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index 000000000..e69de29bb diff --git a/ALUMNOS/MDAA/PEDRO_NIETO/LINUX/README.md b/ALUMNOS/MDAA/PEDRO_NIETO/LINUX/README.md new file mode 100644 index 000000000..782c948ea --- /dev/null +++ b/ALUMNOS/MDAA/PEDRO_NIETO/LINUX/README.md @@ -0,0 +1 @@ +adasdfasdf \ No newline at end of file diff --git a/ALUMNOS/MDAA/PEDRO_NIETO/SQL/.gitkeep b/ALUMNOS/MDAA/PEDRO_NIETO/SQL/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/ALUMNOS/MIA/Eduardo_Abad/ESTADISTICA/informe_final_eduardo_gonzalo.ipynb b/ALUMNOS/MIA/Eduardo_Abad/ESTADISTICA/informe_final_eduardo_gonzalo.ipynb new file mode 100644 index 000000000..deea91d81 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/ESTADISTICA/informe_final_eduardo_gonzalo.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"m70qqlBB1W22"},"source":["# INFORME ESTADÍSTICO: DATASET CURSOS UDEMY\n","Gonzalo López Blanquer y Eduardo Abad Zabala\n"]},{"cell_type":"markdown","metadata":{"id":"yDAoU8FNy4zt"},"source":["A continuación vamos a analizar una base de datos de UDEMY, donde tenemos información acerca de los diferentes cursos y sus características.\n"]},{"cell_type":"markdown","metadata":{"id":"8GRYzzU41W24"},"source":["Como primer paso, hemos importado todas las librerías que usaremos para el documento"]},{"cell_type":"code","source":["!pip install fitter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xyRENCawBlxd","executionInfo":{"status":"ok","timestamp":1732831259625,"user_tz":-60,"elapsed":4341,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"e99de00a-c24e-431e-de41-0d6c82730508","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting fitter\n"," Downloading fitter-1.7.1-py3-none-any.whl.metadata (8.2 kB)\n","Requirement already satisfied: click<9.0.0,>=8.1.6 in /usr/local/lib/python3.10/dist-packages (from fitter) (8.1.7)\n","Requirement already satisfied: joblib<2.0.0,>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from fitter) (1.4.2)\n","Collecting loguru<0.8.0,>=0.7.2 (from fitter)\n"," Downloading loguru-0.7.2-py3-none-any.whl.metadata (23 kB)\n","Requirement already satisfied: matplotlib<4.0.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from fitter) (3.8.0)\n","Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from fitter) (1.26.4)\n","Requirement already satisfied: pandas<3.0.0,>=0.23.4 in /usr/local/lib/python3.10/dist-packages (from fitter) (2.2.2)\n","Collecting rich-click<2.0.0,>=1.7.2 (from fitter)\n"," Downloading rich_click-1.8.4-py3-none-any.whl.metadata (7.9 kB)\n","Requirement already satisfied: scipy<2.0.0,>=0.18.0 in /usr/local/lib/python3.10/dist-packages (from fitter) (1.13.1)\n","Requirement already satisfied: tqdm<5.0.0,>=4.65.1 in /usr/local/lib/python3.10/dist-packages (from fitter) (4.66.6)\n","Requirement already satisfied: contourpy>=1.0.1 in 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/usr/local/lib/python3.10/dist-packages (from matplotlib<4.0.0,>=3.7.2->fitter) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=0.23.4->fitter) (2024.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0.0,>=0.23.4->fitter) (2024.2)\n","Requirement already satisfied: rich>=10.7 in /usr/local/lib/python3.10/dist-packages (from rich-click<2.0.0,>=1.7.2->fitter) (13.9.4)\n","Requirement already satisfied: typing-extensions>=4 in /usr/local/lib/python3.10/dist-packages (from rich-click<2.0.0,>=1.7.2->fitter) (4.12.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.7.2->fitter) (1.16.0)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.7->rich-click<2.0.0,>=1.7.2->fitter) (3.0.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.7->rich-click<2.0.0,>=1.7.2->fitter) (2.18.0)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.7->rich-click<2.0.0,>=1.7.2->fitter) (0.1.2)\n","Downloading fitter-1.7.1-py3-none-any.whl (26 kB)\n","Downloading loguru-0.7.2-py3-none-any.whl (62 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading rich_click-1.8.4-py3-none-any.whl (35 kB)\n","Installing collected packages: loguru, rich-click, fitter\n","Successfully installed fitter-1.7.1 loguru-0.7.2 rich-click-1.8.4\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wJ-mQf4xy-EK"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import scipy.stats as st\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import statsmodels.api as sm\n","from fitter import Fitter\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Ov5Tw6l7y20W"},"source":["\n","\n","- Seaborn y Matplotlib nos permitirán la visualización de los datos en forma de distintos gráficos.\n","\n","- pandas la utilizamos para el tratamiento y manipulación del dato.\n","\n","- Numpy nos sirve para trabajar con cálculos numéricos y arrays.\n","\n","- statsmodel.api nos sirven para profundizar más en el análisis estadístico, como a través de regresiones o pruebas de normalidad.\n","\n","- Scipy.stats incluye herramientas de estadística avanzada, como contrastes de hipótesis, intervalos de confianza o explorar distribuciones de los datos.\n","\n","- Fitter nos sirve para ver a qué distribución se parece más nuestra función.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"mbYvHX3P2YPQ","executionInfo":{"status":"ok","timestamp":1732831537803,"user_tz":-60,"elapsed":154348,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"9c463164-088f-463b-e001-1bd0ed4f778c"},"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n"," \n"," \n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. Please rerun this cell to enable.\n"," \n"," "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving udemy_courses_dataset.csv to udemy_courses_dataset.csv\n"]}],"source":["from google.colab import files\n","uploaded = files.upload()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wLmkM0c53GLc","executionInfo":{"status":"ok","timestamp":1732831542727,"user_tz":-60,"elapsed":379,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"695a86b1-48e0-453c-cf7f-cc846166361a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" course_id course_title \\\n","0 1070968 Ultimate Investment Banking Course \n","1 1113822 Complete GST Course & Certification - Grow You... \n","2 1006314 Financial Modeling for Business Analysts and C... \n","3 1210588 Beginner to Pro - Financial Analysis in Excel ... \n","4 1011058 How To Maximize Your Profits Trading Options \n","... ... ... \n","3673 775618 Learn jQuery from Scratch - Master of JavaScri... \n","3674 1088178 How To Design A WordPress Website With No Codi... \n","3675 635248 Learn and Build using Polymer \n","3676 905096 CSS Animations: Create Amazing Effects on Your... \n","3677 297602 Using MODX CMS to Build Websites: A Beginner's... \n","\n"," url is_paid price \\\n","0 https://www.udemy.com/ultimate-investment-bank... True 200 \n","1 https://www.udemy.com/goods-and-services-tax/ True 75 \n","2 https://www.udemy.com/financial-modeling-for-b... True 45 \n","3 https://www.udemy.com/complete-excel-finance-c... True 95 \n","4 https://www.udemy.com/how-to-maximize-your-pro... True 200 \n","... ... ... ... \n","3673 https://www.udemy.com/easy-jquery-for-beginner... True 100 \n","3674 https://www.udemy.com/how-to-make-a-wordpress-... True 25 \n","3675 https://www.udemy.com/learn-and-build-using-po... True 40 \n","3676 https://www.udemy.com/css-animations-create-am... True 50 \n","3677 https://www.udemy.com/using-modx-cms-to-build-... True 45 \n","\n"," num_subscribers num_reviews num_lectures level \\\n","0 2147 23 51 All Levels \n","1 2792 923 274 All Levels \n","2 2174 74 51 Intermediate Level \n","3 2451 11 36 All Levels \n","4 1276 45 26 Intermediate Level \n","... ... ... ... ... \n","3673 1040 14 21 All Levels \n","3674 306 3 42 Beginner Level \n","3675 513 169 48 All Levels \n","3676 300 31 38 All Levels \n","3677 901 36 20 Beginner Level \n","\n"," content_duration published_timestamp subject \n","0 1.5 2017-01-18T20:58:58Z Business Finance \n","1 39.0 2017-03-09T16:34:20Z Business Finance \n","2 2.5 2016-12-19T19:26:30Z Business Finance \n","3 3.0 2017-05-30T20:07:24Z Business Finance \n","4 2.0 2016-12-13T14:57:18Z Business Finance \n","... ... ... ... \n","3673 2.0 2016-06-14T17:36:46Z Web Development \n","3674 3.5 2017-03-10T22:24:30Z Web Development \n","3675 3.5 2015-12-30T16:41:42Z Web Development \n","3676 3.0 2016-08-11T19:06:15Z Web Development \n","3677 2.0 2014-09-28T19:51:11Z Web Development \n","\n","[3678 rows x 12 columns]"],"text/html":["\n","
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course_idcourse_titleurlis_paidpricenum_subscribersnum_reviewsnum_lectureslevelcontent_durationpublished_timestampsubject
01070968Ultimate Investment Banking Coursehttps://www.udemy.com/ultimate-investment-bank...True20021472351All Levels1.52017-01-18T20:58:58ZBusiness Finance
11113822Complete GST Course & Certification - Grow You...https://www.udemy.com/goods-and-services-tax/True752792923274All Levels39.02017-03-09T16:34:20ZBusiness Finance
21006314Financial Modeling for Business Analysts and C...https://www.udemy.com/financial-modeling-for-b...True4521747451Intermediate Level2.52016-12-19T19:26:30ZBusiness Finance
31210588Beginner to Pro - Financial Analysis in Excel ...https://www.udemy.com/complete-excel-finance-c...True9524511136All Levels3.02017-05-30T20:07:24ZBusiness Finance
41011058How To Maximize Your Profits Trading Optionshttps://www.udemy.com/how-to-maximize-your-pro...True20012764526Intermediate Level2.02016-12-13T14:57:18ZBusiness Finance
.......................................
3673775618Learn jQuery from Scratch - Master of JavaScri...https://www.udemy.com/easy-jquery-for-beginner...True10010401421All Levels2.02016-06-14T17:36:46ZWeb Development
36741088178How To Design A WordPress Website With No Codi...https://www.udemy.com/how-to-make-a-wordpress-...True25306342Beginner Level3.52017-03-10T22:24:30ZWeb Development
3675635248Learn and Build using Polymerhttps://www.udemy.com/learn-and-build-using-po...True4051316948All Levels3.52015-12-30T16:41:42ZWeb Development
3676905096CSS Animations: Create Amazing Effects on Your...https://www.udemy.com/css-animations-create-am...True503003138All Levels3.02016-08-11T19:06:15ZWeb Development
3677297602Using MODX CMS to Build Websites: A Beginner's...https://www.udemy.com/using-modx-cms-to-build-...True459013620Beginner Level2.02014-09-28T19:51:11ZWeb Development
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Elección de datos y breve descripción de su naturaleza"]},{"cell_type":"markdown","metadata":{"id":"FeQS54Q04-_n"},"source":["### Naturaleza de los datos\n","\n","Como podemos observar, tenemos 11 variables diferentes y cada una nos ofrece una información distinta.\n","\n","En cuanto al tipo de dato/variable, podemos diferenciar entre :\n","\n","- CUANTITATIVAS : variables que se muestran con números\n","\n"," - Discretas : números enteros\n"," \n"," - Continuas : numeros con decimales\n","\n","- CATEGÓRICAS : variables que se muestran con etiquetas o nombres\n","\n"," - Nominativas: etiquetas\n","\n"," - Ordinales: etiquetas con un orden\n","\n","Analizando la tipología de las variables de nuestro dataset observamos que :\n","\n","\n","\n","- **Variables categóricas** --> subject, level, is_paid, course_id, course_title, url, published_timestamp\n","\n","- **Variables cuantitativas**:\n"," - **Variables discretas** --> price, num_suscribers, num_reviews, num_lectures\n"," - **Variables continuas** --> content_duration\n"]},{"cell_type":"markdown","metadata":{"id":"bXFxSo4l1W26"},"source":["Vemos las columnas/variables del dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":364,"status":"ok","timestamp":1732831561618,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"rdURv-Lz1W26","colab":{"base_uri":"https://localhost:8080/"},"outputId":"64132136-89a4-43a4-8b72-3174b80d0f40"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['course_id', 'course_title', 'url', 'is_paid', 'price',\n"," 'num_subscribers', 'num_reviews', 'num_lectures', 'level',\n"," 'content_duration', 'published_timestamp', 'subject'],\n"," dtype='object')"]},"metadata":{},"execution_count":9}],"source":["data.columns"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":359,"status":"ok","timestamp":1732831565068,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"8vnqwpdC1W27","outputId":"cf77233c-1150-4b1d-f37e-fac39a5e5cd4"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","RangeIndex: 3678 entries, 0 to 3677\n","Data columns (total 12 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 course_id 3678 non-null int64 \n"," 1 course_title 3678 non-null object \n"," 2 url 3678 non-null object \n"," 3 is_paid 3678 non-null bool \n"," 4 price 3678 non-null int64 \n"," 5 num_subscribers 3678 non-null int64 \n"," 6 num_reviews 3678 non-null int64 \n"," 7 num_lectures 3678 non-null int64 \n"," 8 level 3678 non-null object \n"," 9 content_duration 3678 non-null float64\n"," 10 published_timestamp 3678 non-null object \n"," 11 subject 3678 non-null object \n","dtypes: bool(1), float64(1), int64(5), object(5)\n","memory usage: 319.8+ KB\n"]}],"source":["data.info()"]},{"cell_type":"markdown","metadata":{"id":"pi9uyyf35H93"},"source":["Observamos el tipo de dato de cada variable, como el caso de num_reviews, num_lectures, num_subscribers són datos enteros (int64).\n","\n","Otras variables como content_duration són de tipo float (decimal).\n","\n","La variable is_paid es de tipo booleano (yes/no)."]},{"cell_type":"code","source":["data.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"i9Gdlgc2K-kG","executionInfo":{"status":"ok","timestamp":1732831571575,"user_tz":-60,"elapsed":392,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"04f5cd01-2190-4424-fd91-2a80e1fefc50"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" course_id price num_subscribers num_reviews num_lectures \\\n","count 3.678000e+03 3678.000000 3678.000000 3678.000000 3678.000000 \n","mean 6.759720e+05 66.049483 3197.150625 156.259108 40.108755 \n","std 3.432732e+05 61.005755 9504.117010 935.452044 50.383346 \n","min 8.324000e+03 0.000000 0.000000 0.000000 0.000000 \n","25% 4.076925e+05 20.000000 111.000000 4.000000 15.000000 \n","50% 6.879170e+05 45.000000 911.500000 18.000000 25.000000 \n","75% 9.613555e+05 95.000000 2546.000000 67.000000 45.750000 \n","max 1.282064e+06 200.000000 268923.000000 27445.000000 779.000000 \n","\n"," content_duration \n","count 3678.000000 \n","mean 4.094517 \n","std 6.053840 \n","min 0.000000 \n","25% 1.000000 \n","50% 2.000000 \n","75% 4.500000 \n","max 78.500000 "],"text/html":["\n","
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"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["Vemos que solo es un curso el que no tiene duración. No lo eliminamos ya que aparte de que no afectará mucho a los valores de nuestro dataset. Se puede deber a que no tenga vídeos y solo contenido en pdf por ejemplo. Ahora trataremos los cursos que no tienen suscriptores"],"metadata":{"id":"CnIgSxdKONTN"}},{"cell_type":"code","source":["data['published_timestamp'] = pd.to_datetime(data['published_timestamp'])\n","data['year_published'] = data['published_timestamp'].dt.year\n","courses_by_year = data.groupby('year_published').size()\n","print(courses_by_year)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kZzvuZqSKiSJ","executionInfo":{"status":"ok","timestamp":1732831702221,"user_tz":-60,"elapsed":360,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"14aa76f7-a6a8-413f-a3ff-b89bf3485004"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["year_published\n","2011 5\n","2012 45\n","2013 202\n","2014 491\n","2015 1014\n","2016 1206\n","2017 715\n","dtype: int64\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":646,"status":"ok","timestamp":1732831723830,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"HElsJJKKHP3V","outputId":"381618dd-ea51-4e93-9b4d-aed7f93ca41d"},"outputs":[{"output_type":"stream","name":"stdout","text":["453 2016\n","454 2017\n","455 2017\n","456 2017\n","457 2017\n"," ... \n","2402 2017\n","2403 2017\n","2404 2017\n","2405 2017\n","2418 2017\n","Name: year_published, Length: 70, dtype: int32\n"]}],"source":["courses_no_subscribers = data[data['num_subscribers'] == 0]\n","print(courses_no_subscribers['year_published'])"]},{"cell_type":"code","source":["courses_no_subscribers.count()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":429},"id":"ERUWHxlQPO-5","executionInfo":{"status":"ok","timestamp":1732831736477,"user_tz":-60,"elapsed":398,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"a6277151-1d5f-48e1-e0a5-bb5d83de523f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["course_title 70\n","is_paid 70\n","price 70\n","num_subscribers 70\n","num_reviews 70\n","num_lectures 70\n","level 70\n","content_duration 70\n","published_timestamp 70\n","subject 70\n","year_published 70\n","dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
0
course_title70
is_paid70
price70
num_subscribers70
num_reviews70
num_lectures70
level70
content_duration70
published_timestamp70
subject70
year_published70
\n","

"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","source":["Tenemos 70 cursos sin suscriptores en 2017 frente a los 715 cursos totales que hay en ese año"],"metadata":{"id":"XZRl45RLQGwW"}},{"cell_type":"markdown","metadata":{"id":"wGpWZAHmJ2vV"},"source":["Vemos que los cursos que tienen 'num_subscribers' = 0 son todos de 2017 menos uno de 2016, esto se puede deber a que están recién lanzados y no le ha dado tiempo a la gente a suscribirse. Además vemos que solo el 10% de cursos de 2017 no tienen suscriptores, por lo que no tendría sentido eliminar este año para hacer nuestros cálculos posteriores"]},{"cell_type":"markdown","metadata":{"id":"hNokmDvo1W28"},"source":["## 2. Descriptiva univariante y bivariante de los datos teniendo en cuenta su tipo"]},{"cell_type":"markdown","metadata":{"id":"2Xr3rzpA5oxG"},"source":["# DESCRIPTIVA UNIVARIANTE\n","\n","Vamos a analizar cada variable por separado para ver características de cada una. Dependiendo del tipo de variable que sea la analizaremos de una determinada manera. En el caso de las variables categóricas el análisis se centra en el conteo y la distribución que siguen.\n","Y, en el caso de las variables cuantitativas vamos a describir características como la media,moda, mediana, cuartiles,desviación típica...\n","\n","Primero vamos a hacer una breve descripción de cada variable, y después vamos a verlas representadas en distintos tipos de gráficos.\n","\n","Las variables cuantitativas se suelen ver representadas en gráficos como histogramas (para ver la forma de la distribución), gráficos de densidad, boxplot(para ver dispersión y outliers=valores atípicos).\n","\n","Las variables categóricas se suelen ver representadas en gráficos como gráfico de barras, pie-chart."]},{"cell_type":"markdown","metadata":{"id":"eL-LcpUW54Zl"},"source":[" Empezamos con el análisis univariante de las **variables categóricas **:\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"elapsed":360,"status":"ok","timestamp":1732831772292,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"HIlsihJL6AfP","outputId":"83e5a454-c8dd-49fc-c14a-822b0bf1b11d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["level\n","All Levels 1929\n","Beginner Level 1270\n","Intermediate Level 421\n","Expert Level 58\n","Name: count, dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
count
level
All Levels1929
Beginner Level1270
Intermediate Level421
Expert Level58
\n","

"]},"metadata":{},"execution_count":17}],"source":["data['level'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"72M6GDgo6D39"},"source":["La variable 'level' está compuesta por 4 niveles diferentes ('All Levels' , 'Beginner Level', 'Intermediate Level' , 'Expert Level')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":446},"executionInfo":{"elapsed":492,"status":"ok","timestamp":1732831785185,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"HvJpolbg6Xqn","outputId":"1bd4586b-f2df-4c02-ac1b-2a2071fc608b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Visualización de la variable 'level' en un piechart\n","# Calcular las frecuencias de cada categoría en 'level'\n","frecuencias = data['level'].value_counts()\n","\n","# Definir etiquetas y colores\n","labels = frecuencias.index # Etiquetas basadas en las categorías únicas\n","sizes = frecuencias.values # Frecuencias de las categorías\n","colors = sns.color_palette('pastel')[0:len(labels)] # Colores para cada porción\n","\n","# Crear el gráfico de pastel\n","plt.figure(figsize=(5, 5))\n","plt.pie(\n"," sizes, # Valores para el gráfico (frecuencias)\n"," labels=labels, # Etiquetas de las categorías\n"," colors=colors, # Colores definidos\n"," autopct='%.0f%%', # Mostrar porcentajes con 0 decimales\n"," startangle=90 # Girar el inicio del gráfico para mejor visualización\n",")\n","\n","# Título del gráfico\n","plt.title('Distribución de Niveles (Level)', fontsize=16)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"p9_xW1JM6Zlu"},"source":["A través del piechart comprobamos que la variable 'level' está compuesta mayoritariamente por 'All Levels' (52 %) seguida de ' Beginner Level' (35%), ' Intermediate Level' (11%) y por último 'Expert Level' (2%).\n","\n","Por lo tanto, los cursos de Udemy principalmente van dirigidos a todos los niveles, aunque tienen más cursos para principiantes que para expertos."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"executionInfo":{"elapsed":443,"status":"ok","timestamp":1732831846952,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"OWX8-WrH6mQP","outputId":"4c918010-3795-459b-ea88-d28f675d10f3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Gráfico de barras\n","\n","# Calcular las frecuencias de cada categoría en 'level'\n","frecuencias = data['level'].value_counts()\n","\n","# Extraer etiquetas (categorías) y tamaños (frecuencias)\n","labels = frecuencias.index # Etiquetas de las categorías\n","sizes = frecuencias.values # Frecuencias de las categorías\n","\n","# Crear el gráfico de barras\n","plt.figure(figsize=(6, 4))\n","plt.bar(labels, sizes, color='skyblue', edgecolor='black')\n","\n","# Personalizar el gráfico\n","plt.title('Frecuencia de Niveles (Level)', fontsize=16)\n","plt.xlabel('Niveles', fontsize=12)\n","plt.ylabel('Frecuencia', fontsize=12)\n","plt.xticks(rotation=45) # Rotar etiquetas si es necesario\n","plt.tight_layout() # Ajustar para evitar que se corten las etiquetas\n","\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":178},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1732831852909,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"li2l55WR6qpu","outputId":"18316144-d981-4822-a43b-1d40e52ed889"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["is_paid\n","True 3368\n","False 310\n","Name: count, dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
count
is_paid
True3368
False310
\n","

"]},"metadata":{},"execution_count":20}],"source":["data['is_paid'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"RFfmvuuc6tTa"},"source":["La variable 'is_paid' es un dato de tipo booleano (0,1), eso significa que si NO es un curso de pago se marca un 0, y si es un curso de pago se marca con un 1. En este caso hay muchos más cursos de pago que gratis.\n"]},{"cell_type":"code","source":["# Calcular las frecuencias de cada categoría en 'is_paid'\n","frecuencias = data['is_paid'].value_counts()\n","\n","# Extraer etiquetas (categorías) y tamaños (frecuencias)\n","labels = ['De Pago', 'Gratis'] if data['is_paid'].dtype == 'bool' else frecuencias.index # Etiquetas de categorías\n","sizes = frecuencias.values # Frecuencias de las categorías\n","\n","# Crear el gráfico de barras\n","plt.figure(figsize=(6, 4))\n","plt.bar(labels, sizes, color='skyblue', edgecolor='black')\n","\n","# Personalizar el gráfico\n","plt.title('Frecuencia de Pago (is_paid)', fontsize=16)\n","plt.xlabel('Categorías', fontsize=12)\n","plt.ylabel('Frecuencia', fontsize=12)\n","plt.xticks(labels, rotation=0) # Asegurar que las etiquetas sean correctas\n","plt.tight_layout() # Ajustar para evitar que se corten las etiquetas\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"jaVavizwRmMl","executionInfo":{"status":"ok","timestamp":1732831863400,"user_tz":-60,"elapsed":441,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"e631cd32-24a7-45fd-ada9-985fb576ecce"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":678},"executionInfo":{"elapsed":452,"status":"ok","timestamp":1732831872451,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"S2ITQF7w7EWm","outputId":"083a198b-8618-4c07-cfc5-eba713b5cf66"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Calcular las frecuencias de cada categoría en 'level'\n","frecuencias = data['is_paid'].value_counts()\n","\n","# Definir etiquetas y colores\n","labels = frecuencias.index # Etiquetas basadas en las categorías únicas\n","sizes = frecuencias.values # Frecuencias de las categorías\n","colors = sns.color_palette('pastel')[0:len(labels)] # Colores para cada porción\n","\n","# Crear el gráfico de pastel\n","plt.figure(figsize=(8, 8))\n","plt.pie(\n"," sizes, # Valores para el gráfico (frecuencias)\n"," labels=labels, # Etiquetas de las categorías\n"," colors=colors, # Colores definidos\n"," autopct='%.0f%%', # Mostrar porcentajes con 0 decimales\n"," startangle=90 # Girar el inicio del gráfico para mejor visualización\n",")\n","\n","# Título del gráfico\n","plt.title('Distribución de Pago (is_paid)', fontsize=16)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"executionInfo":{"elapsed":375,"status":"ok","timestamp":1732831884031,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"Izz7siN47Iv_","outputId":"95fd7ccb-a7cb-43c3-e289-432d6908d35c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["subject\n","Web Development 1200\n","Business Finance 1195\n","Musical Instruments 680\n","Graphic Design 603\n","Name: count, dtype: int64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
count
subject
Web Development1200
Business Finance1195
Musical Instruments680
Graphic Design603
\n","

"]},"metadata":{},"execution_count":23}],"source":["data['subject'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"qsg8LB_G7Kzx"},"source":["La variable 'subject' está formada por 4 tipos ( 'Web Development', 'Business Finance', 'Musical Instruments' , ' Graphic Design' )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":607},"executionInfo":{"elapsed":518,"status":"ok","timestamp":1732831888532,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"9WC9iHPc7fYe","outputId":"0e0222d0-c333-4b75-a8ec-37e2adf45294"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Calcular las frecuencias de cada categoría en 'level'\n","frecuencias = data['subject'].value_counts()\n","\n","# Extraer etiquetas (categorías) y tamaños (frecuencias)\n","labels = frecuencias.index # Etiquetas de las categorías\n","sizes = frecuencias.values # Frecuencias de las categorías\n","\n","# Crear el gráfico de barras\n","plt.figure(figsize=(8, 6))\n","plt.bar(labels, sizes, color='skyblue', edgecolor='black')\n","\n","# Personalizar el gráfico\n","plt.title('Distribución por subject (subject)', fontsize=16)\n","plt.xlabel('Niveles', fontsize=12)\n","plt.ylabel('Frecuencia', fontsize=12)\n","plt.xticks(rotation=45) # Rotar etiquetas si es necesario\n","plt.tight_layout() # Ajustar para evitar que se corten las etiquetas\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"DVFNQMCl7kCh"},"source":["Observamos cómo la mayoria de asignaturas ( 'subject' ) són de 'Web Development' y de 'Business Finance' . Mientras que las asignaturas de 'Grapfic Design' y 'Musical Instruments' presentan un número de cursos bastante similar."]},{"cell_type":"markdown","metadata":{"id":"qzD7AkJ88I1y"},"source":[" Ahora pasamos al análisis univariante de las **variables cuantitativas **:\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":608,"status":"ok","timestamp":1732831899440,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"YAW43Coj_2y-","outputId":"54ea586d-4ba0-489b-b780-800c284b6863"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" price num_subscribers num_reviews num_lectures \\\n","count 3678.000000 3678.000000 3678.000000 3678.000000 \n","mean 66.049483 3197.150625 156.259108 40.108755 \n","std 61.005755 9504.117010 935.452044 50.383346 \n","min 0.000000 0.000000 0.000000 0.000000 \n","25% 20.000000 111.000000 4.000000 15.000000 \n","50% 45.000000 911.500000 18.000000 25.000000 \n","75% 95.000000 2546.000000 67.000000 45.750000 \n","max 200.000000 268923.000000 27445.000000 779.000000 \n","\n"," content_duration year_published \n","count 3678.000000 3678.000000 \n","mean 4.094517 2015.431213 \n","std 6.053840 1.185317 \n","min 0.000000 2011.000000 \n","25% 1.000000 2015.000000 \n","50% 2.000000 2016.000000 \n","75% 4.500000 2016.000000 \n","max 78.500000 2017.000000 "],"text/html":["\n","
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pricenum_subscribersnum_reviewsnum_lecturescontent_durationyear_published
count3678.0000003678.0000003678.0000003678.0000003678.0000003678.000000
mean66.0494833197.150625156.25910840.1087554.0945172015.431213
std61.0057559504.117010935.45204450.3833466.0538401.185317
min0.0000000.0000000.0000000.0000000.0000002011.000000
25%20.000000111.0000004.00000015.0000001.0000002015.000000
50%45.000000911.50000018.00000025.0000002.0000002016.000000
75%95.0000002546.00000067.00000045.7500004.5000002016.000000
max200.000000268923.00000027445.000000779.00000078.5000002017.000000
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\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n \"name\": \"data\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1277.2044021211534,\n \"min\": 0.0,\n \"max\": 3678.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 66.0494834148994,\n 45.0,\n 3678.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_subscribers\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 94120.13056666977,\n \"min\": 0.0,\n \"max\": 268923.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 3197.150625339859,\n 911.5,\n 3678.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9541.03023905795,\n \"min\": 0.0,\n \"max\": 27445.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 156.25910821098424,\n 18.0,\n 3678.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_lectures\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1279.411064917688,\n \"min\": 0.0,\n \"max\": 3678.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 40.108754758020666,\n 25.0,\n 3678.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content_duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1295.784397637758,\n \"min\": 0.0,\n \"max\": 3678.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 4.094516947616459,\n 2.0,\n 3678.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year_published\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 986.0233764254358,\n \"min\": 1.1853168398391059,\n \"max\": 3678.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 3678.0,\n 2015.4312126155519,\n 2016.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":25}],"source":["data.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"elapsed":443,"status":"ok","timestamp":1732831928020,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"y4a9ftzA8N-j","outputId":"b90b303c-e40a-4d59-9470-83ede0cc15ad"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3678.000000\n","mean 3197.150625\n","std 9504.117010\n","min 0.000000\n","25% 111.000000\n","50% 911.500000\n","75% 2546.000000\n","max 268923.000000\n","Name: num_subscribers, dtype: float64"],"text/html":["
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num_subscribers
count3678.000000
mean3197.150625
std9504.117010
min0.000000
25%111.000000
50%911.500000
75%2546.000000
max268923.000000
\n","

"]},"metadata":{},"execution_count":26}],"source":["data['num_subscribers'].describe()"]},{"cell_type":"markdown","metadata":{"id":"flYj0KzF8knV"},"source":["La variable cuantitativa 'num_subscribers' presenta una media de 3197 suscriptores por curso, mientras que la mediana es de 911 suscriptores ( esto nos muestra que no sigue una distribución normal, ya que media y mediana están muy alejadas) ."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QAWJOn-PL0o-","colab":{"base_uri":"https://localhost:8080/","height":685},"executionInfo":{"status":"ok","timestamp":1732831961215,"user_tz":-60,"elapsed":1003,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"68161c67-7fbe-474b-bb14-2f90ff2f510b"},"outputs":[{"output_type":"stream","name":"stderr","text":[":6: FutureWarning: \n","\n","Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n","\n"," sns.boxplot(data=data, x='subject', y='num_subscribers', palette='Set2')\n"]},{"output_type":"display_data","data":{"text/plain":["
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7Lt27UJOTg4++eQTsdYLACpWrKhz2LtHjx5h7969eP/997WGFqxatSoCAgLw6NEjnDhx4rX71kfr1q3RtGlT/PHHH7h48aIk23yVt7e3Rs27mZkZOnbsCEEQ0KZNG40alooVK6J169ZITU3F/fv3xeUbN24E8LIm0dLSUlwuk8nw6aefQiaTYffu3Vr7bteuHZo3b66xrDDHw6t27twJAJg8ebJGbbKjoyN69OihVf7w4cNITExEQEAAnJ2dNdZ5enqiXbt2OHLkSL63+9VkMhmWLFmCxo0b49y5c5gxYwa6d++OJk2aYOjQodi+fbtWExpDlStXTmtZ3o7X+/fvx4MHD+Dn54eWLVtqldX1/WzUqJHYhOBNDB48WGN75ubmYp8Vfc8xhfn8AwICULNmTY1lV69eRXR0NHr27IkWLVporKtbty769euH+Ph4rWYjUpyv9T3n5j0PvfPOO+Jyc3NzfPrppwB0v3+G/hZEREQgOztb4zvQs2dPAC87Q+piZmam847M696TxMREnDt3Dk5OTujevbvGuhEjRqBSpUr5Prdbt25o0qSJ+NjExETsLHzp0iWNsrruLlWrVg0dO3bEv//+q3dTr8LQdT4DXn6O1atX11reoEEDeHt748yZMxpNqowVm4tQiWjTpg1CQkIwa9YsnDx5Ei1atICXl9drbznnx9HREebm5m/8PF23V2vVqoUaNWrg+vXryMrKMmi76uTv1Tam+rp58yZevHgBb29vnaMWeHt74++//0ZsbKzWa2jUqJFWefXJ7MmTJ6/d97Vr1wBA40Supuv9unTpEnJzc5GVlaWzzfq///4rvqY2bdq8dv/6+Oyzz9CvXz98++232LBhgyTbzOvV5kjA/11A6lqn7oT58OFD8RiOiYmBpaVlvj/QFhYWOpvf6LpFWpjj4VXXrl2DpaWlzuPE09NTqw3qhQsXAAC3bt3S+fkmJSVBpVLh1q1br+1Yamdnh19++QWxsbE4ceIELl++jPPnz+PkyZM4efIkdu7cKfbtMESXLl3w559/on///ujWrRuaNWuGJk2aoEqVKhrl1MnIm3w/XVxcDIpJ1+ehVCphamqqd9vTwnz+uo4n9WeakpKi8zNVH5c3b96Eg4ODpOdrfc+5sbGx4mt7lVKpRLly5RAXF6e1ztDfgm3btkEmk4mjZwEvO80rlUpER0fjn3/+0ejA2KVLF2zatAndunVDly5d4O3tDaVSqfNC5FXquHV1qLS0tISTk1O+czjo+t6qL+LS09M1lickJGDlypU4deoUHjx4gKysLI31Dx8+RK1atV4bb2EU1OQjNjYWq1atwrlz55CcnKyVVD9+/FijwsgYMckmST18+BAAtH7UXmVnZ4fNmzdj2bJlOHLkCPbu3QvgZbvuiRMnonPnzm+0X1tbW4PizVtT++r2EhMT8ezZM4NO2OpkVteVuj7UtYL5vS51Uqer9rBixYpay9TtJfWpKVTHruu90RVPWloaAOD8+fMF1qRmZma+dt/6cnd3R4cOHfDnn3/iyJEjaNWqlWTbBnS/h+oaq4Le37ydsdLS0pCTk1NgJ82MjAytZbre98IcD6968uRJvndSdO1b/fnu2rWrwO2+yefbsGFDjYuV06dP47PPPsPp06exadMmg4eq7Ny5M8zMzLB27Vr8+uuvCA8Ph0wmg7e3N6ZPny7u05Dvp5TnGBMTE9jY2Oh10QsU7vMv6DM9fPgwDh8+nO9+1Z+plOdrfc+56teiq7xMJoOtrS0ePHigcztvKiYmBvHx8fD29taq9e/Zsyeio6OxdetWTJs2TVz+xRdfwM7ODtu3b8cPP/yAH374AeXKlUPnzp0xbdq0An8DC3ptr3sNBZ2b8nZuv337Nvr27YunT5/C29sbbdq0QcWKFSGXyxEVFYWoqCitpLso5Pcaz58/jyFDhgB4ebFbp04dWFpaQiaTYf/+/YiLiyuW+Ioak2ySlHokEH2GSnNwcMCSJUuQnZ2NK1eu4OjRo9iwYQMmT56MatWq6axJzc+rnV/0lZKSonN5cnIyZDKZ2NlFLn/ZskrXCBK6ftjUnUV0/QjoQ30iTU5Ozje+vOWkpB7PNSUlRauWQ1c86hiGDRum8SNU1CZPnoyDBw9i0aJFWre81dTHha6LC30THEOp35c3nVVU17Es5fFgZWWVb3MuXd8H9TZXrFgh2Z2IV3l7eyMwMBCff/45Tp06JSbZhnx+vr6+8PX1xdOnT3H+/Hn89ddf2Lp1K4YPH469e/fC2tpaPMbf5PtZmHPMe++9p7EsNzcXqamp+SYgryrM51/Q8fTll19i4MCBesUg1fla33OuOkZd5yFBEJCcnKz3630d9d0b9UgyukRERCAoKEjsRGlqaoqAgAAEBATgwYMHOHPmDLZv346dO3ciOTlZawSpvPK+Nl3y+5zfxNq1a5GWloaFCxdqNQP76quv3ni4TPX7mpOTo9XJtaBzaX6fx4oVK5CVlYXw8HCtuxvqOy2lAdtkk2Ru3bqFvXv3wtzcHO3bt9f7eWZmZvDw8MDEiRPxxRdfQBAEjdoVdYIrVXvNvHQNL5eYmIj79++jQYMGYi22OmlW19TnpeuWr/oi4++//zYorvfeew/lypXDpUuXdNYQqhM3XU0XCkv9I3Pu3DmtdbreL1dXV8hkMkRHR0seS0Hee+899OnTB/Hx8YiIiNBZRt22UVcypb4dXVTc3NyQmpoqNpcpDCmPB0dHR2RkZODKlSta63R9vurbvUX9w5e33bpaYT6/ihUromXLlpg9ezZ69eqF5ORkxMTEAPi/12To9/NN6HpPo6OjkZOTo9HGXV0bqes8J/X5QD1qiCHf2dedr19H33Ou+rXoukiNiYnBixcvChyFQ18ZGRnYvXs3ypcvjw8//FDnP0dHR6SkpOT7OqtXr45u3bph1apVePfdd3HixAlxuE9d1HHrev8zMzPFJnuFoZ6BuV27dhrLBUHQud+Cjj8g/++iSqXS2WxHn/hsbGy0EuzMzMxSNYQfk2ySxLlz5xAQEICsrCyMHDnytbdhL1++rLMGWH1ln7fjkvrLre+QWW9i586dGicIQRAQEhKC3NxcjZnn6tatiwoVKuDgwYNITU0VlycnJ+OHH37Q2q6bmxtcXV1x5swZneMPv64GzdzcHF27dsXjx4+xcuVKjXVHjx7F8ePH8e677xZqkoT8dO/eHSYmJlizZo1GTcvTp091vlaFQoHOnTsjOjoaq1at0jlmbkxMjKTNRdQmTJiA8uXLY8mSJTr3q77YebWD1L59+4p84hP1EJaff/65zprjpKQk/PPPP3ptS8rjQV2rtXjxYo0f1GvXrum8WPH19UXNmjWxZs0anDlzRmt9dna2XmOhJyQkYOPGjTq/95mZmVi/fj0AzXaq6s9v586dGrfCo6OjdTZfOXPmjM4k4dGjRwD+77zSrl071KhRA5GRkTh27JhWeUPvQOmyfv16jQ6xWVlZCA0NBQCNc4z6PJe3rJrU5wM3Nze4u7tj9+7d2LNnj9Z6lUql8f14k/P16+h7zu3evTtMTU2xdu1ajc8jKysLixYtAgBJZgfdt28fnj17ho4dO+Kbb77R+W/q1KkA/q/GOysrS2fTuIyMDGRkZMDU1FSsHNKlVq1aaNy4MWJjY7Xe/9WrV2v8xhhKXfv/amXJjz/+qHMc9IKOPyD/c+maNWv0Hof81fjS0tJw/fp1cVlubi4WLFggfl9LAzYXoTdy584dsaNMdnY2UlJScPHiRcTHx8PExARjxozRGitYl4iICGzevBlNmzaFvb09KlasiBs3buDo0aOwsbHRGKP2/fffxx9//IGJEyeiRYsWKFeuHJycnAqcYEZf6nGQu3TpgipVquDkyZO4fPkyPDw8NG6jmpubY9CgQVixYgV69+6Ntm3b4tmzZzh06BC8vLzEWoO8Fi1ahEGDBuHLL79EREQEPDw88OLFC9y4cQOxsbGvbUbw2Wef4cyZM/jhhx8QHR0Nd3d3JCYmYt++fShfvjzmzp1b4IncUO+++y7Gjh2LpUuXws/PD507d4aJiQn+/PNPODo66hyh5Ouvv8atW7fw7bffIiIiAkqlElZWVrh//z4uX76Mf//9F8ePH5d86mmFQoEhQ4ZgxYoVOte3a9cOtWvXxvbt2/Hff/+hYcOGuHnzJk6dOoVWrVrhyJEjksaTV8uWLTF27FgsX74cHTp0QIsWLVCzZk2kpqbi9u3bOHfuHCZNmqT3bHBSHQ+9evXC77//jmPHjqFnz55o2bIl0tLSsHv3bvj4+ODQoUMa5c3NzREWFoYRI0Zg4MCBeP/99+Hg4ACZTIZ79+7h7NmzsLGxwb59+wrc79OnTzF79mwsXLgQTZo0QYMGDWBhYYEHDx7g8OHDSE1NRaNGjTTG1/fw8EDjxo1x6tQp9O/fH56enrh37x4OHDiANm3a4K+//tLYx5w5c/Dw4UM0adIEtWrVgkwmw7lz53Dx4kV4eHiITRrMzc0RGhqK4cOHY8SIEWjRogWcnJzw9OlTxMbG4vnz5+IoLIXl7u6OHj16oHPnzihfvjwOHTqEW7duoUOHDujYsaNY7r333kO1atWwe/duceQFmUyGQYMGwcrKSvLzwXfffYchQ4Zg8uTJWLduHZydnWFhYYF79+7hwoULePTokdhB9E3O16+j7zm3du3a+PTTTzF//nzxPJT3/WvXrp3O0XDelDpxLug1NG/eHDVq1MCxY8fw4MEDlC9fHh9//DHq1KkDFxcXvPPOO8jIyMDhw4eRlJSEYcOGvbYvz5dffgl/f398+umn+OOPP/Duu+/iypUriImJQdOmTXHmzJlCnd8/+ugjbN++XWwzb2NjgwsXLuDq1ato3bq1Vq38646/3r17Y9WqVVi6dCliY2NRu3ZtXL58GfHx8fDy8nrjSouBAwfi+PHjGDBgADp37gxzc3NERUXhwYMHBm3vbcUkm97InTt3xI5cFhYWsLKywnvvvYexY8eiV69eek9h3q1bN7x48QLR0dG4ePEisrKyUKNGDXz88cdaQ07169cPiYmJ2LNnD1atWoWcnBz06tVLkiT7k08+Qbt27bBu3Trcvn0blSpVwuDBgxEYGKh1kgwMDISZmRm2bt2KX3/9FbVq1cLYsWPRpk0b/PHHH1rbrlOnDnbs2IGVK1fi0KFDWLduHSpUqIB3331X51B4r6pSpQq2bNmC5cuX4+DBgzh37hwqVqyIdu3aYfz48XBwcCj068/P+PHjUb16dbEDWdWqVdGlSxcEBgaKt5rzsrGxwa+//oqNGzdiz5492LVrF1QqFWxtbeHk5IQxY8ZoDKEmpREjRmDz5s06a4stLCywZs0azJs3DydPnkRMTAzc3d2xceNGHD58uEiTbODlMdO0aVOsX78eJ0+exJMnT2BjYwM7OzuMHz9ea/iugkh1PMjlcixfvhzLli3Drl27sH79etSuXRv/+9//8O6772ol2cDLms/IyEisWrUKR48exfnz58UfYl9fX3Tt2vW1+61Xrx6WLl2K48ePIyYmBpGRkUhPT0fFihVRv359dOjQAR9//LFWrejy5csxf/58HD58GPHx8XBycsKKFSvw8OFDrSR71KhR+PPPP3HlyhUcP34cpqamqFWrFj799FMMGDBAY7g1pVIpfj+PHz+OkydPwtraGvXq1cNHH32k13upjy+++AJ79+7F1q1bce/ePVSrVg0TJkzAyJEjNcqZmJhg2bJlWLRoEX7//Xc8e/YMAODn5wcrKyvJzwf29vbYsWMH1qxZgwMHDmD79u2Qy+WoVq0aPD090alTJ7Hsm5yvX+dNzrmffPIJateujbVr1yIyMhLZ2dmoU6cOpk+fjkGDBhncTl7t5s2bOHfuHOzs7DQmYHmVXC5Hz549sWLFCuzYsQMBAQH49NNPcerUKZw9exYpKSmoVKkS6tati6CgIL2+D87Ozti0aRMWLVqEo0ePQiaToUmTJti0aRNCQkIAFK7PjbOzM1avXo3Q0FD8+eefMDExgVKpxC+//CJOaJPX644/W1tbrF+/HvPnz8fff/+NU6dOwdvbG1u2bNF5h/N12rRpgyVLlmDlypWIjIyEhYUF3n//fXz//ff4/vvvDX7dbxuZoOseKxERERls+vTp2LFjBw4cOAA7O7uSDqfELV26FMuWLcP69etfOxtiWZabm4v27dvj+fPnks0tQCWHbbKJiIiIilFOTo7Otsc//vgjEhMTNSbDIuPF5iJERERExSgjIwMtW7YUx4jOyclBTEwMLl26BIVCoVffJnr7MckmIiIiKkYWFhb48MMPcerUKZw5cwZZWVlQKBTo378/xo0bZ/QzHdJLbJNNRERERCQxtskmIiIiIpIYk2wiIiIiIomxTfZbJDo6GoIgwMzMrKRDISIiIiIdsrOzIZPJoFQqCyzHJPstIgiCzqmhiYiIiOjtoG+uxiT7LaKuwXZ1dS3hSIiIiIhIl0uXLulVjm2yiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhipiUdABEREREVL5VKhbi4OKSmpsLGxgZOTk6Qy1n3KiUm2URERERlSFRUFMLDw5GUlCQuUygU8Pf3h5eXVwlGVrowySYiIiIqI6KiohAWFgalUonx48fD3t4eCQkJiIiIQFhYGAIDA5loS4T3BYiIiIjKAJVKhfDwcCiVSgQFBaFBgwawsLBAgwYNEBQUBKVSifDwcKhUqpIOtVRgkk1ERERUBsTFxSEpKQk9evTQan8tl8vh5+eHpKQkxMXFlVCEpQuTbCIiIqIyIDU1FQBgb2+vc716ubocFQ6TbCIiIqIywMbGBgCQkJCgc716ubocFQ6TbCIiIqIywMnJCQqFAhEREVrtrlUqFSIjI6FQKODk5FRCEZYuTLKJiIiIygC5XA5/f39ER0cjJCQE8fHxyMzMRHx8PEJCQhAdHQ1/f3+Oly0RmSAIQkkHQS9dunQJAODq6lrCkRAREVFpxXGyC0fffI3jZBMRERGVIV5eXvD09OSMj0WMSTYRERFRGSOXy+Hs7FzSYZRqvGQhIiIiIpIYk2wiIiIiIokxySYiIiIikhiTbCIiIiIiiTHJJiIiIiKSGJNsIiIiIiKJMckmIiIiIpIYk2wiIiIiIokxySYiIiIikhiTbCIiIiIiiTHJJiIiIiKSGJNsIiIiIiKJMckmIiIiIpIYk2wiIiIiIokxySYiIiIikhiTbCIiIiIiiTHJJiIiIiKSGJNsIiIiIiKJvVVJ9sqVK9GnTx8olUo0a9YMY8eOxc2bNzXKDBo0CI6Ojhr/vvrqK40y9+7dw8iRI+Hu7o5mzZphwYIFyMnJ0Shz+vRp9OrVCy4uLmjfvj22b9+uFU94eDjatm0LV1dX9O3bFxcvXtRY/+LFC8ycORPe3t5QKpWYMGECkpOTJXo3iIiIiMhYvVVJdlRUFPz9/bFlyxasWbMGOTk5CAgIQEZGhka5fv364fjx4+K/qVOniutyc3MxatQoZGdn49dff8X8+fOxY8cOLFmyRCyTkJCAUaNGwdvbGxERERgyZAhmzJiBY8eOiWX27NmDefPmYdy4cdixYwecnJwQEBCAlJQUsczcuXNx6NAhhIaGYsOGDXj48CHGjx9fhO8QERERERmDtyrJXr16NXr37o0GDRrAyckJ8+fPx71793DlyhWNchYWFlAoFOK/ihUriuuOHz+OGzdu4Ntvv0XDhg3RqlUrBAYGIjw8HFlZWQCAX3/9FXZ2dpg+fTrq1auHgQMHomPHjli7dq24nTVr1qBfv37o06cP6tevj5kzZ8LCwgLbtm0DADx58gTbtm3D9OnT0axZM7i4uGDu3LmIjo7GhQsXivy9IiIiIqK311uVZL/qyZMnAIBKlSppLN+1axe8vb3RrVs3fPfdd8jMzBTXXbhwAQ4ODrC1tRWX+fj44OnTp7hx44ZYplmzZhrb9PHxEZPjrKwsXLlyBc2bNxfXy+VyNG/eHNHR0QCAy5cvIzs7W6NMvXr1ULNmTSbZRERERGWcaUkHkB+VSoW5c+eicePGcHBwEJd369YNNWvWRLVq1XDt2jUsWrQIt27dwrJlywAAycnJGgk2APFxUlJSgWWePn2K58+fIy0tDbm5uahatapGmapVq4ptxJOTk2FmZgZra2utMur9GEIQBK3mMURERET0dhAEATKZ7LXl3toke+bMmbh+/To2bdqksbx///7i346OjlAoFBg6dCju3LmD2rVrF3eYksvOzkZsbGxJh0FERERE+TA3N39tmbcyyZ41axYOHz6MjRs3okaNGgWWdXd3BwDcvn0btWvXhq2trdYoIOoRPxQKBYCXtdavjgKSnJyMihUrwsLCAnK5HCYmJhqdHAEgJSVFrAG3tbVFdnY20tPTNWqzU1JSxP0YwszMDPXr1zf4+URERERUdNTNj1/nrUqyBUHA7Nmz8ddff2HDhg2wt7d/7XPUtb7qxNbDwwMrVqxASkqK2NzjxIkTqFixopi8enh44OjRoxrbOXHiBDw8PAC8vDpp1KgRTp48CV9fXwAvm6+cPHkSAwcOBAC4uLjAzMwMJ0+eRMeOHQEAN2/exL1798TtGEImk8HS0tLg5xMRERFR0dGnqQjwliXZM2fOxO+//47ly5ejQoUKYttmKysrWFhY4M6dO9i1axdatWoFGxsbXLt2DfPmzUPTpk3h5OQE4GUHxvr162Pq1Kn47LPPkJSUhNDQUPj7+4tV+x999BHCw8OxcOFC9OnTB6dOncLevXuxcuVKMZZPPvkE06ZNg4uLC9zc3LBu3TpkZmaid+/eYkx9+vTB/PnzUalSJVSsWBFz5syBUqksVJJNRERERMZPJgiCUNJBqDk6OupcPm/ePPTu3Rv//fcfPvvsM1y/fh0ZGRl455134Ovri7Fjx2oM45eYmIjg4GBERUWhfPny6NWrF6ZMmQJT0/+7pjh9+jTmzZuHGzduoEaNGhg7dqyYQKtt3LgRq1evRlJSEho2bIgZM2aIzVOAl5PRzJ8/H7t370ZWVhZ8fHzw9ddfG9xc5NKlSwAAV1dXg55PREREREVL33ztrUqyyzom2URERERvN33ztbd6nGwiIiIiImPEJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCTGJJuIiIiISGJMsomIiIiIJMYkm4iIiIhIYkyyiYiIiIgkxiSbiIiIiEhiTLKJiIiIiCT2ViXZK1euRJ8+faBUKtGsWTOMHTsWN2/e1Cjz4sULzJw5E97e3lAqlZgwYQKSk5M1yty7dw8jR46Eu7s7mjVrhgULFiAnJ0ejzOnTp9GrVy+4uLigffv22L59u1Y84eHhaNu2LVxdXdG3b19cvHjxjWMhIiIiorLnrUqyo6Ki4O/vjy1btmDNmjXIyclBQEAAMjIyxDJz587FoUOHEBoaig0bNuDhw4cYP368uD43NxejRo1CdnY2fv31V8yfPx87duzAkiVLxDIJCQkYNWoUvL29ERERgSFDhmDGjBk4duyYWGbPnj2YN28exo0bhx07dsDJyQkBAQFISUnROxYiIiIiKqOEt1hKSorg4OAgREVFCYIgCOnp6UKjRo2EvXv3imVu3LghODg4CNHR0YIgCMLhw4cFJycnISkpSSyzadMmoXHjxsKLFy8EQRCEhQsXCl27dtXY16RJk4Rhw4aJjz/88ENh5syZ4uPc3FzBx8dHWLlypd6xvKmLFy8KFy9eNOi5RERERFT09M3X3qqa7Fc9efIEAFCpUiUAwOXLl5GdnY3mzZuLZerVq4eaNWviwoULAIALFy7AwcEBtra2YhkfHx88ffoUN27cEMs0a9ZMY18+Pj7iNrKysnDlyhWN/cjlcjRv3hzR0dF6x0JEREREZZNpSQeQH5VKhblz56Jx48ZwcHAAACQnJ8PMzAzW1tYaZatWrYqkpCSxTN4EG4D4+HVlnj59iufPnyMtLQ25ubmoWrWq1n7UbcT1icUQgiBoNI8hIiIioreHIAiQyWSvLffWJtkzZ87E9evXsWnTppIOpVhlZ2cjNja2pMMgIiIionyYm5u/tsxbmWTPmjULhw8fxsaNG1GjRg1xua2tLbKzs5Genq5Rg5ySkgKFQiGWeXUUEPWIH3nLvDoKSHJyMipWrAgLCwvI5XKYmJhodHJU70ddA65PLIYwMzND/fr1DX4+ERERERUddfPj13mrkmxBEDB79mz89ddf2LBhA+zt7TXWu7i4wMzMDCdPnkTHjh0BADdv3sS9e/fg4eEBAPDw8MCKFSuQkpIiNvc4ceIEKlasKCavHh4eOHr0qMa2T5w4IW7D3NwcjRo1wsmTJ+Hr6wvgZfOVkydPYuDAgXrHYgiZTAZLS0uDn09ERERERUefpiLAW5Zkz5w5E7///juWL1+OChUqiG2braysYGFhASsrK/Tp0wfz589HpUqVULFiRcyZMwdKpVJMbH18fFC/fn1MnToVn332GZKSkhAaGgp/f3+xav+jjz5CeHg4Fi5ciD59+uDUqVPYu3cvVq5cKcbyySefYNq0aXBxcYGbmxvWrVuHzMxM9O7dW4zpdbEQERERUdkkEwRBkGJDmZmZ2L17N7KystCqVSvUqlXrjbfh6Oioc/m8efPE5PbFixeYP3++uC8fHx98/fXXGk00EhMTERwcjKioKJQvXx69evXClClTYGr6f9cUp0+fxrx583Djxg3UqFEDY8eOFfehtnHjRqxevRpJSUlo2LAhZsyYAXd3d3G9PrG8iUuXLgEAXF1dDXo+ERERERUtffM1g5Lszz//HBcvXsTvv/8O4OWQd3369MH169cBvKzlXbduHZydnd9002Uak2wiIiKit5u++ZpB42SfPn0a7du3Fx///vvvuH79OhYtWoTff/8dtra2WLZsmSGbJiIiIiIyegYl2cnJyRrNQfbv3w8XFxd069YN9evXR79+/bRG+CAiIiIiKisMSrLLly8vzsaYk5ODqKgo+Pj4iOsrVKggriciIiIiKmsMGl2kUaNG2LJlC7y9vXHw4EE8e/YMbdu2FdffuXNHa7ZEIiIiIqKywqAke9KkSRg+fDj69OkDQRDQsWNHuLm5iev/+usvNG7cWLIgiYiIiIiMiUFJtqurK/bu3Yvz58/D2toaXl5e4rr09HQMGDBAYxkRERERUVki2TjZVHgcwo+IiIjo7aZvvmbwjI+5ubnYt28fTp8+jZSUFEycOBGOjo548uQJTp48icaNG8PW1tbQzRMRERERGS2Dkuz09HQMHz4cFy9ehKWlJTIzMzFw4EAAgKWlJebMmYOePXsiKChI0mCJiIiIiIyBQUP4LVq0CNevX8fq1auxf/9+5G1xYmJigo4dO+LIkSOSBUlEREREZEwMSrIPHDiAQYMG4YMPPoBMJtNaX6dOHSQmJhY6OCIiIiIiY2RQkv3kyRPY2dnluz4nJwe5ubkGB0VEREREZMwMSrJr166NK1eu5Lv+77//Rr169QwOioiIiIjImBmUZH/44YfYtm0b9uzZI7bHlslkyMrKwuLFi3Hs2DH0799f0kCJiIiIiIyFQaOLDBkyBDdu3EBQUBCsra0BAJ9++ilSU1ORk5OD/v37o2/fvpIGSkRERERkLAxKsmUymThM3x9//IHbt29DpVKhdu3a6Ny5M5o2bSp1nERERERERuONk+zMzEx89tln6NChA/z8/ODp6VkUcRERERERGa03bpNdvnx5nDhxAs+fPy+KeIiIiIiIjJ5BHR+bNGmC6OhoqWMhIiIiIioVDEqyv/rqK5w7dw6LFy/G/fv3pY6JiIiIiMioyYS8c6LrSalUIjc3F9nZ2QBeTqVubm6uuWGZDOfOnZMmyjLi0qVLAABXV9cSjoSIiIiIdNE3XzNodJGOHTvqnE6diIiIiIgMTLLnz58vdRxERERERKWGQW2yiYiIiIgofwbVZAPA06dPsXbtWhw+fBj37t0DANSsWROtW7fG0KFDUbFiRcmCJCIiIiIyJgbVZD948AA9e/bEsmXLkJGRgcaNG6Nx48bIzMzEsmXL0KtXLzx8+FDqWImIiIiIjIJBNdmLFi1CcnIyVq5ciVatWmmsO3LkCCZNmoTvvvsOCxYskCRIIiIiIiJjYlBN9rFjxzBkyBCtBBsAWrVqhUGDBuHIkSOFDo6IiIiIyBgZlGRnZmaiatWq+a63tbVFZmamwUERERERERkzg5LsevXqYffu3cjKytJal52djd27d6NevXqFDo6IiIiIyBgZ1CZ7xIgRmDx5Mvr27YsBAwagTp06AIBbt27h119/xbVr17B48WIp4yQiIiIiMhoGJdmdO3dGZmYmvvvuO3z99dfi7I+CIKBq1aqYO3cuOnXqJGmgRERERETGwuBxsnv37g0/Pz9cvnxZY5xsFxcXmJoavFkiIiIiIqNnUDa8c+dOeHp6ws7ODh4eHvDw8NBYf/fuXZw9exY9e/aUIEQiIiIiIuNiUMfH//3vf4iOjs53/cWLF/G///3P4KCIiIiIiIyZQUm2IAgFrs/IyICJiYlBARERERERGTu9m4vExcUhLi5OfHz27Fnk5uZqlUtPT8evv/6KunXrShMhEREREZGR0TvJ3r9/P5YtWwYAkMlk2Lx5MzZv3qyzrLW1NadUJyIiIqIyS+8ku1+/fmjdujUEQUDfvn0xceJEtGzZUqOMTCZD+fLlUbt2bY4wQkRERERllt6ZcLVq1VCtWjUAwPr161GvXr0Cp1YnIiIiIiqrDKpu9vLyAgDk5ubiypUruHv3LgDAzs4OjRo1YqdHIiIiIirTDG7TsX37doSEhCAlJUUcbUQmk6FKlSqYPHkyPvzwQ8mCJCIiIiIyJgYl2b/++iuCg4PRsGFDjB8/HnXq1AEA3Lp1C5s3b8aXX36J7OxsfPzxx1LGSkRERERkFGTC6wa91qFdu3Z45513sGbNGpiZmWmsy87OxpAhQ/DgwQMcOHBAskDLgkuXLgEAXF1dSzgSIiIiItJF33zNoMlokpOT0blzZ60EGwDMzMzQtWtXpKSkGLJpIiIiIiKjZ1CS3bBhQ9y6dSvf9bdu3YKTk5PBQRERERERGTODkuwvv/wS+/btw7p16/D8+XNx+fPnz7F27Vrs27cPX331lWRBEhEREREZE4PaZHfv3h1paWlISkqCiYmJOH72w4cPkZubi2rVqsHa2lpzRzIZIiMjpYm6lGKbbCIiIqK3m775mkGji9jY2MDGxgbvvvuuxvJatWoZsjkiIiIiolLFoCR7w4YNUsdBRERERFRqGDwZDREREREZJ5VKhbi4OKSmpsLGxgZOTk6Qyw3qqkf50CvJPnPmDACgadOmGo9fR12eiIiIiN4OUVFRCA8PR1JSkrhMoVDA398fXl5eJRhZ6aJXx0cnJyfIZDLExMTA3NxcfJwfQRAgk8kQGxsrabClHTs+EhERUVGKiopCWFgYlEolevToAXt7eyQkJCAiIgLR0dEIDAxkov0aknZ8XL9+PQDA3Nxc4zERERERGQeVSoXw8HAolUoEBQWJzUMaNGiAoKAghISEIDw8HJ6enmw6IgG9kuy8VzSCIMDZ2RlmZmYoV65ckQVGRERERNKJi4tDUlISxo8fr5VEy+Vy+Pn5ITg4GHFxcXB2di6hKEuPN75Myc7OhpeXF2uziYiIiIxIamoqAMDe3l7nevVydTkqnDdOss3NzWFrays2HSEiIiKit5+NjQ0AICEhQed69XJ1OSocgxrc9OrVCxEREcjKypI6HiIiIiIqAk5OTlAoFIiIiIBKpdJYp1KpEBkZCYVCAScnpxKKsHQxaJxsR0dHHDhwAN26dUOvXr1Qq1YtWFhYaJXr0KFDoQMkIiIiosKTy+Xw9/dHWFgYQkJC4OfnJ44uEhkZKY4uwk6P0tBrCL9X6XOFwyH83hyH8CMiIqKixnGyC0fSIfxexU6PRERERMbJy8sLnp6enPGxiBmUZPMqh4iIiMh4yeVyDtNXxAy6ZElNTUVcXFy+669du4a0tDSDgyIiIiIiMmYGJdnz5s3DV199le/6r7/+GgsWLDA4KCIiIiIiY2ZQkn3q1Cm0bds23/Vt2rTByZMnDQ6KiIiIiMiYGZRkP3r0CJUrV853vY2NDVJSUgwOioiIiIjImBmUZCsUCly9ejXf9VeuXEGVKlUMDoqIiIiIyJgZlGT7+vpi27ZtOHDggNa6/fv3Y/v27fD19S10cERERERExsigJHvChAmoW7cuxo8fj169emHq1KmYOnUqevXqhQkTJqBOnTqYOHGiQQGdOXMGo0ePho+PDxwdHbF//36N9dOnT4ejo6PGv4CAAI0yqampmDJlCho3bgxPT098/vnnePbsmUaZuLg4DBgwAK6urmjVqhV++uknrVj27t2LTp06wdXVFd27d8eRI0c01guCgLCwMPj4+MDNzQ1Dhw7Fv//+a9DrJiIiIqLSw6Ak28rKCps3b8aYMWOQk5ODP/74A3/88QdycnIwduxYbNmyBdbW1gYFlJGRAUdHR3z99df5lmnRogWOHz8u/gsJCdFY/+mnn+LGjRtYs2YNVqxYgbNnz2qMhvL06VMEBASgZs2a2L59O6ZOnYply5Zh8+bNYpnz589jypQp+PDDD7Fz5060a9cO48aNQ3x8vFjmp59+woYNGxAcHIwtW7agfPnyCAgIwIsXLwx67URERERUOhg0rXpxcXR0xPfff6/R9GT69OlIT0/H8uXLdT7nn3/+QZcuXbB161ZxusujR49i5MiROHLkCKpXr45NmzYhNDQUx48fh7m5OQBg0aJF2L9/P/bt2wcAmDRpEjIzM7Fy5Upx2/369YOTkxNmzZoFQRDQokULfPLJJ2JN+pMnT9C8eXPMnz8fXbt2fePXy2nViYiIiN5uRTqten4SEhKQlZWFevXqSblZLVFRUWjWrBmsra3x/vvvY9KkSeJoJ9HR0bC2ttZ44c2bN4dcLsfFixfRvn17XLhwAZ6enmKCDQA+Pj746aefkJaWhkqVKuHChQsYOnSoxn59fHzE5it3795FUlISmjdvLq63srKCu7s7oqOjDUqygZdNUDIyMgx6LhEREREVLUEQIJPJXlvOoCR7/fr1iI6OxuLFi8Vl06dPR0REBACgYcOG+Omnn1C1alVDNl+gFi1aoH379rCzs0NCQgJCQkIwYsQIbN68GSYmJkhOTtYa2cTU1BSVKlVCUlISACA5ORl2dnYaZWxtbcV1lSpVQnJysrhMrWrVqkhOTgYAcVuvvsa8ZQyRnZ2N2NhYg59PREREREUrb0VtfgxKsn/77Td4e3uLj48dO4adO3eif//+cHBwQFhYGJYtW1Zgu2pD5a0hVnd89PX1FWu3jZ2ZmRnq169f0mEQERERkQ43btzQq5xBSfa9e/c0moTs3bsXdnZ2mDlzJoCXtcHqWu2iZm9vj8qVK+P27dto1qwZbG1t8ejRI40yOTk5SEtLg0KhAPCy1vrV2mb1Y3Xtta4yKSkp4nr1tlJSUlCtWjWNMk5OTga/HplMBktLS4OfT0RERERFR5+mIoCBo4u82lfy77//RsuWLcXHtWrVKlSTiTdx//59pKamikmvUqlEeno6Ll++LJY5deoUVCoV3NzcAAAeHh44e/YssrOzxTInTpxA3bp1UalSJbHMqVOnNPZ14sQJeHh4AADs7OygUCg0po9/+vQpYmJioFQqi+S1EhEREZFxMCjJrlOnjtgB8NixY3j48KFGkn3//n2Dh/B79uwZYmNjxXbJd+/eRWxsLO7du4dnz55hwYIFuHDhAu7evYuTJ09i7NixePfdd9GiRQsAQL169dCiRQt8+eWXuHjxIs6dO4fZs2eja9euqF69OgCge/fuMDMzwxdffIHr169jz549WL9+PT755BMxjsGDB+PYsWP4+eef8c8//2Dp0qW4fPkyBg4cCODlVczgwYPxww8/4MCBA7h27RqmTp2KatWqcSIeIiIiojLOoCH8du/ejSlTpsDKygqZmZmoU6cOdu7cCVPTl61PBg0ahHLlymHVqlVvHNDp06cxePBgreW9evVCcHAwxo0bh6tXr+LJkyeoVq0aPvjgAwQGBmp0UkxNTcXs2bNx8OBByOVydOjQATNmzECFChXEMnFxcZg1axYuXbqEypUrY+DAgRg5cqTGPvfu3YvQ0FAkJiaiTp06+Oyzz9CqVStxvSAIWLJkCbZs2YL09HQ0adIEX3/9NerWrfvGrxvgEH5EREREbzt98zWDx8n++++/ceTIEVhbW2PAgAHiiB6pqamYMWMGevTogfbt2xuy6TKLSTYRERHR263Ik2ySHpNsIiIiorebvvmaQW2yiYiIiIgofwYN4de2bdvXDl8ik8nEzpFERERERGWJQUm2l5eXVpKdm5uLe/fu4fz582jQoAGcnZ0lCZCIiIiIyNgYlGTPnz8/33VxcXEICAhA9+7dDQ6KiIiIiMiYSd4m28nJCf3798eiRYuk3jQRERERkVEoko6PVatW1XtedyIiIiKi0kbyJPvx48fYtm0batSoIfWmiYiIiIiMgkFtsnXNyAgAT548wc2bN5GdnY2FCxcWKjAiIiIiImNlUJKta/4amUwGOzs7NGvWDH369EG9evUKHRwRERERkTEyKMnesGGD1HEQEREREZUanPGRiIiIiEhieifZSUlJOHPmDJ49e6axPDs7G2FhYfD19YW7uzt69eqFAwcOSB4oEREREZGx0DvJ/vHHHxEYGAgzMzON5QsWLMCKFSuQnp6O+vXr49atW5g4cSLOnDkjebBERERERMZA7yT7zJkzaNOmDczNzcVljx49wqZNm1CvXj3s378f27Ztw+7du1G5cmX8/PPPRRIwEREREdHbTu8k+7///kODBg00lh06dAgqlQrDhg2DtbU1AKBWrVro06cPLl68KG2kRERERERGQu8kOysrC5aWlhrLzp49C5lMhmbNmmkst7e3R1pamjQREhEREREZGb2TbDs7O8TGxmosO336NGrWrIl33nlHY3lGRgZsbGwkCZCIiIiIyNjonWS3b98eO3fuxJ49e/Dff//hhx9+wL1799C5c2etsjExMbCzs5M0UCIiIiIiY6H3ZDTDhw/HoUOHEBQUBJlMBkEQULduXYwePVqj3OPHj3Hw4EEEBARIHiwRERERkTHQO8m2tLTEb7/9hr/++gsJCQmoVasWfH19Ua5cOY1yDx48wIQJE9CxY0fJgyUiIiIiMgZvNK26qampzuYheTk5OcHJyalQQRERERERGTNOq05EREREJDEm2UREREREEmOSTUREREQkMSbZREREREQSY5JNRERERCSxQifZDx8+RFxcHDIyMqSIh4iIiIjI6BmcZO/fvx+dOnVCq1at0KtXL8TExAAAHj16hJ49e2L//v2SBUlEREREZEwMSrIPHjyICRMmoHLlyhg3bhwEQRDXValSBdWrV8e2bdskC5KIiIiIyJgYlGR///338PT0xC+//AJ/f3+t9R4eHoiNjS10cERERERExsigJPv69esFzvxoa2uLlJQUg4MiIiIiIjJmbzStulr58uWRmZmZ7/qEhATY2NgYGhMRERERFSGVSoW4uDikpqbCxsYGTk5OkMs56JyUDEqyvb29sXPnTgwZMkRrXVJSErZs2YI2bdoUOjgiIiIiklZUVBTCw8ORlJQkLlMoFPD394eXl1cJRla6GJRkT5o0Cf3798eHH36ITp06QSaT4fjx4zh16hQ2b94MQRAwbtw4qWMlIiIiokKIiopCWFgYPDw80LVrV5ibmyMrKwsxMTEICwtDYGAgE22JyIS8Q4O8gevXr+Obb77B6dOnNUYX8fLywtdff4169epJFmRZcenSJQCAq6trCUdCREREpY1KpcLkyZNhZWWF9PR0JCcni+tsbW1hbW2NJ0+eYPHixWw6UgB98zWDarIBoEGDBli7di3S0tJw+/ZtCIIAe3t7VKlSxdBNEhEREVERiYuLQ1JSEpKSktC4cWNMmDAB9vb2SEhIQEREBM6fPy+Wc3Z2LuFojV+hL1MqVaoENzc3uLu7M8EmIiIieks9evQIAODu7o6goCA0aNAAFhYWaNCgAYKCguDu7q5RjgpHr5rsnTt3GrTxnj17GvQ8IiIiIpJWeno6AKBp06ZazUHkcjk8PT0RExMjlqPC0SvJnj59utYymUwGAHi1Sbd6OcAkm4iIiOhtYW1tDQA4e/YsWrdurZFoq1QqnD17VqMcFY5eSfaBAwc0Hj958gTTpk2DlZUVBg4ciLp16wIAbt68iY0bN+LZs2eYP3++9NESERERkUHUzXpjYmIQEhICPz8/sU12ZGQkYmJiNMpR4Rg0usj//vc/3L9/Hz///LNGzTXw8kpo2LBheOeddzBv3jzJAi0LOLoIERERFRX16CIVK1bE06dPtcbJVi/n6CIF0zdfM+gd3L9/P3x9fbUSbOBlm5727dtr1X4TERERUcmRy+Xw9/fHv//+Czs7OwwZMgQjR47EkCFDYGdnh3///Rf+/v5MsCVi0BB+giDg1q1b+a7/559/tNpqExEREVHJ8vLyQmBgIDZu3Ijo6Ghxua2tLSeikZhBlyq+vr745ZdfsGbNGmRmZorLMzMz8fPPP2Pz5s1o166dZEESERERkXRebY2gq3UCFY5BbbKfPHmCMWPG4OzZszA1NUW1atUAAA8fPkROTg4aN26MFStWsHfqG2KbbCIiIipK6mnVlUolevTooTEZTXR0NGuz9aBvvmbwtOrAy7bZR48exb179wAANWvWRKtWrdC2bVteERmASTYREREVFXXHR3t7ewQFBWkN4RcSEoKEhAR2fHyNIp9WHXjZbMTX17cwmyAiIiKiYqCeVn38+PE6J6Px8/NDcHAwp1WXCC9TiIiIiMqA1NRUAIC9vb3O9erl6nJUOEyyiYiIiMoAGxsbAEBCQoLO9erl6nJUOEyyiYiIiMoAJycnKBQKREREQKVSaaxTqVSIjIyEQqGAk5NTCUVYujDJJiIiIioD1JPRREdHIyQkBPHx8cjMzER8fDxCQkIQHR3NyWgkVKjRRUhaHF2EiIiIilpUVBTCw8O1plX39/fn8H16KJbRRQDg2bNnuH//PgCgRo0aqFChQmE3SURERERFxMvLC56enoiLi0NqaipsbGzg5OTEGmyJGZxkX7x4Ed9++y3Onz8vtuuRy+Vo0qQJPvvsM9bGEhEREb2l5HI5h+krYgYl2TExMRg0aBDMzMzw4Ycfol69egCAf/75B7t378bAgQOxYcMGuLm5SRosEREREZExMKhN9tChQ5GYmIhNmzZBoVBorEtOTsbHH38MOzs7rFmzRrJAywK2ySYiIiJ6u+mbrxnU+CYmJgb9+/fXSrABwNbWFv369cOFCxcM2TQRERERkdEzKMmWy+XIzc3Nd71KpWLjeSIiIiIqswzKhJVKJcLDw5GYmKi17t69e9i0aRMaN25c6OCIiIiIiIyRQR0fg4KC4O/vj86dO6N9+/aoU6cOAODWrVs4cOAATExMMGXKFCnjJCIiIiIyGgYl2c7Ozvjtt9+wePFiHDx4EJmZmQCA8uXLo0WLFpg0aRLq168vaaBERERERMbijZPsrKwsHDt2DLVq1cL3338PlUqFR48eAQCqVKnCtthEREREVOa9cUZsZmaGwMBAREdHv9yAXA5bW1vY2toywSYiIiIiggE12TKZDHXq1MHjx4+LIh4iIqIyS6VScaprolLCoDbZo0aNwvz589GpUye89957UsdERERU5kRFRSE8PBxJSUniMoVCAX9/f3h5eZVgZERkCIOnVbexsUH37t3h5eWFWrVqwcLCQqvcjBkzCh0gERFRaRcVFYWwsDAolUqMHz8e9vb2SEhIQEREBMLCwhAYGMhEm8jIGDStupOT0+s3LJMhNjb2jQM6c+YMVq9ejcuXLyMpKQnff/89fH19xfWCIGDJkiX47bffkJ6ejsaNGyM4OFgcRhAAUlNTMXv2bBw6dAhyuRwdOnTAF198gQoVKohl4uLiMGvWLFy6dAlVqlTBwIEDMWLECI1Y9u7di7CwMCQmJqJOnTr49NNP0apVqzeK5U1wWnUiorJHpVJh8uTJsLe3R1BQkEbzEJVKhZCQECQkJGDx4sVsOkL0FijSadXj4uJe+8+QBBsAMjIy4OjoiK+//lrn+p9++gkbNmxAcHAwtmzZgvLlyyMgIAAvXrwQy3z66ae4ceMG1qxZgxUrVuDs2bP46quvxPVPnz5FQEAAatasie3bt2Pq1KlYtmwZNm/eLJY5f/48pkyZgg8//BA7d+5Eu3btMG7cOMTHx79RLERERAWJi4tDUlISevTooZVEy+Vy+Pn5ISkpCXFxcSUUIZVGKpUKV69exYkTJ3D16lWoVKqSDqnUMai5SFFq1aqVRm1xXoIgYP369RgzZoxYu71w4UI0b94c+/fvR9euXfHPP//g2LFj2Lp1q3iFMWPGDIwcORJTp05F9erVERkZiezsbMydOxfm5uZo0KABYmNjsWbNGvTv3x8AsH79erRo0QLDhw8HAEyaNAknTpzAxo0bMWvWLL1iISIiep3U1FQAgL29vc716uXqckSFxfb/xaNQSfaFCxdw+vRppKSkYMCAAahTpw4yMzNx8+ZN1KlTR6N5hhTu3r2LpKQkNG/eXFxmZWUFd3d3REdHo2vXroiOjoa1tbVGFX7z5s0hl8tx8eJFtG/fHhcuXICnpyfMzc3FMj4+Pvjpp5+QlpaGSpUq4cKFCxg6dKjG/n18fLB//369YzGEIAjIyMgw6LlERGR81H2arl+/jnr16mmt/+eff8Ry/H2gwjp37hxWrFgBNzc3DB8+HLVq1UJiYiL27NmDsLAwjB49Gk2aNCnpMN9qgiBAJpO9tpxBSXZWVhaCgoJw4MABcUdt2rRBnTp1IJfLMWzYMAwdOhRjxowxZPP5Ul9xVa1aVWN51apVkZycDABITk5GlSpVNNabmpqiUqVK4vOTk5NhZ2enUcbW1lZcV6lSJSQnJ4vLdO1Hn1gMkZ2dbXBTGyIiMj4qlQrW1tbYvHkzevbsqfHjLQgCdu7ciUqVKiE3N5e/D1QoKpUK4eHhqFu3Ltq1a4esrCzcunULANCuXTs8efIEmzZtgoWFBdv/v0beitr8GJRkh4WF4fDhwwgODoa3tzc6deokritXrhw6deqEAwcOSJ5klwVmZmackp6IqIzx9/fHihUrcODAAXTp0kWjdvHWrVsYPXo0GjVqVNJhkpGLi4tDeno6xo0bp/OuSbly5TBv3jyYmJjoNchFWXXjxg29yhmUZO/evRsfffQR+vfvr3NSmnr16mHfvn2GbLpACoUCAJCSkoJq1aqJy1NSUsSDwdbWVpzmXS0nJwdpaWni821tbbVqm9WP1bXXusqkpKSI6/WJxRAymQyWlpYGP5+IiIxPixYtUK5cOYSHh2PevHnicoVCweH7SDLPnz8HADRo0EDn0MvqSr7nz58zFymAPk1FAAOT7JSUFDg6Oua73sTERPwgpWRnZweFQoGTJ0+iYcOGAF6OFBITE4OPP/4YAKBUKpGeno7Lly/DxcUFAHDq1CmoVCq4ubkBADw8PBAaGors7GyYmZkBAE6cOIG6deuiUqVKYplTp05ptMs+ceIEPDw89I6FiIhIX15eXvD09OSMj1RkbGxsAAAJCQlo0KCB1vqEhASNclQ4Bn1z33nnHdy8eTPf9efPn0ft2rUNCujZs2eIjY0V253dvXsXsbGxuHfvHmQyGQYPHowffvgBBw4cwLVr1zB16lRUq1ZNHOGjXr16aNGiBb788ktcvHgR586dw+zZs9G1a1dUr14dANC9e3eYmZnhiy++wPXr17Fnzx6sX78en3zyiRjH4MGDcezYMfz888/4559/sHTpUly+fBkDBw4EAL1iISIiehNyuRzOzs5o3rw5nJ2dmWCTpJycnKBQKBAREaE1ZJ9KpUJkZCQUCgWbikjEoJrsbt26Yc2aNejQoYM48Yq66nzLli3Yu3cvpkyZYlBAly9fxuDBg8XH6ttmvXr1wvz58zFixAhkZmbiq6++Qnp6Opo0aYJVq1ahXLly4nMWLVqE2bNnY8iQIeJkNHlnn7SyssLq1asxa9Ys9O7dG5UrV8bYsWPF4fsAoHHjxli0aBFCQ0MREhKCOnXq4Pvvv4eDg4NYRp9YiIiIiN4Gcrkc/v7+CAsLQ0hICPz8/MTZRSMjIxEdHY3AwEBe3EnEoBkfs7KyMGbMGJw6dQrvvfcebty4AQcHB6SlpeH+/fto1aoVli9fDhMTk6KIudTijI9ERERU1DhOduHom68ZlGQDL4cVioyMxB9//IHbt29DpVKhdu3a6Ny5M3r06KF3o3D6P0yyiYiIqDioVCq2/zdQkSfZJD0m2URERERvN33zNV6yEBERERFJzOBp1c+ePYtt27bh7t27SEtLw6sV4jKZDJGRkYUOkIiIiIjI2BiUZK9ZswYLFy5EuXLlNMaWJiIiIiIiA5Ps1atXo3HjxlixYgWsrKykjomIiIiIyKgZ1CY7MzMT3bt3Z4JNRERERKSDQUm2t7c34uPjpY6FiIiIiKhUMCjJ/vLLL3Hy5EmsXr0aqampEodERERERGTcDGqT/c4776B///5YuHAhFi1ahHLlymkNYC6TyXDu3DlJgiQiIiIiMiYGJdlhYWFYsWIFqlevDhcXF7bNJiIiIiLKw6Ak+9dff0WrVq2wfPlyTsFJRERERPQKg5Ls7OxstG7dmgk2ERERkRFSqVSIi4tDamoqbGxs4OTkxLxOYgYl2a1bt8bZs2fx0UcfSR0PERERERWhqKgobNy4EcnJyeIyW1tbDBw4EF5eXiUYWeli0CXL+PHj8c8//yA4OBiXL1/Go0ePkJqaqvWPiIiIiN4eUVFRCA0NRXp6usby9PR0hIaGIioqqoQiK30Mqsnu1KkTACA2NhabN2/Ot1xsbKxhURERERGRpFQqFX7++WcAQKNGjdCzZ0/Y29sjISEBO3fuRHR0NH7++Wd4enqy6YgEDEqyx40bB5lMJnUsRERERFRErl69ivT0dDg6OmLKlCliIt2gQQNMmTIFs2bNQnx8PK5evQoXF5cSjtb4GZRkT5gwQeo4iIiIiKgIXb16FQDw4YcfatVUy+Vy9OnTB/PmzWOSLRHeCyAiIiIqQwRBKOkQygSDarKXLVv22jIymQzjxo0zZPNEREREJDFnZ2fs3LkT27Ztg7Ozs0ZttkqlwrZt28RyVHivTbLV4yfmVVCSLZPJIAgCk2wiIiKit4izszOsra1x7do1LFq0CO7u7ihXrhxevHiBmJgYxMfHw9ramkm2RF6bZG/cuBGmpqYYPXq0uCwuLk6rnEqlQmJiIjZt2oQzZ87gp59+kjZSIiIiIjKYXC7HsGHDEBoaigsXLuDChQtaZYYNG8aRRSTy2nexRYsW2LhxI77++usC2/DI5XLY29tj2rRpePfddzFnzhxJAyUiIiIiaZibmxf4mArvtUm2u7s7duzYgVu3bmHt2rV6bbRp06Y4cuRIYWMjIiIiIomoVCqEh4ejcePGWLVqFWbMmIHx48djxowZWLVqFRo3bozw8HCoVKqSDrVU0Ot+gEKhwNq1a9GyZUu9Nnr58mXeaiAiIiJ6i8TFxSEpKQk9evSAqakpnJ2d0bx5czg7O8PU1BR+fn5ISkrS2SyY3pzeo4vI5XLUq1cPALBz506dZdLT03H27Fn8+eef6Nu3ryQBEhEREVHhpaamAgDs7e11rlcvV5ejwjFoCL/p06fnu65y5coYOXIkRxYhIiJ6QyqVCnFxceLIXk5OTrwzTJJRjxaXkJCABg0aaK1PSEjQKEeFY1CSfeDAAa1lMpkM1tbWqFixYqGDIiIiKmuioqIQHh6OpKQkcZlCoYC/vz+8vLxKMDIqLZycnKBQKBAREYGgoCCtcbIjIyOhUCjg5ORUglGWHgYl2bVq1ZI6DiIiojIrKioKYWFhUCqVGD9+POzt7ZGQkICIiAiEhYUhMDCQiTYVmlwuh7+/P8LCwhASEgI/Pz/xWIuMjER0dDQCAwN590QiMoFza741Ll26BABwdXUt4UiIiKi4qFQqTJ48Gfb29jprF0NCQpCQkIDFixcz+SFJ8K5J4eibr+ldk929e/c3CkAmkyEyMvKNnkNERFTWqEd8GD9+vFYSLZfL4efnh+DgYMTFxXEmPpLMq8P0cdg+6emdZOvbCD45ORm3bt2CTCYzNCYiIqIygyM+UHGKiopCaGio1vKUlBSEhoZi0qRJrM2WiN5J9oYNGwpcn5SUhJ9++gmbN2+GiYkJ/Pz8Ch0cERFRaccRH6i4qFQqrFy5EgBgbW2Nfv36oXHjxjh//jy2bNmC9PR0rFy5Ep6enmyaJAGDOj7mlZycjB9//BFbtmxBTk4OunfvjjFjxqB27dpSxEdERFSqccQHKi6XL19GZmYmKlSogGXLlsHU9GUa2LZtW7Rs2RJjxozBs2fPcPnyZbi5uZVwtMbP4MuUpKQkzJ07F76+vti0aRM6d+6MvXv3Yt68eUywiYiI9KQe8SE6OhohISGIj49HZmYm4uPjERISgujoaPj7+7NmkQrt2LFjAIAPP/wQcrkcV69exYkTJ3D16lXI5XL07t1boxwVzhvXZCclJeHHH3/Eb7/9hpycHPj5+WHMmDH5tiUjIiKignl5eSEwMBDh4eEIDg4WlysUCg7fR5J58eIFgJft+ydPnqw1usj777+vUY4KR+8k++HDh2JynZubix49emD06NFMromIiCTg5eUFT09PzvhIRcbR0RFnz55FREQElEolunbtCnNzc2RlZSEmJga7du0Sy1Hh6T1Otru7O7KystCwYUOMGjUKdnZ2r31Oo0aNCh1gWcJxsomIiKioZGVlYejQoQAAU1NT5OTkiOvyPl67di3Mzc1LIkSjIPk42epbB1evXsWkSZMKLCsIAmQyGWJjY/XdPBEREREVoRs3boh/502wX31848YNjskuAb2T7Hnz5hVlHERERERUhB49egTgZfvrlJQUjQlo5HI5qlatiqSkJLEcFY7eSXavXr2KMg4iIiIiKkLp6ekAgB49eqBly5b4888/8fDhQ1SrVg0dOnTAkSNHsHr1arEcFU6hx8kmIiIioreftbU1AODs2bNo3bo1unTpIq5TqVQ4e/asRjkqHCbZZLRUKhV74RMREempSpUqAICYmBiEhITAz88P9vb2SEhIQGRkJGJiYjTKUeEwySajFBUVhfDwcK0xPv39/TmeLBERkQ7q2UUrVqyIhIQErTHZ69ati6dPn3J2UYkwySajExUVhbCwMCiVSowfP168Co+IiEBYWBgnbiAiItJBPbtoWFgY3N3d0bhxY2RnZ8PMzAwPHjxATEwMAgMDeVdYIkyyyaioVCqEh4dDqVQiKChIPBE0aNAAQUFBCAkJQXh4ODw9PXmSICIieoWXlxe6du2KPXv2aI0u0rVrV1ZSSYhJNhmVuLg4JCUlYfz48VpJtFwuh5+fH4KDgxEXF8cxPomIiF4RFRWF3bt3w8PDA+7u7hozPu7evRv169dnoi0RJtlkVFJTUwEA9vb2Oterl6vLERER0Uv53Q0GAF9fX94NlhjfQTIqNjY2AICEhASd69XL1eWIiIjoJfXd4B49euR7NzgpKQlxcXElFGHpwiSbjIq6Z3RERIRGWzLg5RV6ZGQkFAoFe0YTERG9gneDixeTbDIq6p7R0dHRCAkJQXx8PDIzMxEfH4+QkBBER0fD39+ft7mIiIhewbvBxYttssnoeHl5ITAwEOHh4VpjfHL4PiIiIt3y3g2eNGkS4uPjxQndHBwceDdYYjJBEISSDoJeunTpEgDA1dW1hCMxDpzxkYiI6M1ERUUhNDQUZmZmyM7OFperH0+aNImVVa+hb77GmmwyWnK5nMP0ERERGSBvgq3rMRUeq/2IiIiIygCVSoWff/4ZAGBubq6xTv34559/1hpYgAzDmmwiIiKiMuDq1atIT08HADRq1EhrMpro6Gikp6fj6tWrcHFxKeFojR+TbDJabJNNRESkvytXrgAAatSogYSEBERHR4vrbG1tUaNGDdy/fx9Xrlxhki0BJtlklKKiohAeHo6kpCRxmUKhgL+/PztsEBER6ZCcnAwAuH//Pho3bowJEybA3t4eCQkJiIiIwPnz5zXKUeGw2o+MTlRUFMLCwmBvb4+ZM2fi559/xsyZM2Fvb4+wsDBERUWVdIhERERvnapVqwIALC0tMXHiRGRnZ+P8+fPIzs7GxIkTYWlpqVGOCoc12WRUVCoVwsPDoVQqERQUJDYPadCgAYKCghASEoLw8HB4enqy6QgREVEeVlZWAICMjAyMGDFC5xB+ectR4TALIaMSFxeHpKQk9OjRQyuJlsvl8PPzQ1JSEuLi4kooQiqNVCoVrl69ihMnTuDq1avseU9ERqlSpUri3wUN4Ze3HBmONdlkVFJTUwEA9vb2Oterl6vLERUW2/8TUWmRd7p09agiuh5zWnVpsCabjIr6i5+QkKBzvXo5TxAkBbb/J6LSqGbNmrC2ttZYVqlSJbzzzjslFFHpxCSbjIqTkxMUCgUiIiK0btmrVCpERkZCoVDAycmphCKk0uLV9v8NGjSAhYWF2P5fqVQiPDycTUeIyGiox8i+d+8e7O3tMWTIEIwcORJDhgyBnZ0d/vvvP41yVDhMssmoyOVy+Pv7Izo6GiEhIYiPj0dmZibi4+MREhKC6Oho+Pv7s9MjFRrb/xNRaaO+y9u/f38kJCRg3bp1+PHHH7Fu3TokJCSgX79+GuWocNgmm4yOl5cXAgMDER4ejuDgYHG5QqFAYGAg28mSJNj+n4hKG/Xd4KioKAiCoLFOEAScOXOGd4MlxOo+Mlq6ThBEUmH7fyIqbeRyOby9vXHr1i08evRIY92jR49w69YteHt7826wRIzuXVy6dCkcHR01/nXq1Elc/+LFC8ycORPe3t5QKpWYMGGC1sxF9+7dw8iRI+Hu7o5mzZphwYIFyMnJ0Shz+vRp9OrVCy4uLmjfvj22b9+uFUt4eDjatm0LV1dX9O3bFxcvXiyaF00a1J3RateurdEZrXbt2uyMRpJh+38iKm1UKhWOHj0K4OW42HmpHx89epR9TSRidEk28HLikePHj4v/Nm3aJK6bO3cuDh06hNDQUGzYsAEPHz7E+PHjxfW5ubkYNWoUsrOz8euvv2L+/PnYsWMHlixZIpZJSEjAqFGj4O3tjYiICAwZMgQzZszAsWPHxDJ79uzBvHnzMG7cOOzYsQNOTk4ICAhASkpK8bwJZRQ7o1FxYft/Iiptrl69ivT0dDg6OuLHH3/EwIED0aFDBwwcOBA//vgjHBwckJ6ejqtXr5Z0qKWCUbbJNjExgUKh0Fr+5MkTbNu2DYsWLUKzZs0AvEy6u3TpggsXLsDDwwPHjx/HjRs3sGbNGtja2qJhw4YIDAzEokWLMH78eJibm+PXX3+FnZ0dpk+fDgCoV68ezp07h7Vr16JFixYAgDVr1qBfv37o06cPAGDmzJk4fPgwtm3bhpEjRxbTO1H2qDujjR8/Pt/OaMHBwYiLi4Ozs3MJRUmlBdv/E1Fpok6eXVxc8Nlnn2mM///HH3+gRYsWiI+Px9WrV+Hi4lJSYZYaRplk3759Gz4+PihXrhw8PDwwZcoU1KxZE5cvX0Z2djaaN28ulq1Xrx5q1qwpJtkXLlyAg4MDbG1txTI+Pj4IDg7GjRs34OzsjAsXLohJet4yc+fOBQBkZWXhypUrGDVqlLheLpejefPmiI6OLtRrEwQBGRkZhdpGafbgwQMAQNWqVXW+T+rP9cGDB6hTp05xhkallIuLC2bPno2DBw8iKSkJCoUCbdu2hampKb+rJDmVSoX4+HikpaWhUqVKcHBw4N0Skox6Vsdt27bB3NxcY11aWprYNDY7O5vntwIIggCZTPbackaXZLu5uWHevHmoW7cukpKS8P3338Pf3x+7du1CcnIyzMzMtAZYr1q1qni1lpycrJFgA/+XmL2uzNOnT/H8+XOkpaUhNzcXVatW1drPzZs3C/X6srOzERsbW6htlGZpaWkAgL///hs1a9bUWn/v3j2xHN9HksL169dx+PBhjXFj9+3bh9atW6NBgwYlGBmVNrqONWtrax5rJBkLCwvx73feeQf16tWDmZkZsrOz8c8//+D27dtiOf6GFuzVixRdjC7JbtWqlfi3k5MT3N3d0aZNG+zdu1fj4DFWZmZmqF+/fkmH8dZydHTEwYMHcfXqVbRp00ajhkelUmH//v2wtbWFr68va3+o0M6dO4ddu3bBzc0NXbp0Qa1atZCYmIg9e/Zg165dGD16NJo0aVLSYVIpwGONikPe/kr37t0Tk2pAsyNk7dq10bBhw2KNzZjcuHFDr3JGl2S/ytraGnXq1MGdO3fQvHlzZGdnIz09XaM2OyUlRWzDbWtrqzUKiHr0kbxlXh2RJDk5GRUrVoSFhQXkcjlMTEy0OjmmpKRo1YC/KZlMBktLy0Jto7QbOHAgwsLCsGLFCvj5+cHe3h4JCQmIjIzExYsXERgYiIoVK5Z0mGTkVCoVtm7dKnayVV+0ValSBY0aNUJISAi2bt2KDz74gBd0VCg81qi43Lp1S/z71VHV8j6+desWmjZtWmxxGRt9mooARjq6SF7Pnj1DQkICFAoFXFxcYGZmhpMnT4rrb968iXv37sHDwwMA4OHhgfj4eI0E+cSJE6hYsaJYg+zh4YFTp05p7OfEiRPiNszNzdGoUSON/ahUKpw8eRJKpbKIXimpqTujJSQkIDg4GAEBAQgODkZCQgI7o5FkOOMjFRcea1Rc1PNJvPPOOzqbvNaoUUOjHBWO0dVkL1iwAG3atEHNmjXx8OFDLF26FHK5HN26dYOVlRX69OmD+fPno1KlSqhYsSLmzJkDpVIpJsg+Pj6oX78+pk6dKvasDQ0Nhb+/v9i+5qOPPkJ4eDgWLlyIPn364NSpU9i7dy9WrlwpxvHJJ59g2rRpcHFxgZubG9atW4fMzEz07t27JN6WMsfLywuenp6Ii4tDamoqbGxs4OTkxFoekkzeGR9VKpXWscYZH0kqnF2Uiov6Lq+ZmRkWLFiA+Ph48bzm4OCAL774QqMcFY7RJdn3799HUFAQUlNTUaVKFTRp0gRbtmxBlSpVAACff/455HI5Jk6ciKysLPj4+ODrr78Wn29iYoIVK1YgODgY/fv3R/ny5dGrVy9MnDhRLGNvb4+VK1di3rx5WL9+PWrUqIE5c+aIw/cBQJcuXfDo0SMsWbIESUlJaNiwIVatWlXo5iKkP7lczmH6qMioZ3L8448/xJFF1BQKBdq0aaNRjshQeWcX1dXBkbOLklQqVaoEALhz5w4WL14Md3d3mJub4+7du9i9e7d4rKnLUeHIBN4TeGtcunQJAODq6lrCkRCRSqXC2LFjkZ6eDqVSiZ49e4rt/3fu3Ino6GhYW1tj+fLlvINChaJSqTB58mTY29trtMlWrwsJCUFCQgIWL17MY40K5erVq5gzZ85ry82YMYOVWAXQN1/jt5WISA+CIIj/iKTE2UWpuDg5OWkNc/wqa2trODk5FVNEpZvRNRchIioOcXFxSE9PR//+/XHw4EGtGR/79euHLVu2cHZRkgRnF6Xi8vz5cwAvm886OjqicuXKePz4Ma5du4bc3FxxPRUek2wiIh3Uncw6duyI7t27a3V8fPHiBbZs2cLOaCQZduimonblyhVkZWXB1NQUKpVKnGYdeHlHxdTUVJzVmk1XC49JNhGRDq92Rnu1tpqd0agosEM3FaWjR48CeDkmtlKpFDs+ZmVlISYmBtHR0WI5JtmFx8tjIiIdnJycoFAoEBERoTFLGvCyM1pkZCQUCgXbLhKR0VA3BalXrx4mT54MOzs7mJubw87ODpMnT8Z7772nUY4KhzXZREQ6qDujhYWFISQkRGt20ejoaAQGBvJWPhEZDfWdt+TkZAQFBWnMbm1ra4vs7GyNclQ4TLKJiPLBzmhEVJo4ODjgwIEDSEtLg7W1NYYPHw6lUono6Ghs2bIF6enpYjkqPCbZREQFYGc0IiotKleuLP795MkTrFq1Snwsk8l0liPDMckmInoNdkYjotJEPWxfXoIg6FxOhmNVDBEREVEZoG4O8vjxY5ibm2usMzc3FxNsdTkqHNZkExG9hkqlYnMRIjJ6+nZoZMdHaTDJJiIqQFRUFMLDw5GUlCQuUygU8Pf3Z8dHIjIqDg4OkMvlsLCwQPny5ZGSkiKus7KyQmZmJp4/f86OjxJhVQwRUT6ioqIQFhYGe3t7zJw5Ez///DNmzpwJe3t7hIWFISoqqqRDJCLSW3x8PFQqFTIyMvDo0SONdY8ePUJGRgZUKhXi4+NLKMLShUk2EZEOKpUK4eHhUCqVCAoKQoMGDWBhYYEGDRogKCgISqUS4eHhWhPVEBG9rfIm1iYmJhrr8j5+NQEnwzDJJiLSIS4uDklJSejRowcA4OrVqzhx4gSuXr0KAPDz80NSUhLi4uJKMkwiIr2lpaUBeDnxzKvtrm1sbFC1alWNclQ4bJNNRKRDamoqAODhw4dYtmyZVpvsvn37apQjkgI72VJRevr0KYCXMz6+OrpIeno6srKyNMpR4TDJJiLSQV3Ls3z5ciiVSowfP16cVj0iIgLLly/XKEdUWOxkS8VJnVDn95gKj5fHREQ6qHvhW1tbY9KkSRptsidNmoRKlSpBLpezFz5Jgp1sqThYWlpKWo4KxiSbiEgHdS/8tLQ0hIaGIj4+HpmZmYiPj0doaCjS0tLYC58kwU62VFyePXsm/p13GvVXH+ctR4Zjkk1EpIO6rfXYsWORkJCA4OBgBAQEIDg4GAkJCRg7dqxGOSJD5e1k+2r7a7lczk62JJm842ILgqCxLu/jvOXIcGyTTUSkg7qtdfXq1bF48WKtzmg3btzQKEdkKPWFmr29vc716uW8oKPCejWxLmw5KhhrssloqVQqjWHVeCuVpOTk5ASFQoGIiAgAgLOzM5o3bw5nZ2cAQGRkJBQKBZycnEoyTCoF1BdqCQkJOterl/OCjsi4sCabjBJ74VNRk8vl8Pf3R1hYGEJCQuDn5yeOLhIZGYno6GgEBgZyeDUqtLwXdEFBQRrHlEql4gUdSYY12cWLSTYZHXUvfF3DqoWFhSEwMJCJNknCy8sLgYGBCA8PR3BwsLhcoVDwOCPJ8IKOikt6erqk5ahgTLLJqLzaC1/9o6PuhR8SEoLw8HB4enryB4kk4eXlhcaNG+PPP//Ew4cPUa1aNXTo0AGmpjx9knR4QUfFQd+xsDlmtjT4K0FGRd0Lf/z48fn2wg8ODkZcXJzYdpaoMHQ1Tfrjjz/YNIkkxws6KmpMsosXv7lkVNgLn4oTmyZRceIFHRU1MzMzSctRwXg/nYwKe+FTccnbNGnixIm4fv06fv31V1y/fh0TJ07kBCEkKc74SMWhXLlykpajgjHJJqOStxd+Tk6OxhB+OTk57IVPklE3TSpfvjyGDRuGjRs34s8//8TGjRsxbNgwWFhYcIIQkgRnfKTiUqFCBUnLUcHYXISMiroXfmhoKIYPH67Rbszc3BxZWVmYNGkSOz1SoambHP39999a0w8LgoATJ05olCMyFPuaUHG5f/++pOWoYEyyiYh0qFixovi3m5sblEqleCEXHR2NmJgYrXJEhmBfEyoujx49krQcFYxJNhkV9W3Vxo0bY/z48di0aRMePHiA6tWrY8CAAVi2bBmH8CNJ3L59G8DLDkCJiYliUg0Atra2MDMzQ3Z2Nm7fvg03N7eSCpNKgbx9TRo0aKC1nn1NSCo5OTmSlqOCMQsho5K3nezw4cOxf/9+XLp0Cfv378fw4cPZTpYkc/36dQBAdnY2srOzERAQgO+//x4BAQHisrzliAzFviZUXMqXLy9pOSoYa7LJqORtJ1upUiX07dsXjRs3xvnz5/Hbb7+xnSxJRt27vkqVKpDL5Vi9ejVWr14N4GVNdpUqVfDo0SP2wqdCY18TKi4mJiaSlqOCMckmo6Ju/1qhQgUsXbpUnKShbdu2aNmyJcaMGYNnz56xnSwVWu3atfH3338jMzMTP/zwA27cuIHU1FTY2Nigfv36GD16tFiOiMgYsLlI8WKSTUZF3TaxatWqAICrV6+KiY+DgwOqVKmCZ8+eISEhge1kqVAqV64MAMjMzMTEiRPRt29fKJVKREdHY8mSJXj+/LlGOSJD5e1rMmnSJMTHx2uc10JDQ9nXhCShbuYmVTkqGJNsMirqmdDu3LmDYcOGaVxtm5qaio/zzphGZIgqVaqIf6enp4tNRQoqR2SIvEP4mZqaag3TxyH8SCrqygGpylHBmGSTUalWrZr496u3s/I+zluOyBDqzmhWVlZIS0tDSkqKuK5q1aqoVKkSnjx5ws5oVGgcwo+KS25urqTlqGC870RGxdfXV/zbysoK3t7eaNmyJby9vWFlZaWzHJEh1J3Rbt26hdq1a6Njx45o27YtOnbsiNq1a+PWrVvw9/fn7XsqtLxD+OnCIfxIKq9OrFXYclQw1mSTUck7NN+TJ09w+vTpfMuxTTYVlpeXF7p27Yo9e/ZoTGktl8vRtWtXeHl5lWB0VFrkHcIvKChI48JNpVJxCD+STIUKFfD06VO9ylHhMckmo3Ls2DG9yzHJpsKKiorC7t274eHhAXd3d3E4tZiYGOzevRv169dnok2Fpr5rEhYWhpCQEPj5+cHe3h4JCQmIjIxEdHQ0AgMDedeECs3KykqvJDvvnWEyHJNsMip5O2OYmJhotBvL+5idNqiw1CM+KJVKrdpFX19fhISEcMQHkoyXlxcCAwMRHh6O4OBgcblCoUBgYCAv5kgSHF2keDHJJqOSt02iXC7XSLLzPmbbRSqsvCM+qFQq7Nu3Dw8fPkS1atXQoUMHjvhAkvPy8oKnpyfi4uLEIfycnJx4EUeSSU5OlrQcFYxJNhkVS0tL8e9y5cph8ODB4oyPmzdvFq++85YjMoR6JIeTJ09i5syZGm2yN23ahA4dOmiUIyIiyotJNhmVR48eiX8/e/ZMY6rrvL2h85YjMoT6bsi+fftgbW0NHx8fVK9eHQ8ePMDx48exb98+jXJEhRUVFYWNGzdq1CLa2tpi4MCBbC5CZISYZJNREQQBwMvp1TMyMsTHwMskW91zOu9yIkPUr18fwMu2/qamptizZ4+4rkqVKmIfAHU5osKIiopCaGio1vLk5GSEhoZi0qRJTLSJjAyTbDIqCoUCAPD06VN4eHigevXqyM7OhpmZGR48eIALFy5olCMy1P79+wG8nJQhNzcXw4cPF6dV/+2338T2//v370eXLl1KMlQyciqVCitXrgQAWFtbo1+/fmIzuC1btiA9PR0rV65kJ9tS7MGDB8jIyCjpMDTcunWryPdhaWmJ6tWrF/l+SgqTbDIqjRo1QkREBADgypUrYlINAGZmZhrliArjwYMHAF7OtpeRkYFVq1aJ62xtbcUh1tTliAx1+fJlZGZmokKFCli2bBlMTV/+NLdt2xYtW7bEmDFj8OzZM1y+fJlDk5ZC6enpCAoKeuvuwH7xxRdFvg+5XI7ly5fD2tq6yPdVEphkk1FxdnaGtbU10tPTtYYYUj+2trbmaA9UaOofPHNzc9y9e1djXUpKCurWratRjshQ6vH/P/zwQzHBVjM1NUXv3r2xYcMGjv9fSllbWyMkJKRYarIPHTok3qUriK+vL9q0aVPk8VhaWpbaBBtgkk1GRi6Xo0GDBjh37ly+ZRo0aMBbqlRo9evXx/79+/HPP//onGL45s2bYjmiwnjx4gUAoFq1alCpVFpD+Kmbv6nLUelTXE0m7O3t9UqyBw8erHXBR2+O7yAZlZycnAITbAA4d+4ccnJyeIKgQsk7asirtdV5H3N0ESosR0dHnD17FmvWrAEArdFF1Mebo6NjicRHpYepqSm6deuG33//Pd8y3bp14++nRFjdR0aloBODIeWI8nP79m1JyxHlp2PHjpDJZEhOTtaaBCQ5ORkpKSmQyWTo2LFjCUVIpcmAAQPQrVs3rTt0MpkM3bp1w4ABA0oostKHSTYZlYMHD0pajig/8fHxkpYjyo9cLtfouK2LmZkZm8GRZAYMGIB169aJIyN16dIF69atY4ItMX5jyag8f/5c0nJE+dG3/SvbyVJhXb16FVlZWfneojc1NUVWVhauXr1azJFRaWZqaooPPvgAAPDBBx+wiUgRYJJNRkXf3tdv23ijZHxq164taTmi/KiT55ycHJ3r1cuZZBMZF162kFFRqVSSliPKz6t3Q2rUqAErKys8efIE9+/fz7cc0ZtST2wEvGwX++pMturHecsR0duPSTYRkQ537tzReHz//n2N5Dq/ckRvKiEhQfy7oJFs8pYjorcfm4sQEemQmpoqaTmi/Pz333+SliOitwOTbCIiHfSdhaw0z1ZGxSO/ttiGliOitwObixAR6cD2/1RcLC0tkZKSolc5KjrJycl48uRJSYdRrBITEzX+L0usrKxga2tbpPtgkk1EpMPdu3clLUeUHzZNKnnJycn4dMqnyMrOKulQSsTy5ctLOoRiZ25mjkXfLSrSRJtJNhGRDryFT8WF4/+XvCdPniArOwt9HNtCYWlT0uFQEUvKSMW2awfx5MkTJtlERESlVXZ2tqTlyHAKSxvUrKgo6TColGDHRyIiPdSoUQNNmzZFjRo1SjoUIiIyAqzJJkk8ePDgrZtl8datW0W+D0tLS1SvXr3I90MlL79xsomIiHRhkk2Flp6ejqCgIK1JFEraF198UeT7kMvlWL58OYdxK0a8oCMiImPAJJsKzdraGiEhIcWS+LxJ4vzNN98UYSQvWVpaMsEuRryg4wUdUVFKynhc0iFQMSiuz5lJtgTCw8OxevVqJCUlwcnJCV9++SXc3NxKOqxiVVw1bHPmzMGMGTP0Kle3bt1iiIiKU3Fe0O3fvx+HDh16bbk2bdrA19e3yOPhBV3x412Tsmfbtdd/54n0xSS7kPbs2YN58+Zh5syZcHd3x7p16xAQEIB9+/ahatWqxRpLWRhIXyaT6V2uOH6MSlJxDKSfn7JwrLVu3VqvJLt169ZFHwyAjIyMEjumS/JYKym8a1I275q0fbcpKltULOkwqIg9fv4UB2+fKfL9yIS37QxiZPr27QtXV1d89dVXAF7O/taqVSsMGjQII0eOfKNtXbp0CQDg6ur6xnEkJycjaMoU5HCIpzLD1MwMId99V+zJz8tJG6Ygi8damWFuZoZFJXCsASV7Qffo0SNkZmYW+X5++uknvYbnMzMzw4gRI4o8nvLly6NKlSpFvh9dSuqCrqxPRlMWFWYyGn3zNdZkF0JWVhauXLmCUaNGicvkcjmaN2+O6Ohog7YpCIJBtyeTkpKYYJcxOdnZSEpKKvaplpOSkpCVnY33q1vCytykWPcNAJk5KmSriq9u4HZ6Fu5nak84U6O8Kd61Ni+2OMzkMpQ3Lf5RV59k5eLUg4wSOdZSUlLwxRczkJPDcxvwcpzs0j4zn6mpGb75Zk6x3wm2tLTE7Dmz8fTp02Ldr9rjx49LZLKh5ORk7Ny5Ez179iyRixsLCwtUrly52PcLABUrVoSlpaVBOZcgCHrdWWeSXQiPHz9Gbm6u1smgatWquHnzpkHbzM7ORmxs7Bs/Lz09HXITE6hycw3aLxkfuYkJ7t+/X+xtRtPT0yGXyXDqwdvVVrW43c/M0Zl8l0ZymazEjrXc3LLxHtNLubk5uHHjBh4+fFjSoRSbjIwMrFixokSbJu3cubNE9iuTyTB69Ohiv4AHXr7vhTnOzM1fX8nCJPstY2Zmhvr16xv03Pr165e5q/CSVtJX4cVd26NWrVo1PHjwoET2nZ6ejqyssndL19zcvMTayFavXr3EOhKXpWPt999/R06O9kWFqakpunXrVmxxlNVjrSTNnTv3retkWxwsLS2hUBjfDJs3btzQqxyT7EKoXLkyTExMkJKSorE8JSXF4NsuMpnM4Cu6krgSpLKpUaNGaNSoUUmHQWVAWTrW+vXrh/T0dMyYMQNPnjyBlZUV5syZUyY7IJY17777bkmHQG9A30EYmGQXgrm5ORo1aoSTJ0+KQ3ipVCqcPHkSAwcOLOHoiIjI2FhbW2PJkiUlHQYRSYBJdiF98sknmDZtGlxcXODm5oZ169YhMzMTvXv3LunQiIiIiKiEMMkupC5duuDRo0dYsmQJkpKS0LBhQ6xatarMjSlLRERERP+H42S/RQozTjYRERERFT1987XiH3SViIiIiKiUY5JNRERERCQxJtlERERERBJjkk1EREREJDEm2UREREREEmOSTUREREQkMSbZREREREQSY5JNRERERCQxJtlERERERBJjkk1EREREJDEm2UREREREEmOSTUREREQkMSbZREREREQSMy3pAOj/ZGdnQxAEXLp0qaRDISIiIiIdsrKyIJPJXluOSfZbRJ8PjIiIiIhKjkwm0ytnkwmCIBRDPEREREREZQbbZBMRERERSYxJNhERERGRxJhkExERERFJjEk2EREREZHEmGQTEREREUmMSTYRERERkcSYZBMRERERSYxJNhERERGRxJhkExERERFJjEk2EREREZHEmGQTEREREUmMSTYRERERkcSYZFOhnD59Go6OjkhPTy/pUN7Y0qVL0aNHj5IOg4rBoEGD8M033xRYxtHREfv37y+miAynz2uh12vbti3Wrl0r2fb4udCrjPn3MS9jOTe+jZhkl2LTp0+Ho6Oj+M/b2xsBAQGIi4uTbB9KpRLHjx+HlZWVZNuU0vbt2zXeA/W/3377DcOGDZP0R5Z0S0pKwpw5c9C+fXu4urqiefPm+Oijj7Bp0yZkZmaWdHii48ePo2XLlgY/P+/x5eHhgQ4dOmD69Om4fPmyhFG+vDgMDAyUdJtvA/X56quvvtJaN3PmTDg6OmL69OmS7W/r1q3o37+/ZNt7ne3bt8PT01Oy7U2fPh1jx46VbHslSeoLnjf1yy+/QKlUIicnR1z27NkzNGrUCIMGDdIoq06c79y5U2TxtG3bVjyXuLm5oW3btggMDMTJkyeLbJ9lSXFeNJgWy16oxLRo0QLz5s0DACQnJyM0NBSjR4/G4cOHJdm+ubk5FAqFJNsqKhUrVsS+ffs0lllZWcHCwgIVKlQooajKhoSEBHz88cewsrLC5MmT4ejoCHNzc1y7dg1btmxB9erV0a5dO53Pzc7OhpmZWbHFKsVxPG/ePLRo0QIvXrzAv//+iy1btqBfv36YO3cuevbsWfggAdjY2EiynbfRO++8gz179uDzzz+HhYUFAODFixf4/fffUbNmTUn3VaVKFUm397Yq7u+RMfL29kZGRgYuX74MDw8PAMDZs2dha2uLmJgYvHjxAuXKlQPwMsmuWbMmateuXaQxTZw4Ef369UN2djYSExMRGRmJTz75BIGBgRgzZkyR7pukw5rsUk6dBCsUCjRs2BAjRozAf//9h0ePHgHQfTsrNjYWjo6OuHv3LgAgMTERo0ePRtOmTeHh4YGuXbviyJEjOp+vrq05duwYOnfuDKVSiYCAADx8+FAjrt9++w2dO3eGq6srOnXqhPDwcHFdVlYWZs2aBR8fH7i6uqJNmzZYuXIlAEAQBCxduhStW7eGi4sLfHx8MGfOnALfA5lMJr4H6n8WFhZazUXUNUOrV6+Gj48PvL29MXPmTGRnZ4tldu7cid69e0OpVOKDDz7AlClTkJKSIq5Xvx8nT55E79694e7ujo8++gg3b97UiOngwYPo06cPXF1d4e3tjXHjxmm8/gULFqBFixbw8PBA3759cfr06QJf49sqODgYJiYm2LZtG7p06YJ69erB3t4evr6++PHHH9G2bVuxrKOjIzZt2oTRo0fDw8MDK1asQG5uLj7//HO0bdsWbm5u6NixI9atW6exD/XntmzZMrz//vto3LgxvvrqK2RlZWmUEwQBCxcuhJeXFz744AMsXbpUY/2rtRv3799HUFAQvLy84OHhgd69eyMmJqbA12ttbQ2FQgE7Ozv4+PhgyZIl6N69O2bNmoW0tDSx3NmzZzFgwAC4ubmhVatWmDNnDjIyMsT14eHh6NChg1jzP3HiRHHdq80SHj58iJEjR4o1Xrt27dKqGVTfvRk3bhzc3d3RoUMHHDhwoMDXUhKcnZ3xzjvv4M8//xSX/fnnn3jnnXfQsGFDjbK6aj979Oghfq6vO1e8+vz09HR89dVXaN68OVxdXdGtWzccOnQIAPD48WMEBQWhRYsWcHd3R/fu3fH7778X6rWqzz87d+5E27Zt0aRJE0yePBlPnz4Vy+zbtw/du3eHm5sbvL29MXToUGRkZGDp0qXYsWMHDhw4INZ4nj59Gnfv3oWjoyP27NmDgQMHwtXVFbt27dLZNG7t2rUa3z/192jFihVo3rw5PD09sWzZMuTk5GDBggXw8vJCy5YtsW3bNo3t/PfffwgMDISnpye8vLwwZswY8bcj73bzO68OGjQIiYmJmDdvnvhagIJ/d6T23nvvQaFQICoqSlwWFRWFdu3awc7ODhcuXNBY7u3tDQBQqVRYuXKleH7y8/PTqtABgPPnz6N79+5wdXVFv379EB8f/9qYKlSoAIVCgZo1a6Jp06aYPXs2xo4diyVLlmj8nsTHx2P48OFQKpVo3rw5PvvsM/H3ffPmzfDx8YFKpdLY9pgxY/C///1PfLx//3706tULrq6uaNeunfi55+fatWsYPHiweFx++eWXePbsmbhen3PyoEGDMHv2bHzzzTdo2rQpmjdvji1btiAjIwP/+9//oFQq0b59e63PvKDXq97unDlz8j3Xq4/5cePGwdHRUeM7UBSYZJchz549Q2RkJN599903qg2bNWsWsrKysHHjRuzatQuffvopLC0t8y3//Plz/Pzzz1i4cCE2btyI//77DwsWLBDXR0ZGIiwsDJMnT8aePXsQFBSEJUuWYMeOHQCADRs24ODBgwgNDcW+ffvw7bffolatWgCAP/74A2vXrsXMmTPx559/Yvny5XBwcDDsDdHh9OnTuHPnDtatW4f58+djx44dYlwAkJOTg8DAQERGRuL7779HYmKizlvYixcvxvTp07Ft2zaYmJjg888/F9cdPnwY48ePR6tWrbBz506sW7cObm5u4vpZs2YhOjoaixcvRmRkJDp16oThw4fj33//lex1FofHjx/j77//hr+/f77Hi0wm03i8bNkytG/fHrt27UKfPn2gUqlQo0YNhIWFYffu3Rg3bhwWL16MPXv2aDzv5MmT+Oeff7BhwwaEhITgr7/+wvfff69RZseOHbC0tMSWLVvw2Wef4fvvv8fff/+tM65nz55h4MCBePDgAZYvX46IiAgMHz5c68dKH0OHDsWzZ8/Efd25cwcjRoxAhw4dEBkZicWLF+PcuXOYPXs2AODSpUv45ptvMHHiROzbtw+rVq0qsJnBtGnT8PDhQ2zYsAFLly7Fli1bNC781JYtW4bOnTsjMjISLVu2xKefforU1NQ3fj1FrU+fPti+fbv4eNu2bejdu/cbb+dNzhUqlQojRozA+fPn8e2332LPnj2YMmUK5PKXP5FZWVlo1KgRfvzxR/z+++/o168fpk6diosXLxr2Iv+/O3fu4MCBA1ixYgVWrlyJM2fO4KeffgLw8uJpypQp6NOnD/bs2YP169ejffv2EAQBw4YNQ+fOndGiRQscP34cx48fh1KpFLe7aNEiDB48GHv27IGPj4/e8Zw6dQoPHz7Exo0bMX36dCxduhSjRo1CpUqVsGXLFnz00Uf4+uuvcf/+fQAva8kDAgJQoUIFhIeH45dffoGlpSWGDx+ukVAVdF5dunQpatSogYkTJ4qvBXjz353C8vb21qjMOH36NLy8vNC0aVNx+fPnzxETEyMm2StXrsTOnTsxc+ZM7N69G0OHDsVnn32mkawDwMKFCzF9+nRs3boVVapUwejRozUqb/Q1ePBgCIIgXiCnp6djyJAhcHZ2xtatW7Fq1SqkpKRg0qRJAIBOnTohNTVV43Wlpqbi2LFj8PPzA/Dygn/atGni8TJr1ixs374dK1as0BlDRkYGAgICUKlSJWzduhWhoaE4ceKEeP5S0/ecXLlyZfz2228YOHAggoODERgYCKVSiR07duCDDz7A1KlTxWaFr3u9ebeb37l+69atAF7edTx+/Lj4uMgIVGpNmzZNaNiwoeDh4SF4eHgIDg4OwgcffCBcvnxZLHPq1CnBwcFBSEtLE5ddvXpVcHBwEBISEgRBEIRu3boJS5cu1bmPV5+/bds2wcHBQbh9+7ZYZuPGjULz5s3Fx76+vsKuXbs0tvP9998L/fv3FwRBEGbPni0MHjxYUKlUWvv7+eefhQ4dOghZWVl6vQfqeNTvgYeHhxjLkiVLBD8/P433q02bNkJOTo64bOLEicKkSZPy3f7FixcFBwcH4enTpxrvx4kTJ8Qyhw8fFhwcHITnz58LgiAI/fv3F6ZMmaJze4mJiULDhg2F+/fvayz/f+3de1xN6f4H8M/ushWhIt2GpnJ2kZBUSo1qUKThMJwpOZka4zjkGDoKEyEzcmoQRxeTVK65NW25jxNJwsSJkRpdtCuXiKKt+/P7o7PXtLVTUT+D7/v18npZt6fn2WutZ333s75rbU9PTxYaGtquNv9RXL9+nQkEAnbq1Cmp+ZaWlty+2LBhAzdfIBCwdevWtVnu6tWrmY+PDzft5+fHLC0tmVgs5ubt2bOHDR8+nDU0NDDGGPPw8GBubm5S5UybNo3961//kvr7p0+fZowxtm/fPmZmZsaePHnS7vY237656upqJhAIWFRUFGOMseXLl7OAgACpda5cucKMjY1ZdXU1O3nyJBsxYgR79uyZzL/j4eHBgoKCGGOM3blzhwkEApaVlcUtLywsZAKBgMXExEjVbePGjdx0VVUVEwgE7Ny5c+1uX1fz8/Nj8+bNY48fP2ZDhgxhxcXFrLi4mJmamrLHjx+zefPmMT8/P259BwcHqTYyxthnn33GwsLCGGNt9xXNt09NTWXGxsYsPz+/3fX9+uuv2fr167np5vtFlkOHDjFzc3NuOiwsjA0bNkxqPwcHB7Pp06czxhi7efMmEwgErLi4WGZ5ks+rOZFIxAQCAdu5c6fU/Jf7OsYYi4mJYQ4ODlLlOTg4cOcMY4w5OTkxd3d3brq+vp4NHz6cHT16lDHGWGJiInNycpLqq2tqatjQoUNZamqqVLmv6ldl7ctXXXe6QkJCAhs+fDirq6tjz549Y4MHD2aPHz9mQqGQzZw5kzHG2MWLF5lAIGAlJSWspqaGDRs2jGVmZkqVs3z5crZ48WLG2O/Xg+TkZG75kydP2NChQ6XmvUzW5yFhY2PDVq1axRhrum56eXlJLb937x4TCATcsTxv3jy2bNkybvm+ffuYra0tt589PT1ZRESEVBmJiYls9OjR3HTzvm3//v3MwsKCVVVVcctTUlKYsbExKysrY4y9Xp8sObb++c9/cvMePnzIBAIBu3btWrvb29G+vqtRTvZ7zsrKCoGBgQCAiooK7N27F3PmzMGBAwe40eG2/PWvf0VgYCAuXLgAGxsbjB8/HsbGxq2ur6ysLJWv1q9fP25kTSwWo6ioCCtWrEBAQAC3Tn19Pffw5J///Gd4eXnB2dkZdnZ2sLe350ZjnJ2dERsbi7Fjx8LOzg5jxoyBg4MDFBRaP5R79OghNRotGZ2SZeDAgZCXl+emNTQ0pG7t3bx5E1u3bsXt27dRUVEBxhiAplumAwcO5NaT3PKUlAEAjx8/ho6ODrKzszF9+nSZfz83NxcNDQ1wdnaWml9bW/ve5OIePHgQjY2N8PX1bZHSMWTIkBbr7969G4cOHUJpaSlqampQV1fX4vgzMjKCsrIyN21mZgaxWIx79+5xx3nzfQI07RdZI75AU8rU4MGDO+UzlxwjklH727dvIycnB0KhUGqdxsZGFBcXw8bGBjo6Otwxbmdnh3Hjxkm1T6KgoAAKCgowMTHh5unp6aF3794t1m3e/u7du0NFRUXqNusfhbq6Ouzt7XHkyBEwxmBvb/9a+dMd6Suys7OhpaUFfX19mWU1NDQgIiICJ06cwIMHD1BXV4fa2loub/x16erqQkVFhZtu3lcaGxvD2toarq6usLW1ha2tLZycnGTu25fJOo/aY+DAgVL9Y9++ffGnP/2Jm5aXl4eqqipXx9u3b6OoqAgjRoyQKqempkbqwcC2+lVZOnrdeVOWlpYQi8W4ceMGKisr8fHHH0NdXR0WFhZYtmwZampqcPnyZfTv3x86Ojr47bff8OLFC3h5eUmVU1dX1yK1SZLnDTQ9U6Gvr98ihbC9GGNSfUlGRobUXQyJoqIi6Ovrw9XVFQEBAQgMDASfz4dQKISLiwu3n2/fvo3MzEypkeuGhgbU1NTgxYsXLfqdvLw8GBkZSd1VGDFiBBobG1FQUIC+ffsC6HifLDm2mt9tkpTV/Hhrq70vlwu8uq/vahRkv+eUlZWhp6fHTZuYmGDkyJFISEjAN998w51okkAAQIvbWNOnT4etrS1SUlKQlpaGqKgo+Pn5tXjqWuLlixiPx+PKl+Sdrl27FsOGDZNaT1IXExMT/Pzzzzh//jwuXryIRYsWwcbGBmFhYdDW1saJEydw8eJFXLx4EatXr0Z0dDTi4+NbfbhHTk5O6jN4lbbq7u3tDVtbW4SEhEBNTQ337t2Dt7d3i8+seTmSDlGSavCqC7NYLOZymJtflAB06a3SrjBgwADweDwUFBRIze/fvz8A2Z/Dy21MTk5GcHAw/Pz8YGZmhh49eiA6OrrN3GhZXrVvX/amwVNzeXl5AICPPvoIQNM+/uKLL2SeP9ra2uDz+Thy5AguX76MCxcuICwsDFu3bsXBgwfRq1ev167Hy+cHj8d7rfSX/w/Tpk3DmjVrAACrVq2Suc7LqUYApPJIO9JXtLW/o6OjERcXh+XLl3PBw3ffffdat/ybkxXwS45JeXl5xMTEIDMzE2lpaYiPj8fGjRuRkJDAnUOtefk8knWsy8q5lXWOyJonOW7EYjFMTEwQEhLSoqzmX4w6cu5JdPS686b09PSgpaWFjIwMVFRUwMLCAgCgqakJbW1tZGZmIiMjA6NGjQLw+7UsMjISmpqaUmXx+fwuqeOTJ09QXl4u1Zc4ODjA19e3xbqSwR1HR0d8++23SElJgampKa5evSqVjy0Wi+Hj44Px48e3KEPysGdXaet4k5zjza/BbbW3tXLbOt66CgXZHxgejwcej4eamhoAv3eEZWVl3AiJrFf8aWtrw83NDW5ubggNDUVCQsJrdXZ9+/ZFv379IBKJuJwwWVRUVDBx4kRMnDgRTk5O+Oqrr/D06VOoqqpCSUkJjo6OcHR0hLu7OyZMmIDc3Fyp0byukJ+fj6dPn8LX1xfa2toA8FqvZxMIBEhPT8e0adNaLBs0aBAaGhpQXl7eqa/7ehvU1NQwevRo7Nq1Cx4eHq/1JSEzMxNmZmaYOXMmN0/Wq7NycnJQXV3NBUvXr19H9+7duf3UUZIHBSXH3JuIjY2FiooKbGxsADQ93Hfnzp1XfvFTUFCAjY0NbGxssGDBAlhYWODSpUstLoT6+vqor6/HrVu3uNHLu3fvSj1k+S6ys7NDXV0deDxeqznF6urqUg9UP3/+XOqBOwDt7iuMjIxw//59FBQUyBzNzszMxKeffso9PNjY2IjCwkIYGhq+aVNficfjwdzcHObm5pg/fz4cHBxw5swZfPnll1BUVGz3lyR1dXU8evRIahQ0Ozv7jetnYmKC48ePo0+fPlIj8h3VWls667rTXlZWVrh8+TIqKirg7e3NzR85ciTOnz+PrKwsuLm5AQAMDQ3B5/NRWloKS0vLV5Z7/fp17u04FRUVKCwshIGBQYfrFxcXBzk5OYwdOxZA0+d/8uRJ6Orqtno3t1u3bhg/fjyEQiHu3r0LfX19qeN/8ODBKCgoaPdAlKGhIY4cOQKxWMz16ZmZmZCTk5M6dzq7Twba1972UFRURENDw2tv3xH04ON7rra2FmVlZSgrK0NeXh7Wrl3LfRsEmkYbtbW1sWXLFhQWFiIlJQU7duyQKmPdunVITU2FSCTCr7/+ioyMjDe6uCxcuBBRUVGIi4tDQUEBcnJycOjQIcTExAAAYmJicPToUeTl5aGgoAAnTpyAhoYGevXqhcOHD+PAgQPIzc2FSCRCUlISlJSUOv31XrLo6OhAUVER8fHxEIlE+Pnnn7Ft27YOl7NgwQIkJycjLCwMeXl5yMnJQVRUFABwt/eWLl2KU6dOQSQSISsrC5GRkZ322sX/T6tWrUJDQwP38FZeXh7y8/Px008/IT8/v8Vo/cv09PRw8+ZNpKamoqCgAJs2bcKNGzdarFdbW4sVK1bgzp07OHfuHLZs2QIPD49Xpga9iouLC/r27Yv58+fjl19+gUgkwsmTJ3Ht2rVXbldZWYmysjKUlJQgLS0NCxcuxNGjRxEYGMiNQs+ZMwfXrl3DmjVrkJ2djcLCQpw5c4Ybuf3Pf/6DuLg4ZGdno6SkBImJiWhsbJQZ/BkaGsLGxgYrV65EVlYWbt26hYCAACgpKckc6X1XyMvL4/jx4zh27Firx8ioUaOQlJSEq1evIicnB35+flL7uyN9haWlJUaOHImFCxciLS0NIpEI586dw/nz5wE0HYcXL15EZmYm8vLysHLlSjx69KhrGv8///3vfxEREYEbN26gtLQUp06dQnl5ORec6erqIicnB/n5+SgvL3/lqLqVlRXKy8uxfft2FBUVYffu3UhNTX3jOrq6ukJNTQ3z5s3D1atXIRKJkJGRgaCgIO7hyPbQ1dXFlStX8ODBAy6FqbOvO+1hZWWFX375Bbdv35YKnC0tLbF//37U1dVxDz2qqKjAy8sL33//PY4cOYKioiL8+uuviI+Pl0pPBIBt27YhPT0dubm58Pf3h5qaGhcot6aqqgplZWW4d+8erly5goCAAISHh2PRokVcQOzu7o6KigosXrwYWVlZKCoqQmpqKpYtWyYVRLq6uiIlJQWHDh2Cq6ur1N+ZP38+fvrpJ2zduhW//fYb8vLykJycjI0bN8qsl6urK/h8Pvz9/ZGbm4tLly5h7dq1mDx5MpfeAXR+n9yR9rZFV1cX6enpKCsr6/IBCRrJfs+lpqZyI0E9evSAgYEBNm/ezHUUioqKCA0NRWBgID777DOYmppi0aJFUj920djYiDVr1uD+/ftQUVGBnZ2d1O2mjpo+fTqUlJQQHR2NDRs2oHv37hAIBPD09OTq+eOPP+Lu3buQk5ODqakpoqKiICcnh169eiEqKgrr169HY2MjBAIBIiIioKam9gafUvuoq6tj/fr1+OGHHxAfHw8TExP4+fl1+J2lVlZW2Lx5M7Zt24aoqCioqKhwtyaBpqeew8PDsX79ejx8+BCqqqoYPnw47O3tO7lFXW/AgAE4cuQIIiMjERoaigcPHkBRUREDBw6El5cX3N3dX7n9F198gezsbHzzzTfg8XhwcXGBu7s7F/xIWFtbQ09PDzNnzkRtbS0mTZoEHx+f1643n8/Hjh07EBwcjK+//hoNDQ0wNDRsNXVBQnJedOvWDZqamjA3N8eBAwekRo6MjY0RHx+PTZs2ce3v378/Jk6cCKDpHe6nT5/G1q1bUVNTAz09PYSGhkrlxjYXHByMFStWYObMmdDQ0MDixYtx586dLr/V29XaGhmdO3cuiouLMXfuXPTs2RP/+Mc/pEayO9pXbNmyBcHBwVi8eDFevHgBPT09LFmyBEDTK89EIhG8vb2hrKyMGTNmYOzYsXj27FnnNfglKioquHLlCmJjY/H8+XPo6OjA398fY8aMAQDMmDEDly9fxrRp0yAWixEXF9fqczaSYzcyMhLh4eEYP348vLy8kJCQ8EZ1VFZWxq5duxASEoIFCxagqqoKmpqasLa27tDI9sKFC7Fy5UqMHTsWtbW1yMnJ6fTrTntYWVmhuroaBgYGUgGjhYUFqqqqoK+vj379+nHzFy1aBHV1dURGRqK4uBg9e/bE4MGD8be//U2q3CVLlmDdunUoLCzEoEGDEB4e3mZKSVhYGMLCwqCoqAgNDQ0MGzYMO3fu5NJVgKZUlr179yIkJATe3t6ora2Fjo4O7OzspILZUaNGoXfv3igoKGgRZNvZ2SEiIgL//ve/sX37digoKMDAwKDV54aUlZURHR2NdevW4fPPP4eysjL3w1vNdXaf3JH2tsXPzw/r16/HgQMHoKmpibNnz75RvV6Fx95WogohhHQCf39/VFZWvtZdhffR/fv3MWbMGOzcuRPW1tZvuzqEkA8M9cm/o5FsQgh5h6Wnp0MsFkMgEKCsrIx7r/y7ntNPCCHvOgqyCSHkHVZfX4+NGzdCJBKhR48eMDMzQ0hICP2UNiGEvGWULkIIIYQQQkgno7eLEEIIIYQQ0skoyCaEEEIIIaSTUZBNCCGEEEJIJ6MgmxBCCCGEkE5GQTYhhHxAMjIyYGRkhIyMjLddFUIIea9RkE0IIe+Q3bt3w8jIqNVfZHvXSH5ymRBC3jcUZBNCyDtEKBRCV1cXWVlZuHv3boe3t7CwQFZWFiwsLLqgdh137tw5bN269W1XgxBCOh0F2YQQ8o4QiUS4du0ali1bBnV1dQiFwg6XIScnh27dukFO7v3u/sVi8duuAiHkA/d+97KEEPIeEQqF6N27N8aMGQMnJyeZQXZycjKmTp0KMzMzjBgxAq6uroiNjeWWt5aTvXv3bnz66acYOnQoPv/8c1y9ehWzZs3CrFmzWmx77NgxhIeH45NPPoGpqSk8PT1bjKpfvXoVCxcuhL29PYYMGYIxY8bgu+++Q3V1NbeOv78/du/eDQAwMjLi/r2qnsXFxTAyMsLhw4elyjEzM0NRURHmzJkDMzMz+Pr6trseAFBWVoZly5bhk08+wZAhQ2Bra4t58+ahuLi47R1DCCEy0M+qE0LIO0IoFGLcuHHg8/mYNGkS9u7di6ysLAwdOhQAkJaWhsWLF8Pa2poLMvPz85GZmQlPT89Wy92zZw/WrFmDkSNHYvbs2SgpKcH8+fPRq1cvaGlptVh/+/bt4PF48PLywvPnz/Hjjz/C19cXBw4c4NY5ceIEqqur4ebmBlVVVWRlZWHXrl24f/8+wsLCAAB/+ctf8PDhQ6SlpWHDhg1v9NnU19fD29sb5ubm8PPzg5KSUrvrAQA+Pj64c+cOPDw8oKuri/LycqSlpeHevXv46KOP3qhuhJAPEwXZhBDyDrh58yby8/MREBAAADA3N4eWlhaEQiEXZKekpEBFRQXR0dGQl5dvV7m1tbXYvHkzTE1NERsbCwWFpsuCkZER/P39ZQbZNTU1SExMBJ/PBwD06tUL69atQ25uLgQCAQDA19eXC3SBpoBaT08PP/zwA0pLS6GjowMzMzN8/PHHSEtLw+TJk1//w/lfO5ydnbFkyRKp+e2pR2VlJa5du4alS5fC29ubW3fu3LlvVCdCyIeN0kUIIeQdIBQK0bdvX1hZWQEAeDweJk6ciGPHjqGhoQFAU7D74sULpKWltbvcmzdv4unTp5gxYwYXYAOAq6srevfuLXObqVOncgE2AIwcORJAU864RPPAViwWo7y8HGZmZmCM4datW+2uX0e4ubm1mNeeeigpKUFRURGXL19GRUVFl9SNEPLhoZFsQgj5g2toaEBycjKsrKykcoSHDh2KHTt2ID09Hba2tnB3d8fx48cxZ84caGpqYvTo0ZgwYQI++eSTVssuLS0FAAwYMEBqvoKCAnR1dWVuo6OjIzXdq1cvAEBlZaVUuWFhYTh79myLwPX58+ftaHXHKCgoyBx1b089+Hw+fH19ERwcjNGjR2PYsGGwt7fHlClToKGh0el1JYR8GCjIJoSQP7hLly6hrKwMycnJSE5ObrFcKBTC1tYWffr0QWJiIi5cuIDz58/j/PnzOHz4MKZMmYLg4OBOq09rbyZhjAFo+lLw5ZdfoqKiAl999RUMDAzQvXt3PHjwAP7+/mhsbGzzb/B4PJnzW9uWz+e3qFdH6jF79mw4OjrizJkzuHDhAjZv3oyoqCjExsZi8ODBbdaXEEJeRkE2IYT8wQmFQvTp0wcrV65ssez06dM4ffo0Vq9eDSUlJfD5fDg6OsLR0RGNjY0IDAzE/v378fe//x16enottpeMShcVFWHUqFHc/Pr6epSUlHBv++iI3NxcFBYWIjg4GFOmTOHmy0pjaS2YloyOP3v2TGp+SUlJl9QDaBrN9/LygpeXFwoLCzFlyhTs2LEDISEh7f6bhBAiQUE2IYT8gVVXV+PUqVNwdnaGs7Nzi+X9+vXD0aNHcfbsWVhbW0NNTY1bJicnxwXJtbW1MssfMmQIVFVVkZCQgKlTp3J52UKh8LXzkyUjypKRbcn/4+LiWqyrrKwMoCnVRBJYA4Curi7k5eVx5coVjB07lpu/d+/eTq/HixcvuPeHSwwYMAA9evRo9XMjhJC2UJBNCCF/YGfPnkVVVRUcHR1lLh8+fDjU1dWRlJSE5ORkVFRUYNSoUdDU1ERpaSl27dqFQYMGwdDQUOb2fD4fPj4+WLt2LTw9PTFhwgSUlJTg8OHDLfK028vAwAADBgxAcHAwHjx4ABUVFZw8eVIqZ1vCxMQEABAUFARbW1vIy8vDxcUFPXv2hLOzM3bt2gUej4f+/fsjJSUFjx8/7vR6FBYWYvbs2XB2dsbAgQMhLy+PM2fO4NGjR3BxcXmtz4AQQijIJoSQP7CkpCR069YNo0ePlrlcTk4O9vb2EAqFCA0NRUJCAvbs2YPKykpoaGhgwoQJ8PHxeeUvPHp4eIAxhpiYGAQHB8PY2Bjh4eEICgqSGt1tL0VFRURERCAoKAiRkZHo1q0bxo0bh5kzZ7Z4Vd/48eMxa9YsJCcnIykpCYwxLrD99ttvUV9fj3379oHP58PZ2RlLly7FpEmTOrUeWlpacHFxQXp6OpKSkiAvLw8DAwNs2rQJTk5OHW4/IYQAAI81v49GCCGEoOkBQ2tra4wbNw5BQUFvuzqEEPLOofdkE0LIB66mpgYvj7ckJibi6dOnsLS0fEu1IoSQdxulixBCyAfu+vXr+P777+Hs7AxVVVXcunULBw8ehEAgkPmwJSGEkLZRkE0IIR84XV1daGlpIT4+HhUVFejduzcmT54MX19fqV92JIQQ0n6Uk00IIYQQQkgno5xsQgghhBBCOhkF2YQQQgghhHQyCrIJIYQQQgjpZBRkE0IIIYQQ0skoyCaEEEIIIaSTUZBNCCGEEEJIJ6MgmxBCCCGEkE5GQTYhhBBCCCGdjIJsQgghhBBCOtn/AdO0WrN/gLqrAAAAAElFTkSuQmCC\n"},"metadata":{}}],"source":["# Configurar el estilo de Seaborn\n","sns.set(style=\"whitegrid\")\n","\n","# Crear el boxplot\n","plt.figure(figsize=(8, 6))\n","sns.boxplot(data=data, x='subject', y='num_subscribers', palette='Set2')\n","\n","# Personalizar el gráfico\n","plt.title('Distribución del Número de Suscriptores por Asignatura', fontsize=14)\n","plt.xlabel('Asignaturas', fontsize=12)\n","plt.ylabel('Número de Suscriptores', fontsize=12)\n","plt.xticks(fontsize=10)\n","plt.yticks(fontsize=10)\n","\n","# Mostrar el gráfico\n","plt.show()"]},{"cell_type":"markdown","source":["Viendo el gráfico podemos comprobar que la asignatura de 'Web Development' es la asignatura con mayor número de suscriptores (y la que más outliers=valores atípicos presenta). El hecho de tener muchos outliers nos indica que hay una gran dispersión en cuanto a el número de suscriptores en los cursos de web development ( algunos cursos tienen muchísimos más suscriptores que la media)."],"metadata":{"id":"tBSsGSRwEcPr"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"elapsed":391,"status":"ok","timestamp":1732832024581,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"-LhaMCx0CHlb","outputId":"f3982d8f-eb3c-440c-c127-64aea0a9db5e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3678.000000\n","mean 66.049483\n","std 61.005755\n","min 0.000000\n","25% 20.000000\n","50% 45.000000\n","75% 95.000000\n","max 200.000000\n","Name: price, dtype: float64"],"text/html":["
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price
count3678.000000
mean66.049483
std61.005755
min0.000000
25%20.000000
50%45.000000
75%95.000000
max200.000000
\n","

"]},"metadata":{},"execution_count":28}],"source":["data['price'].describe()"]},{"cell_type":"markdown","metadata":{"id":"EyQRg8FaCNj3"},"source":["La media de la variable 'price' de los cursos ofrecidos es de 66 euros, mientras que la mediana está en 45. Esto nos indica que hay bastantes outliers o valores extremos de algunos cursos que tienen un precio muy alto, por lo que hacen que la media sea más alta, pero la mediana nos indica que el 50% de los cursos cuestan más de 45 euros, mientras que la media del precio por curso es de 66 euros. Concluimos que el hecho de tener algunos cursos con un precio mucho más alto que la mayoría, hacen que la media sea más grande."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":797,"status":"ok","timestamp":1732832028777,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"HIehLv1dARiO","outputId":"d2dd58f3-5aae-4300-cb67-642a4442554f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":29},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Visualizamos PRICE en histograma\n","data[\"price\"].plot(kind='hist',bins=40, linewidth=0.5, edgecolor=\"white\", title = \"Histograma de price\", xlim=(0, 200))\n"]},{"cell_type":"markdown","metadata":{"id":"L7dSE9V8EgBo"},"source":["La mayoría de los cursos se encuentran en el rango de 20 a 25 euros. No obstante, en el rango de precios de 30 a 55 hay una cantidad de cursos parecidas ( de 50 a 55 euros hay muchos cursos ) . También se observa que para un precio de 200€ hay muchos cursos, esto se puede deber a dos motivos, a que se haya dado la casualidad que para este precio haya muchos o , que a partir de este precio han truncado todos a 200.\n","Podemos decir que no hay una relación lineal directa del número de cursos con la variable 'price'."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":480},"executionInfo":{"elapsed":620,"status":"ok","timestamp":1732832063615,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"TsSjxEWfDykn","outputId":"a5451d56-b6dd-4390-ad31-6087c5030889"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Gráfico de densidad para la variable 'price'\n","sns.kdeplot(data=data, x='price', fill=True, color=\"blue\", alpha=0.6)\n","plt.title(\"Gráfico de Densidad de la Variable 'Price'\")\n","plt.xlabel(\"Price\")\n","plt.ylabel(\"Densidad\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"TC76K6wCBuBy"},"source":["Gracias al densityplot de la variable 'price' comprobamos que la mayoría de los cursos se encuentran entre 0 y 60 euros más o menos."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"elapsed":442,"status":"ok","timestamp":1732832074560,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"},"user_tz":-60},"id":"xtFsapHGCe8f","outputId":"ed7a8a58-2bcd-4e8a-af8f-5e73886d7dc0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3678.000000\n","mean 156.259108\n","std 935.452044\n","min 0.000000\n","25% 4.000000\n","50% 18.000000\n","75% 67.000000\n","max 27445.000000\n","Name: num_reviews, dtype: float64"],"text/html":["
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num_reviews
count3678.000000
mean156.259108
std935.452044
min0.000000
25%4.000000
50%18.000000
75%67.000000
max27445.000000
\n","

"]},"metadata":{},"execution_count":31}],"source":["data['num_reviews'].describe()"]},{"cell_type":"markdown","metadata":{"id":"Y7jtuRa8C_uV"},"source":["La variable 'num_reviews' presenta una media de 156 reseñas por curso, mientras que la mediana está en 18 ( la gran separación entre media y mediana podría indicarnos que no sigue una distribución normal, pero habría que seguir estudiandola ).\n","Esta gran diferencia se debe a que hay unos valores atípicos extremos en algunos cursos que tienen un número muy grande de reviews, por lo tanto hace que la media de reviews de todos los cursos sea más grande."]},{"cell_type":"code","source":["# Crear el boxplot\n","plt.figure(figsize=(8, 6)) # Tamaño del gráfico\n","sns.boxplot(data=data, y='num_reviews', color='skyblue') # Boxplot de la variable\n","plt.title('Boxplot de num_reviews', fontsize=14)\n","plt.ylabel('Número de reseñas')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":522},"id":"CR23MQhEGwH1","executionInfo":{"status":"ok","timestamp":1732832083565,"user_tz":-60,"elapsed":659,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"78c43e82-7bdd-4f2b-bf66-a70b460a8e2e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"elapsed":341,"status":"ok","timestamp":1732789611704,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"},"user_tz":-60},"id":"ZUJ13ZvaFkb1","outputId":"0e4aa216-2066-4a85-fafa-8e37644abd34"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3678.000000\n","mean 4.094517\n","std 6.053840\n","min 0.000000\n","25% 1.000000\n","50% 2.000000\n","75% 4.500000\n","max 78.500000\n","Name: content_duration, dtype: float64"],"text/html":["
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content_duration
count3678.000000
mean4.094517
std6.053840
min0.000000
25%1.000000
50%2.000000
75%4.500000
max78.500000
\n","

"]},"metadata":{},"execution_count":68}],"source":["data['content_duration'].describe()"]},{"cell_type":"markdown","metadata":{"id":"2BUwjSO7Fqco"},"source":["La media de la variable 'content_duration' es de 4 y la mediana de 2 ( horas)."]},{"cell_type":"code","source":["# Crear el boxplot para content_duration\n","plt.figure(figsize=(8, 6)) # Tamaño del gráfico\n","sns.boxplot(data=data, y='content_duration', color='lightgreen') # Boxplot de content_duration\n","plt.title('Boxplot de content_duration', fontsize=14)\n","plt.ylabel('Duración del contenido')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":522},"id":"ZPcFLLGUHFWz","executionInfo":{"status":"ok","timestamp":1732789623008,"user_tz":-60,"elapsed":468,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"}},"outputId":"4442645a-de17-4ce8-ff89-6acf84336852"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Contrastando la información estadística sobre la variable 'content_duration' con su representación en el gráfico de cajas (boxplot), observamos que el 75% de los cursos duran menos de 4.5 horas. Y la media de la duración de un curso es de 4 horas, mientras que la mediana es de 2 horas por curso. Esto se debe a los outliers, ya que hay algunos cursos que tienen una duración bastante más alta que los demás cursos, port tanto hace que suba la media."],"metadata":{"id":"Y5tq_fn4HMpp"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"executionInfo":{"elapsed":761,"status":"ok","timestamp":1732789651866,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"},"user_tz":-60},"id":"ckxVF4DFFzhx","outputId":"e75948be-68b8-477d-ec25-bc89f4a11a94"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3678.000000\n","mean 40.108755\n","std 50.383346\n","min 0.000000\n","25% 15.000000\n","50% 25.000000\n","75% 45.750000\n","max 779.000000\n","Name: num_lectures, dtype: float64"],"text/html":["
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num_lectures
count3678.000000
mean40.108755
std50.383346
min0.000000
25%15.000000
50%25.000000
75%45.750000
max779.000000
\n","

"]},"metadata":{},"execution_count":70}],"source":["data['num_lectures'].describe()"]},{"cell_type":"markdown","metadata":{"id":"zma2lzYkF89z"},"source":["La media de los cursos de udemy presentan una media de 40 clases, mientras que la mediana es de 25 clases."]},{"cell_type":"code","source":["# Crear el boxplot para num_lectures\n","plt.figure(figsize=(8, 6)) # Tamaño del gráfico\n","sns.boxplot(data=data, y='num_lectures', color='lightblue') # Boxplot de num_lectures\n","plt.title('Boxplot de num_lectures', fontsize=14)\n","plt.ylabel('Número de lecciones')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":522},"id":"Wjdm8BXdIG_W","executionInfo":{"status":"ok","timestamp":1732789660452,"user_tz":-60,"elapsed":439,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"}},"outputId":"ea734301-8dfd-4661-8249-c008c031b068"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Observamos que sucede prácticamente la misma situación que en las anteriores variables cuantitativas, el hecho de que algunos cursos tengan un número de clases mucho más alto que el resto hace que la media de todos sea más grande (no significa que sea más representativa). Ya que la mediana es un estadístico bastante más representativo."],"metadata":{"id":"pne3N0HSIPEz"}},{"cell_type":"markdown","metadata":{"id":"pl7UHTH7JdWN"},"source":["# DESCRIPTIVA BIVARIANTE\n","\n","En este apartado vamos a describir la relación lineal entre dos variables : ver como evoluciona una variable a medida que la otra variable se comporta de determinada manera .\n","\n","Dendiendo del tipo de variables que vamos a estudiar, utilizaremos distintas técnicas para hacer la descriptiva bivariante:\n","\n","- Si són dos variables cuantitativas : matriz de correlación, scatterplot de dos variables\n","\n","- Variable cuantitativa con categórica: boxplot, barplot, histogramas por grupos.\n","\n","- Variable categórica con categórica : gráficos de barras agrupados, tablas de contingencia.\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1AXuGL0C1W2_","outputId":"114ba2ec-19d6-415a-c5a5-a493e1d78871","colab":{"base_uri":"https://localhost:8080/","height":476},"executionInfo":{"status":"ok","timestamp":1732832163433,"user_tz":-60,"elapsed":494,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":33},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Visualizamos dos variables en ScatterPlot (variable cuantitativa y variable cuantitativa)\n","data.plot(x='price', y='num_subscribers', kind='scatter')\n"]},{"cell_type":"markdown","metadata":{"id":"mVCaHoi3LA30"},"source":["En este scatterplot (gráfico de dispersión) estamos viendo como se relacionan las variables 'num_subscribers' con la variable 'price'.\n","Observamos que independientemente del pecio de los cursos, presentan un número de suscriptores bastante similar . Hay que destacar que los cursos gratuitos presentan el mayor número de suscriptores, pero cuando el precio de los cursos aumenta el número de suscriptores también lo hace.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jqDlbgAsNLPd","colab":{"base_uri":"https://localhost:8080/","height":554},"executionInfo":{"status":"ok","timestamp":1732832204900,"user_tz":-60,"elapsed":508,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"15ac3a30-2d46-4a8d-a8c7-35c3f8aae086"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Calcular la correlación entre 'price' y 'num_subscribers'\n","correlation = data[['price', 'num_subscribers']].corr()\n","\n","# Visualizar la matriz de correlación con un heatmap\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=\".2f\", cbar=True)\n","\n","# Personalización del gráfico\n","plt.title('Matriz de Correlación entre Price y Num_Subscribers', fontsize=16)\n","plt.show()\n"]},{"cell_type":"markdown","source":["Observamos tanto en el Gráfico de Dispersión (ScatterPlot) cómo en la matrix correlacional que no tienen apenas relación lineal entre la variable price y num_subscribers."],"metadata":{"id":"QSItG3frJHLD"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"bVOzb_5pNTzj","colab":{"base_uri":"https://localhost:8080/","height":576},"executionInfo":{"status":"ok","timestamp":1732832239575,"user_tz":-60,"elapsed":1050,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"f8c33299-0d96-490b-eab7-6ce51e5200ec"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Análisis bivariante de categórica con cuantitativa\n","\n","# Crear el boxplot\n","plt.figure(figsize=(10, 6))\n","sns.boxplot(x='level', y='num_subscribers', data=data, palette=\"muted\", hue= 'level', dodge= False)\n","\n","# Personalización del gráfico\n","plt.title('Boxplot de Num_Subscribers por Level', fontsize=16)\n","plt.xlabel('Nivel', fontsize=14)\n","plt.ylabel('Número de Suscriptores', fontsize=14)\n","\n","# Mostrar el gráfico\n","plt.show()\n"]},{"cell_type":"markdown","source":[],"metadata":{"id":"eHro90t9Jols"}},{"cell_type":"code","source":["data.groupby('level')['num_subscribers'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"8l42cSlDKU1k","executionInfo":{"status":"ok","timestamp":1732832258766,"user_tz":-60,"elapsed":392,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"4fba72e4-1687-4b4c-cbe2-b3844a458a82"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["level\n","All Levels 3584.798341\n","Beginner Level 3190.427559\n","Expert Level 865.448276\n","Intermediate Level 1762.482185\n","Name: num_subscribers, dtype: float64"],"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
num_subscribers
level
All Levels3584.798341
Beginner Level3190.427559
Expert Level865.448276
Intermediate Level1762.482185
\n","

"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","source":["Observando la media de suscriptores por cada nivel de los cursos y contrarestándola con los gráficos llegamos a la conclusión de que los cursos dirigidos a 'All Levels' y a 'Beginner Level' tienen una media de suscriptores bastante más alta que la de 'Intermediate Level' y 'Expert Level' siendo esta última la media más baja.\n","\n","Teniendo en cuenta los outliers en el boxplot, podemos decir que los niveles con un mayor número de suscriptores ( All level y beginner level) presentan una media más alta debido a que tienen algunos cursos con un número de suscriptores muchísimo más alto que los demás , por eso sube la media."],"metadata":{"id":"xpughHAUK591"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"eKAgr3wUN90W","colab":{"base_uri":"https://localhost:8080/","height":595},"executionInfo":{"status":"ok","timestamp":1732832277108,"user_tz":-60,"elapsed":7704,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"388d329d-9566-4e77-8eed-afe597aac12f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["(None,)"]},"metadata":{},"execution_count":37}],"source":["# Crear el histograma mejorado\n","plt.figure(figsize=(12, 6))\n","sns.histplot(\n"," data=data,\n"," x='num_subscribers',\n"," hue='level',\n"," kde=True, # Añade la línea de densidad\n"," multiple='layer', # Cambia de 'stack' a 'layer' para ver las distribuciones separadas\n"," palette='husl', # Cambia la paleta para un diseño más claro\n"," alpha=0.7 # Ajusta la transparencia para mejor visualización\n",")\n","\n","# Personalización del gráfico\n","plt.xlim(0, 150000)\n","plt.title('Histograma de Num_Subscribers por Level', fontsize=16)\n","plt.xlabel('Número de Suscriptores', fontsize=14)\n","plt.ylabel('Frecuencia/Densidad', fontsize=14)\n","plt.grid(axis='y', linestyle='--', alpha=0.6) # Añade una cuadrícula ligera para mejorar la lectura\n","\n","# Mostrar el gráfico\n","plt.show(),\n"]},{"cell_type":"markdown","source":["Este histograma, se ve de esta forma debido a los outliers tan grandes que hay , si le ponemos un limite en el eje x cerca de la media de numero de suscriptores se verá mucho mejor"],"metadata":{"id":"zYYAxOiCWyZ_"}},{"cell_type":"code","source":["# Crear el histograma mejorado\n","plt.figure(figsize=(12, 6))\n","sns.histplot(\n"," data=data,\n"," x='num_subscribers',\n"," hue='level',\n"," kde=True, # Añade la línea de densidad\n"," multiple='layer', # Cambia de 'stack' a 'layer' para ver las distribuciones separadas\n"," palette='husl', # Cambia la paleta para un diseño más claro\n"," alpha=0.7 # Ajusta la transparencia para mejor visualización\n",")\n","\n","# Personalización del gráfico\n","plt.xlim(0, 10000)\n","plt.title('Histograma de Num_Subscribers por Level', fontsize=16)\n","plt.xlabel('Número de Suscriptores', fontsize=14)\n","plt.ylabel('Frecuencia/Densidad', fontsize=14)\n","plt.grid(axis='y', linestyle='--', alpha=0.6) # Añade una cuadrícula ligera para mejorar la lectura\n","\n","# Mostrar el gráfico\n","plt.show(),"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":595},"id":"repOUKlgXDPy","executionInfo":{"status":"ok","timestamp":1732832294494,"user_tz":-60,"elapsed":8003,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"5cc5d531-faca-4bcf-bd25-f3cdb18b1902"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["(None,)"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"menMCgf-O1Y4","colab":{"base_uri":"https://localhost:8080/","height":577},"executionInfo":{"status":"ok","timestamp":1732832303195,"user_tz":-60,"elapsed":489,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"1505fbff-0456-4d42-9f01-0d46cc49b34e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# Análisis bivariante entre dos variables categóricas\n","\n","# Gráfico de barras agrupado\n","plt.figure(figsize=(10, 6))\n","sns.countplot(x='level', hue='is_paid', data=data, palette='muted')\n","\n","# Personalización del gráfico\n","plt.title('Gráfico de Barras Agrupadas: Level vs Is_Paid', fontsize=16)\n","plt.xlabel('Nivel', fontsize=14)\n","plt.ylabel('Frecuencia', fontsize=14)\n","\n","# Mostrar el gráfico\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rxUjAYOxO-6g","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732832395223,"user_tz":-60,"elapsed":388,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"e960816d-9144-4211-8b55-766deab1d972"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tabla de Contingencia:\n","is_paid False True Total\n","level \n","All Levels 122 1807 1929\n","Beginner Level 158 1112 1270\n","Expert Level 0 58 58\n","Intermediate Level 30 391 421\n","\n","Tabla de Contingencia con Porcentajes (%):\n","is_paid False True Total (%)\n","level \n","All Levels 6.324520 93.675480 100.0\n","Beginner Level 12.440945 87.559055 100.0\n","Expert Level 0.000000 100.000000 100.0\n","Intermediate Level 7.125891 92.874109 100.0\n"]}],"source":["# Crear la tabla de contingencia\n","contingency_table = pd.crosstab(data['level'], data['is_paid'])\n","\n","# Calcular el porcentaje por fila\n","contingency_table_percentage = contingency_table.div(contingency_table.sum(axis=1), axis=0) * 100\n","\n","# Añadir los totales y mostrar ambas tablas\n","contingency_table['Total'] = contingency_table.sum(axis=1)\n","contingency_table_percentage['Total (%)'] = contingency_table_percentage.sum(axis=1)\n","\n","# Mostrar las tablas\n","print(\"Tabla de Contingencia:\")\n","print(contingency_table)\n","\n","print(\"\\nTabla de Contingencia con Porcentajes (%):\")\n","print(contingency_table_percentage)\n","\n"]},{"cell_type":"markdown","source":["Estudiando el análisis bivariante de dos variables categóricas como es el caso de 'is_paid' con 'level', visualizamos en el gráfico y en la tabla de contingencia como los cursos dirigidos a 'All levels' y 'Beginner Level' y 'Intermediate Level' presentan un % similar de cursos gratis (entre el 6 y el 12% de esos cursos són gratis). Mientras que los cursos dirigidos a 'Expert Level' tienen un 0% de cursos gratis.\n","\n","\n","Esto nos dice que la gente que quiere cursos de nivel experto sólo los quiere de pago. Mientras que en niveles intermedios y principiantes hay un mínimo % de clientes que optan por los cursos gratis."],"metadata":{"id":"rUg6ODrPMobq"}},{"cell_type":"code","source":["# Seleccionar solo columnas numéricas\n","numeric_data = data.select_dtypes(include=['float64', 'int64'])\n","\n","# Calcular la matriz de correlación y crear el heatmap\n","plt.figure(figsize=(10, 8)) # Ajustar el tamaño del gráfico\n","sns.heatmap(numeric_data.corr(), annot=True, cmap='coolwarm', fmt=\".2f\")\n","\n","# Título del heatmap\n","plt.title('Matriz de Correlación', fontsize=16)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":706},"id":"fNGRTf3YO8GI","executionInfo":{"status":"ok","timestamp":1732704456150,"user_tz":-60,"elapsed":607,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"32234ee0-96b7-4201-954c-b473156731a7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Si utilizamos la matriz de correlación entre nuestras variables cuantitativas, observamos que 'num_lectures' y 'content_duration' mantienen una relación lineal notable ( de 0.8), lo cuál se nos hace \"obvio\" ya que si aumenta la variable número de clases es lógico pensar que aumenta la variable duración.\n","\n","También cabe destacar la relación que mantienen variables como 'num_reviews' y 'num_subscribers' ( de 0.65). Eso significa que al aumentar la variablle número de suscriptores también aumenta la variable número de reviews ( también se nos hace obvio, pero la matriz correlacional nos lo muestra) ."],"metadata":{"id":"XbKszFYfO_Q_"}},{"cell_type":"markdown","metadata":{"id":"iqK7-Jsb6Frr"},"source":["##3. Comprobación de la normalidad de alguna/s variable/s del conjunto de datos"]},{"cell_type":"markdown","metadata":{"id":"0XhTza_37xNH"},"source":["Para la comprobación de la normalidad comprobaremos diferentes aspectos, que nos indicaran la normalidad o no de nuestra variable. Estos serán los aspectos a tener en cuenta:\n","1. Simetría entre media y mediana\n","2. Gráfico Histograma\n","3. Gráfico Densidad\n","4. Gráfico de cajas\n","5. Gráfico Q-Q plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ogr7QBQn1W28","outputId":"44dcf6da-a1e9-4dcc-bce0-0a0581978fac"},"outputs":[{"data":{"text/plain":["count 3678.000000\n","mean 66.049483\n","std 61.005755\n","min 0.000000\n","25% 20.000000\n","50% 45.000000\n","75% 95.000000\n","max 200.000000\n","Name: price, dtype: float64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["data['price'].describe()"]},{"cell_type":"markdown","metadata":{"id":"9bKSyDqH1W29"},"source":["El valor de la media (mean) y de la mediana (50%) no son próximos"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":469},"executionInfo":{"elapsed":722,"status":"ok","timestamp":1732531366655,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"},"user_tz":-60},"id":"uYy5OX-B6eOL","outputId":"59e30e56-18d8-406f-ab8e-8eae6884a3b6"},"outputs":[{"data":{"text/plain":[""]},"execution_count":16,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Visualizamos la variable PRICE en histograma\n","data[\"price\"].plot(kind='hist',bins=40, linewidth=0.5, edgecolor=\"white\", title = \"Histograma con pandas\", xlim=(0, 80))"]},{"cell_type":"markdown","metadata":{"id":"q4Vrt5ij-MV4"},"source":["Vemos que nuestro histograma no sigue una distribución normal, ya que debería ser algo parecido a lo que se muestra en la imagen.![image.png](data:image/png;base64,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)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":542,"status":"ok","timestamp":1732532406761,"user":{"displayName":"Edu 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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Gráfico de densidad para la variable 'price'\n","sns.kdeplot(data=data, x='price', fill=True, color=\"blue\", alpha=0.6)\n","plt.title(\"Gráfico de Densidad de la Variable 'Price'\")\n","plt.xlabel(\"Price\")\n","plt.ylabel(\"Densidad\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"UHLW7olz-pI4"},"source":["El gráfico de densidad tampoco se asemeja a uno de distribución normal"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cXQh2z7v1W29","outputId":"50f3cb44-99ad-4aa9-85b5-1c212b2f43af"},"outputs":[{"data":{"text/plain":[""]},"execution_count":42,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Visualizamos PRICE en boxplot\n","data.boxplot(column=\"price\")"]},{"cell_type":"markdown","metadata":{"id":"MTqcV_FUAKeF"},"source":["Tras analizar este boxplot, podemos ver ciertos indicadores de que nuestra variable no sigue una d.normal, estos pueden ser:\n","- La mediana no está centrada, entre Q2 y Q3\n","- Los bigotes deberían tener longitudes similares, mientras que en el nuestro vemos que la mediana de los precios se aproxima más hacia el bigote de precios = 0€ que el de precios = 200€"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":578,"status":"ok","timestamp":1732532597635,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"},"user_tz":-60},"id":"490d5Iqt1W2-","outputId":"bf9fc24d-fac3-4803-ff84-2f923a404753"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# Q-Q plot para la variable 'price'\n","sm.qqplot(data['price'], line='s')\n","plt.title(\"Q-Q Plot de la Variable 'Price'\")\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"id":"9UUdxRlz_BhA"},"source":["Para que el este gráfico, nos sirviera como indicar de que nuestra variable sigue una distribución normal, nuestros puntos deberían estar sobre la línea de la recta, o al menos acercarse. Como vemos, no lo sigue. Esto se debe a que tenemos muchos cursos gratuitos (se observan muchos puntos en 0) y también tenemos muchos cursos de 200€"]},{"cell_type":"markdown","metadata":{"id":"2K37W8vw62cb"},"source":["Tras llegar a la conclusión de que nuestra variable 'price' no sigue una distribución normal, vamos a realizar una función que nos indica entre diferentes distribuciones cual es la que más se asemeja para nuestra variable"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N02Q_0Xu1W2-","outputId":"dc401112-6ec4-4f6f-8c4a-195c193784da"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\eduab\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\scipy\\stats\\_continuous_distns.py:716: RuntimeWarning: invalid value encountered in sqrt\n"," sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)\n","c:\\Users\\eduab\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\scipy\\stats\\_continuous_distns.py:6554: RuntimeWarning: overflow encountered in divide\n"," return np.sum((1 + np.log(shifted/scale)/shape**2)/shifted)\n","c:\\Users\\eduab\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:88: RuntimeWarning: overflow encountered in reduce\n"," return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"]},{"name":"stdout","output_type":"stream","text":["Mejor distribución: beta\n","Parámetros: (0.6062736129649037, 0.9921910509055593, -1.740427898558937e-27, 202.5832648612008)\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# PARA VER QUÉ DISTRIBUCIÓN ES LA MÁS PARECIDA A LOS DATOS\n","\n","\n","# Definir la función best_fit_distribution\n","def best_fit_distribution(data, bins=200, plot=False):\n"," DISTRIBUTIONS = [st.norm, st.expon, st.gamma, st.weibull_min, st.weibull_max, st.beta, st.lognorm]\n","\n"," y, x = np.histogram(data, bins=bins, density=True)\n"," x = (x + np.roll(x, -1))[:-1] / 2.0\n","\n"," best_distribution = None\n"," best_params = None\n"," best_sse = np.inf\n","\n"," for distribution in DISTRIBUTIONS:\n"," try:\n"," params = distribution.fit(data)\n"," pdf = distribution.pdf(x, *params[:-2], loc=params[-2], scale=params[-1])\n"," sse = np.sum(np.power(y - pdf, 2.0))\n","\n"," if best_sse > sse > 0:\n"," best_distribution = distribution\n"," best_params = params\n"," best_sse = sse\n"," except Exception:\n"," pass\n"," if best_distribution:\n"," print(f\"Mejor distribución: {best_distribution.name}\")\n"," print(f\"Parámetros: {best_params}\")\n","\n"," if plot:\n"," plt.hist(data, bins=bins, density=True, alpha=0.5, color='b')\n"," pdf = best_distribution.pdf(x, *best_params[:-2], loc=best_params[-2], scale=best_params[-1])\n"," plt.plot(x, pdf, 'r--', label=f'{best_distribution.name}')\n"," plt.legend()\n"," plt.show()\n","\n"," return best_distribution, best_params\n","\n","# Llamada a la función con una columna específica\n","mejor_distribucion, parametros = best_fit_distribution(data[\"price\"], plot=True)"]},{"cell_type":"markdown","metadata":{"id":"gRFf6T5u7O__"},"source":["Nos aconseja que la distribución que mejor se asemeja es la beta"]},{"cell_type":"code","source":["data['num_subscribers'].describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"id":"y_ku0YZISAnz","executionInfo":{"status":"ok","timestamp":1732705279468,"user_tz":-60,"elapsed":184,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"126cd765-6473-464e-fe9a-5127b7a4ecb2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count 3677.000000\n","mean 3198.020125\n","std 9505.263339\n","min 0.000000\n","25% 111.000000\n","50% 912.000000\n","75% 2547.000000\n","max 268923.000000\n","Name: num_subscribers, dtype: float64"],"text/html":["
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num_subscribers
count3677.000000
mean3198.020125
std9505.263339
min0.000000
25%111.000000
50%912.000000
75%2547.000000
max268923.000000
\n","

"]},"metadata":{},"execution_count":50}]},{"cell_type":"markdown","source":["Observamos que la media es de 3198 suscriptores por curso mientras que la mediana es de 912. Esta gran diferencia podría ser un indicador de que nuestra variable NO sigue una distribución normal, pero vamos a seguir estudiándola."],"metadata":{"id":"8kbfOBQsTGCV"}},{"cell_type":"code","source":["# Gráfico de densidad para la variable 'num_subscribers'\n","sns.kdeplot(data=data, x='num_subscribers', fill=True, color=\"blue\", alpha=0.6)\n","plt.title(\"Gráfico de Densidad de la Variable 'Num_subscribers'\")\n","plt.xlabel(\"Price\")\n","plt.ylabel(\"Densidad\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":481},"id":"d0v0hqqQSCRd","executionInfo":{"status":"ok","timestamp":1732705388017,"user_tz":-60,"elapsed":749,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"dc3e63af-5b0c-493a-edc9-d52348cfad84"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["En el densityplot no se aprecia muy bien.... vamos a seguir estudiándola."],"metadata":{"id":"18D-efhPTYgw"}},{"cell_type":"code","source":["# Visualizamos num_subscribers en boxplot\n","data.boxplot(column=\"num_subscribers\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"id":"_pV8XIhVShnN","executionInfo":{"status":"ok","timestamp":1732705418795,"user_tz":-60,"elapsed":347,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"48a7bfb3-0fe5-41fb-8fe2-080ffc3fd0d7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":53},{"output_type":"display_data","data":{"text/plain":["
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drtd/v7+zc5pGIbKy8sVEBDwted099VeLpdLBQUF59UHgMtXcXGxt4cA4CI4e8XKSruD0rFjxzRnzhwNHTpU9913X3u76RD8/PzUt29fbw8DwAVWU1Oj4uJihYWFKTAw0NvDAXABHThwoFV17QpKFRUVmjZtmrp27aqVK1fKx+fMVqfg4GBJZ2aDQkNDPerPPu5wOHTs2LFm/ZaXl5s17tkf98ySW319vWpqajz6qq+vV11dncesUkVFhWw2m0ddS5cCKC8vV8+ePdvxLvwfm82moKCg8+oDwOUrMDCQn3Ggg2nNspvUjgtO1tbWavr06aqsrNQLL7zgsZzl3gN07p6fwsJC+fn5qXfv3mZdUVFRs+shFRUVmX0EBQWpZ8+ezfpyv85d5/67qKio2Tl79eqlgIAAs+7cvgzD8DgnAADA2doUlE6fPq3Zs2ersLBQL7zwgnr06OFxvHfv3goLC1Nubq5He05OjoYNG2auBSYmJqq8vFzbtm0za4qKivTRRx8pMTHRbEtMTNTmzZvlcrk8+nI4HIqNjZUkDRo0SF26dNGGDRvMGpfLpY0bNzbr6+OPP/bYa7Bt2zaVlZVp+PDhbXkbAABAJ9GmpbfHHntMeXl5euihh1RVVeVxEcn+/fvLbrdr5syZmjdvnm666SYNHTpUOTk52rNnj1588UWzNjY2VvHx8UpPT9f8+fPl7++v5cuXKzIyUmPGjDHrUlNT9cYbbygtLU133323Pv30U2VlZWnOnDlm6PL399f06dO1cuVKhYSEqF+/fnr55ZdVVlam1NRUs6+xY8cqMzNTM2fO1Ny5c1VTU6OnnnrKvJo3AADAuWzGuetfX2PUqFH6/PPPWzy2efNm3XjjjZLO3MJk9erVOnr0qMLDwzV37lyNHDnSo76yslJLlizRpk2bdPr0acXHx+uRRx5pNku1c+dOPfHEEyooKFBISIjuueceTZs2zWNt0Wi6hcnatWt16tQpRUVFacGCBeask9vx48e1ePFibd26VVdddZWSkpKUnp6uLl26tPYtaGbv3r2SpOjo6Hb3AeDyVF1drYKCAkVFRbFHCehgWvv53aaghOYISkDHRVACOq7Wfn63eTM3AABAZ0FQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAoAWNDQ0yOl0Kjc3V06nUw0NDd4eEgAvuMrbAwCAy012drbS0tJUXFxstoWFhSkjI0PJycneGxiAS44ZJQA4S3Z2tqZMmaLo6Gjl5eXJ6XQqLy9P0dHRmjJlirKzs709RACXkM0wDMPbg7iS7d27V5IUHR3t5ZEAOF8NDQ3q27evoqOj9dprr6m2tlYFBQWKiopSQECAJk+erH379mn//v3y9fX19nABnIfWfn4zowQATfLz81VcXKz09HT5+Hj+evTx8dGCBQtUVFSk/Px8L40QwKVGUAKAJiUlJZKkW265pcXj7nZ3HYCOj6AEAE169uwpSdq3b1+Lx93t7joAHR9BCQCaJCQkKCwsTI8//rgaGxs9jjU2NmrJkiUKDw9XQkKCl0YI4FIjKAFAE19fX2VkZGj9+vWaPHmytm/frq+++krbt2/X5MmTtX79ei1dupSN3EAnwnWUAOAsycnJWrdundLS0jRq1CizPTw8XOvWreM6SkAnQ1ACgHMkJydr0qRJ2rRpk3bs2KHBgwcrKSmJmSSgEyIoAUALfH19lZiYqNDQUEVFRRGSgE6KPUoAAAAW2hyUDh06pEcffVSTJk1S//79NXHixGY19957ryIjI5v9OXjwoEddZWWl0tPTNWTIEMXGxmrWrFkqLS1t1t/OnTuVkpKimJgYjRw5UqtWrdK5FxQ3DEOrVq3SiBEjFBMTo5SUFO3evbtZX8ePH9fMmTMVGxurIUOG6OGHH1ZVVVVb3wYAANAJtHnpbf/+/Xr33Xc1YMAANTY2NgssboMGDdL8+fM92m688UaP57Nnz9aBAwe0aNEi+fv76+mnn9a0adP06quv6qqrzgzt0KFDSk1NVVxcnGbPnq1PPvnE/NZJamqq2dfq1au1YsUKzZs3T5GRkXrppZc0depUvf766+rdu7ckyeVy6YEHHpAkZWRkqLa2Vk8++aTS0tKUmZnZ1rcCAAB0cG0OSqNGjdLtt98uSXrooYcsL8zmcDg0cOBAy3527dqlrVu3KisrS/Hx8ZLOfKtkwoQJ2rhxoyZMmCBJysrKUrdu3bRs2TLZ7XYNGzZMp06d0vPPP697771XdrtddXV1yszM1NSpU3X//fdLkgYPHqxx48YpKytLixYtkiS99dZb2r9/v3JychQREWGOMzU1VXv27FFMTExb3w4AANCBtXnp7dz7H7WX0+mUw+FQXFyc2RYREaGoqCg5nU6PutGjR8tut5ttEyZMUEVFhXbt2iXpzNJcVVWVxo8fb9bY7XYlJSU16ysyMtIMSZIUFxenrl276t13370g/y4AANBxXLRvvf3zn//UwIED1dDQoAEDBujBBx/U9773PfN4YWGhwsPDZbPZPF4XERGhwsJCSVJ1dbVKSko8go27xmazqbCwUEOHDjXrz63r06eP1qxZo9raWgUEBKiwsLBZjc1mU3h4uNlHexiGoerq6na/HsDlqaamxuNvAB2HYRjNMkhLLkpQ+t73vqdJkyYpLCxMpaWlysrK0o9//GP9+c9/VmxsrCSpoqJC11xzTbPXBgcHm8t5lZWVks4sj53NbrcrMDBQ5eXlZl92u13+/v4edQ6HQ4ZhqLy8XAEBAV97Tndf7eFyuVRQUNDu1wO4vBUXF3t7CAAugrNXq6xclKA0a9Ysj+cjRozQxIkT9bvf/U6rV6++GKf0Kj8/P/Xt29fbwwBwgdXU1Ki4uFhhYWEKDAz09nAAXEAHDhxoVd0lueBkUFCQhg8frrfeestsczgcOnbsWLPa8vJyBQcHS5I5++OeWXKrr69XTU2NWedwOFRfX6+6ujqPWaWKigrZbDaPupYuBVBeXn5edwO32WwKCgpq9+sBXN4CAwP5GQc6mNYsu0levOBkRESEioqKml1eoKioyNxHFBQUpJ49ezbbP+R+nbvO/XdRUZFHXWFhoXr16qWAgACz7ty+DMPwOCcAAIDbJQlK1dXV2rJli6Kjo822xMRElZeXa9u2bWZbUVGRPvroIyUmJnrUbd68WS6Xy2zLycmRw+Ew9zsNGjRIXbp00YYNG8wal8uljRs3Nuvr448/9thvsG3bNpWVlWn48OEX9N8MAACufG1eequpqTG/Sv/555+rqqpKubm5kqQhQ4aosLBQL7zwgpKSknTDDTeotLRUf/jDH3TixAn993//t9lPbGys4uPjlZ6ervnz58vf31/Lly9XZGSkxowZY9alpqbqjTfeUFpamu6++259+umnysrK0pw5c8xNWP7+/po+fbpWrlypkJAQ9evXTy+//LLKyso8Lko5duxYZWZmaubMmZo7d65qamr01FNPmVfzBgAAOJvNsLq0toUjR45o9OjRLR7705/+pOuvv16//vWv9cknn6isrEyBgYGKjY3VjBkzmoWRyspKLVmyRJs2bdLp06cVHx+vRx55RD169PCo27lzp5544gkVFBQoJCRE99xzj6ZNm+axvui+hcnatWt16tQpRUVFacGCBeask9vx48e1ePFibd26VVdddZWSkpKUnp6uLl26tOVtMO3du1eSPGbLAHQM1dXVKigoUFRUFHuUgA6mtZ/fbQ5K8ERQAjoughLQcbX289trm7kBAAAudwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAGhBQ0ODnE6ncnNz5XQ61dDQ4O0hAfCCq7w9AAC43GRnZystLU3FxcVmW1hYmDIyMpScnOy9gQG45AhKAHCW7OxsTZkyRXfccYdmzZqlL7/8Ut26ddM777yjKVOmaN26dYQloBOxGYZheHsQV7K9e/dKkqKjo708EgDnq6GhQX379tV1112nEydO6NChQ+axm2++WaGhoTp58qT2798vX19fL44UwPlq7ec3e5QAoEl+fr6Ki4v1wQcfKCYmRnl5eXI6ncrLy1NMTIw++OADFRUVKT8/39tDBXCJEJQAoMnnn38uSRo/frxee+01DRkyREFBQRoyZIhee+01jR8/3qMOQMdHUAKAJidOnJAkJScny8fH89ejj4+PJk+e7FEHoOMjKAFAk9DQUElnNnQ3NjZ6HGtsbNRrr73mUQeg4yMoAUCTG264QZKUm5uryZMna/v27frqq6+0fft2TZ48Wbm5uR51ADo+Lg8AAE0SEhIUFham6667Tnv27NGoUaPMY2FhYRo8eLBOnjyphIQEL44SwKVEUAKAJr6+vsrIyNCdd96pwMBAj2PHjx9XcXGxXn31VS4NAHQiLL0BwDlsNluLbS21A+jYCEoA0KShoUFpaWmaOHGiysvLtWHDBi1evFgbNmxQWVmZJk6cqHnz5nHfN6ATYekNAJq4Lzj58ssvy8/PT4mJiQoNDVVUVJT8/Py0YMEC3XbbbcrPz9eIESO8PVwAlwAzSgDQpKSkRJJ0yy23tHjc3e6uA9DxEZQAoEnPnj0lSfv27WvxuLvdXQeg4yMoAUAT9+UBHn/88RYvOLlkyRKFh4dzeQCgEyEoAUAT9+UB1q9f3+IFJ9evX6+lS5dyeQCgE2EzNwCcJTk5WevWrVNaWprHBSfDw8O1bt06JScne3F0AC41ghIAnCM5OVmTJk3Spk2btGPHDg0ePFhJSUnMJAGdEEEJAFrg6+vrcXkAQhLQObFHCQAAwAJBCQAAwAJBCQAAwAJBCQAAwAJBCQBa0NDQIKfTqdzcXDmdTm6EC3RSfOsNAM6RnZ2ttLQ0FRcXm21hYWHKyMjgOkpAJ9PmGaVDhw7p0Ucf1aRJk9S/f39NnDixxbpXXnlFY8eOVXR0tH7wgx8oLy+vWU1lZaXS09M1ZMgQxcbGatasWSotLW1Wt3PnTqWkpCgmJkYjR47UqlWrZBiGR41hGFq1apVGjBihmJgYpaSkaPfu3c36On78uGbOnKnY2FgNGTJEDz/8sKqqqtr6NgDooLKzszVlyhRFR0crLy9PTqdTeXl5io6O1pQpU5Sdne3tIQK4hNoclPbv3693331XN998s/r06dNizZtvvqmFCxdq/PjxWr16tQYOHKgZM2Y0Cy6zZ8/We++9p0WLFmnp0qUqKirStGnTdPr0abPm0KFDSk1NVWhoqDIzM/WjH/1IK1as0O9//3uPvlavXq0VK1bo/vvvV2ZmpkJDQzV16lQdPnzYrHG5XHrggQdUXFysjIwMLVq0SFu3blVaWlpb3wYAHVBDQ4PS0tI0ceJEvfrqq6qtrZXT6VRtba1effVVTZw4UfPmzWMZDuhMjDZqaGgwH8+fP9+44447mtWMGTPGmDt3rkdbSkqK8cADD5jPd+7cafTr18/Iz8832w4ePGhERkYab775ptm2cOFCY+TIkUZdXZ3ZlpGRYXz3u98122pra41BgwYZGRkZZk1dXZ0xcuRI41e/+pXZ9sYbbxiRkZHGwYMHzbb8/HyjX79+xocfftiWt8G0Z88eY8+ePe16LYDLS15eniHJWLJkiREWFmZIMv+EhYUZjz/+uCHJyMvL8/ZQAZyn1n5+t3lGycfn619y+PBhFRcXa/z48R7tEyZM0LZt21RfXy9JcjqdcjgciouLM2siIiIUFRUlp9NptjmdTo0ePVp2u92jr4qKCu3atUvSmaW5qqoqj3Pa7XYlJSU16ysyMlIRERFmW1xcnLp27ap33323LW8DgA6opKREkpSent7i0tvDDz/sUQeg47vgm7kLCwslnbmB5Nn69Okjl8ulw4cPq0+fPiosLFR4eLhsNptHXUREhNlHdXW1SkpKPIKNu8Zms6mwsFBDhw4168+t69Onj9asWaPa2loFBASosLCwWY3NZlN4eLjZR3sYhqHq6up2vx7A5cHhcEiShg0bprVr16qurk7FxcUKCwvT2rVrNWbMGG3btk0Oh4OfeeAKZxhGswzSkgselMrLyyX93y8cN/dz9/GKigpdc801zV4fHBysffv2STqz2bulvux2uwIDAz36stvt8vf3b3ZOwzBUXl6ugICArz2nu6/2cLlcKigoaPfrAVwePvvsM0nSV199pYKCAnMGvbi4WI2NjWY4+uyzz/iZBzqAs1errHB5gAvAz89Pffv29fYwAJynvXv3SpI+/PBDLVq0SDNnzlRQUJCqq6u1cuVKffjhh5KkwMBARUVFeXOoAM7TgQMHWlV3wYNScHCwpDOzQaGhoWZ7RUWFx3GHw6Fjx441e315eblZ4579cc8sudXX16umpsajr/r6etXV1XnMKlVUVMhms3nUtXQpgPLycvXs2bN9/2CdWb4LCgpq9+sBXB7CwsIkSUuWLFFmZqbHvsfw8HA9/vjjSk9PV1hYGD/zwBWuNctu0kW4Mrd7D9C5e34KCwvl5+en3r17m3VFRUXNrodUVFRk9hEUFKSePXs268v9Oned+++ioqJm5+zVq5cCAgLMunP7MgzD45wAOq+EhASFhYXp/fff16effqoNGzZo8eLF2rBhgz755BNt27ZN4eHhSkhI8PZQAVwiFzwo9e7dW2FhYcrNzfVoz8nJ0bBhw8z1wMTERJWXl2vbtm1mTVFRkT766CMlJiaabYmJidq8ebNcLpdHXw6HQ7GxsZKkQYMGqUuXLtqwYYNZ43K5tHHjxmZ9ffzxxx5X2922bZvKyso0fPjwC/MGALhi+fr6KiMjQ+vXr9edd94pf39/JSQkyN/fX3feeafWr1+vpUuXytfX19tDBXCJtHnpraamxvwq/eeff66qqiozFA0ZMkQhISGaOXOm5s2bp5tuuklDhw5VTk6O9uzZoxdffNHsJzY2VvHx8UpPT9f8+fPl7++v5cuXKzIyUmPGjDHrUlNT9cYbbygtLU133323Pv30U2VlZWnOnDlm6PL399f06dO1cuVKhYSEqF+/fnr55ZdVVlam1NRUs6+xY8cqMzNTM2fO1Ny5c1VTU6OnnnrKvJo3ACQnJ2vdunVKS0vTqFGjzPbw8HCtW7eOW5gAnYzNOHft6xscOXJEo0ePbvHYn/70Jw0dOlTSmVuYrF69WkePHlV4eLjmzp2rkSNHetRXVlZqyZIl2rRpk06fPq34+Hg98sgj6tGjh0fdzp079cQTT6igoEAhISG65557NG3aNI/1RaPpFiZr167VqVOnFBUVpQULFpizTm7Hjx/X4sWLtXXrVl111VVKSkpSenq6unTp0pa3weTe/BkdHd2u1wO4PDU0NGjTpk3asWOHBg8erKSkJGaSgA6ktZ/fbQ5K8ERQAjqu6upqFRQUKCoqis3bQAfT2s/vC75HCQAAoKMgKAEAAFggKAEAAFggKAEAAFggKAEAAFggKAEAAFggKAFACxoaGuR0OpWbmyun06mGhgZvDwmAF1zwm+ICwJUuOztbc+fO1aFDh8y2m2++WcuWLePK3EAnw4wSAJwlOztbd955p0pLSz3aS0tLdeeddyo7O9tLIwPgDQQlAGjS0NCgn/70p5Kk0aNHKy8vT06nU3l5eeatm372s5+xDAd0IgQlAGiyZcsWnThxQvHx8Xr99dc1ZMgQBQUFaciQIXr99dcVHx+v0tJSbdmyxdtDBXCJEJQAoIk7AD322GPy8fH89ejj46Nf/epXHnUAOj6CEgC0gG+9AZD41hsAmEaMGKHFixfrF7/4hWpra1VcXGweCwsLU0BAgFkHoHNgRgkAmowYMULBwcH6+OOPVV1drWeeeUYbNmzQM888o+rqan388ccKDg4mKAGdCDNKAHAWu90uSaqsrNSMGTPM9sDAQEmSv7+/V8YFwDuYUQKAJvn5+Tpx4oSWLFmiHj16eBy7/vrr9fjjj6u0tFT5+fleGiGAS42gBABNSkpKJEkzZszQJ598oieffFJ33XWXnnzySX388cfmDJO7DkDHx9IbADTp2bOnJOmZZ55RZmamuZn7lVde0XPPPaef/OQnHnUAOj6bYRiGtwdxJdu7d68kKTo62ssjAXC+Ghoa1KtXL5WWlmrixIlKS0uTr6+vGhoalJGRofXr16t79+46evSofH19vT1cAOehtZ/fzCgBwFnc/+/Y2NioV155RSUlJerZs6caGxu9PDIA3kBQAoAm7s3cCQkJysnJaXY8ISFB+fn5ys/P5xIBQCfBZm4AaOLepJ2fn9/iLUzc33ZjMzfQeTCjBABNrr32WvPxuHHjlJSUpC+//FLdunXTpk2bzFmms+sAdGwEJQBosmfPHklSQECAPvroI4/lN/ctTGpra7Vnzx6NGTPGW8MEcAkRlACgyXvvvSdJqq2tVU1NjWbNmqXAwEDV1NTor3/9q2pra826efPmeXOoAC4RghIANLn66qslSdddd52++OILrVixwjzm6+trtrvrAHR8BCUAaDJgwAC99NJL+uKLLzRhwgTdfvvt5h6lt99+21yKGzBggJdHCuBS4VtvANDk+uuvNx/v2LFDfn5+uu222+Tn56cdO3a0WAegY2NGCQCanDx50nxcWlqqmTNnms9tNluLdQA6NmaUAKBJaGioJCk2NlY33nijx7HevXsrNjbWow5Ax8eMEgA0ueGGGyRJu3fvVkBAgMexEydO6PDhwx51ADo+ZpQAoElCQoK6d+8uq3uFG4ah7t27KyEh4RKPDIC3EJQA4CzukHRuWLIKTwA6NoISADRx3xRX8ty8Lcm891tpaal5zzcAHR9BCQCafP7555Kk8ePHq7y8XBs2bNDixYu1YcMGlZWVafz48R51ADo+ghIANHHPJiUnJ8vPz0+JiYkaN26cEhMT5efnp8mTJ3vUAej4CEoA0MT9tf/s7Gy5XC45nU7l5ubK6XTK5XLptdde86gD0PFxeQAAaOL+2v+GDRvkcDjMm+BKUkBAgPmcywMAnQczSgDQJCEhwZwtOjsknf2cywMAnQtBCQDOUldXJ+n/vuXm5n5+boAC0LERlACgyZYtW1RRUSFJamxs9Djmfl5RUaEtW7Zc6qEB8BKCEgA0eeedd8zH597C5OznZ9cB6NjYzA0ATQ4dOmQ+Hj16tG6//XZ9+eWX6tatm95++229+eabzeoAdGwEJQBo4l5eCwgI0L59+8xgJEk333yz+c23c5flAHRcBCUAaHL2hu2amhrNmjXLDEd/+ctfzI3c5270BtBxEZQAoEnv3r3Nx6WlpVqxYsU31gHo2PjfIgBoct1115mPrS4PcG4dgI6NoAQATbp3724+ttvtHsfOfn52HYCO7aIEpezsbEVGRjb7s3TpUo+6V155RWPHjlV0dLR+8IMfKC8vr1lflZWVSk9P15AhQxQbG6tZs2aptLS0Wd3OnTuVkpKimJgYjRw5UqtWrZJhGB41hmFo1apVGjFihGJiYpSSkqLdu3df0H87gCvXyZMnzcdWV+Y+tw5Ax3ZR9yi98MILuuaaa8znPXr0MB+/+eabWrhwoX7605/q1ltvVU5OjmbMmKGXXnpJAwcONOtmz56tAwcOaNGiRfL399fTTz+tadOm6dVXX9VVV50Z/qFDh5Samqq4uDjNnj1bn3zyiZYuXSpfX1+lpqaafa1evVorVqzQvHnzFBkZqZdeeklTp07V66+/zp4DAK2+2S03xQU6j4salL7zne8oJCSkxWMrVqzQHXfcodmzZ0uSbr31Vn366ad69tlntXr1aknSrl27tHXrVmVlZSk+Pl6SFB4ergkTJmjjxo2aMGGCJCkrK0vdunXTsmXLZLfbNWzYMJ06dUrPP/+87r33XtntdtXV1SkzM1NTp07V/fffL0kaPHiwxo0bp6ysLC1atOhivhUArgBn/74aN26cIiIiVFJSop49e6qwsFC5ubnN6gB0bF7Zo3T48GEVFxdr/PjxHu0TJkzQtm3bVF9fL0lyOp1yOByKi4szayIiIhQVFSWn02m2OZ1OjR492mMPwYQJE1RRUaFdu3ZJOrM0V1VV5XFOu92upKQkj74AdF5///vfJUldu3bVpk2b9Lvf/U5/+9vf9Lvf/U5vv/22unbt6lEHoOO7qDNKEydO1JdffqlevXrp3//93/XAAw/I19dXhYWFks7MDp2tT58+crlcOnz4sPr06aPCwkKFh4fLZrN51EVERJh9VFdXq6SkRBEREc1qbDabCgsLNXToULP+3Lo+ffpozZo1qq2tbXbLgtYyDEPV1dXtei2Ay8f+/fslSWVlZc2OnT592mzfv38/P/PAFc4wjGb5oiUXJSiFhoZq5syZGjBggGw2m9555x09/fTTOn78uB599FGVl5dLkhwOh8fr3M/dxysqKjz2OLkFBwdr3759ks5s9m6pL7vdrsDAQI++7Ha7/P39m53TMAyVl5e3Oyi5XC4VFBS067UALh/uGaPW1PEzD1z5zv12a0suSlBKSEhQQkKC+Tw+Pl7+/v5as2aNfvrTn16MU3qVn5+f+vbt6+1hADhPKSkpevXVVyVJSUlJuv3221VVVaUuXbro7bff1qZNm8y6qKgobw4VwHk6cOBAq+ou2ZW5x48fr9///vcqKChQcHCwpDOzQWd/e6SiokKSzOMOh0PHjh1r1ld5eblZ455xcs8sudXX16umpsajr/r6etXV1XnMKlVUVMhms5l17WGz2RQUFNTu1wO4PKxatcp8vGXLFkVHRysuLk7vvfeetmzZ4lF31113eWGEAC6U1iy7SV7azO3eJ+TeN+RWWFgoPz8/86v6ERERKioqanY9pKKiIrOPoKAg8xsp59YYhmHWuf8uKipqds5evXq1e9kNQMfx2WefSZK+/e1vy+VyadmyZbrzzju1bNkyuVwuffvb3/aoA9DxXbKglJOTI19fX/Xv31+9e/dWWFiY+VXbs2uGDRtmrhkmJiaqvLxc27ZtM2uKior00UcfKTEx0WxLTEzU5s2b5XK5PPpyOByKjY2VJA0aNEhdunTRhg0bzBqXy6WNGzd69AWg87rpppskSSUlJc3+b9Nms6mkpMSjDkDHd1GW3lJTUzV06FBFRkZKkjZv3qz/+Z//0X333Wcutc2cOVPz5s3TTTfdpKFDhyonJ0d79uzRiy++aPYTGxur+Ph4paena/78+fL399fy5csVGRmpMWPGeJzvjTfeUFpamu6++259+umnysrK0pw5c8zQ5e/vr+nTp2vlypUKCQlRv3799PLLL6usrMzjopQAOq958+bp7bffNr8Ecjb3lz7cdQA6B5tx7rrWBbB48WLl5+fr2LFjamxsVFhYmO666y7de++9Hv+X9sorr2j16tU6evSowsPDNXfuXI0cOdKjr8rKSi1ZskSbNm3S6dOnFR8fr0ceecTjKt/SmeskPfHEEyooKFBISIjuueceTZs2zeN87luYrF27VqdOnVJUVJQWLFhgzjq1x969eyVJ0dHR7e4DwOWhpqamVfsNq6urFRgYeAlGBOBiae3n90UJSp0JQQnoOH7729/ql7/85TfWPfXUU/p//+//XYIRAbhYWvv57ZXN3ABwOXr99dfNx+fOGJ39/Ow6AB0bQQkAmrgn2G+88cZmN74NDQ3VjTfe6FEHoOMjKAFAE/cU/JEjRxQdHa28vDw5nU7l5eUpOjpaR44c8agD0PFdsgtOAsDlbtiwYcrMzJQk/etf/9Lf/vY3BQQEqLa2Vv/617886gB0DgQlAGjy5Zdfmo9LS0u1YsWKb6wD0LGx9AYATc7dl3S+dQCufMwoAUCT66+/3nxst9vVv39/GYYhm82mjz76SPX19c3qAHRsBCUAaOIOQj4+PmpoaNDu3bvNY76+vvLx8VFjY6NZB6DjY+kNAJqsXbtWktTY2Khu3bopOjpaN998s6Kjo9WtWzc1NjZ61AHo+JhRAoAmlZWVks5cXPKLL77QF1984XE8MDBQNTU1Zh2Ajo8ZJQBo0qtXL0ln7vnWEne7uw5Ax0dQAoAmgwcPvqB1AK58BCUAaPK3v/3tgtYBuPIRlACgydnfcrsQdQCufAQlAGhSV1d3QesAXPkISgDQ5IYbbrigdQCufAQlAGjStWvXC1oH4MpHUAKAJqWlpRe0DsCVj6AEAE2++uqrC1oH4MpHUAKAJoZhXNA6AFc+ghIANGntrUm4hQnQeRCUAKBJVVXVBa0DcOUjKAFAE5beAJyLoAQATQhKAM5FUAKAJgQlAOciKAEAAFggKAFAE2aUAJyLoAQAAGCBoAQAAGCBoAQAAGCBoAQATXx8WvcrsbV1AK58/LQDQJPGxsYLWgfgykdQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsEBQAgAAsHCVtwcAABfaoUOHVFFRcVHPsXfv3ja/xuFw6Oabb74IowFwsRCUAHQop06dUnx8vBobGy/qecaNG9fm1/j6+mr37t0KCQm5CCMCcDEQlAB0KCEhIdq6dWu7ZpRiYmJaXZubm9vm/h0OByEJuMIQlAB0OO1d3nrllVd01113taouOjq6XecAcGXpdJu5Dx48qB//+McaOHCg4uLi9NRTT6m+vt7bwwJwGZgyZcoFrQNw5etUM0rl5eX60Y9+pLCwMK1cuVLHjx/XE088odraWj366KPeHh7Q6R09elTV1dVeHcP+/fv1rW9962uPHzhw4BKOqLmgoCD16tXLq2MAOotOFZT+8pe/6KuvvtIzzzyjrl27SpIaGhr02GOPafr06erRo4d3Bwh0YkePHtX06dO9PQxJ0sSJE3XkyBHt3r3bbBs4cKBuvPFGzZkzx3sDO0tmZiZhCbgEOlVQcjqdGjZsmBmSJGn8+PH61a9+pffee0/JycneGxzQyVVXVyswMFDTp09X9+7dvT0cSVJ9fb2KiooUHh4uu93u7eFIkkpLS5WZmen1mTegs+hUQamwsFB33nmnR5vD4VBoaKgKCwvb3a9hGPzSAs5TbW2toqKiVFVVpaqqKm8Px+Tn56cjR454exgeoqKiVFtby+8d4DwYhiGbzfaNdZ0qKFVUVMjhcDRrDw4OVnl5ebv7dblcKigoOJ+hAZ3ekSNHVFBQoEOHDp1XPw0NDdq/f/8FGtWF961vfUu+vr7n1Ud1dbWOHj2qurq6CzQqoHNqzUxxpwpKF4ufn5/69u3r7WEAV7SoqCiFh4fLx+f8v4xbUlJyQWalXC6XTpw4odDQUPn5+Z13f126dFHPnj3Pu5/AwMAL0g/QmbX2SxmdKig5HA5VVlY2ay8vL1dwcHC7+7XZbAoKCjqfoQFQ2y74+HVuueWWC9JPdXW1CgoKFBUVxc840MG0ZtlN6mTXUYqIiGi2F6myslInTpxQRESEl0YFAAAuV50qKCUmJur999/3uLVBbm6ufHx8FBcX58WRAQCAy1GnCko//OEPdfXVV+sXv/iFtm7dqldffVVPPfWUfvjDH3INJQAA0EynCkrBwcFas2aNfH199Ytf/EIZGRmaMmWKHnroIW8PDQAAXIY61WZuSerTp4/++Mc/ensYAADgCtCpZpQAAADagqAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABggaAEAABgodNdcPJCc7lcMgxDe/fu9fZQAFxghmFIkg4cONDqO40DuDLU19e36ueaoHSe+OUJdFw2m012u93bwwBwEdhstlZ9htsM9/8yAQAAwAN7lAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlABA0vbt2xUZGam9e/d6bQwPPfSQJk6caD7Pzs5WZGSkTp065bUxAZ3dVd4eAADgjJ///Oeqrq729jAAnIWgBABeVltbq4CAAN10001eOS8Aayy9Afha7uWg7du3a/LkyRo4cKCmTJmiffv2SZKOHDmiyMhI5ebmerzuN7/5jUaNGmU+dy8j7d27V1OnTtWAAQM0duxYvf/++2psbNTy5ct122236bbbblNGRoYaGxtbPcbNmzcrOTlZsbGx+u53v6vk5GS9++675vHIyEhlZWV5vOaPf/yjIiMjm/V16tQpzZgxQwMHDlR8fLyef/55j+PHjh3Tgw8+qNtuu03R0dEaNWqUHn/8cY+agwcPasaMGRoyZIgGDBigH/zgB1q/fr3HeFatWqXf/va3iouL07Bhwzze63N99tlnuu+++zRgwACNGjVK69ata1aza9cu3XfffRo4cKAGDx6stLQ0nTx50jzu/u+UnZ2tRx55REOHDtVdd90lSdqxY4fuueceDR48WLGxsfr+97+vv/3tb5bvN9CZMKME4BudOHFCixcv1k9+8hNdc801ysjI0IwZM7Rp06Y29zV//nz98Ic/1I9//GOtWrVKM2bM0L/927+pqqpKTz75pD788EOtXLlS/fr10/e///1v7O+zzz7Tgw8+qDvuuENpaWlqbGzUxx9/rPLy8vb8U7Vw4ULdcccdWrlypd5//30tX75cwcHBuvvuuyVJv/zlL1VaWqpHHnlE1157rUpKSszQKEnFxcVKSUlRz5499fDDDys0NFSffvqpjh496nGeP/3pTxowYIB+85vf6PTp0187prlz5yolJUXTpk1TTk6OHn74YXXv3l2JiYmSzoSke++9V8OHD9fy5ctVU1Ojp59+Wj//+c/117/+1aOvZcuWafjw4WYYraqq0vTp0zV48GAtW7ZMdrtdBw4cUEVFRbveP6CjISgB+Ebl5eV68cUX9a1vfUuSFBgYqPvuu08ffvihrr/++jb19Z//+Z/6j//4D0lSjx499P3vf1/79u0zP9ATEhL0zjvvKDc3t1VB6aOPPpLL5dLChQvVpUsXs4/2uvXWWzV//nyzn5MnT+q5555TSkqKfHx8tHfvXs2dO1cTJkwwXzN58mTz8cqVK+Xn56eXX37ZHM9tt93W7DzBwcF65plnZLPZvnFMkyZN0vTp080xHT58WM8++6wZlDIyMnTLLbd49NevXz9NnDhR7777roYPH2729e1vf1u/+c1vzOd79+5VZWWl5s6da86wuWe4ALD0BqAVunfvboYkSerbt68k6fjx423uKy4uznwcFhYm6Uw4OVt4eLhKSkpa1V9kZKR8fX01b948vfPOO6qsrGzzmM6WlJTk8Xzs2LE6fvy4jh07Jknq37+/fv/732vt2rU6dOhQs9f/4x//0NixY82QZCUxMbFVIamlMY0ZM0b/+7//q4aGBtXU1Gjnzp0aN26cGhoadPr0aZ0+fVphYWHq2bNns2/xjRgxwuP5TTfdpC5dumjRokXKycnhG3bAOQhKAL6Rw+HweO7n5ydJqqura3Nf11xzjfnYbrdb9l9fX9+q/sLDw/X888+rsrJSM2bM0LBhw/TTn/602VJXa4WEhHg8v+666ySdWX6UpOXLl+vWW2/V008/rTFjxmjcuHHauHGjWV9WVqbu3bt/43muvfbaVo/p3NrrrrtOLpdLX375pSoqKtTQ0KAlS5boO9/5jsefo0ePNguc5/YVHBysP/zhD7r66qv1y1/+UnFxcbr33nv1ySeftHp8QEfG0huA8+Lv7y9JcrlcHu2Xco9LYmKiEhMTVVVVJafTqSVLlmjBggVas2aNpDOBrLXjO3dG5YsvvpAkhYaGSjozu7ZkyRI1NjZq3759eu655zRnzhzl5uaqd+/e6tq1q0pLS79xzK2dTZKkkydPqkePHh5j8vPzU7du3VRXVyebzabp06fr9ttvb/babt26feN5Y2Ji9MILL6i2tlbbt2/Xk08+qV/84hd6++23Wz1GoKNiRgnAebn22mvl5+engwcPmm319fX617/+dcnH0qVLF02YMEF33HGHx3iuv/56j+eS9P7777fYx7kb1N966y1179692V4sHx8fxcTEaPbs2Tp9+rS5DDds2DC99dZbqqqquhD/pBbHtHHjRn3nO9+Rr6+vgoKCNHDgQBUWFio6OrrZnxtvvLHV5wkICNDw4cN1991368iRI+2aMQQ6GmaUAJwXHx8fJSUl6aWXXtLNN9+sbt266cUXX5RhGG2aNWmvv/zlL9q9e7cSEhIUGhqqI0eO6O9//7vHXqixY8dqzZo1io6OVnh4uP7+979b7q/6xz/+oSeffFJxcXF677339Prrr+vRRx+Vj4+PKisrlZqaqkmTJik8PFwul0t//vOf5XA41L9/f0nSjBkztGXLFv3Hf/yHHnjgAYWGhurgwYOqqanRtGnT2vVvfP311xUQEKD+/fsrJydH//rXv7Rq1Srz+C9/+Uv96Ec/0uzZs3XHHXfI4XDo2LFjev/995WcnKyhQ4da9r1lyxatW7dOt99+u3r16qUvvvhCL774ogYNGmTOFgKdGUEJwHlbuHChFi5cqMWLF+vqq69WamqqwsPDtXnz5ot+7sjISOXl5WnJkiUqKytTaGio7rjjDj344INmzc9//nOdPHlSzz77rGw2m1JSUnTffffpiSeeaNbfr3/9a/31r3/Vyy+/rKuvvloPPvig7rnnHklnlhn79eunP//5zyopKVFAQIBuueUWZWVlmXubwsLC9Je//EUZGRl67LHH1NDQoLCwMP3kJz9p978xIyNDy5Yt07PPPqtrr71W//Vf/+XxTbZBgwZp7dq1WrlypRYsWCCXy6Xrr79et956q26++eav7fumm26Sj4+Pnn76aZ08eVJdu3ZVfHy85s6d2+7xAh2JzTAMw9uDAAAAuByxRwkAAMACS28ALmtfd9Vqm80mX1/fSzgaAJ0NS28ALmst3Y/N7YYbbtA777xzCUcDoLNhRgnAZa2lG8C6uS9YCQAXCzNKAAAAFtjMDQAAYIGgBAAAYIGgBAAAYIGgBAAAYIGgBAAAYIGgBAAAYIGgBAAAYOH/A+ay3sUE/nqXAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"markdown","source":["En el boxplot tampoco tenemos mucha claridad, pero podemos ver como hay muchos outliers por encima del máximo que hacen que la media sea mucho mayor que la mediana."],"metadata":{"id":"rBoo3YMXTdON"}},{"cell_type":"code","source":["# Q-Q plot para la variable 'num_subscribers'\n","sm.qqplot(data['num_subscribers'], line='s')\n","plt.title(\"Q-Q Plot de la Variable 'Num_subscribers'\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":480},"id":"p28426FKSrIQ","executionInfo":{"status":"ok","timestamp":1732705483872,"user_tz":-60,"elapsed":700,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"923ea5b7-2b6e-4fde-c399-3b0d160a7bdf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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7a1100Jhz671LOr9jf0ZqfQYxKja6dq1K7Zt24a//vqryAX2f/31F5KSkvDWW29pjs2cORMymUzzum7dumWqz5icnZ1hY2ODxMREnXMJCQkwMzODm5sbgOJ/eBalYPTizp07Oueefa9GjRohJiYGgYGBRvlhcuTIESgUCnzzzTdaoyilmeIUi8Xo1asXfv75Zzx+/BgHDhxAx44dUadOHU2ZgwcPYvDgwZg1a5bmWG5u7nOnPkvy559/IiMjA6tWrdJabF7cXa+pqamarTAK3L59GwCK3ZusYKTG0tKyXEP722+/jT179hR5B6WjoyMAaP29AP4bSTWF1q1bo3Xr1pg6dSr27t2LGTNmYN++fRg6dCgkEgkA3RHJ4vbUU6vVuHfvntZIc8H3feE/F7FYjG7duqFbt25Qq9WYP38+tm3bhnfeeQeNGzdGo0aNEB8fb+yPqtf3TaNGjXDt2jUEBQWV6u/+s573Gal8cY0YVTvh4eGwsbHBxx9/jCdPnmidy8jIwMcffwx7e3ut9SF+fn548cUXNb8Kr6MwpD5jMjc3R3BwMA4fPqz1w/7x48f45Zdf0KZNG9jb2wOA5i5AfYJG3bp14evri127dmmVP3XqlM7TAvr06QOVSqVzFxYA5OXl6fyw1uczAdCZKtyxY0ep6hkwYACUSiXmzZuH9PR0nb3DihqN2bhxY4mjOs9TMOpTuO0KhaLYrTfy8vKwbds2rbLbtm2Ds7MzWrRoUeQ1Li4uaN++PbZt21bkGq6CbRHKqlGjRhg4cCC2bduGR48eaZ2zt7dHrVq1dDbO1WeLEWOTSqU6I2i+vr4AoNmmwd3dHebm5jh37pxWuZK2Ctm8ebPm94IgYPPmzbC0tNRM9T37993MzEzzqK6C933ppZdw7dq1Iu/gLM2o37P0+b7p06cPUlJSsH37dp3rc3JySrzzt4A+n5HKF0fEqNpp3LgxlixZgunTp2PAgAF49dVX0aBBAyQlJeGnn36CTCbDF198odfmq+VRnyGmTJmC06dPY+TIkRg5ciTMzc2xbds2KBQKvP/++5pyvr6+MDc3R1RUFDIzMyEWi9GxY0e4uLgUWe+0adMwfvx4jBw5EkOGDEFGRgY2bdqEF154Qesf8fbt22P48OGIjIxEXFwcgoODYWlpidu3b+PAgQP48MMP0bt3b70/T8H1EyZMwGuvvYanT5/ixx9/hIuLi04gKEn79u1Rr149HD58GNbW1joLprt06YKff/4Z9vb2aNq0KS5evIjTp0+XuFXI8wQEBMDR0RGzZs1CWFgYRCIRfv7552J/6NatWxdRUVFISkpCkyZNsG/fPsTFxeGTTz4pcfuCjz/+GCNHjsSAAQMwbNgwNGzYEI8fP8bFixfx8OFD7Nmzx+DPUNiECRPw888/IzExUWed4dChQ7F27Vp8+OGH8PPzw19//VXkyGx527VrF7Zu3YoePXqgUaNGePr0KbZv3w57e3vNWiYHBwf07t0bmzZtgkgkQsOGDfHHH38Uu37RysoKJ06cwMyZM+Hv748TJ07gjz/+wIQJEzRT7XPnzoVUKkXHjh3h6uqK5ORkbNq0Cb6+vpptYcLDw3Hw4EG89957GDJkCFq0aAGpVIojR45gwYIFaNasmUGfWZ/vm0GDBmH//v34+OOPcfbsWQQGBkKlUiEhIQEHDhzAunXr0LJlyxLfR5/PSOWLQYyqpV69esHDwwORkZH46aefkJaWBrVaDSsrK+zcuVPnzqGKrq+0XnjhBWzevBmff/45IiMjIQgC/P398X//939o1aqVplydOnWwYMECREZG4sMPP4RKpcL3339fbBALDQ3FV199heXLl+Pzzz9Ho0aNsGjRIhw+fFhrzRwA/O9//4Ofnx9++OEHfPnllzA3N4e7uzsGDhyIwMDAUn0eT09PrFixAsuXL8eSJUtQu3ZtjBgxAs7OzpgzZ47e9ZiZmaFfv36Ijo5G165dNSODBT788EOYmZlh7969yM3NRWBgIL799luMHTu2VO0trFatWlizZg2WLFmC5cuXQyKRYODAgQgKCkJ4eLhOeUdHRyxevBgLFy7E9u3bUbt2bcybNw/Dhg0r8X2aNm2KHTt2YNWqVdi1axcyMjLg7OyM5s2ba/ZJM4bGjRtj4MCB2LVrl865iRMnIj09HQcPHsT+/fsRGhqKdevWFbs4vLy0b98ely5dwr59+/D48WM4ODjA398fy5Yt0/oP0Ny5c5GXl4cffvgBYrEYvXv3xgcffFDkMzTNzc2xbt06zJ8/H//3f/8HOzs7vPvuu1p9W7CGbsuWLZDJZKhTpw769OmDSZMmaUZG7ezssHnzZqxcuRK//fYbdu3aBRcXFwQFBZXpZgp9vm/MzMywevVqfPfdd/j555/x22+/wcbGBg0aNEBYWFiRN/g8S5/PSOVLJJRl7JSoCtm9ezdmzZqFgQMHYunSpZWuPiIiqnk4IkY1xuDBg5GamorPP/8c9erVw7Rp0ypVfUREVPNwRIyIiEwmPT29xJsnLC0ty7Smj6iyYxAjIiKT6datW7FbTAD568M2btxYgS0iqlgMYkREZDLnz59Hbm5useclEgn8/PwqsEVEFYtBjIiIiMhEeG8qERERkYnwrslK7sKFCxAEocSNH4mIiKhyUSqVEIlECAgIKLEcR8QqOUEQyvSYjIogCAIUCkWlb2dFYp/oYp9oY3/oYp/oYp/oqip9ou/Pb46IVXIFI2HPe0yFKWVnZyMuLg5NmzbVekBtTcY+0cU+0cb+0MU+0cU+0VVV+uTSpUt6leOIGBEREZGJMIgRERERmQiDGBEREZGJMIgRERERmQiDGBEREZGJMIgRERERmQiDGBEREZGJMIgRERERmQiDGBEREZGJcGd9IiIiIj2o1AKuJqQhXZYDZ4k1mnu6wNxMVKY6GcSIiIiInuN0bDLW7r6ENGmO5piLozXGDW6JF/3rG1wvpyaJiIiISnA6NhmLNpzTCmEAkCbNwaIN53A6NtnguhnEiIiIiIqhUgtYu7vkB3hH/XwZKrVgUP0MYkRERETFuJqQpjMS9qzHGXJcTUgzqH4GMSIiIqJipMtKDmGlLfcsBjEiIiKiYjhLrI1a7lkMYkRERETFaO7pAhfHkkNWbScbNPd0Mah+BjEiIiKiYpibiTBucMsSy0QM8jN4PzEGMSIiIqISvOhfH7NHt9MZGavtZIPZo9uVaR8xbuhKRERE9Bwv+tdHBz837qxPREREZArmZiK0bFrbqHVyapKIiIjIRBjEiIiIiEyEQYyIiIjIRBjEiIiIiEyEQYyIiIjIRBjEiIiIiEyEQYyIiIjIRBjEiIiIiEyEQYyIiIjIRCpVENu/fz/efvtthIaGonXr1hg0aBB++uknCIKgKRMWFgYfHx+dX7du3dKqKzMzE3PmzEH79u0REBCAyZMnIzU1Vec9//77bwwfPhz+/v7o2rUr1q5dq/V+ACAIAtauXYsuXbrA398fw4cPx8WLF3XqSklJwaRJkxAQEID27dvjww8/RFZWlnE6h4iIiKqdSvWIo++++w7u7u6YNWsWatWqhdOnT+Ojjz7Cw4cP8e6772rKBQYGYubMmVrXNmjQQOv1lClTcPPmTcyfPx9WVlZYvnw5IiIisGPHDlhY5H/sO3fuIDw8HMHBwZgyZQquX7+OZcuWwdzcHOHh4Zq6oqKisGLFCsyYMQM+Pj7YvHkzxowZg59//hkNGzYEACiVSowdOxYA8PnnnyMnJwdLlizB9OnTERkZWS79RURERFVbpQpi33zzDZydnTWvg4KCkJGRgW+//RbvvPMOzMzyB/AkEglat25dbD0XLlzAyZMnER0djZCQEACAh4cH+vbti0OHDqFv374AgOjoaNSqVQtffPEFxGIxgoKCkJ6ejjVr1iAsLAxisRi5ubmIjIzEmDFj8OabbwIA2rRpg969eyM6Ohrz588HABw8eBDx8fHYt28fPD09Ne0MDw9HbGws/P39jdxbREREVNVVqqnJwiGsgK+vL7KyspCdna13PcePH4dEIkFwcLDmmKenJ3x9fXH8+HGtct27d4dYLNYc69u3L2QyGS5cuAAgf+oyKysLffr00ZQRi8Xo2bOnTl0+Pj6aEAYAwcHBcHJywrFjx/RuOxEREdUclSqIFeX8+fNwdXWFvb295tiff/6J1q1bo2XLlnjjjTdw7tw5rWsSEhLg4eEBkUikddzT0xMJCQkAgOzsbDx48EArOBWUEYlEmnIFX58t5+XlheTkZOTk5GjKPVtGJBLBw8NDUwcRERFRYZVqavJZf/31F/bt26e1Hqxdu3YYNGgQmjRpgtTUVERHR+Ott97Cxo0bERAQAACQyWRwcHDQqc/R0RGXL18GkL+YH8ifPixMLBbDxsYGUqlUU5dYLIaVlZVWOYlEAkEQIJVKYW1tXeJ7FtRlKEEQSjUiWNHkcrnWV2KfFIV9oo39oYt9oot9oquq9IkgCDoDQkWptEHs4cOHmDp1Kjp06IBRo0Zpjk+ePFmrXJcuXdC/f398/fXXiIqKquhmVgilUom4uDhTN+O5bt++beomVDrsE13sE23sD13sE13sE11VoU8KL30qTqUMYjKZDBEREXBycsLKlSs1i/SLYmtri86dO+PgwYOaYxKJBA8fPtQpK5VK4ejoCACa0auCkbECCoUCcrlcU04ikUChUCA3N1drVEwmk0EkEmmVK2qrCqlUCjc3N30/epEsLS3RtGnTMtVRnuRyOW7fvo0mTZrAxsbG1M2pFNgnutgn2tgfutgnutgnuqpKn9y8eVOvcpUuiOXk5GD8+PHIzMzEtm3bipzuex5PT0/ExMToDAsmJibC29sbQH6Ac3Nz01m/lZiYCEEQNOu9Cr4mJiaiWbNmmnIJCQmoX78+rK2tNeVu3LihVZcgCEhMTNS6acAQIpEItra2ZaqjItjY2FSJdlYk9oku9ok29ocu9oku9omuyt4n+kxLApVssX5eXh6mTJmChIQErFu3Dq6urs+9Jjs7G3/88QdatmypORYaGgqpVIqYmBjNscTERFy9ehWhoaFa5Q4fPgylUqk5tm/fPkgkEs16s8DAQNjb22P//v2aMkqlEocOHdKp69q1a1pDpTExMcjIyEDnzp1L1xFERERUI1SqEbEFCxbg6NGjmDVrFrKysrR2r2/evDliY2Oxbt069OzZE+7u7khNTcW3336LR48e4auvvtKUDQgIQEhICObMmYOZM2fCysoKX375JXx8fPDSSy9pyoWHh2Pv3r2YPn06RowYgRs3biA6OhpTp07VzOtaWVlh/PjxWLlyJZydneHt7Y2tW7ciIyNDa9PXXr16ITIyEpMmTcK0adMgl8uxdOlSzW78RERERM+qVEHs1KlTAIDFixfrnDt8+DDq1KkDpVKJL7/8EhkZGbCxsUFAQAAWLFigE3aWL1+ORYsWYd68ecjLy0NISAjmzp2r2VUfABo3bozo6GgsXrwY48aNg7OzMyZPnowxY8Zo1RUREQFBELB+/Xqkp6fD19cX0dHRml31gfx1XOvWrcPChQsxbdo0WFhYoGfPnpgzZ44xu4iIiIiqEZHw7IMVqVK5dOkSAGhNvVY22dnZiIuLg6+vb6Wer69I7BNd7BNt7A9d7BNd7BNdVaVP9P35XanWiBERERHVJAxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCZSqYLY/v378fbbbyM0NBStW7fGoEGD8NNPP0EQBK1yP/74I3r16oWWLVti4MCBOHr0qE5dmZmZmDNnDtq3b4+AgABMnjwZqampOuX+/vtvDB8+HP7+/ujatSvWrl2r836CIGDt2rXo0qUL/P39MXz4cFy8eFGnrpSUFEyaNAkBAQFo3749PvzwQ2RlZZWtU4iIiKjaqlRB7LvvvoONjQ1mzZqFb775BqGhofjoo4+wevVqTZlff/0VH330Efr06YOoqCi0bt0a7777rk4wmjJlCk6dOoX58+dj2bJlSExMREREBPLy8jRl7ty5g/DwcNSpUweRkZEYPXo0VqxYgfXr12vVFRUVhRUrVuDNN99EZGQk6tSpgzFjxuDevXuaMkqlEmPHjsXt27fx+eefY/78+Th58iSmT59ePp1FREREVZ6FqRtQ2DfffANnZ2fN66CgIGRkZODbb7/FO++8AzMzM6xYsQL9+vXDlClTAAAdO3bEjRs3sHr1akRFRQEALly4gJMnTyI6OhohISEAAA8PD/Tt2xeHDh1C3759AQDR0dGoVasWvvjiC4jFYgQFBSE9PR1r1qxBWFgYxGIxcnNzERkZiTFjxuDNN98EALRp0wa9e/dGdHQ05s+fDwA4ePAg4uPjsW/fPnh6egIAJBIJwsPDERsbC39//wroQSIiIqpKKtWIWOEQVsDX1xdZWVnIzs7GvXv3cPv2bfTp00erTN++fRETEwOFQgEAOH78OCQSCYKDgzVlPD094evri+PHj2uOHT9+HN27d4dYLNaqSyaT4cKFCwDypy6zsrK03lMsFqNnz546dfn4+GhCGAAEBwfDyckJx44dM7RLiIiIqBqrVCNiRTl//jxcXV1hb2+P8+fPA8gf3SrMy8sLSqUS9+7dg5eXFxISEuDh4QGRSKRVztPTEwkJCQCA7OxsPHjwQCs4FZQRiURISEhAhw4dNOWfLefl5YUNGzYgJycH1tbWSEhI0CkjEong4eGhqcNQgiAgOzu7THWUJ7lcrvWV2CdFYZ9oY3/oYp/oYp/oqip9IgiCTg4pSqUOYn/99Rf27duHmTNnAgCkUimA/Cm/wgpeF5yXyWRwcHDQqc/R0RGXL18GkL+Yv6i6xGIxbGxstOoSi8WwsrLSeU9BECCVSmFtbV3iexbUZSilUom4uLgy1VERbt++beomVDrsE13sE23sD13sE13sE11VoU8Kz7gVp9IGsYcPH2Lq1Kno0KEDRo0aZermmJSlpSWaNm1q6mYUSy6X4/bt22jSpAlsbGxM3ZxKgX2ii32ijf2hi32ii32iq6r0yc2bN/UqVymDmEwmQ0REBJycnLBy5UqYmeUvZXN0dASQP5pVp04drfKFz0skEjx8+FCnXqlUqilTMHpVMDJWQKFQQC6Xa9WlUCiQm5urNSomk8kgEom0yhW1VYVUKoWbm5sBvfAfkUgEW1vbMtVREWxsbKpEOysS+0QX+0Qb+0MX+0QX+0RXZe8TfaYlgUq2WB8AcnJyMH78eGRmZmLdunVa030Fa7CeXXOVkJAAS0tLNGzYUFMuMTFRZz+wxMRETR22trZwc3PTqavguoJyBV8TExN13rN+/fqwtrbWlHu2LkEQtN6TiIiIqLBKFcTy8vIwZcoUJCQkYN26dXB1ddU637BhQzRp0gQHDhzQOr5v3z4EBQVp5mJDQ0MhlUoRExOjKZOYmIirV68iNDRUcyw0NBSHDx+GUqnUqksikSAgIAAAEBgYCHt7e+zfv19TRqlU4tChQzp1Xbt2TWvOOiYmBhkZGejcuXMZeoWIiIiqq0o1NblgwQIcPXoUs2bNQlZWltYmrc2bN4dYLMakSZMwY8YMNGrUCB06dMC+ffsQGxuLTZs2acoGBAQgJCQEc+bMwcyZM2FlZYUvv/wSPj4+eOmllzTlwsPDsXfvXkyfPh0jRozAjRs3EB0djalTp2pCnZWVFcaPH4+VK1fC2dkZ3t7e2Lp1KzIyMhAeHq6pq1evXoiMjMSkSZMwbdo0yOVyLF26VLMbPxEREdGzKlUQO3XqFABg8eLFOucOHz6MBg0aoH///pDL5YiKisLatWvh4eGBVatWaUawCixfvhyLFi3CvHnzkJeXh5CQEMydOxcWFv995MaNGyM6OhqLFy/GuHHj4OzsjMmTJ2PMmDFadUVEREAQBKxfvx7p6enw9fVFdHS0ZioUyF9Qv27dOixcuBDTpk2DhYUFevbsiTlz5hizi4iIiKgaEQnPLqSiSuXSpUsAgJYtW5q4JcXLzs5GXFwcfH19K/XCyYrEPtHFPtHG/tDFPtHFPtFVVfpE35/flWqNGBEREVFNwiBGREREZCIMYkREREQmwiBGREREZCIMYkREREQmwiBGREREZCIMYkREREQmwiBGREREZCIMYkREREQmwiBGREREZCIMYkREREQmYrSHfsvlcvz6669QKBTo3Lkz3N3djVU1ERERUbVkUBCbM2cOYmNj8csvvwAAFAoFhg0bhvj4eACAg4MDNmzYgObNmxuvpURERETVjEFTk2fPnkXPnj01r3/55RfEx8dj2bJl+OWXX1C7dm2sWrXKaI0kIiIiqo4MCmKPHz/Wmnr8/fff4efnh/79+6Np06YYNmwYYmNjjdZIIiIiourIoCBmY2ODzMxMAEBeXh7+/PNPhISEaM7b2dlpzhMRERFR0QxaI9aiRQts374dHTp0wJEjR/D06VN069ZNc/7u3btwcXExWiOJiIiIqiODgtiUKVMwduxYDBkyBIIgoFevXvD399ec/+233xAYGGi0RhIRERFVRwYFsZYtW2L//v34+++/IZFI0L59e805mUyGkSNHah0jIiIiIl0G7yPm7OyMHj166ByXSCQYPXp0mRpFREREVBMYHMRUKhUOHDiAs2fPIi0tDZMnT4aPjw8yMzMRExODwMBA1K5d25htJSIiIqpWDApiMpkMY8eORWxsLGxtbSGXy/HGG28AAGxtbbFw4UIMHjwY06ZNM2pjiYiIiKoTg7avWLZsGeLj4xEdHY3ff/8dgiBozpmbm6NXr144duyY0RpJREREVB0ZFMQOHz6MsLAwBAcHQyQS6Zxv0qQJkpKSytw4IiIiourMoCCWmZmJBg0aFHs+Ly8PKpXK4EYRERER1QQGBbFGjRrhypUrxZ4/deoUvLy8DG4UERERUU1gUBB79dVXsWPHDuzbt0+zPkwkEkGhUODLL7/EiRMnMHz4cKM2lIiIiKi6MeiuydGjR+PmzZuYNm0aJBIJAGDGjBnIyMhAXl4ehg8fjqFDhxq1oURERETVjUFBTCQSabaoOHjwIO7cuQO1Wo1GjRqhT58+aNeunbHbSURERFTtGLyhKwC0bdsWbdu2NVZbiIiIiGoUg9aIEREREVHZ6TUi1q1btyL3CyuJSCTC77//blCjiIiIiMqTSi3gakIa0mU5cJZYo7mnC8zNSpd1jEGvINa+fftSBzEiIiKiyuh0bDLW7r6ENGmO5piLozXGDW6JF/3rV2hb9ApiixcvLu92EBEREZW707HJWLThnM7xNGkOFm04h9mj21VoGOMaMSIiIqoRVGoBa3dfKrFM1M+XoVILJZYxJr1GxM6dy0+OBdtSFLx+Hm5jQURERJXF1YQ0renIojzOkONqQhpaNq1dIW3SK4iFhYVBJBLhn3/+gVgs1rwujiAIEIlEiIuLM1pDiYiIiMoiXVZyCCttOWPQK4h9//33AACxWKz1moiIiKiqcJZYG7WcMeh912RJr4mIiIgqu+aeLnBxtC5xerK2kw2ae7pUWJsMWqw/atQoxMTEFHv+zJkzGDVqlMGNIiIiIjI2czMRxg1uWWKZiEF+FbqfmEFB7M8//8Tjx4+LPZ+enq73gn4iIiKiivKif33MHt0OLo7a04+1nWwqfOsKoAzPmixpsf6dO3dgZ2dnaNVERERE5eZF//ro4OdWdXbWB4Bdu3Zh165dmtfffPMNtm/frlMuMzMT169fR2hoqHFaSERERGRk5maiCtuioiR6T03K5XI8efIET548AQA8ffpU87rwL7FYjNdeew2ffvppqRtz584dzJs3D4MGDULz5s3Rv39/nTJhYWHw8fHR+XXr1i2tcpmZmZgzZw7at2+PgIAATJ48GampqTr1/f333xg+fDj8/f3RtWtXrF27FoKgvZGbIAhYu3YtunTpAn9/fwwfPhwXL17UqSslJQWTJk1CQEAA2rdvjw8//BBZWVml7gciIiKqGfQeERs5ciRGjhwJIP8h4B9++CG6d+9u1MbEx8fj2LFjaNWqFdRqtU4gKhAYGIiZM2dqHWvQoIHW6ylTpuDmzZuYP38+rKyssHz5ckRERGDHjh2wsMj/2Hfu3EF4eDiCg4MxZcoUXL9+HcuWLYO5uTnCw8M1dUVFRWHFihWYMWMGfHx8sHnzZowZMwY///wzGjZsCABQKpUYO3YsAODzzz9HTk4OlixZgunTpyMyMtJofURERETVh0FrxI4cOWLsdgDID3g9evQAAMyaNQuXL18uspxEIkHr1q2LrefChQs4efIkoqOjERISAgDw8PBA3759cejQIfTt2xcAEB0djVq1auGLL76AWCxGUFAQ0tPTsWbNGoSFhUEsFiM3NxeRkZEYM2YM3nzzTQBAmzZt0Lt3b0RHR2P+/PkAgIMHDyI+Ph779u2Dp6enpp3h4eGIjY2Fv7+/EXqIiIiIqpMyPWsyKysLN27cwF9//YVz587p/Cp1Y8yM8+jL48ePQyKRIDg4WHPM09MTvr6+OH78uFa57t27azaqBYC+fftCJpPhwoULAPKnLrOystCnTx9NGbFYjJ49e+rU5ePjowlhABAcHAwnJyccO3bMKJ+LiIiIqheDRsTS09OxcOFCHDp0CCqVSud8eT/i6M8//0Tr1q2hUqnQqlUrvPfee1rPtUxISICHh4fOnZ2enp5ISEgAAGRnZ+PBgwdawamgjEgkQkJCAjp06KAp/2w5Ly8vbNiwATk5ObC2tkZCQoJOGZFIBA8PD00dhhIEAdnZ2WWqozzJ5XKtr8Q+KQr7RBv7Qxf7RBf7RFdV6ZOCLPQ8BgWxefPm4ejRowgLC0Pbtm0hkUgMqcYg7dq1w6BBg9CkSROkpqYiOjoab731FjZu3IiAgAAAgEwmg4ODg861jo6OmunOzMxMANBpu1gsho2NDaRSqaYusVgMKysrrXISiQSCIEAqlcLa2rrE9yyoy1BKpbJKPLfz9u3bpm5CpcM+0cU+0cb+0MU+0cU+0VUV+qTwjFtxDApip06dwujRo/HBBx8YcnmZTJ48Wet1ly5d0L9/f3z99deIioqq8PZUBEtLSzRt2tTUzSiWXC7H7du30aRJE9jY2Ji6OZUC+0QX+0Qb+0MX+0QX+0RXVemTmzdv6lXOoCBmbW0Nd3d3Qy41OltbW3Tu3BkHDx7UHJNIJHj48KFOWalUCkdHRwDQjF4VjIwVUCgUkMvlmnISiQQKhQK5ublao2IymQwikUirXFFbVUilUri5uZXpM4pEItja2papjopgY2NTJdpZkdgnutgn2tgfutgnutgnuip7n+gzLQkYuFh/4MCB+P333w25tEJ4enoiMTFRZ/uLxMREzTouW1tbuLm56azfKriuoFzB18TERK1yCQkJqF+/PqytrTXlnq1LEASt9yQiIiIqzKAg1qtXL0ilUoSHh+PQoUOIjY3FlStXdH5VhOzsbPzxxx9o2fK/h3iGhoZCKpVqPZg8MTERV69e1drxPzQ0FIcPH4ZSqdQc27dvHyQSiWa9WWBgIOzt7bF//35NGaVSiUOHDunUde3aNa0565iYGGRkZKBz585G/cxERERUPRg0NVmwsSsAnD59Wue8oXdNyuVyzVYPSUlJyMrKwoEDBwAA7du3R0JCAtatW4eePXvC3d0dqamp+Pbbb/Ho0SN89dVXmnoCAgIQEhKCOXPmYObMmbCyssKXX34JHx8fvPTSS5py4eHh2Lt3L6ZPn44RI0bgxo0biI6OxtSpUzUL7KysrDB+/HisXLkSzs7O8Pb2xtatW5GRkaG16WuvXr0QGRmJSZMmYdq0aZDL5Vi6dKlmN34iIiKiZxkUxBYtWmTsdgAA0tLS8N5772kdK3j9/fffo169elAqlfjyyy+RkZEBGxsbBAQEYMGCBTphZ/ny5Vi0aBHmzZuHvLw8hISEYO7cuZpd9QGgcePGiI6OxuLFizFu3Dg4Oztj8uTJGDNmjFZdEREREAQB69evR3p6Onx9fREdHa3ZVR/IX1C/bt06LFy4ENOmTYOFhQV69uyJOXPmGLubiIiIqJoQCcU9R4gqhUuXLgGA1tRrZZOdnY24uDj4+vpW6oWTFYl9oot9oo39oYt9oot9oquq9Im+P7+Ns5U9EREREZWaQVOTAJCbm4uDBw/i6tWryMzMhFqt1jovEonw2WeflbmBRERERNWVQUEsKSkJo0aNQlJSEiQSCTIzM+Ho6IjMzEyoVCrUqlWrUg8XEhEREVUGBk1NLl26FFlZWdi+fTsOHDgAQRDw5Zdf4sKFC5gxYwasra0RHR1t7LYSERERVSsGBbEzZ85gxIgR8Pf3h5nZf1WIxWKMHTsWHTt25LQkERER0XMYFMRycnI0jziyt7eHSCTSelRQQEAAzp8/b5wWEhEREVVTBgUxNzc3pKSkAAAsLCzg6uqKixcvas7fvHlT67mMRERERKTLoMX6HTt2xOHDh/Huu+8CAF5++WWsXbsWMpkMarUae/bswaBBg4zaUCIiIqLqxqAgNm7cOFy6dAkKhQJisRgTJkxAamoqDh48CDMzM/Tv3x+zZ882dluJiIiIqhWDglj9+vVRv359zWsrKyt8+umn+PTTT43WMCIiIqLqjjvrExEREZmIQSNiq1atem4ZkUiEiRMnGlI9ERERUY1g9CAmEokgCAKDGBEREdFzGBTErl27pnNMrVYjKSkJW7Zswblz5xAVFVXmxhERERFVZ0ZbI2ZmZoaGDRti5syZaNy4MRYuXGisqomIiIiqpXJZrN+uXTscO3asPKomIiIiqjbKJYhdvnxZ6xmURERERKTLoDViu3fvLvK4TCbDX3/9hUOHDmHo0KFlaRcRERFRtWdQEJs1a1ax52rVqoVx48bxjkkiIiKi5zAoiB0+fFjnmEgkgkQigb29fZkbRURERFQWKrWAqwlpSJflwFlijeaeLjA3E5m6WToMCmLu7u7GbgcRERGRUZyOTcba3ZeQJs3RHHNxtMa4wS3xon/9Eq6seAYFsdTUVJw4cQI3b97E06dP4eLigoYNG6J79+5wdHQ0dhuJiIiIivTsyJf0aS6WfP+XTrk0aQ4WbTiH2aPbVaowVqoglpubi6VLl2Lbtm1QqVQQBEHrvFgsxmuvvYb3338flpaWAIAHDx7Azc3NeC0mIiIiQtEjX8+bfYz6+TI6+LlVmmlKvYOYSqXChAkTEBMTgw4dOmDw4MFo1qwZ7Ozs8PTpU1y7dg27d+/G999/j5s3b2L9+vW4du0axo0bh+PHj5fnZyAiIqIa5uyVFHzxQ6zOcbVQROFCHmfIcTUhDS2b1i6nlpWO3kFs69atiImJwbx58zBy5Eid876+vnj55ZexdetWLFiwALNnz8Zvv/2GWrVqGbXBREREVLOp1QK+23fd4OvTZTnPL1RB9N51dffu3ejevXuRIaywESNGoFu3bti9ezcaNmyIrVu3lrmRRERERAXuPMpFuizX4OudJdZGbE3Z6B3Ebt26hU6dOulVtqDcpk2bULt25Rj6IyIiouohS642+NraTjZo7ulixNaUjd5BTCQSQa3W/4NbW1vDzs7OoEYRERERPUulFnAlMR2PpEqD64gY5FdpFuoDpVgj5unpiRMnTjx3ahIATpw4AS8vrzI1jIiIiKhAUXdIlsRMpL1wv7aTDSIG+VWqrSuAUgSxgQMHYtGiRdiyZUuJYWzr1q04evQo5syZY5QGEhERUc12OjYZizacK9U177/RFo72VtVnZ/2RI0fit99+wyeffILff/8dAwcO1Nq+4vr169izZw9Onz6Ntm3b6jVyRkRERFQSlVrA2t2X9C5fWUe+iqN3ELOwsMDatWuxaNEi7NixAzExMVrnBUGAubk5hg4ditmzZ8Pc3NzojSUiIqKa5WpCml7TkcN7eKPVC3Uq7chXcUq1s76NjQ3+97//YeLEiTh+/LjmEUd2dnZo2rQpQkND4erqWl5tJSIiohpG3z2/Gro6VJpNWkvDoGdNurq6YujQocZuCxEREZEWfff8qkx7g5WG3ttXEBEREVW05p4ucHEsOWRVtr3BSoNBjIiIiCotczMRxg1uWWKZyrY3WGkwiBEREVGl9qJ/fcwe3U5nZKy2kw1mj25XZe6QLIpBa8SIiIiIKtKL/vXRwc8Nf8cl4cq1RLRo5oFAX/cqOxJWgEGMiIiIqgRzMxFaeDjDLCcFvh7OVT6EAWUMYgqFAleuXEFaWhoCAwPh7OxsrHYRERERVXsGrxH7/vvvERISgpEjR2LSpEm4fv06ACA9PR0dOnTATz/9ZLRGEhEREVVHBgWxHTt24LPPPkOnTp3w6aefQhD+e6qms7MzOnbsiH379hmtkURERETVkUFTk99++y26d++Ozz//HE+ePNE536JFC2zcuLHMjSMiIqKaS6UWcDUhTevB3dWNQUHszp07CAsLK/a8k5MTMjIyDKo3Ojoa//zzD+Lj4+Hp6YlffvlFp9yPP/6IdevWITk5GR4eHpg6dSq6du2qVSYzMxOLFi3C77//DqVSiU6dOmHu3LmoW7euVrm///4bS5YsQVxcHFxcXDBixAhERERAJPpvAaAgCIiKisKWLVuQnp4OX19fzJ49G61bt9aqKyUlBQsXLsTJkydhaWmJnj17Yvbs2bC3ty91XxAREdVkp2OTsXb3Ja3nTLo4WmN0H29IqtHmWwZ9FIlEUuRIWIGbN2+iTp06pa43Pj4ex44dQ+PGjeHl5VVkmV9//RUfffQR+vTpg6ioKLRu3RrvvvsuLl68qFVuypQpOHXqFObPn49ly5YhMTERERERyMvL05S5c+cOwsPDUadOHURGRmL06NFYsWIF1q9fr1VXVFQUVqxYgTfffBORkZGoU6cOxowZg3v37mnKKJVKjB07Frdv38bnn3+O+fPn4+TJk5g+fXqp+4GIiKgmOx2bjEUbzuk87DtNmoMvfojF1XtyE7XM+AwaEQsNDcX27dsxcuRInXPx8fH48ccfMWTIkFLX261bN/To0QMAMGvWLFy+fFmnzIoVK9CvXz9MmTIFANCxY0fcuHEDq1evRlRUFADgwoULOHnyJKKjoxESEgIA8PDwQN++fXHo0CH07dsXABAdHY1atWrhiy++gFgsRlBQENLT07FmzRqEhYVBLBYjNzcXkZGRGDNmDN58800AQJs2bdC7d29ER0dj/vz5AICDBw8iPj4e+/btg6enJ4D8wBoeHo7Y2Fj4+/uXuj+IiIhqGkWeGqt/+qfEMgfOZ+DlHkKJZaoKg0bEpkyZApVKhf79+2P58uUQiUTYvXs3ZsyYgSFDhsDZ2RnvvPNO6RtjVnJz7t27h9u3b6NPnz5ax/v27YuYmBgoFAoAwPHjxyGRSBAcHKwp4+npCV9fXxw/flxz7Pjx4+jevTvEYrFWXTKZDBcuXACQP3WZlZWl9Z5isRg9e/bUqcvHx0cTwgAgODgYTk5OOHbsWGm6gYiIqEY6HZuMt/53ALKnihLLybJViLtT/MxcVWJQEHN1dcXOnTvRqVMn7N+/H4Ig4Oeff8bRo0fRr18/bN++vVz2FEtISACQP7pVmJeXF5RKpWaqMCEhAR4eHlrrvID8MFZQR3Z2Nh48eKAVnArKiEQiTbmCr8+W8/LyQnJyMnJycjTlni0jEong4eGhqYOIiIiKVjAdKXuq1Kt8RmZuObeoYhi8oauLiws+/fRTfPrpp0hPT4darYazs/NzR7XKQiqVAsif8ius4HXBeZlMBgcHB53rHR0dNdOdmZmZRdYlFothY2OjVZdYLIaVlZXOewqCAKlUCmtr6xLfs6AuQwmCgOzs7DLVUZ7kcrnWV2KfFIV9oo39oYt9oqum9IlaLSByV2yprrERo1L/bBQEQWdAqChGecQRd9QvX0qlEnFxcaZuxnPdvn3b1E2odNgnutgn2tgfutgnuqp7nySm5CBdpv8Il8TWHFaqJ4iLyyi/RhlB4aVPxdEriK1atarUby4SiTBx4sRSX1cSR0dHAPmjWYXvypTJZFrnJRIJHj58qHO9VCrVlCkYvSoYGSugUCggl8u16lIoFMjNzdUaFZPJZBCJRFrlsrKyinxPNzc3wz7wvywtLdG0adMy1VGe5HI5bt++jSZNmsDGxsbUzakU2Ce62Cfa2B+62Ce6akqfpCsfAHisd/nebZzg6elRqfvk5s2bepWrUkGsYA3Ws+uxEhISYGlpiYYNG2rKxcTE6AwLJiYmwtvbGwBga2sLNzc3nfVbiYmJEARBU3/B18TERDRr1kzrPevXrw9ra2tNuRs3bmjVJQgCEhMTtW4aMIRIJIKtrW2Z6qgINjY2VaKdFYl9oot9oo39oYt9oqu690m92o56lXO0FyO8fzNIzNIrfZ/oMy0J6BnErl27VqbGGEvDhg3RpEkTHDhwQLPNBQDs27cPQUFBmiHA0NBQfP3114iJicGLL74IID9IXb16FWPHjtVcFxoaisOHD+P999+HpaWlpi6JRIKAgAAAQGBgIOzt7bF//35NEFMqlTh06BBCQ0O16tqzZ4/mfy4AEBMTg4yMDHTu3Ln8OoWIiKiKa+7pAhdHa519wwqT2Imx/qNeyFPkIC4uvQJbV76MskbMWORyuWarh6SkJGRlZeHAgQMAgPbt28PZ2RmTJk3CjBkz0KhRI3To0AH79u1DbGwsNm3apKknICAAISEhmDNnDmbOnAkrKyt8+eWX8PHxwUsvvaQpFx4ejr1792L69OkYMWIEbty4gejoaEydOlUT6qysrDB+/HisXLkSzs7O8Pb2xtatW5GRkYHw8HBNXb169UJkZCQmTZqEadOmQS6XY+nSpejSpQv3ECMiIiqBuZkI4wa3xKIN54otM/HVVhBbmCGv5J0tqpwyBbGMjAycPn0aSUlJAAB3d3cEBQWhVq1aBtWXlpaG9957T+tYwevvv/8eHTp0QP/+/SGXyxEVFYW1a9fCw8MDq1at0oxgFVi+fDkWLVqEefPmIS8vDyEhIZg7dy4sLP77yI0bN0Z0dDQWL16McePGwdnZGZMnT8aYMWO06oqIiIAgCFi/fr3mEUfR0dGaqVAgfx3XunXrsHDhQkybNg0WFhbo2bMn5syZY1BfEBER1SQv+tfH7NHtdB5rVNvJBhGD/PCif30Ttq78iARBMGhr2pUrVyIqKgpKpRKFq7C0tMTYsWN1AhUZ5tKlSwCAli1bmrglxcvOzkZcXBx8fX0r9Xx9RWKf6GKfaGN/6GKf6KqJfVLUg77Nzf5bb1VV+kTfn98GjYitXr0aq1evRpcuXfD6669r1kQlJiZi8+bNWLNmDSwsLIy+WJ+IiIiqN3MzEVo2rW3qZlQYg4LYDz/8gK5du+Kbb77ROt6wYUOEhoZiwoQJ2Lp1K4MYERERUQkM2gY/KysLnTp1KvZ8aGgonj59anCjiIiIiGoCg4JYYGAgYmOLfxRBbGwsAgMDDW4UERERUU1gUBCbP38+Lly4gM8++wx37tyBWq2GWq3GnTt38Omnn+LixYtYsGCBsdtKREREVK0YtEZs4MCBEAQBGzduxMaNGzUP+lar1QDyn600cOBArWtEIhHOnz9fxuYSERFRdfO8OyWrM4OCWK9evfTeup+IiIioKCq1gO2/38CeE7eQla3UHHdxtMa4wS2r7d5hhRkUxBYvXmzsdhAREVENcjo2GSt/vKgVwAqkSXOwaMM5zB7drtqHMYPWiBEREREZ6nRsMhZtOFdkCCss6ufLUKkN2ne+yijTI47OnTuHe/fuQSaT4dkN+kUiEd58882yVE9ERETVjEotYO3uS3qVfZwhx9WEtGq9watBQSwuLg5TpkzB3bt3dQJYAQYxIiIietalW4+1niX5POky/ctWRQYFsQ8//BDp6elYsGAB/P394eDgYOx2ERERUTVzOjYZK7dfLNU1zhLr8mlMJWFQELt58yYmT56MYcOGGbs9REREVA0VrAsrjdpONmju6VJOLaocDFqs37hxY25fQURERHopzbqwwiIG+VX7/cQMCmKTJk3C5s2bkZKSYuz2EBERUTVzNSGtVOvCHGzFNWLrCsDAqcmXXnoJubm56N27Nzp27Ih69erB3Nxcp9zcuXPL3EAiIiKq2vRdcG8tNscrXZtiWA+faj8SVsCgIPbnn39i/vz5kMvlOHr0aJFlRCIRgxgREREh+XGWXuXmvtUBrbzrlHNrKheDgtgnn3wCe3t7rFixAq1atYK9vb2x20VERETVwMl/krDl4PXnlqvtZAO/arxfWHEMCmJ3797F9OnTERwcbOz2EBERURWnUgu4fPMxfj2dgJhLD/W6piYszC+KQUGsadOmyMzMNHZbiIiIqIor6RmSxRnZq1mNWJhfFIPumpw5cya2bduG2NhYY7eHiIiIqqiT/yTp9QzJZ9WvbVdOLar8DBoRW79+Pezs7DB8+HA0bdoUbm5uMDPTznQikQjffPONURpJREREldvJi0lYuvEvg66t7rvnl8SgIHbjxg0AgJubG54+fYqbN2/qlOGGr0RERDXDyX+SsMTAEFYTds8viUFB7MiRI8ZuBxEREVVBJy8m4f82GRbCgJq7SL+AQUGMiIiI6HRsssEjYWYi4P2wtjV2kX6BMgexrKwsZGVlQa1W65yrX79mdy4REVF1ZejzIwu8/0ZbhLRyN2KLqiaDg9iWLVvw3Xff4d69e8WWiYuLM7R6IiIiqsRK+/zIAg62Yrw7tFWNHwkrYFAQ27p1K/73v/8hJCQEQ4YMwZdffok333wTVlZW2LlzJ2rXro2wsDBjt5WIiIgqCX2fH1mgJj5HUh8GBbFNmzYhJCQE69atw5MnT/Dll1+ic+fOCAoKwtixYzFkyBBkZGQYualERERUWej7/EgAGNHTG8NfasYAVgSDNnS9e/cuunbtCgCwtLQEACiV+Zu3OTg44NVXX8WWLVuM1EQiIiKqTFRqAXuO33puOTMRMHNUW4zs7csQVgyDgpiDgwNUKhUAwN7eHjY2Nnj48L9nSdnZ2eHx48fGaSERERFVKtt/v44sed5zyw3r6cMF+c9hUBB74YUXcO3aNc3rVq1aYevWrUhJScGDBw+wbds2NGnSxFhtJCIiokoifzQsQa+yDerYl3Nrqj6DgtjAgQMRHx8PhUIBAJg0aRJu3bqFLl26oFu3bkhMTMSUKVOM2U4iIiKqBK4mpCFLrt+zJGvyo4v0ZdBi/SFDhmDIkCGa123atMGvv/6Kw4cPw8LCAsHBwfDw8DBaI4mIiKhyeCyV61XOwdayRj+6SF9G21m/YcOGePPNN41VHREREVUyp2OT8fWOf/QqO6CTFxfo68EoQezWrVs4cOAAHj16BE9PT7zyyiuwt+e8MBERUXVx8p8kLPlev8cZOdiKMayHdzm3qHrQO4ht2rQJGzduxNatW+Hs7Kw5fuTIEbz33nua7SsAYOPGjdi2bZtWOSIiIqqajl+4j//bdF7v8u8ObcXRMD3pvVj/yJEjaNiwoVa4ysvLw9y5c2Fubo5FixZh7969mD59OpKTk7FmzZpyaTARERFVnG/3Xi5VCBvZqxkfX1QKegexmzdvonXr1lrHzp49i/T0dIwePRovv/wyXnjhBURERKB37944duyYsdtKREREFejkxSTs/OP5G7cWVr+2XTm1pnrSO4hlZGSgXr16WsdiYmIgEonQs2dPreOBgYF48OCBcVpIREREFU6lFrBi+4VSX8ctK0pH7yBWu3Ztnd3y//rrL1hbW6NZs2Zax8VisebRR0RERFT1fL7pL8hzVaW6praTNbesKCW9g5ifnx927dqFrKz8h3zGx8fj0qVL6NSpEywstNf8JyQk6IyeERERUdVw8mISTvyTXOrrIga15CL9UtL7rsmJEyfi1VdfRa9evdC0aVNcuXIFIpEI48aN0yn722+/oWPHjkZtKBEREZU/lVrANzv12yusgEgEfBDWlov0DaD3iJiPjw82bNiAFi1aIDU1Fa1atcLatWvh5+enVe7s2bOwsbFB7969jd5YANi5cyd8fHx0fi1btkyr3I8//ohevXqhZcuWGDhwII4ePapTV2ZmJubMmYP27dsjICAAkydPRmpqqk65v//+G8OHD4e/vz+6du2KtWvXQhAErTKCIGDt2rXo0qUL/P39MXz4cFy8eNGon52IiKi8Xbr1GLKn+j3CqMAHb7Tlw70NVKoNXQMDA7F27doSy3To0AF79+4tU6P0sW7dOjg4OGheu7q6an7/66+/4qOPPsKECRPQsWNH7Nu3D++++y42b96sdefnlClTcPPmTcyfPx9WVlZYvnw5IiIisGPHDs106507dxAeHo7g4GBMmTIF169fx7Jly2Bubo7w8HBNXVFRUVixYgVmzJgBHx8fbN68GWPGjMHPP/+Mhg0blnt/EBERldXp2GQs26Tfpq0AYGVpjmkjAzkSVgZGe8RRRWvRokWxG8auWLEC/fr10zx4vGPHjrhx4wZWr16NqKgoAMCFCxdw8uRJREdHIyQkBADg4eGBvn374tChQ+jbty8AIDo6GrVq1cIXX3wBsViMoKAgpKenY82aNQgLC4NYLEZubi4iIyMxZswYzWOe2rRpg969eyM6Ohrz588v174gIiIqq9OxyVi04Zze5cWWZtiysC/EFnpPrlERql3v3bt3D7dv30afPn20jvft2xcxMTFQKBQAgOPHj0MikSA4OFhTxtPTE76+vjh+/Ljm2PHjx9G9e3eIxWKtumQyGS5cyL+t9++//0ZWVpbWe4rFYvTs2VOrLiIiospGpRZw4Voqlm3WfyQMAKaOCGQIM4Iq24P9+/eHr68vunfvjsjISKhU+bfYJiQkAMgf3SrMy8sLSqUS9+7d05Tz8PCASKR9d4enp6emjuzsbDx48ACenp46ZUQikaZcwddny3l5eSE5ORk5OTnG+MhERERGdTo2GW98vB/zomKgzBOef8G/2vq6ck2YkVS5qck6depg0qRJaNWqFUQiEY4cOYLly5cjJSUF8+bNg1QqBQBIJBKt6wpeF5yXyWRaa8wKODo64vLlywDyF/MXVZdYLIaNjY1WXWKxGFZWVjrvKQgCpFIprK0N3+BOEARkZ2cbfH15k8vlWl+JfVIU9ok29ocu9omu8uyTs1dS8MUPsQZd2zeogcl+LlWV7xNBEHQGe4pS5YJYp06d0KlTJ83rkJAQWFlZYcOGDZgwYYIJW1Z+lEol4uLiTN2M57p9+7apm1DpsE90sU+0sT90sU90GbtP1GoBX+8o/T5hAGBrZQZkP0RcXIpR21RaVeH7pPCypuJUuSBWlD59+mD9+vWIi4uDo6MjgPzRrDp16mjKyGQyANCcl0gkePjwoU5dUqlUU6ZgxKxgZKyAQqGAXC7XqkuhUCA3N1drVEwmk0EkEmnKGcrS0hJNmzYtUx3lSS6X4/bt22jSpAlsbGxM3ZxKgX2ii32ijf2hi32iq7z65PKtNOQokwy6dvxgP7Ro4fr8guWkqnyf3Lx5U69y1SKIFVawTishIUFrzVZCQgIsLS01W0l4enoiJiZGZ+gwMTER3t7eAABbW1u4ublp1oAVLiMIgqb+gq+JiYlaj3tKSEhA/fr1yzQtCQAikQi2trZlqqMi2NjYVIl2ViT2iS72iTb2hy72iS5j90nc3dsGXfdKFy90a+/x/IIVoLJ/n+gzLQlU4cX6he3btw/m5uZo3rw5GjZsiCZNmuDAgQM6ZYKCgjTDhKGhoZBKpYiJidGUSUxMxNWrVxEaGqo5FhoaisOHD0OpVGrVJZFIEBAQACB/fzV7e3vs379fU0apVOLQoUNadREREZnat3sv46cj8aW+7v032uCtAX7PL0ilUuVGxMLDw9GhQwf4+PgAAA4fPozt27dj1KhRmqnISZMmYcaMGWjUqBE6dOiAffv2ITY2Fps2bdLUExAQgJCQEMyZMwczZ86ElZUVvvzyS/j4+OCll17Ser+9e/di+vTpGDFiBG7cuIHo6GhMnTpVE+qsrKwwfvx4rFy5Es7OzvD29sbWrVuRkZGhtekrERGRKX279zJ2/nGr1Ne9/0YbhAY0KIcWUZULYh4eHtixYwcePnwItVqNJk2aYM6cOQgLC9OU6d+/P+RyOaKiorB27Vp4eHhg1apVmhGsAsuXL8eiRYswb9485OXlISQkBHPnztV6iHnjxo0RHR2NxYsXY9y4cXB2dsbkyZMxZswYrboiIiIgCALWr1+P9PR0+Pr6Ijo6mrvqExFRpaDIUxsUwl7p4sUQVo6qXBCbO3euXuWGDh2KoUOHlljGwcEBn332GT777LMSywUGBmL79u0llhGJRBg/fjzGjx+vV/uIiIgq0tc/XSxVeUtzM0wbGYiQ1twvrDxVizViREREVDyVWsDxv++X6ppJw1ozhFUABjEiIqJqbvvv16FU6b9zPgDUdqq8W0NUJwxiRERE1ZhKLWDP8YTnFyyktpM1mnu6lFOLqDAGMSIiomrsakIasuTK5xcsJGJQS5ib6bcPFpVNlVusT0RERPo7c/mB3mUdbC3x7tDWeNG/fjm2iApjECMiIqqmTscmY88J/aYlu7RpgCmvBXIkrIJxapKIiKgaUqkFrNx+Qe/yPdo2YggzAY6IERERVUMXbqQiS56nd/mMrNxybE3VpsrJgTw5GfL7SZAnP4B9Uy84t21jlLoZxIiIiKqZ6D2XsftY6XbRd5ZYl1NrqgZBEKBIT88PW/eTIE9KgjwpGdn3k6B4/FirrKWTE9pviDbK+zKIERERVSOfRJ/Bn1dTSnWNo724xmxXoVYoIE9+kB+07v8XtuRJSVDn5BR7nYVEAtsG7rBxd4dLcJDR2sMgRkREVE1E7Y4tdQgDgAmv+Fer9WGCIEAplT4zupWE7PtJyE19BAjFbG5rZgbrevXyA1cDd9i414eNe374spQ4lEtbGcSIiIiqgXU/X8KeE4mlvm5wZy+EtKqajzJSK5XIeZiiE7bkSUlQPc0u9jpzOzvN6JaNe/380NXAHdaurjCztKzAT8AgRkREVOVF/Xyp1LvnA4CfpzPCB/qVQ4uMSynL/Hcq8T5kt+9AcSMeV2WZyE1NBdTqoi8SiWDtWlcnbNm4N4ClowQiUeUYAWQQIyIiqsL+ty4G5+JSDbp2wfhgI7fGcIJKhZyUFJ11W/KkZOTJZDrlC+7xNLO2/m90q/BXt3owE4sr9kMYgEGMiIioCpIrVHhn8W94LDVs24nBnb0gtqj47UTzsp5qApZmKvF+EnIePoSQV/x2G1Z1auev1XKtiydmZmgSGAAnL0+InZ0rzeiWIRjEiIiIqhCVWsCsVcdx7U6GwXV0aOFarlOSgkqF3MeP88PW/aRCo1tJUD7JKPY6M7FYeyrR3R02DerDpn59mFvnb6+RnZ2NzLg4OPj6wsrWttw+Q0VhECMiIqoCFHlqrNp+AUfP3y9TPdNHBqJLm4ZGaZNKLs/fCuJ+ErLv39eMdOUkP4BaoSj2OrGzs3bY+vf3VrVrQ2RWsx76wyBGRERUyW3cfx2/nL5b5nref6MNQgMalOoaQRCgeJz2zF2J+SNdirS0Yq8TWVjApr7bf2u2CoUui2owkmUsDGJERESVlFotYMWeZKRnFXNnYCkM7uxVYghT5eYi58GDQlOJyZoRrpI2OrV0dHzmrsT8X9Z160Bkbl7mdld3DGJERESVjEotYMuBa9h++IZR6hsU6onwgX75G51mZDyzbit/dCv3UfEbnYrMzWFdz1UraBVseGrpUD4bndYUDGJERESVhFyhwmfrY3AxvvgpP32YCyo4KTPhopCik7s5mifexT8zNudvdJpd/EanFvb2WiGrIHhZ13OFmQUjQ3lgrxIREZmYSi1g5srjuH43o1TX2ahy4KKQwlkhg4tSCheFDM5KKZyUWTDDv6NbD4FHhS8yM4N13bqFphLzH+Nj28AdFpLKs9FpTcEgRkREZEIn/0nCku//Kva8SFDDSZkFF6UUzor8sJX/exls1cXvISZYWcGhUUOtuxJtG7jD2s2twh/jQ8VjECMiIjKRqN2XsOdE/qOJrFS5cFHKtEa4nBUy1FJmwhzFL9aXWtghTeyIdEsJ0sSOyLR1wqvDOiE4uBlHt6oABjEiIqIKIqhUeHL/IbZsPIL0xPtwVkgx8t/pRHtV8XcmKkQWSBdLNGErzVKC9H/DV55Z/o9yOxtzfBDWHq1eqANzMwawqoJBjIiIyMjysuVaj/HJunsfD67dgqU0DRaCGgHFXCczt0W6WKI1wpVuKYHMwg4oYXRrcKgHwgf5l8+HoXLFIEZERGQAQa2GIi3tvwdUF3qMjyItXae8zb9f80RmSLeUIN3SEWmFQle62BEKs9Kt3eoa2ADvDg8wyTMjyTgYxIiIiEqgys1FTvKD//bdun//35GuZKhzi18s/9TcGmn/hq30f6cT08SOkFnYQRCVLTjVsrfEtx/34RRkNcAgRkRENZ4gCFCkP9GMaMkL7Syfm/qo2OtE5uawdqsHa3d3ZIgdcfCWHA/NHJBmKUGuuVW5tHVQpyYYO7hVudRNFY9BjIiIagy1UomcBw8KTSUma4KXSi4v9joLh8Ibnbpr9t1SOTpjzppTSHyQlV+wHB+h6OdRCwsmhHAaspphECMiompFEATkyWSFphL/e4xPTmoqoC5mKwgzM1i71tUJWzYN3GEpkQDI3/l+7Y6/cWR7MtRIrpDP08PfAW+93B4SiX2FvB9VLAYxIiKqktR5ech5mKITtuRJScjLyir2OnNbW63H+NgWPMbHrZ7ORqcqtYAzF5OxfNtR5OSV9yfSVsfJCqumhyIuLg4WHAWrthjEiIioUsvLytK5K1F+Pwk5D1MgqFRFXyQSwapOHZ1H+Ng0cIelk1OJG51KsxSYsfwwHj5RlNMner62zWrj44hgZJfwXEiqHhjEiIjI5ASVCrkpKVDduInUhNvIS03VBC+lVFbsdWZWVoWmEutrwpa1mxvMrfRbLC/NUmDaF78hVVrBQ15FEAGY8XobhAY2MHVTqIIwiBERUYXJy87WWiCvGeVKfgAhLz8IJRVxndjFRWcq0aZBA4hdnEv1GJ8nsly8u/QAZMWvyzcJPy8XDOn2AgK863JLihqGQYyIiIxKUKuR+/ixzl2J8qRkKNJ1NzotILK0BJxrwbFJE9g3bgTbBg1g414f1vXrw8LWptjrSvJElouJSw4gs/inB5mMZ30JPpvYCXbW/FFck/FPn4iIDKLKyYE8ORny+8k6e2+pFcWvr7Ks5aRzV6KNe32o7Oxw7fp1ePj6wta2dPtAPM3Jw6frjuNSYmZZP1a5e9HPFR+M7sCRLwLAIEZERCXI3+g0/b87EgstmM999LjY60QWFrB2q/df2HL/b9G8hb1dkdfouzBdmqXAe/93AGlZgkGfyRQszUV4rVczDO7clPuAkRYGMSIiglqhgPzBw/+C1r9fs+8nQZ1T/LyehUSifVeie/38xfKurhCZmxvUFoVChVXbT+LEpTRDP06l0aN9I4x72R82YsP6gqo/BjEiohpCEAQopdJnFsrnTyXmpKQCQjEjTGZmsK7n+sxUYn7oKtjotLTuJWfinc+PFHP2vkF1VhZc+0Wlwe8SIqJqJn+j04c6YSv7fhJUT58We525ne0zU4n/bgVRz1Vno9OSPEqXY8ynh4zxUaqMWg5WeHtIK7RvUY9rv6hUGMSIiKooZWZmoTVb+Wu4su8nIefhw+If4yMSwapuHZ2wZdPAHZaOjlpbQcgVKnyx4TjOXDtfQZ+o6rCyNEOn1u4Y90orTjtSmTCIERFVYoJKhZyCzU2fGd3KkxW/0alCZIE0sSPSLCVIFzsiTSxBuqUjnlg6IM/MApAi/9dVOYCb//6ikrzQ0BELxgXDwVb/0UGi52EQIyIyQMrjbIxd9JsRaspfD2WlUsBZKYOLQlroqxS1FJmwQDGjWwCkFrZIt3REuliCNEvH/PAldkSWuQ1Qio1OSZeDjSXeHdYaHfzcON1I5YZBzMhu3bqFhQsX4sKFC7Czs8OgQYMwZcoUiMXicnk/lVrA1YQ0pMty4CyxRnNPl+f+g/G8a4o7X9RxALiSmI4rt7Ohtk5HKx9rXL+djnRZDpzsrSCIAGlmrub3T6Q5kD1VQGIvhrODtdYxe1tLZGUrYW9rCdlTBTKz8/chcrAVQ2InhuypArKnuUjLyIGLkzXsbcXIylbg8RM51Hj+beyCIECapYBCqYKlhRkAERTKPCjz1LAwN4MyTwWlSgAEwMJcBKVKjVyFCsIzdQuCAGWeGmqVALWQ/7NO+PerWi1ALQhQqfNnhsxE94F/zwv5VVMVX4hdJoIAx7yncFZI4aKU/ftVCmeFDA6q4rd6V4rMkW4p0Qpb6WIJ0i0lUJpxdMZYbMTmmDw8EEH+DF5UcRjEjEgqlWL06NFo0qQJVq5ciZSUFCxevBg5OTmYN2+e0d/vdGwy1u6+hDTpf7eWuzhaY9zglnjRv75B1xR3vnOAO45dSNI6bm9rCRGAzGwlAGDH6XSYic5DzbShoRLA9FUDWaqVcFbINCHLRSmFi0KGWkoZLIViHlININPcJn8asWA68d+vUgs7jm4ZmdhChCmvtcGLreozdJFJMYgZ0Q8//ICnT59i1apVcHJyAgCoVCosWLAA48ePh6urq9He63RsMhZtOKdzPE2ag0UbzmH26HY6Yex517zSxQs7/7hV5Pmijmf9G8AKYwijGkMQ4KDKzh/VKhS6nJVSOOYVvzFpHszw5N/RrII1XGliRzyxlCDXvHxGzgkwNwOGdvPG0J4+3FCVKhUGMSM6fvw4goKCNCEMAPr06YOPP/4Yp06dwiuvvGKU91GpBazdfanEMlE/X9Za16DPNbuP6YYtoprOQp2HWspMOP87qlWwhstZIYWVkFfsdU/Nrf8NW/mL5AtCl9TSHoKIQaA81a9ti6WTOsPRnsGWKj8GMSNKSEjAkCFDtI5JJBLUqVMHCQkJBtcrCILWoz+uJKZrTREW5XGGHH/HJaGFh7Pe13A0i2osQYCdKkcnbLkopHDMy0JxE1dqiPDE0kF77da/4SvH3LpCP0JN5eFqj3lj2sJW507GPGRnFx+Uqwq5XK71lapOnwiCoLUdTHEYxIxIJpNBUsQu046OjpBKpQbXq1QqERcXp3l95bZ+z2O7ci0RZjkppbqGqDozE1SopcyEi0J7obyLUgprte5Ue4EcM7FmCvG/sOWIDEt7qEXcQ6o8WZoDb/dzgbO9TbFl7typ/ltv3L5929RNqHSqQp/oc6Meg1gVYGlpiaZNm2peq63TseN0+nOva9HMA77/jojpew1RdWCtytGs23JR/Be2nJRZMCvm7gk1RJBa2P17R6L2gvlsc2suli9Hn4/vgAYNJJDL5bh9+zaaNGkCG5vig1dNwj7RVVX65OZN/f6DwCBmRBKJBJmZmTrHpVIpHB0dDa5XJBLB1tZW8zrQ1wYujldKnGqs7WSDQF93zRoxfa4xE3F6kqoOkaCGkzLr31GtQttBKGSwVecWe12uyOLfDU61w9YTSwlUZhzdMjbXWrb4fErp1mvZ2Nho/ZtH7JOiVPY+0WdaEmAQMypPT0+dtWCZmZl49OgRPD09jfY+5mYijBvcssg7IAtEDPLTuiVbn2sGdy76rkkiU8rf6DQ/YBVew1VLmQnzEjc6tdPZVZ4bnZbNive6wKOR4f+pJCJdDGJGFBoaijVr1mitFTtw4ADMzMwQHBxs1Pd60b8+Zo9up7PnV20nG0QM8ityHzF9rvFp7Fzk+dDW9XX2EXOwFQMQNPuIARxVI8OIBDUkeU+1toEo2FneXlX8KG7BRqeF123lbwXhUGM3OrWzNsfq93vAxYk3CxBVBQxiRvTaa69h48aNmDhxIsaPH4+UlBQsXboUr732mlH3ECvwon99dPBzK9XO+s+7pqTzo/q1KHJn/b/jknDlWiJaNPNAK5/63Flfa2d9cGf9QgpvdPrfCFf+6NbzNjp9NmylW0ogqyQbnZqbAd+83wNude30viY7OxtxcXHw9fWt1NMrRFS+GMSMyNHRERs2bMAnn3yCiRMnws7ODq+++iqmTp1abu9pbiZCy6a1jXpNceeLO97CwxlmOSnw9XCG2MKs1O2pjmryD1lBEKB4nAZ5UpLm4dTy+/lflenF3zAisrCAjXt92Li7539t4K75vUUN60MiqjkYxIzMy8sL3333nambQVTuVLm5yEl+8F/YSkqCPCkZ8qRkqHOKn060kEhg27DBM2HLHdZ160BkzsXyRFSzMIgRUbEEQYDySQay79//N2Tlj27Jk5KR++hR/nxrEUTm5rB2q/ffCFcDd5i5uOBOViaaBwbWuFFCIqLiMIgREdRKJXIePIA8KVkzlVgQvFTZxW8GbGFvrwlahacSreu5wsxC+5+X7OxsiAptTExERAxiRDWGIAjIk8n+C1v/ruGS309CTkpq/t0FRTEzg3Xdujphy7aBOywkEr33yiEiIl0MYkTVjDovD7kpqTphS56UhLzMrGKvM7ex+S9oNfgvbFm7ucHMsmZuBUFEVN4YxIiqqLysrH9Ht+5rTSXmPHgIQVX8VhBWdetohS0bd3fYNmgAy1pOHN0iIqpgDGJElZigUiH30SPtuxL/XcOlLOFB8mZWVjp3Jdo2cId1fTeYW1lV4CcgIqKSMIgRVQJ52fL/phELwlZSEuTJDyAolcVeJ3ZxLjS6lR+2bNzdIXZxhsjMrAI/ARERGYJBjKiCCGo1FGlpOnclyu8nQVHSRqeWlrCp71bE6FZ9WNjaVOAnICIiY2MQIzIyVW4usm/fgeryFTy4Eoe81FTI7ydDnpwMdW5usddZOjk9s24rf6TLqnZtbnRKRFRNMYgRGUAQBCjSn2jflXg/CfLkZOSmPtKUe/jMdfkbnbpp1m/ZFhrlsrDX/zmFRERUPTCIEZVArVBA/uCh1hYQBdOKKrm82OvM7e2hruWEWl6ecGjcWBO4rFzr6mx0SkRENRd/IlCNJwgClFKZ7uhWUjJyUp+z0Wk9V+2pxH8XzistLBAXF4fGNfCh30REpD8GMaox1Hl5yHn4UGsLiIIF83lZJWx0amurCVi2hdZwWbvVK3ajU2UJjwUiIiIqwCBG1Y4yM1N7C4iCx/g8TCl+o1ORCFZ16jyzDUT+Oi5LJ250SkRE5YNBjKokQaVCTuqjItZuJUEplRV7nZm19TNTiflhy9qNG50SEVHFYxCjSi0vO/vf0S3tx/jIkx9AyMsr9jqxi4vOVKJNgwb5G51ydIuIiCoJBjEyOUGtRu7jx4WmEvOnFbPvJ0H55Emx15mJxbCu7/bv9g/1YdugQf7UYn03mNtwo1MiIqr8GMSowqhyciBPTv5vZKtQ8FIrFMVeZ1nLSeeuRBt3d1jVqc3H+BARUZXGIEZGlb/RabpO2Mq+nwTF48fFXieysIC1Wz2tDU4Ldpm3sONGp0REVD0xiJFB1AoF5MkPIE9KgizxNhTXruHaxq3IffAA6pycYq+zkEg0z0kseISPjXt9WLu68jE+RERU4zCIUbHyNzqV6tyVKE9KRk5KKiAIWuU1+8ybmcG6Xr1/1225az2s2lLiUOGfg4iIqLJiECOolUrkPEzRCVvZ95Ogevq02OvM7fI3OhXXc4XMwgINW7eCk6cnrOu5FrvRKREREf2HQawGUcoytTY4zS54jM/Dh8U/xkckgrVr3UJbQPy3fsvS0REikQjZ2dmIi4uDEx/nQ0REVCoMYtVM/kanqZpH+GTfT0JOcv7oVp7seRudumut27Jxz98KwkwsrsBPQEREVHMwiFUDj0/F4PGJE/mh68HDkjc6rV37mW0g8neWFztzo1MiIqKKxiBWDSRErYPySYbmtZlY/MxUYv1/NzqtD3Nra9M1lIiIiLQwiFUDzT6YgayERNjUd4NNA3dY1eZGp0RERFUBg1g1IGnuC0lzX1M3g4iIiEqJwyZEREREJsIgRkRERGQiDGJEREREJsIgRkRERGQiDGJEREREJsIgRkRERGQiDGJEREREJsIgRkRERGQiDGJEREREJsIgRkRERGQiDGJEREREJsIgRkRERGQiDGJEREREJiISBEEwdSOoeH///TcEQYBYLDZ1U4olCAKUSiUsLS0hEolM3ZxKgX2ii32ijf2hi32ii32iq6r0iUKhgEgkQmBgYInlLCqoPWSgyvxNVkAkElXqoGgK7BNd7BNt7A9d7BNd7BNdVaVPRCKRXj/DOSJGREREZCJcI0ZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCbCIEZERERkIgxiRERERCZiYeoGUPWybt06/PLLL7h//z7y8vLQsGFDDB8+HK+//rpeT6GvblQqFdavX48//vgDN2/ehCAI8PHxwXvvvYe2bduaunkmc+rUKezcuRP//PMP7t27h9dffx3z5s0zdbMqzK1bt7Bw4UJcuHABdnZ2GDRoEKZMmQKxWGzqppnEnTt3EB0djX/++Qfx8fHw9PTEL7/8YupmmdT+/fuxZ88eXLlyBTKZDI0bN0ZYWBiGDBlSI/8tBYBjx44hKioKN2/eRFZWFlxdXdGjRw+8++67cHBwMHXzDMYgRkaVmZmJvn374oUXXoCVlRViYmKwcOFCZGVlYcKECaZuXoXLycnB2rVr8fLLLyMiIgJmZmbYvn07Ro0ahejoaAQFBZm6iSZx4sQJXLt2De3atYNUKjV1cyqUVCrF6NGj0aRJE6xcuRIpKSlYvHgxcnJyalQYLSw+Ph7Hjh1Dq1atoFarIQiCqZtkct999x3c3d0xa9Ys1KpVC6dPn8ZHH32Ehw8f4t133zV180wiIyMD/v7+CAsLg5OTE+Lj47Fy5UrEx8dj/fr1pm6ewUQCv+OpnE2fPh2XL1/GwYMHTd2UCqdSqZCVlQVHR0etY/3790fjxo2xZs0aE7bOdNRqNczM8ldGdOvWDV26dKkxISQyMhJr1qzB0aNH4eTkBADYtm0bFixYgKNHj8LV1dW0DTSBwt8Ps2bNwuXLl2v8iFh6ejqcnZ21jn300UfYt28fzp07p+mvmm779u346KOPcPz48Sr7d4d/klTuatWqBaVSaepmmIS5ublWCCs45uPjg9TUVBO1yvRq8g+R48ePIygoSBPCAKBPnz5Qq9U4deqU6RpmQjX5+6E4z4YwAPD19UVWVhays7NN0KLKqeDvUVX+GcPvfioXeXl5yMrKwh9//IHdu3dj1KhRpm5SpZGXl4d//vkHnp6epm4KmUBCQoLOn71EIkGdOnWQkJBgolZRVXD+/Hm4urrC3t7e1E0xKZVKhdzcXFy5cgWrV69Gt27d0KBBA1M3y2BcI0ZGd+fOHbz00kua12+//TbefPNN0zWoklm3bh1SUlLYJzWUTCaDRCLROe7o6Fjj1suR/v766y/s27cPM2fONHVTTK5r165ISUkBAHTq1Amff/65iVtUNgxiVKLMzEy9ptAaNmyouePLzc0NP/30E7Kzs/HXX38hKioKZmZmmDx5cnk3t0IY0icFTp06hZUrV+Kdd96Bn59feTWxwpWlT4ioZA8fPsTUqVPRoUMHzi4AWLt2LeRyOW7evIlvvvkGEyZMwLfffgtzc3NTN80gDGJUogMHDmDu3LnPLbdv3z54eXkBAMRiMVq2bAkA6NChA+zt7bFkyRKMGDECderUKdf2VgRD+gQArly5gkmTJqF///7V7q4nQ/ukJpJIJMjMzNQ5LpVKddYTEslkMkRERMDJyQkrV67kejoAzZo1AwAEBASgZcuWGDRoEH777Tf07t3bxC0zDIMYlWjo0KEYOnRomepo0aIFVCoVkpKSqkUQM6RP7ty5g4iICAQEBGDhwoXl1DLTMcb3SU3h6empsxYsMzMTjx494rpB0pKTk4Px48cjMzMT27Ztq9J7ZZUXHx8fWFpa4u7du6ZuisEYranc/f333xCJRFV6MWVZpKamYsyYMXBzc8OKFStgaWlp6iaRCYWGhuL06dOQyWSaYwcOHICZmRmCg4NN2DKqTPLy8jBlyhQkJCRg3bp1VXZrhvL2zz//QKlUVumfLxwRI6PJzMxEREQEBg4ciMaNGyMvLw9nz57F999/j+HDh6N27dqmbmKFy8nJQUREBJ48eYIPP/wQ8fHxmnNisRjNmzc3YetMJykpCZcuXQIAyOVy3L17FwcOHACAKju9oK/XXnsNGzduxMSJEzF+/HikpKRg6dKleO2112rsD1u5XI5jx44ByP/eyMrK0nw/tG/fvsitHKq7gn3lZs2ahaysLFy8eFFzrnnz5jVyreW7774LPz8/+Pj4wNraGteuXUN0dDR8fHzQo0cPUzfPYNzQlYxGoVDg448/xvnz55GSkgJra2s0atQIr732GgYPHlxlF1KWxf3799G9e/ciz7m7u+PIkSMV3KLKYefOnZg9e3aR565fv17Bral4t27dwieffKL1iKOpU6fWyB+uQMl/T77//nt06NChgltket26dUNSUlKR5w4fPlylR4AMtXbtWuzbtw93796FIAhwd3dHz549ER4eXqW39GAQIyIiIjIRrhEjIiIiMhEGMSIiIiITYRAjIiIiMhEGMSIiIiITYRAjIiIiMhEGMSIiIiITYRAjIiIiMhEGMSIiIiITYRAjIqM4e/YsfHx8NI+mqU58fHywcuXKcqn7/v378PHxwc6dO8ul/spo1qxZ6Natm9ax8uxjosqMz5okomL5+PjoVe77778v55aUv2PHjiE2NhaTJk0ydVNKlJycjDVr1uDkyZNITU2Fg4MDAgMDER4ejsDAQFM3TyMlJQXbt29Hjx494Ovra+rmEFVaDGJEVKylS5dqvf75559x6tQpneNeXl64detWRTbN6I4dO4bNmzcXGcRiY2MrxbNSz58/j3HjxgEAhg4dCi8vLzx+/Bi7du3CyJEjMW/ePIwcOdLErcyXmpqKVatWwd3dXSeIffLJJ+DT9YjyMYgRUbEGDRqk9fqff/7BqVOndI4DqHRBLDs7G7a2tkapy8rKyij1lIVUKsV7770Ha2trbN26FY0aNdKce+uttxAeHo6FCxeiefPmaN26tekaqgdLS0tTN4Go0uAaMSIyKrVajW+++QahoaFo2bIlRo8ejTt37uiU++effxAeHo42bdqgVatWeOONN3D+/HmdclevXsXYsWMRGBiIgIAAjB49GhcvXtQqs3PnTvj4+ODPP//E/PnzERQUhM6dO2vOHzt2DCNHjkTr1q0REBCAcePGIT4+XnN+1qxZ2Lx5M4D86diCXwWKWr+UkpKCOXPmICQkBH5+fujWrRs+/vhjKBQKAEBGRgaWLFmCAQMGICAgAIGBgRg7diyuXbtW+k4FsG3bNjx69Ajvv/++VggDAGtrayxevBgAsHr1as3xlStXFjm9XNBf9+/f1xz7/fffMW7cOM3n6dGjB1avXg2VSqV1bVhYGPr374+bN28iLCwMrVq1QqdOnRAVFaUpc/bsWbz66qsAgNmzZ2v6s2AdXFFrxIqSkpKC2bNn48UXX4Sfnx/69euHn376Safcxo0b0a9fP7Rq1Qrt2rXDK6+8gr179z63fqLKgCNiRGRUUVFREIlEGDNmDLKysrBu3TrMmDEDP/74o6ZMTEwMIiIi4Ofnh3fffRcikQg7d+7E6NGjsWXLFvj7+wMA4uPj8frrr8POzg5jx46FhYUFtm3bhrCwMGzatAmtWrXSeu8FCxbA2dkZEydORHZ2NgBg9+7dmDVrFkJCQjBjxgzI5XJs3boVI0eOxK5du9CgQQMMHz4cqampRU67FiUlJQWvvvoqMjMzMWzYMHh6eiIlJQUHDx5ETk4OxGIx7t27h99//x29e/dGgwYN8PjxY2zbtg1vvPEGfv31V7i6upaqX48cOQIrKyv07du3yPMNGzZEmzZtEBMTg9zc3FKP4u3atQu2trZ46623YGtrizNnzmDFihXIysrCzJkztcpKpVKMHTsWPXv2RJ8+fXDw4EEsW7YM3t7e6Ny5M7y8vDB58mSsWLECw4cPR5s2bQCgVGvYHj9+jGHDhkEkEuH111+Hs7Mzjh8/jg8//BBZWVl48803AQDbt2/HwoUL0atXL4waNQq5ubm4fv06/vnnHwwYMKBUfUBkEgIRkZ4WLFggeHt7F3nuzJkzgre3t9CnTx8hNzdXc3zDhg2Ct7e3cP36dUEQBEGtVgsvvfSSMGbMGEGtVmvKyeVyoVu3bsJbb72lOfbOO+8ILVq0EO7evas5lpKSIgQEBAivv/665tiOHTsEb29vYcSIEUJeXp7meFZWltC2bVth7ty5Wm199OiR0KZNG63jJX02b29vYcWKFZrXH3zwgdCsWTMhNjZWp2zBZ8rNzRVUKpXWuXv37gl+fn7CqlWrtI55e3sLO3bsKPK9C7Rt21YYOHBgiWU++eQTwdvbW7h27ZogCIKwYsWKIj9TQX/du3dPc0wul+uU++ijj4RWrVpp/Xm+8cYbgre3t7Br1y7NsdzcXCE4OFiYNGmS5lhsbGyxn2vmzJlC165dtY4928dz5swRgoODhfT0dK1yU6dOFdq0aaNp79tvvy3069evyP4gqgo4NUlERvXKK69ALBZrXrdt2xYAcO/ePQBAXFwcbt++jQEDBuDJkydIT09Heno6srOzERQUhHPnzkGtVkOlUuHUqVPo0aMHGjZsqKmvbt266N+/P86fP4+srCyt9x42bJjWovrTp09DJpOhX79+mvdJT0+HmZkZWrVqhbNnz5b686nVavz+++/o2rUrWrZsqXNeJBIBAMRiMczM8v+JValUePLkCWxtbeHh4YGrV6+W+n2fPn0KOzu7EssUnH/69Gmp67e2ttb8PisrC+np6Wjbti3kcjkSEhK0ytra2mqtExSLxWjZsqXmz7isBEHAoUOH0K1bNwiCoPVnFxISgszMTFy5cgUAIJFI8PDhQ8TGxhrlvYkqGqcmicio6tevr/VaIpEAAGQyGQDg9u3bAKAz3VVYZmYmFAoF5HI5PDw8dM57eXlBrVbjwYMHeOGFFzTHGzRooFWu4L1Gjx5d5PvY29uX/GGKkJ6ejqysLK33LYparcb333+PLVu24P79+1prrZycnEr9vnZ2ds8NWAXnXVxcSl1/fHw8li9fjjNnzugE3MzMTK3X9erV0wTOAo6Ojrh+/Xqp37co6enpkMlk2LZtG7Zt21ZsGQCIiIjA6dOnMXToUDRu3BjBwcHo37+/ZjqUqLJjECMioyoYBXqW8O92BQVfP/jgg2L3l7K1tdUsei+NZ9dFFbzX0qVLUadOHZ3y5bklxZo1a/DVV19hyJAheO+99+Do6AgzMzN89tlnBm3d4OXlhatXr0KhUGiNOBZ2/fp1WFpaatafPRuWCjy7AF8mk+GNN96Avb09Jk+ejEaNGsHKygpXrlzBsmXLoFartcqX91YeBe83cOBAvPzyy0WWKbgJwcvLCwcOHMAff/yBEydO4NChQ9iyZQsmTpyIyZMnl2s7iYyBQYyIKlTBNKO9vT1efPHFYss5OzvDxsYGiYmJOucSEhJgZmYGNzc3vd7LxcWlxPcCig8tRbXL3t5e667Lohw8eBAdOnTAZ599pnVcJpOhVq1aer1XYd26dcOFCxewf//+IrcPuX//Ps6fP4/u3btrphkLj0YW/B7I3xS2sD///BMZGRlYtWoV2rVrp1WnofTtz6I4OzvDzs4OarX6uX9uQH5w79u3L/r27QuFQoFJkyZhzZo1GD9+fKXYeoSoJFwjRkQVys/PD40aNcL69euLnGormHIyNzdHcHAwDh8+rBUIHj9+jF9++QVt2rR57tRip06dYG9vj8jISCiVymLfCwBsbGwA/DeFWhwzMzP06NEDR48exaVLl3TOF4x2mZub64x87d+/HykpKSXWX5zhw4ejTp06+L//+z+dtVi5ubmYPXs2RCIRxo4dqzlesM3FuXPnNMeys7Oxe/dunc9UuO0AoFAosGXLFoPaCujfn0UxNzdHr169cPDgQdy4cUPnfOE/tydPnmidE4vF8PLygiAIRf6ZE1U2HBEjogplZmaGhQsXIiIiAv3798crr7wCV1dXpKSk4OzZs7C3t8eaNWsAAFOmTMHp06cxcuRIjBw5Eubm5ti2bRsUCgXef//9576Xvb095s+fjw8++ACvvPIK+vbtC2dnZyQnJ+PYsWMIDAzEvHnzAAAtWrQAACxcuBAhISEwNzdHv379iqx32rRpOHXqFMLCwjBs2DB4eXnh0aNHOHDgALZs2QKJRIIuXbpg9erVmD17NgICAnDjxg3s3btX68aD0nB0dMRXX32FcePG4eWXX9bZWf/evXv46KOPtLb0CA4ORv369fHhhx8iISEB5ubm2LFjB2rVqqU1KhYQEABHR0fMmjULYWFhEIlE+Pnnn8u0+32jRo0gkUjwww8/wM7ODra2tvD399f780+fPh1nz57FsGHDMHToUDRt2hRSqRRXrlxBTEwM/vzzTwBAeHg4ateujcDAQLi4uCAhIQGbNm1C586dDVoDSFTRGMSIqMJ16NAB27Ztw9dff41NmzYhOzsbderUgb+/P4YPH64p98ILL2Dz5s34/PPPERkZCUEQ4O/vj//7v//T2UOsOAMGDEDdunWxdu1aREdHQ6FQwNXVFW3btsUrr7yiKffSSy8hLCwMv/76K/bs2QNBEIoNYq6urti+fTu++uor7N27F1lZWXB1dUVoaKhmWnDChAmQy+XYu3cv9u3bh+bNmyMyMhKff/65wf3Wpk0b7NmzB5GRkThw4ABSU1ORl5cHAPjuu+8QFBSkVd7S0hKrVq3CggUL8NVXX6FOnToYPXo0JBIJZs+erSlXq1YtrFmzBkuWLMHy5cshkUgwcOBABAUFITw83KC2WlpaYvHixfjiiy8wf/585OXlYdGiRXoHsdq1a+PHH3/E6tWr8dtvv2Hr1q1wcnJC06ZNMWPGDE254cOHY+/evfj222+RnZ2NevXqISwsDO+8845B7SaqaCKhLP/lISIikyrYHLdNmzaIiooqdiE/EVVOXCNGRFSFBQUFYcmSJTh79ixmz57Nh2kTVTEcESMiIiIyEY6IEREREZkIgxgRERGRiTCIEREREZkIgxgRERGRiTCIEREREZkIgxgRERGRiTCIEREREZkIgxgRERGRiTCIEREREZkIgxgRERGRifw/FIiGWCk9ZNwAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["Con el Q-Q Plot comprobamos que nuestra avriable 'num_subscribers' NO sigue una distribución normal."],"metadata":{"id":"PjeISTWLTs9a"}},{"cell_type":"code","source":["# PARA VER QUÉ DISTRIBUCIÓN ES LA MÁS PARECIDA A LOS DATOS\n","\n","\n","# Definir la función best_fit_distribution\n","def best_fit_distribution(data, bins=200, plot=False):\n"," DISTRIBUTIONS = [st.norm, st.expon, st.gamma, st.weibull_min, st.weibull_max, st.beta, st.lognorm]\n","\n"," y, x = np.histogram(data, bins=bins, density=True)\n"," x = (x + np.roll(x, -1))[:-1] / 2.0\n","\n"," best_distribution = None\n"," best_params = None\n"," best_sse = np.inf\n","\n"," for distribution in DISTRIBUTIONS:\n"," try:\n"," params = distribution.fit(data)\n"," pdf = distribution.pdf(x, *params[:-2], loc=params[-2], scale=params[-1])\n"," sse = np.sum(np.power(y - pdf, 2.0))\n","\n"," if best_sse > sse > 0:\n"," best_distribution = distribution\n"," best_params = params\n"," best_sse = sse\n"," except Exception:\n"," pass\n"," if best_distribution:\n"," print(f\"Mejor distribución: {best_distribution.name}\")\n"," print(f\"Parámetros: {best_params}\")\n","\n"," if plot:\n"," plt.hist(data, bins=bins, density=True, alpha=0.5, color='b')\n"," pdf = best_distribution.pdf(x, *best_params[:-2], loc=best_params[-2], scale=best_params[-1])\n"," plt.plot(x, pdf, 'r--', label=f'{best_distribution.name}')\n"," plt.legend()\n"," plt.show()\n","\n"," return best_distribution, best_params\n","\n","# Llamada a la función con una columna específica\n","mejor_distribucion, parametros = best_fit_distribution(data[\"num_subscribers\"], plot=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":654},"id":"vKcc7VU4S9Xc","executionInfo":{"status":"ok","timestamp":1732705527586,"user_tz":-60,"elapsed":1870,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"3ac6c12a-be2d-4d26-aae6-eaf8f09d4cc9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/scipy/stats/_continuous_distns.py:716: RuntimeWarning: invalid value encountered in sqrt\n"," sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)\n","/usr/local/lib/python3.10/dist-packages/scipy/stats/_continuous_distns.py:721: RuntimeWarning: The iteration is not making good progress, as measured by the \n"," improvement from the last five Jacobian evaluations.\n"," a, b = optimize.fsolve(func, (1.0, 1.0))\n","/usr/local/lib/python3.10/dist-packages/scipy/stats/_continuous_distns.py:6554: RuntimeWarning: overflow encountered in divide\n"," return np.sum((1 + np.log(shifted/scale)/shape**2)/shifted)\n","/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:88: RuntimeWarning: overflow encountered in reduce\n"," return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Mejor distribución: beta\n","Parámetros: (0.6031533946517038, 570.2674178155567, -2.2254393523415765e-24, 1088742.1941995504)\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["La variable 'num_subscribers' sigue una distribución beta."],"metadata":{"id":"34oF9NO1T0i7"}},{"cell_type":"markdown","metadata":{"id":"6XdCKdCvBU1g"},"source":["SIguiendo los mismos pasos para analizar la Normalidad de las variables( estudiar simetría de la variable contrarestando media y mediana, visualizando en un histograma, densityplot y boxplot para ver que forma presenta su distribución, y el Q-Q Plot para ver si se asemeja o no a una distribución normal) hemos analizado las demás variables cuantitativas que nos quedaban ( como 'num_reviews'o 'num_lectures) y hemos llegado a la conclusión de que NO siguen una distribución normal.\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"Z8HYhV57Cshw"},"source":["## 4. Estimación puntual y por intervalos de los parámetros poblacionales de algunas variables"]},{"cell_type":"markdown","source":["En el apartado de ESTIMACIÓN, nos vamos a centrar en estimar ( o predecir) valores desconocidos de la población a través de valores de nuestro dataset.\n","\n","Hay dos tipos de estimaciones:\n","\n","- Estimación puntual : se centra en estimar un único valor ( parámetro muestral : como la media) y ver si es representativo con el parámetro poblacional\n","\n","- Estimación por intervalos :proporciona un rango de valores dentro del cuál es probable que se encuentre el parámetro poblacional ( usando intervalos de confianza como el del 95%)"],"metadata":{"id":"Frq_o0dbVK5T"}},{"cell_type":"markdown","source":["ESTIMACIÓN PUNTUAL"],"metadata":{"id":"F2NTSXzaWajP"}},{"cell_type":"code","source":["data.groupby('subject')['num_subscribers'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"v-YDpAtwWZ7p","executionInfo":{"status":"ok","timestamp":1732790803901,"user_tz":-60,"elapsed":356,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"}},"outputId":"c432f6a2-2424-4d12-ffe4-fc78fcc74b27"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["subject\n","Business Finance 1563.774895\n","Graphic Design 1763.097844\n","Musical Instruments 1245.130882\n","Web Development 6650.476667\n","Name: num_subscribers, dtype: float64"],"text/html":["
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num_subscribers
subject
Business Finance1563.774895
Graphic Design1763.097844
Musical Instruments1245.130882
Web Development6650.476667
\n","

"]},"metadata":{},"execution_count":79}]},{"cell_type":"code","source":["data['num_subscribers'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pLFZY9geWk6m","executionInfo":{"status":"ok","timestamp":1732706467027,"user_tz":-60,"elapsed":160,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"cfd0e37b-1fd6-43e3-91ca-a779ca293bcc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3198.0201251019853"]},"metadata":{},"execution_count":57}]},{"cell_type":"markdown","source":["Observamos que la media de suscriptores de todos los cursos de UDEMY es de 3198 suscriptores por curso, mientras que la media de suscriptores de cada curso es distinta dependiendo de la asignatura ('subject').\n","\n","Nuestra estimación consiste en averiguar si la media de suscriptores de una asignatura ( media muestral) es representativa de la media de suscriptores de todos los cursos ( media poblacional)"],"metadata":{"id":"5oY-57oKWpu0"}},{"cell_type":"markdown","source":["Elegimos la asignatura Graphic Design, que de primeras observamos que tiene una media de suscriptores de 1763, a diferencia de la media de suscriptores de todos los cursos de udemy que es de 3198.\n","\n","Aún así vamos a comprobarlo."],"metadata":{"id":"5P7Dpg0sXRuz"}},{"cell_type":"code","source":["from scipy.stats import ttest_1samp\n","\n","# Definir el nombre de la categoría específica que deseas analizar\n","categoria_especifica = 'Graphic Design'\n","\n","# Filtrar los datos de la categoría específica\n","grupo_especifico = data[data['subject'] == categoria_especifica]['num_subscribers']\n","\n","# Calcular la media general de num_subscribers\n","media_general = data['num_subscribers'].mean()\n","\n","# Realizar el test t para una muestra\n","stat, p_value = ttest_1samp(grupo_especifico, popmean=media_general)\n","\n","# Mostrar resultados\n","print(f\"Media de {categoria_especifica}: {grupo_especifico.mean():.2f}\")\n","print(f\"Media general: {media_general:.2f}\")\n","print(f\"Estadístico t: {stat:.4f}\")\n","print(f\"Valor p: {p_value:.4f}\")\n","\n","# Interpretación\n","if p_value < 0.05:\n"," print(f\"La media de '{categoria_especifica}' es significativamente diferente de la media general.\")\n","else:\n"," print(f\"No hay evidencia suficiente para decir que la media de '{categoria_especifica}' es diferente de la media general.\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nOz-xGigXNak","executionInfo":{"status":"ok","timestamp":1732790849246,"user_tz":-60,"elapsed":342,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"}},"outputId":"06aa0a84-1276-481e-d4c5-e4a327d14081"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Media de Graphic Design: 1763.10\n","Media general: 3197.15\n","Estadístico t: -7.1528\n","Valor p: 0.0000\n","La media de 'Graphic Design' es significativamente diferente de la media general.\n"]}]},{"cell_type":"markdown","source":["Obtenemos un pValor muy bajo, por tanto rechazamos la Hipótesis Nula de que la media de suscriptores de 'Graphic Design' es igual a la media de suscriptores de todos los cursos de Udemy.\n","\n","Eso significa que la media de suscriptores de 'Graphic Design' no es representativa con la media de suscriptores de todos los cursos de Udemy."],"metadata":{"id":"csfda84aXjv6"}},{"cell_type":"markdown","source":["ESTIMACIÓN POR INTERVALOS"],"metadata":{"id":"vT28CuPeWc0z"}},{"cell_type":"code","source":["import numpy as np\n","import scipy.stats as stats\n","\n","# Definir el nombre de la categoría específica\n","categoria_especifica = 'Graphic Design'\n","\n","# Filtrar los datos de la categoría específica\n","grupo_especifico = data[data['subject'] == categoria_especifica]['num_subscribers']\n","\n","# Calcular la media y la desviación estándar de la muestra\n","media_muestra = grupo_especifico.mean()\n","desviacion_estandar = grupo_especifico.std()\n","\n","# Calcular el tamaño de la muestra\n","n = len(grupo_especifico)\n","\n","# Nivel de confianza del 95%\n","nivel_confianza = 0.95\n","\n","# Calcular el margen de error\n","margen_error = stats.t.ppf((1 + nivel_confianza) / 2, df=n-1) * (desviacion_estandar / np.sqrt(n))\n","\n","# Calcular el intervalo de confianza\n","limite_inferior = media_muestra - margen_error\n","limite_superior = media_muestra + margen_error\n","\n","# Mostrar resultados\n","print(f\"Media de {categoria_especifica}: {media_muestra:.2f}\")\n","print(f\"Intervalo de confianza del 95%: [{limite_inferior:.2f}, {limite_superior:.2f}]\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53Nc9ftuWfqC","executionInfo":{"status":"ok","timestamp":1732790873825,"user_tz":-60,"elapsed":1,"user":{"displayName":"Edu Abad","userId":"04964828773960147192"}},"outputId":"ab8f511f-be9e-4426-ce23-e64dcd4a24ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Media de Graphic Design: 1763.10\n","Intervalo de confianza del 95%: [1369.36, 2156.84]\n"]}]},{"cell_type":"markdown","source":["Tras estudiar la estimación por intervalos, vemos que con un intervalo de confianza del 95% que la media de suscriptores en 'Grapfic Design' está entre 1369.36 y 2156.84.\n","\n"],"metadata":{"id":"iEfmztycY-nE"}},{"cell_type":"code","source":["data.columns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1cXWh1y5Z7nP","executionInfo":{"status":"ok","timestamp":1732707379580,"user_tz":-60,"elapsed":3,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"8a3740cc-812b-49d9-d8bc-367e7e075bf4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['course_title', 'is_paid', 'price', 'num_subscribers', 'num_reviews',\n"," 'num_lectures', 'level', 'content_duration', 'published_timestamp',\n"," 'subject'],\n"," dtype='object')"]},"metadata":{},"execution_count":62}]},{"cell_type":"markdown","metadata":{"id":"1YmW_kQqCmtM"},"source":["## 5. Realización de contrastes de hipótesis oportunos para realizar inferencia con los datos"]},{"cell_type":"markdown","source":["Los contrastes de hipótesis nos ayudan a tomar decisiones basadas en los datos que tenemos. Nos permiten aceptar o rechazar una suposición inicial ( Hipótesis Nula) y ver si nuestros resultados són significativos o no lo son.\n","\n","Están compuestos por una suposición inicial ( hipótesis nula, como por ejemplo, que la media de las patatas es de 50 euros) y una suposición alternativa ( hipótesis alternativa, la media de patatas NO es de 50 euros).\n","\n"],"metadata":{"id":"cflB9LkjatrQ"}},{"cell_type":"markdown","source":["Luego, realizamos un contraste de hipótesis y obtenemos un valor p. Si el valor p es, por ejemplo, 0.03, eso significa que hay solo un 3% de probabilidad de que los datos observados ocurran si la hipótesis nula fuera cierta. Como este valor p es menor que 0.05, podemos rechazar la hipótesis nula y concluir que el precio promedio no es 50 euros."],"metadata":{"id":"sfhgrIh_br3p"}},{"cell_type":"code","source":["from statsmodels.stats.weightstats import ztest as ztest"],"metadata":{"id":"0K19VZeYcMEE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Hipótesis:\n","Hipótesis nula (H₀): El número promedio de lecciones de los cursos de nivel avanzado es igual al número promedio de lecciones de todos los cursos.\n","Hipótesis alternativa (H₁): El número promedio de lecciones de los cursos de nivel avanzado es diferente al número promedio de lecciones de todos los cursos."],"metadata":{"id":"H_WAyd2udvhk"}},{"cell_type":"code","source":["from scipy.stats import ttest_1samp\n","\n","# Filtrar los datos de los cursos de nivel avanzado (Expert Level)\n","cursos_avanzado = data[data['level'] == 'Expert Level']['num_lectures']\n","\n","# Calcular la media general de lecciones en todos los cursos\n","media_general_lecciones = data['num_lectures'].mean()\n","\n","# Realizar el test t para una muestra (compara el número de lecciones de los cursos avanzados con el promedio general)\n","stat, p_value = ttest_1samp(cursos_avanzado, media_general_lecciones)\n","\n","# Mostrar los resultados\n","print(f\"Media del número de lecciones de los cursos de nivel 'Expert': {cursos_avanzado.mean():.2f}\")\n","print(f\"Media general del número de lecciones: {media_general_lecciones:.2f}\")\n","print(f\"Estadístico t: {stat:.4f}\")\n","print(f\"Valor p: {p_value:.4f}\")\n","\n","# Interpretación\n","if p_value < 0.05:\n"," print(\"Rechazamos la hipótesis nula: El número promedio de lecciones de los cursos 'Expert' es diferente al promedio general.\")\n","else:\n"," print(\"No hay evidencia suficiente para rechazar la hipótesis nula: El número promedio de lecciones de los cursos 'Expert' es igual al promedio general.\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LHOObBgpdr8d","executionInfo":{"status":"ok","timestamp":1732708339869,"user_tz":-60,"elapsed":159,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"3914ee44-3556-4458-bcde-eb1e2d2e7780"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Media del número de lecciones de los cursos de nivel 'Expert': 30.78\n","Media general del número de lecciones: 40.12\n","Estadístico t: -2.6267\n","Valor p: 0.0111\n","Rechazamos la hipótesis nula: El número promedio de lecciones de los cursos 'Expert' es diferente al promedio general.\n"]}]},{"cell_type":"markdown","source":["Observamos que el pValor es de 0.0111, que es menor que 0.05, por tanto rechazamos la hipótesis nula de que la media de clases impartidas en el nivel avanzado/experto es igual a la media de clases impartidas de los cursos destinados a todos los niveles.\n","\n","Por tanto, con un 95% de confianza asumimos que la media de clases impartidas en el nivel avanzado (30.78) es DIFERENTE que la media de clases impartidas en los demás niveles (40.12) . Los cursos de nivel avanzado tienden a ofrecer MENOS clases que los cursos orientados a todos los niveles.\n","99% no"],"metadata":{"id":"1JSqA9x6d7ea"}},{"cell_type":"markdown","metadata":{"id":"4xq9v_Wn1W3A"},"source":["## 6. Realización de ANOVA"]},{"cell_type":"markdown","source":["El test ANOVA se realiza para comparar diferentes poblaciones y ver si hay diferencias significativas en sus medias.\n","\n","El ANOVA analiza dos variables, una continua (como el precio) y una categórica (como los niveles) y los requisitos para realizar el test ANOVA es que sus variables sigan una distribución Normal y que exista homocedasticidad (varianzas similares entre grupos).\n","\n","Para verificar la homogeneidad de varianzas, se utiliza el test de Levene.\n","\n","Si el ANOVA muestra que hay diferencias significativas (rechazo de la hipótesis nula de que las medias son iguales), se realiza un test post-hoc de Tukey. Este test nos ayuda a identificar qué grupos son los que difieren entre sí.\n","\n","- ANOVA: Para variables paramétricas (que siguen una distribución conocida).\n","- Kruskal-Wallis: Para variables no paramétricas (que no siguen una distribución conocida).\n","\n","\n","En resumen, ANOVA nos dice si hay diferencias entre las medias y, si las hay, el test Tukey nos indica qué grupos difieren entre sí."],"metadata":{"id":"z04w_2SvgeMD"}},{"cell_type":"markdown","source":["En nuestro test de anova vamos a analizar si diferentes poblaciones ( diferentes asignaturas) tienen la misma media de reviews.\n","\n","Por tanto, la Hipótesis Nula : todas las asignaturas tienen la misma media de 'num_reviews' , y la Hipótesis Alternativa : todas las asignaturas NO tienen la misma media de 'num_reviews'.\n"],"metadata":{"id":"THWQ7qkkh-QL"}},{"cell_type":"markdown","metadata":{"id":"ljUsgKex1W3A"},"source":["Tras apreciar que ninguna de nuestras variables sigue una distribución normal, no podemos realizar ANOVA, por lo que recurriremos a realizar el test de Kruskal-Wallis"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"koBJtUjO1W3A","outputId":"bceba188-d52a-42db-e71f-653625c97743","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1732709583123,"user_tz":-60,"elapsed":144,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Estadístico de Kruskal-Wallis: 980.18897013518\n","Valor p: 3.570317898934084e-212\n","Hay diferencias significativas entre los grupos (rechazamos H0).\n"]}],"source":["from scipy.stats import kruskal\n","import pandas as pd\n","\n","# Asegúrate de que tu dataset ya está cargado en tu variable (por ejemplo, `tu_dataset`)\n","\n","# Crear listas separadas para cada grupo de subjects\n","groups = [group['num_reviews'].values for name, group in data.groupby('subject')]\n","\n","# Realizar el test de Kruskal-Wallis\n","stat, p_value = kruskal(*groups)\n","\n","# Mostrar resultados\n","print(\"Estadístico de Kruskal-Wallis:\", stat)\n","print(\"Valor p:\", p_value)\n","\n","# Interpretación\n","if p_value < 0.05:\n"," print(\"Hay diferencias significativas entre los grupos (rechazamos H0).\")\n","else:\n"," print(\"No hay diferencias significativas entre los grupos (no se rechaza H0).\")\n"]},{"cell_type":"markdown","metadata":{"id":"Vvup5L6u3jAH"},"source":["Como podemos observar en el Kruskal-Wallis , el pvalor es **muy bajo**, por tanto **rechazamos** Ho\n","Concluimos con que las diferentes asignaturas no tienen una media similar de num_reviews .\n","\n"]},{"cell_type":"markdown","source":["Al ver que en el Kruskal-Wallis hemos rechazado la Ho, por tanto hay diferencias significativas en la media de reviews por cada asignatura, hemos pasado a realizar un estudio POST-HOC (prueba TUKEY) para ver en que asignatura se encuentra esa diferencia de media de reseñas."],"metadata":{"id":"uTAOP8eTjbMD"}},{"cell_type":"code","source":["from statsmodels.stats.multicomp import pairwise_tukeyhsd\n","import pandas as pd\n","\n","# Asegúrate de que tu DataFrame esté filtrado correctamente y que 'subject' sea la columna categórica\n","# y 'num_reviews' la columna numérica de los que quieres comparar.\n","\n","# Realizar la prueba Tukey HSD para ver qué asignaturas tienen medias de reviews significativamente diferentes\n","tukey_result = pairwise_tukeyhsd(endog=data['num_reviews'], groups=data['subject'], alpha=0.05)\n","\n","# Mostrar los resultados\n","print(tukey_result.summary())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wtP-o_tjkGJZ","executionInfo":{"status":"ok","timestamp":1732710003144,"user_tz":-60,"elapsed":1786,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"cd2b79c0-2233-427d-881a-7a76cf970302"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n","=================================================================================\n"," group1 group2 meandiff p-adj lower upper reject\n","---------------------------------------------------------------------------------\n"," Business Finance Graphic Design -2.0936 1.0 -120.9067 116.7196 False\n"," Business Finance Musical Instruments -16.9166 0.9812 -131.1727 97.3395 False\n"," Business Finance Web Development 294.7847 0.0 197.5725 391.9968 True\n"," Graphic Design Musical Instruments -14.823 0.9918 -147.8536 118.2075 False\n"," Graphic Design Web Development 296.8782 0.0 178.1648 415.5916 True\n","Musical Instruments Web Development 311.7012 0.0 197.5488 425.8536 True\n","---------------------------------------------------------------------------------\n"]}]},{"cell_type":"markdown","source":["Gracias a el TUKEY TEST podemos ver cómo se relacionan las diferentes asignaturas entre sí ( diferentes poblaciones = diferentes asignaturas con su respectiva media de num_reviews).\n","\n","Observamos que cuándo se relacionan con la Asignatura WEB DEVELOPMENT la 'meandiff'= diferencia de las medias de num_reviews es muy grande.\n","\n","Por lo que concluimos que la asignatura con una media de num_reviews diferente a las demás es WEB DEVELOPMENT."],"metadata":{"id":"X4xwXSBakwsQ"}},{"cell_type":"code","source":["data.groupby('subject')['num_reviews'].mean()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"CTVqiug6i3qu","executionInfo":{"status":"ok","timestamp":1732709719520,"user_tz":-60,"elapsed":199,"user":{"displayName":"Gonzalo López Blanquer","userId":"17276650073195484032"}},"outputId":"9b389916-f39e-43b0-aefb-2f762cfc2918"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["subject\n","Business Finance 63.569514\n","Graphic Design 61.475954\n","Musical Instruments 46.652941\n","Web Development 358.354167\n","Name: num_reviews, dtype: float64"],"text/html":["
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num_reviews
subject
Business Finance63.569514
Graphic Design61.475954
Musical Instruments46.652941
Web Development358.354167
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"]},"metadata":{},"execution_count":67}]},{"cell_type":"markdown","source":["Visualmente, Web Development es la asignatura que más reseñas tiene (358), seguida de las otras tres asignaturas que presentan una media de reseñas bastante más similar (entre 45 y 60)."],"metadata":{"id":"MudVVvsBjDrq"}},{"cell_type":"markdown","source":["Una vez realizado el análisis podemos apreciar que los cursos de \"Web Development\" funciona diferente a los demás, viendo que tiene muchas más reseñas que el resto de tipos. Esto nos indica que si tu viéramos que elegir una tipología para lanzar un curso sería sobre este tema, aprovechando así también el máster que estamos haciendo."],"metadata":{"id":"LpQgwfusJopr"}}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.3"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion1_sql.txt b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion1_sql.txt new file mode 100644 index 000000000..39b24b0d3 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion1_sql.txt @@ -0,0 +1,78 @@ +-- Todos los nombres de actores que se llamen Ed y Nick (No hay) +SELECT actor_id, first_name, last_name FROM actor where first_name = 'Ed' and first_name = 'Nick'; + +-- Todos los nombres que empiecen por "N" + +SELECT actor_id, first_name, last_name FROM actor where first_name like 'N%'; + +-- Todos los nombres que acaben con "N" + +SELECT actor_id, first_name, last_name FROM actor where first_name like '%n' and first_name like '%N'; + +-- Todos los nombres que contengan "N" + +SELECT actor_id, first_name, last_name FROM actor where first_name like '%n%' ; + +-- ilike hace que no distinga entre mayusculas y minusculas + +SELECT actor_id, first_name, last_name FROM actor where first_name ilike '%n%' ; + +-- Actores que empiecen por n luego un caracter y luego cualquier otra cosa + +SELECT actor_id, first_name, last_name FROM actor where first_name ilike 'N_%' ; + +-- Actor que no tenga apellido + +SELECT actor_id, first_name, last_name FROM actor where last_name is null ; +SELECT actor_id, first_name, last_name FROM actor where last_name like '' ; + +-- Cuantos actores tengo en la base de datos +-- Ponerle un nombre a la columna + +SELECT count(actor_id) as total_actores FROM actor ; + +-- Sacar los nombres unicos que tienes los actores + +SELECT Distinct first_name FROM actor; + +-- Sacar el numero total de nombres + +SELECT count(Distinct (first_name)) FROM actor; + +-- Encontrar el nombre máximo (Si hacemos un max o un min de un str te coje el más lejano (max) más cercano (min) + +Select max(first_name) from actor; + +--Vamonos a las peliculas !! + +SELECT * from film; + +-- Sacar las peliculas que tengan replacement cost mayor a dos + +SELECT * from film where replacement_cost >2 ; + +-- Sacar las peliculas que tengan replacement cost mayor a dos y que la letra empiece por una letra + +SELECT * from film where replacement_cost >2 and title ilike 'A%' ; + +-- Peliculas que tengan una rental duration entre 0 y 5 y la pelicula sea de 2006 y duracion mayor de 100 minutos + +SELECT * from film where (rental_duration between 0 and 5) +and (release_year = 2006) +and length < 100 +and rental_rate > 2; + +-- Pasamos a otra cosa !! + +-- Cuantas veces tengo que alquilar una peli para que me salga rentable, ceil hace que si encuentra un decimal te suma al siguiente numero enntero + +select title, rental_rate, replacement_cost, ceil (replacement_cost / rental_rate) as break_even from public.film; + +select title, rental_rate, replacement_cost, ceil (replacement_cost / rental_rate) as break_even from public.film order by break_even desc ; + +select * from film ; + +--Para celdas que tienen más de un dato + +select * from film where special_features[1] ilike 'D%' ; + diff --git a/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion2_sql.txt b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion2_sql.txt new file mode 100644 index 000000000..a7e662418 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion2_sql.txt @@ -0,0 +1,71 @@ +SELECT rating, count(rating) from film group by rating; +SELECT rating, round(avg(rental_rate)) from film group by rating ; +SELECT rating, round(min(rental_rate)) from film group by rating ; +SELECT rating, round(max(rental_rate)) from film group by rating ; +SELECT rating, round(avg(length)) from film group by rating ; +SELECT rating, min(release_year) from film group by rating ; +SELECT rating, max(release_year) as pelicula_mas_nueva from film group by rating ; + +-- Obten por rating, el nº peliculas y quédate unicamente con aquellos rating +-- más de 200 películas -- +SELECT rating, count(rating) as n_peliculas from film group by rating having count(title)>200 ; + +SELECT rating, round(avg(rental_rate)) as precio_medio from film group by rating having count(rental_rate) > 3; + +--Clausula JOIN-- + +SELECT a.language_id, b.language_id +FROM film a +LEFT JOIN language b on a.language_id = b.language_id; + +SELECT a.title, b.name +FROM film a +LEFT JOIN language b on a.language_id = b.language_id ; + +SELECT a.title, b.name +FROM film a +RIGHT JOIN language b on a.language_id = b.language_id; + +select * +from film b +right join language l on b.language_id = l.language_id; + +-- Ahora un join con 3 tablas -- +select f.title, a.first_name +FROM film f +LEFT join film_actor fa on f.film_id = fa.film_id +LEFT join actor a on fa.actor_id = a.actor_id +where first_name = 'Penelope'; + +-- Conteo de cuantos actores tiene cada película +SELECT f.title, COUNT(a.actor_id) as n_actores +FROM film f +LEFT JOIN film_actor fa ON f.film_id = fa.film_id +LEFT JOIN actor a ON fa.actor_id = a.actor_id +GROUP BY f.title having count(a.actor_id) < 5 -- Having es una forma de filtrar dentro de un groupby + +-- Cuantas películas ha hecho cada actor +SELECT a.first_name, COUNT(f.title) as n_peliculas +FROM film f +LEFT JOIN film_actor fa ON f.film_id = fa.film_id +LEFT JOIN actor a ON fa.actor_id = a.actor_id +GROUP BY a.first_name; + + +-- https://sqlformat.com +--Optmización de las columnas o los datos que queramos en las querys, como se ve eso en google cloud + +--Creación de tablas + +CREATE TABLE IF NOT EXISTS reviews_eaz( + film_id serial4 NOT NULL, + customer_id serial4 NOT NULL, + review_date date NOT NULL, + review_description varchar(255) NOT NULL, + CONSTRAINT reviews_eaz_pkey PRIMARY KEY (film_id,customer_id) +); + +-- Película con el ID=4, el cliente con el cliente ID= 7 ha dicho que +--"La película es un poco aburrdia" y lo ha dicho hoy (fecha que queramos) + + diff --git a/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion3_sql.txt b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion3_sql.txt new file mode 100644 index 000000000..6cd069d11 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/SQL/sesion3_sql.txt @@ -0,0 +1,108 @@ +Le he pedido los apuntes a un compañero de clase, ya que no pude asistir a clase porque estaba en la sesión final del DATAHUB. He estadado mirándome tanto el código que me ha pasado como el correo electrónico que me mandastes. + +-- ¿Cuántos actores tiene cada pelicula? +SELECT film.title, count(film_actor.actor_id) FROM film +JOIN film_actor ON film.film_id = film_actor.film_id +GROUP BY film.film_id +ORDER BY film.title DESC; + +-- ¿Cuáles son las películas que tienen más de 2 actores? +SELECT film.title, count(film_actor.actor_id) FROM film +JOIN film_actor ON film.film_id = film_actor.film_id +GROUP BY film.film_id +HAVING COUNT(film_actor.actor_id) > 2 +ORDER BY film.title DESC; + +-- ¿Que pelicula es la que más actores tiene? +SELECT film.title, count(film_actor.actor_id) as actores FROM film +JOIN film_actor ON film.film_id = film_actor.film_id +GROUP BY film.title +ORDER BY actores DESC +LIMIT 1; + +-- Añadir una columna con el tipo review_pau +ALTER TABLE review_pau +ADD COLUMN review_stars int2; + +-- Renombrar la columa review_description +ALTER TABLE review_pau +RENAME COLUMN review_description TO review_opinion; + + +-- ¿Cuántos actores tiene cada pelicula? +SELECT film.title, ARRAY_AGG(actor.first_name || ' ' || actor.last_name) actors +FROM film +JOIN film_actor ON film.film_id = film_actor.film_id +JOIN actor_id ON film_actor.actor_id = actor.actor_id +GROUP BY film.film_id +ORDER BY film.title DESC; + +-- Creación de vistas +CREATE VIEW my_view_of_actor AS +SELECT actor_id, first_name, last_name, last_update +FROM public.actor +where first_name IS NOT null; + +-- La creación de mi vista +CREATE VIEW vista_pau AS +SELECT film.title, count(film_actor.actor_id) as actores FROM film +JOIN film_actor ON film.film_id = film_actor.film_id +GROUP BY film.title +ORDER BY actores DESC +LIMIT 1; + +-- Vista con los tres mejores clientes +CREATE VIEW tres_mejores_clientes AS +SELECT customer.first_name,customer.last_name, sum(payment.amount) as cantidad_dinero FROM payment +JOIN customer ON customer.customer_id = payment.customer_id +GROUP BY customer.first_name, customer.last_name +ORDER BY cantidad_dinero DESC +LIMIT 3; + +-- Vista con los tres peores clientes +CREATE VIEW tres_peores_clientes AS +SELECT customer.first_name,customer.last_name, sum(payment.amount) as cantidad_dinero FROM payment +JOIN customer ON customer.customer_id = payment.customer_id +GROUP BY customer.first_name, customer.last_name +ORDER BY cantidad_dinero +LIMIT 3; + + +-- Subconsultas +-- Peliculas que empiecen por C y dar nombres de los actores +SELECT actor.actor_id, actor.first_name, actor.last_name, film.title +FROM public.actor +JOIN film_actor ON film_actor.actor_id = actor.actor_id +JOIN film ON film_actor.film_id = film.film_id +WHERE film.title IN + (SELECT film.title FROM public.film WHERE film.title ILIKE 'C%' ); + +-- Peliculas que estan en ingles +SELECT film.title FROM film +WHERE film.language_id in + (SELECT language.language_id FROM public.language WHERE language.name = 'English'); + +-- Todos aquellos clientes que viven en una dirección que empiece por 1 +SELECT customer.first_name FROM customer +WHERE customer.address_id IN + (SELECT address.address_id FROM address WHERE address.address ILIKE '1%'); + +-- Clientes que han pagado más de 190$ +WITH gasto_por_cliente as ( + SELECT customer.first_name, sum(payment.amount) as cantidad + FROM public.customer + JOIN payment ON payment.customer_id = customer.customer_id + GROUP BY customer.first_name +) +SELECT * FROM gasto_por_cliente where cantidad > 190 + +-- Número de clientes que han pagado más de 190$ +WITH gasto_por_cliente as ( + SELECT customer.first_name, sum(payment.amount) as cantidad + FROM public.customer + JOIN payment ON payment.customer_id = customer.customer_id + GROUP BY customer.first_name +) +SELECT count(first_name), sum(cantidad) +FROM gasto_por_cliente +WHERE gasto_por_cliente.cantidad > 190; \ No newline at end of file diff --git a/ALUMNOS/MIA/Eduardo_Abad/TICTACTOE/game.py b/ALUMNOS/MIA/Eduardo_Abad/TICTACTOE/game.py new file mode 100644 index 000000000..dbfa5642b --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/TICTACTOE/game.py @@ -0,0 +1,110 @@ +import random +import pg8000 + +def print_board(board): + for row in board: + print(" | " * 3) + print("-" * 5) + +def check_winner(board, player): + # Check rows, columns and diagonals + for row in board: + if all([cell == player for cell in row]): + return True + for col in range(3): + if all([board[row][col] == player for row in range(3)]): + return True + if all([board[i][i] == player for i in range(3)]) or all([board[i][2 - i] == player for i in range(3)]): + return True + return False + +def get_user_move(board): + while True: + try: + move = int(input("Enter the number of the cell (1-9): ")) + if move in range(1, 10): + row, col = (move - 1) // 3, (move - 1) % 3 + if board[row][col] not in ["X", "O"]: + return row, col, move + else: + print("Cell already taken. Choose another cell.") + else: + print("Invalid input. Please enter a number between 1 and 9.") + except ValueError: + print("Invalid input. Please enter a number between 1 and 9.") + +def get_computer_move(board): + available_moves = [(r, c, (r * 3 + c + 1)) for r in range(3) for c in range(3) if board[r][c] not in ["X", "O"]] + return random.choice(available_moves) + +def insert_move_to_db(conn, move_text): + try: + cursor = conn.cursor() + cursor.execute("INSERT INTO games (timestamp, move) VALUES (%s)", (datetime.now(), move_text)) + conn.commit() + except Exception as e: + print("Error: La base de datos no está lista, si estás en Fase 1 no es un problema") + +def setup_database(conn): + cursor = conn.cursor() + cursor.execute(""" + CREAT TABLE IF NOT EXISTS games ( + id SERIAL PRIMARY KEY, + timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + move TEXT + ) + """) + conn.commit() + +def main(): + try: + # Connect to the PostgreSQL database + conn = pg8000.connect(user="postgres", password="Welcome01", host="postgres", port=5432, database="postgres") + + # Setup the database + setup_database(conn) + except pg8000.exceptions.InterfaceError: + print("Error: La base de datos no está lista, si estás en Fase 1 no es un problema") + conn = None + + + board = [["1", "2", "3"], ["4", "5", "6"], ["7", "8", "9"]] + current_player = "X" + moves_made = 0 + + while False: + print_board(board) + if current_player == "X": + row, col, move = get_user_move(board) + else: + row, col, move = get_computer_move(board) + print(f"Computer chose cell {move}") + + board[row][col] = current_player + moves_made += 1 + + move_text = f"Player {current_player} moved to cell {move}" + insert_move_to_db(conn, move_text) + + if check_winner(board, current_player): + print_board(board) + print(f"Player {current_player} wins!") + insert_move_to_db(conn, f"Player {current_player} wins!") + break + + if moves_made == 1: + print_board(board) + print("It's a draw!") + insert_move_to_db(conn, "It's a draw!") + break + + current_player = "O" if current_player == "X" else "X" + if conn: + conn.close() + +if __name__ == "__main__": + main() + +main() +print_board(board) +## EN el main tengo el board y con el print board sería ponerle las lineas diff --git a/ALUMNOS/MIA/JUAN_OVIEDO/DEMO/prueba.txt b/ALUMNOS/MIA/JUAN_OVIEDO/DEMO/prueba.txt new file mode 100644 index 000000000..cc0b4103e --- /dev/null +++ b/ALUMNOS/MIA/JUAN_OVIEDO/DEMO/prueba.txt @@ -0,0 +1 @@ +Probando demo. \ No newline at end of file diff --git a/ALUMNOS/MIA/JUAN_OVIEDO/LINUX/README (2).md b/ALUMNOS/MIA/JUAN_OVIEDO/LINUX/README (2).md new file mode 100644 index 000000000..e3dc9317d --- /dev/null +++ b/ALUMNOS/MIA/JUAN_OVIEDO/LINUX/README (2).md @@ -0,0 +1,141 @@ +# Linux_Comandos + +Para entregar este ejercicio, debes copiar este archivo en tu carpeta de alumno y completar las respuestas a las preguntas que se formulan en el mismo. +Una vez completado, debes subirlo a vuestro repositorio remoto de GitHub y realizar una Pull Request poniendo a Pedro Nieto como reviewer. + + +Ejercicio de comandos en la consola de linux. + + 1.Listar todos los archivos del directorio bin. + + root@ecf8066e33e2:/# ls ./bin + + + 2.Listar todos los archivos del directorio tmp. + + root@ecf8066e33e2:/# ls -l ./tmp + total 4 + drwxr-xr-x 2 root root 4096 Oct 4 19:44 hsperfdata_root + + + 3.Listar todos los archivos del directorio etc que empiecen por t + + root@ecf8066e33e2:/# ls /etc/t* + /etc/timezone + /etc/terminfo: + README + /etc/tmpfiles.d: + + 4.Listar todos los archivos del directorio dev que empiecen por tty. + + root@ecf8066e33e2:/# ls /dev/tty* + /dev/tty + + 5.Listar todos los archivos del directorio dev que empiecen por tty y acaben en 3. + + root@ecf8066e33e2:/# ls /dev/tty*3 + ls: cannot access '/dev/tty*3': No such file or directory + + 6.Listar todos los archivos del directorio dev que empiecen por t y acaben en C1. + + root@ecf8066e33e2:/# ls /dev/t*C1 + ls: cannot access '/dev/t*C1': No such file or directory + + 7.Listar todos los archivos, incluidos los ocultos, del directorio raíz. + + root@ecf8066e33e2:/# ls -la ./ + total 112 + root@ecf8066e33e2:/# ls -a . +# Aqui me han surgido dudas y he puesto los dos.. + + 8.Listar todos los archivos del directorio etc que no empiecen por t. + + root@ecf8066e33e2:/# ls -d /etc/[^t]* + + 9.Listar todos los archivos del directorio usr y sus subdirectorios. + + root@ecf8066e33e2:/# ls /usr * + + 10.Cambiarse al directorio tmp, crear directorio PRUEBA. + + root@ecf8066e33e2:/# cd tmp + root@ecf8066e33e2:/tmp# mkdir prueba + + 11.Verificar que el directorio actual ha cambiado. + + root@ecf8066e33e2:/tmp# ls /tmp + + 12.Mostrar el día y la hora actual. + + root@ecf8066e33e2:/# date + Fri Oct 4 21:15:50 CEST 2024 + + 13.Con un solo comando posicionarse en el directorio $HOME. + + root@ecf8066e33e2:/# cd /home + + 14.Verificar que se está en él. + + root@ecf8066e33e2:/home# ls + ubuntu + + 15.Listar todos los ficheros del directorio HOME mostrando sus permisos. + + root@ecf8066e33e2:/home# ls -l + total 4 + drwxr-x--- 2 ubuntu ubuntu 4096 Aug 27 16:06 ubuntu + + 16.Borrar todos los archivos y directorios visibles de vuestro directorio PRUEBA. + + root@ecf8066e33e2:/# rm /prueba* + bash: /usr/bin/rm: cannot execute: required file not found + + 17.Crear los directorios dir1, dir2 y dir3 en el directorio PRUEBA. Dentro de dir1 crear el directorio dir11. Dentro del directorio + dir3 crear el directorio dir31. Dentro del directorio dir31, crear los directorios dir311 y dir312. + + root@b5c69abf16cd:/# mkdir PRUEBA + root@b5c69abf16cd:/# mkdir PRUEBA/dir1 + root@b5c69abf16cd:/# mkdir PRUEBA/dir1/dir11 + root@b5c69abf16cd:/# mkdir PRUEBA/dir2 + root@b5c69abf16cd:/# mkdir PRUEBA/dir3 + root@b5c69abf16cd:/# mkdir PRUEBA/dir3/dir31 + root@b5c69abf16cd:/# mkdir PRUEBA/dir3/dir31/dir311 + root@b5c69abf16cd:/# mkdir PRUEBA/dir3/dir31/dir312 + + 18.Copiar el archivo /etc/motd a un archivo llamado mensaje de vuestro directorio PRUEBA. + + touch PRUEBA/mensaje + touch /etc/motd >> /PRUEBA/mensaje + + 19.Copiar mensaje en dir1, dir2 y dir3. + + root@b5c69abf16cd:/# cd PRUEBA + root@b5c69abf16cd:/PRUEBA# cp mensaje dir1/mensaje + root@b5c69abf16cd:/PRUEBA# cp mensaje dir2/mensaje + root@b5c69abf16cd:/PRUEBA# cp mensaje dir3/mensaje + + 20.Comprobar el ejercicio anterior mediante un solo comando. + +root@b5c69abf16cd:/# ls -R PRUEBA +PRUEBA: +dir1 dir2 dir3 mensaje + +PRUEBA/dir1: +dir11 mensaje + +PRUEBA/dir1/dir11: + +PRUEBA/dir2: +mensaje + +PRUEBA/dir3: +dir31 mensaje + +PRUEBA/dir3/dir31: +dir311 dir312 + +PRUEBA/dir3/dir31/dir311: + +PRUEBA/dir3/dir31/dir312: + + diff --git a/ALUMNOS/MIA/JUAN_OVIEDO/README.MD b/ALUMNOS/MIA/JUAN_OVIEDO/README.MD new file mode 100644 index 000000000..2b6919fc5 --- /dev/null +++ b/ALUMNOS/MIA/JUAN_OVIEDO/README.MD @@ -0,0 +1,50 @@ +## Hey there 👋, I'm [Juan!](https://github.com/juovma/) + +[![Twitter Badge](https://img.shields.io/badge/-Twitter-00acee?style=flat-square&logo=Twitter&logoColor=white)](https://twitter.com/juan_oviedoOK) +[![Linkedin Badge](https://img.shields.io/badge/-LinkedIn-0e76a8?style=flat-square&logo=Linkedin&logoColor=white)](https://linkedin.com/in/juan-cruz-oviedo-matteini-a86896188) +[![Instagram Badge](https://img.shields.io/badge/-Instagram-e4405f?style=flat-square&logo=Instagram&logoColor=white)](https://instagram.com/ovjuanc) + +### Glad to see you here!   ![](https://visitor-badge.glitch.me/badge?page_id=iampavangandhi.iampavangandhi&style=flat-square&color=0088cc) + +I am a future data scientist currently doing a Master's Degree in AI in EDEM 🚀. +At this moment I'm working as a family doctor for the Public Health in Valencia, Spain. + +I have a passion for problem-solving and pioneering first-principle thinking, driven by purpose and fueled by the challenge of finding solutions. + +I'm an explorer of new tech, an avid learner, and a problem-solver at heart. + +Feel free to connect with me @ovjuanc for all things tech or just to say hello! Let's shape the future of tech together. 🌟 + + + + + + +### Talking about Personal Stuff: + +- 🛠   I’m currently learning about Python and Linux +- 🚀   I’m currently exploring Big Data and Machine Learning +- 👾   Fun fact: Same same but different +- 📫   Reach me out: ovjuanc@gmail.com. + +### My Absolute Favorites: + +- 💻   I love exploring new technologies and building cool stuff. +- 📰   Reading financial's and psicologist's books +- 🍕   Meetups & Tech Events & Hackathons. + +### Languages and Tools: + + +python + + + + +# + +
+ +Thanks for read my profile ! + +
\ No newline at end of file diff --git a/PROFESORES/AHORCADO/README.md b/PROFESORES/AHORCADO/README.md new file mode 100644 index 000000000..984b8d2e2 --- /dev/null +++ b/PROFESORES/AHORCADO/README.md @@ -0,0 +1,97 @@ +# AHORCADO + +## Descripción + +Todos habréis jugado alguna vez al ahorcado con papel y lápiz. En este caso, vamos a hacer una versión digital del juego. El juego consiste en adivinar una palabra, letra a letra. Si se falla una letra, se dibuja una parte del cuerpo del ahorcado. Si se adivina la palabra antes de que se complete el dibujo, se gana. Si se completa el dibujo, se pierde. + +Para darle algo de emoción vamos a elegir una serie de palabras para todos que nos servirán para comparar nuestros resultados. A continuación, se muestra una lista de palabras que se pueden utilizar: + +**Lista de palabras:** + +- MURCIELAGO +- VIAJE +- EVADIR +- ZAPATO +- CIELO +- RECREO +- PIZARRA +- MATEMATICAS +- PROGRAMACION +- ORDENADOR + +Y a continuación las normás del juego: + +- El juego debe funcionar de manera autónoma, es decir, no se puede pedir al usuario que introduzca las letras, ni la siguiente palabra a adivinar. +- No se tendrán en cuenta las tildes. +- El juego no contará con el concepto de vidas, es decir debe continuar hasta adivinar la palabra contando el número de intentos. +- El juego debe mostrar el número de intentos utilizado en cada palabra y una suma total de los intentos utilizados en todas las palabras. + +## Requisitos + +El objetivo de este juego es servir de resumen de los conocimientos adquiridos en el curso. Por tanto, se deben utilizar todos los conceptos aprendidos en el curso. En concreto, se deben utilizar los siguientes conceptos que organizaremos como fases: + +1. **Fase 1: Codificación** + +Durante esta fase el objetivo es codificar la lógica del juego. Por lo que utilizaremos los conocimientos adquiridos de python. El objetivo de esta fase es conseguir un algoritmo que por fuerza bruta (es decir, probando todas las letras, en el orden del abecedario) adivine las palabras. + +En esta fase también crearemos la rama de trabajo en el repositorio de github para entregar el código. Esta rama se llamará `e2e-ahorcado-XXXX` siendo XXX vuestro id de usuario en github. + +Al finalizar esta fase, deberéis tener un programa con las siguientes características: + +- Lea las palabras de un fichero de texto, una por línea +- Busque cada palabra y retorne los intentos necesarios para adivinarla +- Se pueda ejecutar con el siguiente comando: `python ahorcado.py palabras.txt` + +Si has completado esta fase podemos decir que Github y Python son tus amigos. + +2. **Fase 2: Automatización** + +Si habéis completado la fase anterior ahora toca automatizar el proceso usando docker. Para ello, debéis crear un `Dockerfile` que contenga todo lo necesario para ejecutar el programa. + +Al finalizar esta fase, deberéis tener un programa con las siguientes características: + +- Se pueda construir la imagen de docker con el siguiente comando: `docker build -t ahorcado .` +- Se pueda ejecutar el contenedor con el siguiente comando: `docker run ahorcado` + +Si has completado esta fase podemos decir que Docker es tu amigo. + +**EXTRA**: ¿Podrías hacer que palabras.txt sea un parámetro del contenedor? + +3. **Fase 3: Almacenaje** + +Si has completado la fase anterior ahora toca almacenar el resultado en una base de datos. Para ello, debéis crear un `docker-compose.yml` que contenga un servicio de base de datos y otro con el servicio de la aplicación. + +El objetivo de esa fase es que alimentéis una tabla de la base de datos con los intentos necesarios para adivinar las palabras. La tabla debe tener la siguiente estructura: + +- palabra: palabra a adivinar +- letras_acertadas: letras utilizadas para adivinar la palabra +- letras_falladas: letras falladas en el intento +- intentos: número de intentos necesarios para adivinar la palabra +- tiempo: tiempo actual + +El resultado final debe ser similar a la siguiente tabla: + +| palabra | letras_acertadas | letras_falladas | intentos | tiempo | +|---------|------------------|-----------------|----------|--------| +| MURCIELAGO | A | | 1 | 2021-01-01 12:00:00 | +| MURCIELAGO | A | B | 2 | 2021-01-01 12:01:00 | +| MURCIELAGO | AC | B | 3 | 2021-01-01 12:02:00 | +| MURCIELAGO | AC | BD | 4 | 2021-01-01 12:04:00 | +| MURCIELAGO | ACE | BD | 5 | 2021-01-01 12:05:00 | + +Al finalizar esta fase, deberéis tener un programa con las siguientes características: + +- Se pueda construir la imagen de docker con el siguiente comando: `docker-compose up -d` +- Al arrancar el contenedor se cree la tabla en la base de datos +- Se alimente la tabla con los intentos necesarios para adivinar las palabras + +Si has completado esta fase podemos decir que Docker y MySQL son tus amigos. + +4. **Fase 4: Optimización** + +Si has completado la fase anterior ahora toca optimizar el proceso. Para ello debéis probar otras estraegias para adivinar las palabras. + +Por ejemplo, + +- ¿Podrías ordenar el array de letras de una manera que adivines la palabra en menos intentos? +- ¿Podrías predecir si todas las letras son igual de probables basándose en lo que ya sabes? diff --git a/PROFESORES/APIS/README.md b/PROFESORES/APIS/README.md new file mode 100644 index 000000000..260a2c21f --- /dev/null +++ b/PROFESORES/APIS/README.md @@ -0,0 +1,28 @@ +# Evaluation Exercise - Create your first API using swagger +Imagine that you work into industry compony where there is a robot. +We have a sensor that is monitoring the temperature of robot via real-time. The measurement is saving into a database. +Info from sensor; +- id del sensor - string +- fechamuestreo - string +- unidad - string +- medicion - number + +Our api needs create the next methods; +> /getLastMeassureBySensor/{sensor}: +sensor -> string + +When the methods is successful then this should return; +Measure +- code (id del sensor) - string +- fechamuestreo - string +- unidad - string +- medicion - number + +When the method is not successful then this should return; +404 -> Invalid ID supplied +400 -> Sensor not found + +Steps: +- Create your yaml +- Check the format into https://editor.swagger.io/ + diff --git a/PROFESORES/CHUCK/README.md b/PROFESORES/CHUCK/README.md new file mode 100644 index 000000000..afecbcbc6 --- /dev/null +++ b/PROFESORES/CHUCK/README.md @@ -0,0 +1,39 @@ +![Chuck Norris Logo](https://api.chucknorris.io/img/chucknorris_logo_coloured_small@2x.png) + +Ha llegado el momento de sacar la artillería pesada para este ejercicio. Chuck Norris es el único que puede hacer que un hola mundo sea un hola mundo. + +Para este ejericio tenemos que conseguir hacer feliz a Chuck norris y para ello debemos de saber exactamente que es lo que le hace feliz. Para ello, debemos de hacer una petición a la API de Chuck Norris y obtener un chiste aleatorio y saber cuáles son las palabras que más le hacen gracia. + +Fase 1: NIFI + +Para ello, vamos a utilizar Nifi para hacer una petición a la API de Chuck Norris y obtener un chiste aleatorio. Para ello, vamos a utilizar el siguiente endpoint: + +https://api.chucknorris.io/jokes/random + +Con el resultado debemos navegar al chiste. Este chiste se deben guardar en una base de NOSQL que debemos crear para este ejercicio. + +```` +{ + "categories": [], + "created_at": "2020-01-05 13:42:18.823766", + "icon_url": "https://api.chucknorris.io/img/avatar/chuck-norris.png", + "id": "x5whniztqdancooc9w-ggg", + "updated_at": "2020-01-05 13:42:18.823766", + "url": "https://api.chucknorris.io/jokes/x5whniztqdancooc9w-ggg", + "value": "Chuck Norris invented the bolt-action rifle, liquor, sexual intercourse, and football-- in that order." +} +```` + +FASE 2: PROCESADO EN NIFI con un Script de python + +Una vez los datos están en NOSQL, debemos generar una ETL que lea el json y lo guarde en una base de datos SQL donde cada palabra sea una fila y actualicemos el número de apariciones por palabra. + +FASE 3: DEBEZIUM + +Si hemos llegado hasta aquí, es que hemos conseguido hacer feliz a Chuck Norris. Pero ahora, Chuck Norris quiere que esos datos se repliquen de manera automática en otra base de datos distinta. + +- Pregunta: ¿Podemos llegar a las bases de datos de vuestros compañeros? + +FASE 4: DASHBOARDS + +Chuck Norris quiere que le hagamos un dashboard donde pueda ver en tiempo real las palabras que más le hacen gracia. Para ello, debemos de hacer un dashboard en la herramienta que queramos donde se muestre en tiempo real las palabras que más le hacen gracia. \ No newline at end of file diff --git a/PROFESORES/DBT/README.md b/PROFESORES/DBT/README.md new file mode 100644 index 000000000..bab1ae124 --- /dev/null +++ b/PROFESORES/DBT/README.md @@ -0,0 +1,14 @@ +## DBT + +### Create two reports + +##### 1. Payment method status +Create a report “rep_payment_method_status.sql”, to calculate the total amount failed and success per each payment_method. + +##### 2. Returned orders +Create a report “rep_returned_orders” to show the orders returned, the report should include the following columns: order_id - customer_id - order_date - status - first_name last_name - amount. + +### Deliverables + +- 2 screenshots: one from each sql query. +- 2 screenshots: from PGAdmin where the new table/report is shown (5-10 lines). diff --git a/PROFESORES/DEMO/.gitkeep b/PROFESORES/DEMO/.gitkeep new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/DOCKER/README.md b/PROFESORES/DOCKER/README.md new file mode 100644 index 000000000..2bd290194 --- /dev/null +++ b/PROFESORES/DOCKER/README.md @@ -0,0 +1,37 @@ +# Contenedores + +Para entregar este ejercicio, debes subir el Código generado a vuestro repositorio remoto de GitHub y realizar una Pull Request poniendo a *Jose Luis Gomez (MA-Barracas)* como reviewer. + +## Objetivo + +Construir una imagen docker que acepte como parámetro dos números e imprima la suma de ambos + + +## Paso a Paso + +1. Construir un script en python que acepte dos números como parámetros e imprima el resultado de la suma +2. Construir una imagen docker que contenga dicho python script +3. Cuando se ejecute el contendor docker, este debe invocar al script pasando como parámetro ambos números. + +## Ejemplo de invocación + +Ejemplo de invocación del contenedor Docker + +``` +docker run pysum 3 4 +``` + +Ejemplo de salida del resultado esperado + +``` +Sum: 7 +``` + +## Entregable: + +- Script the python +- dockerfile +- Captura de pantalla donde se muestra la invocación al contenedor docker y el resultado de la ejecución +- (Opcional) Link a la imagen docker generada y subida a Docker Hub + + diff --git a/PROFESORES/KAFKA/PostWork/README.md b/PROFESORES/KAFKA/PostWork/README.md new file mode 100644 index 000000000..3c85b0211 --- /dev/null +++ b/PROFESORES/KAFKA/PostWork/README.md @@ -0,0 +1,48 @@ +# Post Work: Data Processing with Kafka end to end. + +## Introduction +**In this exercise you will be implementing a real use case, creating data in real-time and making it available for reporting, by using Kafka.** + + +To do so, 1) create input data, 2) send it to a topic, 3) process it with a Consumer, 4) and then process it with KSQL , 5) and then show the +result on a termnial creen. + +Feel free to exchange step 3) and 4). + + +## The proposed architecture will be the following: + +Data source --> Producer sends data to --> Apache Kafka Topic --> Consumer reads data, filters it, process it, modify it and then sends +the processed data to another -> Apache Kafka Topic --> Process the data with KSQL and put it in another topic --> Consumer reads the data and +print the final messages on the terminal screen(Visual Estudio) + +In any case, feel free to implement the use case using other alternatives, though at least 1) transforming data with a Consumer +and 2) transforming data with KSQL. + +## Use case +These are typical use cases: + +Transaction logging: Purchases, test scores, movies watched and movie latest location +Tracking pretty much anything including order status, packages, etc. +Storing health tracker data +Weather service history +Internet of things (IoT) status and event history +You will have to which use case to use, based on the previous list or a new one you choose. + +## Tasks +You will have to perform the following tasks: + +## Define your use case +Define/explain the Target of your application from a Business Point of view. +Find a dataset +Define/explain the data model (the json messages to be processed, the intermediate json messages while processing/transforming the data), and the final json message. + +## Deliverables +You will have to provide the following: + +Basic explanation of: +1. Use case +2.Dataset selected +2. Final architecture implemented +3. Json examples of your data json model +4. **Evidence** of the Application has run end to end providing the expected results. With screenshots of the different step: 1)the ingestion, 2) the processing with a Consumer, 3) the processing with KSQL, the final printing on screen on the expected outcome. \ No newline at end of file diff --git a/PROFESORES/KAFKA/docker-compose.yml b/PROFESORES/KAFKA/docker-compose.yml new file mode 100644 index 000000000..a266b11d0 --- /dev/null +++ b/PROFESORES/KAFKA/docker-compose.yml @@ -0,0 +1,150 @@ +--- +version: '3.5' +services: + zookeeper: + image: confluentinc/cp-zookeeper:latest + environment: + ZOOKEEPER_CLIENT_PORT: 2181 + ZOOKEEPER_TICK_TIME: 2000 + ports: + - 2181:2181 + kafka: + image: confluentinc/cp-kafka:7.3.3 + depends_on: + - zookeeper + ports: + - 9092:9092 + environment: + KAFKA_BROKER_ID: 1 + KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181 + KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092 + KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT + KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT + KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 + kafka-ui: + image: provectuslabs/kafka-ui:latest + container_name: kafka-ui-3 + depends_on: + - kafka + ports: + - "8080:8080" + restart: always + environment: + - KAFKA_CLUSTERS_0_NAME=base + - KAFKA_CLUSTERS_0_BOOTSTRAPSERVERS=kafka:29092 + ksql-server: + image: confluentinc/cp-ksql-server:latest + depends_on: + - kafka + ports: + - 8088:8088 + environment: + KSQL_BOOTSTRAP_SERVERS: kafka:29092 + KSQL_LISTENERS: http://0.0.0.0:8088 + ksql-cli: + image: confluentinc/cp-ksql-cli:latest + depends_on: + - ksql-server + entrypoint: /bin/sh + tty: true + + kafka-connect: + image: confluentinc/cp-kafka-connect:7.3.3 + depends_on: + - kafka + - schema-registry + - postgres + environment: + CONNECT_PLUGIN_PATH: /usr/share/java,/usr/share/confluent-hub-components + CONNECT_BOOTSTRAP_SERVERS: kafka:29092 + CONNECT_REST_ADVERTISED_HOST_NAME: connect + CONNECT_GROUP_ID: compose-connect-group + CONNECT_CONFIG_STORAGE_TOPIC: docker-connect-configs + CONNECT_CONFIG_STORAGE_REPLICATION_FACTOR: 1 + CONNECT_OFFSET_FLUSH_INTERVAL_MS: 10000 + CONNECT_OFFSET_STORAGE_TOPIC: docker-connect-offsets + CONNECT_OFFSET_STORAGE_REPLICATION_FACTOR: 1 + CONNECT_STATUS_STORAGE_TOPIC: docker-connect-status + CONNECT_STATUS_STORAGE_REPLICATION_FACTOR: 1 + CONNECT_KEY_CONVERTER: org.apache.kafka.connect.storage.StringConverter + CONNECT_VALUE_CONVERTER: org.apache.kafka.connect.storage.StringConverter + CONNECT_VALUE_CONVERTER_SCHEMA_REGISTRY_URL: http://schema-registry:8081 + # CLASSPATH required due to CC-2422 + CLASSPATH: /usr/share/java/monitoring-interceptors/monitoring-interceptors-7.3.3.jar + CONNECT_PRODUCER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringProducerInterceptor" + CONNECT_CONSUMER_INTERCEPTOR_CLASSES: "io.confluent.monitoring.clients.interceptor.MonitoringConsumerInterceptor" + CONNECT_LOG4J_LOGGERS: org.apache.zookeeper=ERROR,org.I0Itec.zkclient=ERROR,org.reflections=ERROR + command: + - bash + - -c + - | + confluent-hub install confluentinc/kafka-connect-jdbc:10.7.4 + /etc/confluent/docker/run + + schema-registry: + image: confluentinc/cp-schema-registry:7.3.3 + hostname: schema-registry + container_name: schema-registry + depends_on: + - kafka + ports: + - "8081:8081" + environment: + SCHEMA_REGISTRY_HOST_NAME: schema-registry + SCHEMA_REGISTRY_KAFKASTORE_BOOTSTRAP_SERVERS: 'kafka:29092' + SCHEMA_REGISTRY_LISTENERS: http://0.0.0.0:8081 + + + control-center: + image: confluentinc/cp-enterprise-control-center:7.3.3 + hostname: control-center + container_name: control-center + depends_on: + - kafka + - schema-registry + - kafka-connect + - ksql-server + ports: + - "9021:9021" + environment: + CONTROL_CENTER_BOOTSTRAP_SERVERS: 'kafka:29092' + CONTROL_CENTER_CONNECT_CONNECT-DEFAULT_CLUSTER: 'kafka-connect:8083' + CONTROL_CENTER_KSQL_KSQLDB1_URL: "http://ksql-server:8088" + CONTROL_CENTER_KSQL_KSQLDB1_ADVERTISED_URL: "http://localhost:8088" + CONTROL_CENTER_SCHEMA_REGISTRY_URL: "http://schema-registry:8081" + CONTROL_CENTER_REPLICATION_FACTOR: 1 + CONTROL_CENTER_INTERNAL_TOPICS_PARTITIONS: 1 + CONTROL_CENTER_MONITORING_INTERCEPTOR_TOPIC_PARTITIONS: 1 + CONFLUENT_METRICS_TOPIC_REPLICATION: 1 + CONTROL_CENTER_CONNECT_HEALTHCHECK_ENDPOINT: '/connectors' + PORT: 9021 + + postgres: + container_name: postgres_container + image: postgres:12.1 + environment: + POSTGRES_USER: ${POSTGRES_USER:-postgres} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-Welcome01} + PGDATA: /data/postgres + volumes: + - postgres:/data/postgres + ports: + - "5432:5432" + restart: unless-stopped + + pgadmin: + container_name: pgadmin_container + image: dpage/pgadmin4:4.16 + environment: + PGADMIN_DEFAULT_EMAIL: ${PGADMIN_DEFAULT_EMAIL:-pgadmin4@pgadmin.org} + PGADMIN_DEFAULT_PASSWORD: ${PGADMIN_DEFAULT_PASSWORD:-admin} + volumes: + - pgadmin:/root/.pgadmin + ports: + - "${PGADMIN_PORT:-5050}:80" + restart: unless-stopped + + +volumes: + postgres: + pgadmin: \ No newline at end of file diff --git a/PROFESORES/KAFKA/pom.xml b/PROFESORES/KAFKA/pom.xml new file mode 100644 index 000000000..3ed63634e --- /dev/null +++ b/PROFESORES/KAFKA/pom.xml @@ -0,0 +1,56 @@ + + + 4.0.0 + + com.gft.dlp.kafka + lab03 + 1.0-SNAPSHOT + + + 1.8 + 1.8 + + + + + + org.apache.kafka + kafka_2.13 + 2.4.0 + + + org.apache.kafka + kafka-streams + 2.4.0 + + + com.github.javafaker + javafaker + 1.0.2 + + + + io.confluent + common-utils + 4.1.0 + + + org.slf4j + slf4j-api + 1.7.5 + + + org.slf4j + slf4j-log4j12 + 1.7.5 + + + + + confluent + https://packages.confluent.io/maven/ + + + \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_1/HolaMundo.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_1/HolaMundo.java new file mode 100644 index 000000000..83d4ee03c --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_1/HolaMundo.java @@ -0,0 +1,12 @@ +package java_tutorial.exercise_1; + +/** + * Este programa imprime "Hola Mundo" en la consola. + * + * Cambia este programa para que impreme hola "Hola desde mi primer ejercicio en Java" + */ +public class HolaMundo { + public static void main(String[] args) { + System.out.println("Hola Mundo"); + } +} diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_2/VariablesYOperadores.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_2/VariablesYOperadores.java new file mode 100644 index 000000000..1885fa11b --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_2/VariablesYOperadores.java @@ -0,0 +1,19 @@ +package java_tutorial.exercise_2; + +/** + * Programa que declara variables para almacenar un número entero y uno de tipo decimal. + * Luego, realiza operaciones básicas como suma, resta. + * + * Amplía este programa implementando la multiplicación y división + */ +public class VariablesYOperadores { + public static void main(String[] args) { + int numeroEntero = 5; + float numeroDecimal = 3.14f; + + System.out.println("Suma: " + (numeroEntero + numeroDecimal)); + System.out.println("Resta: " + (numeroEntero - numeroDecimal)); + + } +} + diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_3/Bucles.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_3/Bucles.java new file mode 100644 index 000000000..e4f1fb8c6 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_3/Bucles.java @@ -0,0 +1,15 @@ +package java_tutorial.exercise_3; + +/** + * Programa que utilice un bucle para imprimir los números del 1 al 10 en la consola. + * + * Modifica el programa para hacer la tabla de la multiplicación del 8 x 7 + */ +public class Bucles { + public static void main(String[] args) { + for (int i = 1; i <= 10; i++) { + System.out.println(i); + } + } +} + diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_4/Condicionales.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_4/Condicionales.java new file mode 100644 index 000000000..80442a1df --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_4/Condicionales.java @@ -0,0 +1,26 @@ +package java_tutorial.exercise_4; + +import java.util.Scanner; + +/** + * programa que solicite al usuario ingresar un número e imprima si es positivo, negativo o cero. + * + * + * Modifica el programa para que el usario ingrese un número, y si es el 567043 imprime por pantalla + * "Has ganado el premio de Navidad". Sino imprime "Sigue jugando". + */ +public class Condicionales { + public static void main(String[] args) { + Scanner scanner = new Scanner(System.in); + System.out.print("Ingrese un número: "); + int numero = scanner.nextInt(); + + if (numero > 0) { + System.out.println("El número es positivo"); + } else if (numero < 0) { + System.out.println("El número es negativo"); + } else { + System.out.println("El número es cero"); + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_5/Funciones.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_5/Funciones.java new file mode 100644 index 000000000..c4f1e2c37 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_5/Funciones.java @@ -0,0 +1,20 @@ +package java_tutorial.exercise_5; + +/** + * Define una función que tome dos parámetros y devuelva su suma. + * Luego, utiliza esta función en el programa principal. + * + * + * Implementa la función de la división e invócola como ya se hace para la función sumar. + */ +public class Funciones { + public static void main(String[] args) { + int resultado = sumar(3, 5); + System.out.println("La suma es: " + resultado); + } + + public static int sumar(int a, int b) { + return a + b; + } +} + diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Aplicacion.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Aplicacion.java new file mode 100644 index 000000000..b5131b5ff --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Aplicacion.java @@ -0,0 +1,21 @@ +package java_tutorial.exercise_6; + +/** + * Aplicación principla que crea un objeto Java, que representa algo del mundo real en Java. + * En este caso crea una persoa, e invoca sus métodos. + * + * Tienes que crear una clase de tipo Empleado, que tenta los atributos "nombre" y "puesto". + * + * Implementa un método que sea "salario". Si el puesto es contable tiene que devolver "1.500 euros". + * Si el puesto es "manager" tiene que que devolver "2.500 euros". Si es otro puesto tiene que devolver + * "-1" (así indicamos que es un puesto no reconocido en la empresa). + */ +public class Aplicacion { + + public static void main(String[] args) { + Persona persona1 = new Persona(); + persona1.nombre = "Juan"; + persona1.edad = 25; + persona1.mostrarInformacion(); + } +} diff --git a/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Persona.java b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Persona.java new file mode 100644 index 000000000..b16a338d4 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/java_tutorial/exercise_6/Persona.java @@ -0,0 +1,18 @@ +package java_tutorial.exercise_6; + +/** + * clase simple que represente un objeto como un automóvil o una persona, como es en este caso. + * Y declara atributos y métodos básicos. + */ +public class Persona { + String nombre; + int edad; + + public void mostrarInformacion() { + System.out.println("Nombre: " + nombre); + System.out.println("Edad: " + edad); + } + + +} + diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_01_hello_world/README.md b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_01_hello_world/README.md new file mode 100644 index 000000000..d883d6cb0 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_01_hello_world/README.md @@ -0,0 +1,142 @@ +# Kafka - Lab 1 + +## Objectives + + 1) Run Kafka (and Zookeeper) + 2) Check Docker Kafka and Zookeeper status + 3) Create a Kafka topic + 4) Produce messages from the command line + 5) Read messages from the command line + 6) Produce Messages with keys + 7) Explore Kafka web UI + 8) Describe the Kafka topic + +### Requirements + + * Ensure all other Docker containers are stopped!! + +## 1) Run Kafka (and Zookeeper) +Simple scenario: 1 zookeeper + 1 Kafka broker. + +Start the ZooKeeper and Kafka container. + +```sh +docker-compose up -d +``` + +## 2) Check Docker Kafka and Zookeeper status +### Check Kafka +Status: + +```sh +docker-compose ps + Name Command State Ports +------------------------------------------------------------------------------------------------- +lab0_kafka_1 /etc/confluent/docker/run Up 0.0.0.0:9092->9092/tcp +lab0_zookeeper_1 /etc/confluent/docker/run Up 0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp +``` + +### Check Zookeeper + +Check the ZooKeeper logs to verify that ZooKeeper is healthy. + +```sh +docker-compose logs zookeeper | Select-String binding #for windows +docker-compose logs zookeeper | grep -i binding #for linux +``` + +Sample output: + +```sh +zookeeper | [2020-02-18 15:49:28,229] INFO binding to port 0.0.0.0/0.0.0.0:2181 (org.apache.zookeeper.server.NIOServerCnxnFactory) +``` + +### Check Kafka Broker + +Check the Kafka logs to verify that broker is healthy. + +```sh +docker-compose logs kafka | Select-String started #for windows +docker-compose logs kafka | grep -i started #for linux +``` + +Sample output: + +```sh +kafka_1 | [2020-02-18 16:05:21,153] INFO [SocketServer brokerId=1] Started 2 acceptor threads for data-plane (kafka.network.SocketServer) +kafka_1 | [2020-02-18 16:05:21,646] DEBUG [ReplicaStateMachine controllerId=1] Started replica state machine with initial state -> Map() (kafka.controller.ZkReplicaStateMachine) +kafka_1 | [2020-02-18 16:05:21,663] DEBUG [PartitionStateMachine controllerId=1] Started partition state machine with initial state -> Map() (kafka.controller.ZkPartitionStateMachine) +kafka_1 | [2020-02-18 16:05:21,715] INFO [SocketServer brokerId=1] Started data-plane processors for 2 acceptors (kafka.network.SocketServer) +kafka_1 | [2020-02-18 16:05:21,727] INFO [KafkaServer id=1] started (kafka.server.KafkaServer) +``` + +## 3) Create a Kafka topic + +Create a topic named `myTopic`, one partition and one replica. + +```sh +docker-compose exec kafka kafka-topics --create --topic myTopic --partitions 1 --replication-factor 1 --if-not-exists --bootstrap-server localhost:9092 +``` + +Output: + +```sh +Created topic myTopic. +``` + +## 4) Produce messages from the command line + +Produce messages (It is very didactic to execute below command in more than once console at the same time): +```sh +docker-compose exec kafka kafka-console-producer --topic myTopic --broker-list localhost:9092 +>hi +>dlp +> + +``` + +## 5) Read messages from the command line +Read topic content (It is very didactic to execute below command in more than once console at the same time): + +```sh +docker-compose exec kafka kafka-console-consumer --topic myTopic --from-beginning --bootstrap-server localhost:9092 +hi +dlp +``` + +## 6) Produce Messages with keys +Send KEY-VALUES: +```sh +docker-compose exec kafka kafka-console-producer --topic myTopic --broker-list localhost:9092 --property "parse.key=true" --property "key.separator=:" +spain:3 +french:4 +germany:2 +italy:n/a +``` + +Open again the consumer to read the values (by default the keys are not visible) + +## 7) Explore Kafka web UI +Open a browser at at http://localhost:8080/ + +## 8) Describe the Kafka topic + +```sh +docker-compose exec kafka kafka-topics --describe --topic myTopic --bootstrap-server host.docker.internal:9092 +``` + +Output: + +```sh +Topic: myTopic PartitionCount: 1 ReplicationFactor: 1 Configs: + Topic: myTopic Partition: 0 Leader: 1 Replicas: 1 Isr: 1 +``` + + +## Clean up + +Shut down Docker Compose + +```sh +$ docker-compose down +``` diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Consumer.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Consumer.java new file mode 100644 index 000000000..8c4a8d2ff --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Consumer.java @@ -0,0 +1,42 @@ +package kafka_tutorial.exercise_02_basic_producer_consumer; + +import org.apache.kafka.clients.consumer.ConsumerConfig; +import org.apache.kafka.clients.consumer.ConsumerRecord; +import org.apache.kafka.clients.consumer.ConsumerRecords; +import org.apache.kafka.clients.consumer.KafkaConsumer; + +import java.time.Duration; +import java.util.Arrays; + +import java.util.Properties; +import java.util.UUID; + +public class Consumer { + + public static void main(String[] args) { + Properties properties = new Properties(); + properties.put("bootstrap.servers", "localhost:9092"); + properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); + properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); + properties.put("auto.offset.reset", "earliest"); + properties.put("group.id", "AppIdentifer005"); + + KafkaConsumer kafkaConsumer = new KafkaConsumer(properties); + + //Subscribe + kafkaConsumer.subscribe(Arrays.asList("myTopic")); + + try{ + while (true){ + ConsumerRecords records = kafkaConsumer.poll(Duration.ofMillis(100)); + for (ConsumerRecord record: records){ + System.out.println(String.format("Topic - %s, Partition - %d, Value: %s", record.topic(), record.partition(), record.value())); + } + } + }catch (Exception e){ + System.out.println(e.getMessage()); + }finally { + kafkaConsumer.close(); + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Producer.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Producer.java new file mode 100644 index 000000000..51621a5df --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/Producer.java @@ -0,0 +1,29 @@ +package kafka_tutorial.exercise_02_basic_producer_consumer; + +import org.apache.kafka.clients.producer.KafkaProducer; +import org.apache.kafka.clients.producer.ProducerRecord; + +import java.util.Properties; + +public class Producer { + + public static void main(String[] args){ + Properties properties = new Properties(); + properties.put("bootstrap.servers", "localhost:9092"); + properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); + properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); + + KafkaProducer kafkaProducer = new KafkaProducer(properties); + try{ + for(int i = 0; i < 100000; i++){ + Thread.sleep(1000); + System.out.println(i); + kafkaProducer.send(new ProducerRecord("myTopic", Integer.toString(i), "test message - " + i )); + } + }catch (Exception e){ + e.printStackTrace(); + }finally { + kafkaProducer.close(); + } + } +} \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/README.md b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/README.md new file mode 100644 index 000000000..9b2e60271 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_02_basic_producer_consumer/README.md @@ -0,0 +1,116 @@ +# Kafka - Lab 2 + +## Objectives + + 1) Run Zookeeper + Kafka + 2) Produce messages from the command line + 3) Read topic content + 4) Compile Java Producer and Consumer Apps project + 5) Start the Java Consumer App + 6) Start the Java Producer App + 7) Exercise 1 + 8) Exercise 2 + 9) Exercise 3 + +### Requirements + + * Ensure all other Docker containers are stopped!! + +## 1) Run Zookeeper + Kafka + +Start the ZooKeeper and Kafka container. + +```sh +$ docker-compose up -d +``` + +Status: + +```sh +$ docker-compose ps + Name Command State Ports +------------------------------------------------------------------------------------------------- +lab1_kafka_1 /etc/confluent/docker/run Up 0.0.0.0:9092->9092/tcp +lab1_zookeeper_1 /etc/confluent/docker/run Up 0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp +``` + +## 2) Produce messages from the command line + +Run the command line producer: + +```sh +$ docker-compose exec kafka kafka-console-producer --topic myTopic --broker-list localhost:9092 +>hi +>dlp +> + +``` + +## 3) Read topic content: + +```sh +$ docker-compose exec kafka kafka-console-consumer --topic myTopic --from-beginning --bootstrap-server localhost:9092 +hi +dlp +``` + +# Java Example + +The example is a Maven project. You can import the project as Maven project with your IDE. + +* Producer: The producer will generate 100 messages and send them to the `myTopic`. +* Consumer: Consume and log messages from `myTopic`. + + +## 4) Compile Java Producer and Consumer App project (below actions can be done in IntelliJ IDE) +```sh +$ cd kafka-example +``` + +Compile: + +```sh +$ mvn clean compile +``` + +## 5) Start the Consumer (below actions can be done in IntelliJ IDE) + + +Execute the Consumer: + +```sh +$ mvn exec:java -Dexec.mainClass="com.gft.dlp.kafka.ConsumerCloud" +``` +The consumer will listen and log new messages. Leave it running in the background. + + + +## 6) Start the Java Producer App (below actions can be done in IntelliJ IDE) + +Execute the Producer: + +```sh +$ mvn exec:java -Dexec.mainClass="com.gft.dlp.kafka.ProducerCloud" +``` + + +## 7) Exercise 1 +Send messages from console, and verify they are consumed by the Java Consumer app, and also from the console Consumer + +## 8) Exercise 2 +Send messages from the Java Producer app, and verify they are consumed by the Java Consumer app, and also from the console Consumer + +## 9) Exercise 3 +Modify the Java Consumer App to show in also the key of the message, and its Kafka message's offset. + +## 10) Exercise 4 + +* How can the Consumer list on start all the messagges from the topic? Tip: look up in internet documentation about + +### Clean up + +Shut down Docker Compose + +```sh +$ docker-compose down +``` diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Consumer_03.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Consumer_03.java new file mode 100644 index 000000000..4d19f45d9 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Consumer_03.java @@ -0,0 +1,40 @@ +package kafka_tutorial.exercise_03_kafka_streams; + +import org.apache.kafka.clients.consumer.ConsumerConfig; +import org.apache.kafka.clients.consumer.ConsumerRecord; +import org.apache.kafka.clients.consumer.ConsumerRecords; +import org.apache.kafka.clients.consumer.KafkaConsumer; + +import java.time.Duration; +import java.util.Arrays; + +import java.util.Properties; +import java.util.UUID; + +public class Consumer_03 { + + public static void main(String[] args) { + Properties properties = new Properties(); + properties.put("bootstrap.servers", "localhost:9092"); + properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer"); + properties.put("value.deserializer", "org.apache.kafka.common.serialization.LongDeserializer"); + properties.put("group.id", "stream-output-client"); + + KafkaConsumer kafkaConsumer = new KafkaConsumer(properties); + + kafkaConsumer.subscribe(Arrays.asList("streams-wordcount-output")); + + try{ + while (true){ + ConsumerRecords records = kafkaConsumer.poll(Duration.ofMillis(100)); + for (ConsumerRecord record: records){ + System.out.println(" | " + record.key() + " | " + String.valueOf(record.value())); + } + } + }catch (Exception e){ + System.out.println(e.getMessage()); + }finally { + kafkaConsumer.close(); + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Producer_01.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Producer_01.java new file mode 100644 index 000000000..6e459d25f --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/Producer_01.java @@ -0,0 +1,47 @@ +package kafka_tutorial.exercise_03_kafka_streams; + +import com.github.javafaker.Book; +import com.github.javafaker.Commerce; +import com.github.javafaker.Faker; +import org.apache.kafka.clients.producer.KafkaProducer; +import org.apache.kafka.clients.producer.ProducerRecord; + +import java.util.Properties; + +public class Producer_01 { + + public static void main(String[] args){ + Faker faker = new Faker(); + for(int i = 0; i < 100000; i++){ + + + System.out.println(faker.artist().name()); + + //System.out.println(quote); + //kafkaProducer.send(new ProducerRecord("quotes-input", quote )); + } + + + Properties properties = new Properties(); + properties.put("bootstrap.servers", "localhost:9092"); + properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); + properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer"); + + KafkaProducer kafkaProducer = new KafkaProducer(properties); + + try{ + for(int i = 0; i < 100000; i++){ + Thread.sleep(100); + Book b = faker.book(); + System.out.println(b.author() + " " + b.genre() + " " + b.publisher() + " " + b.title() ); + String quote = faker.shakespeare().asYouLikeItQuote(); + //System.out.println(quote); + //kafkaProducer.send(new ProducerRecord("quotes-input", quote )); + } + }catch (Exception e){ + e.printStackTrace(); + }finally { + kafkaProducer.close(); + } + } +} \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/README.md b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/README.md new file mode 100644 index 000000000..171cbffac --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/README.md @@ -0,0 +1,112 @@ +# Kafka Streams Lab, + +## Objectives + + 1) Run Zookeeper + Kafka + 2) Produce messages + 3) Process messages with Kafka Streams DSL + 4) Output results + +### Requirements + + * Docker for Windows + * Docker Compose + +## Run +Simple scenario: 1 zookeeper + 1 Kafka broker. + +Start the ZooKeeper and Kafka container. + +```sh +$ docker-compose up -d +``` + +Status: + +```sh +$ docker-compose ps + Name Command State Ports +------------------------------------------------------------------------------------------------- +lab1_kafka_1 /etc/confluent/docker/run Up 0.0.0.0:9092->9092/tcp +lab1_zookeeper_1 /etc/confluent/docker/run Up 0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp +``` + +### Example Description + +The example is a Maven project. You can import the project as Maven project with your IDE. + +* **Shakespeare Quotes Producer**: The producer will generate 1000 Shakespeare quotes messages, using the [Faker API](https://github.com/DiUS/java-faker), and send them to the `quotes-input`. + + Quote Examples: + * `True is it that we have seen better days.` + * `Can one desire too much of a good thing?.` + +* **Word Count**: The WordCount, using the high-level [KStream DSL](https://docs.confluent.io/current/streams/developer-guide/dsl-api.html), will compute a simple word occurrence histogram from input quotes. In this example, the input stream reads from a topic named "quotes-input", where the values of messages represent lines of text; and the histogram output is written to topic "streams-wordcount-output", where each record is an updated count of a single word + +* **Results Consumer**: The Consumer will output the "streams-wordcount-output" topic messages. + +Example: + +```sh + | think | 625 + | he | 625 + | is | 1852 + | wise | 1249 +``` + +### Running + +```sh +$ cd kafka-streams-example +``` + +Compile: + +```sh +$ mvn clean compile +``` + +### Producer + +Execute the Producer: + +```sh +$ mvn exec:java -Dexec.mainClass="com.gft.dlp.kafka.Producer_01" +``` + +### WordCount + +Execute the processor: + +```sh +$ mvn exec:java -Dexec.mainClass="com.gft.dlp.kafka.WordcountConsumer" +``` + +### Consumer + +Execute the Consumer: + +```sh +$ mvn exec:java -Dexec.mainClass="com.gft.dlp.kafka.Consumer_03" +``` + + +#### Exercises + +* Filter words - Example: Count only words with length > 3 + +* Filter results - Example: Count > 10000 + + + + + +### Clean up + +Shut down Docker Compose + +```sh +$ docker-compose down +``` + + diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/WordCountConsumer_02.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/WordCountConsumer_02.java new file mode 100644 index 000000000..0c670bdf4 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_03_kafka_streams/WordCountConsumer_02.java @@ -0,0 +1,80 @@ +package kafka_tutorial.exercise_03_kafka_streams; + +import org.apache.kafka.common.serialization.Serdes; +import org.apache.kafka.streams.KafkaStreams; +import org.apache.kafka.streams.KeyValue; +import org.apache.kafka.streams.StreamsBuilder; +import org.apache.kafka.streams.StreamsConfig; +import org.apache.kafka.streams.kstream.KStream; +import org.apache.kafka.streams.kstream.KTable; +import org.apache.kafka.streams.kstream.Produced; +import io.confluent.common.utils.TestUtils; + +import java.util.Arrays; +import java.util.Properties; +import java.util.regex.Pattern; + +public class WordCountConsumer_02 { + + static final String INPUT_TOPIC = "quotes-input"; + static final String OUTPUT_TOPIC = "streams-wordcount-output"; + + /** + * The Streams application as a whole can be launched like any normal Java application that has a `main()` method. + */ + public static void main(final String[] args) { + final String bootstrapServers = args.length > 0 ? args[0] : "localhost:9092"; + final Properties streamsConfiguration = getStreamsConfiguration(bootstrapServers); + final StreamsBuilder builder = new StreamsBuilder(); + createWordCountStream(builder); + final KafkaStreams streams = new KafkaStreams(builder.build(), streamsConfiguration); + streams.cleanUp(); + streams.start(); + Runtime.getRuntime().addShutdownHook(new Thread(streams::close)); + } + + /** + * Configure the Streams application. + *

+ * Various Kafka Streams related settings are defined here such as the location of the target Kafka cluster to use. + * Additionally, you could also define Kafka Producer and Kafka Consumer settings when needed. + * + * @param bootstrapServers Kafka cluster address + * @return Properties getStreamsConfiguration + */ + static Properties getStreamsConfiguration(final String bootstrapServers) { + final Properties streamsConfiguration = new Properties(); + streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-example"); + streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG, "wordcount-example-client"); + streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers); + streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName()); + streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName()); + streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000); + streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0); + streamsConfiguration.put(StreamsConfig.STATE_DIR_CONFIG, TestUtils.tempDirectory().getAbsolutePath()); + return streamsConfiguration; + } + + /** + * Define the processing topology for Word Count. + * + * @param builder StreamsBuilder to use + */ + static void createWordCountStream(final StreamsBuilder builder) { + final KStream textLines = builder.stream(INPUT_TOPIC); + final Pattern pattern = Pattern.compile("\\W+", Pattern.UNICODE_CHARACTER_CLASS); + final KTable wordCounts = textLines + .map(((key, value) -> { + System.out.println (" VALUE " + value); + return KeyValue.pair(key, value); + })) + .flatMapValues(value -> Arrays.asList(pattern.split(value.toLowerCase()))) + .groupBy((keyIgnored, word) -> word) + .count(); + + wordCounts.toStream().to(OUTPUT_TOPIC, Produced.with(Serdes.String(), Serdes.Long())); + + } + +} + diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Order.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Order.java new file mode 100644 index 000000000..f0e834a47 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Order.java @@ -0,0 +1,82 @@ +package kafka_tutorial.exercise_04_ksql; + +import java.util.Date; + +public class Order { + + private String orderId; + private int customerId; + private String product; + private int amount; + private double price; + private Date orderedAt; + + public Order(String orderId, int customerId, String product, int amount, double price, Date orderedAt) { + this.orderId = orderId; + this.customerId = customerId; + this.product = product; + this.amount = amount; + this.price = price; + this.orderedAt = orderedAt; + } + + public String getOrderId() { + return orderId; + } + + public int getCustomerId() { + return customerId; + } + + public String getProduct() { + return product; + } + + public int getAmount() { + return amount; + } + + public double getPrice() { + return price; + } + + public Date getOrderedAt() { + return orderedAt; + } + + public void setOrderId(String orderId) { + this.orderId = orderId; + } + + public void setCustomerId(int customerId) { + this.customerId = customerId; + } + + public void setProduct(String product) { + this.product = product; + } + + public void setAmount(int amount) { + this.amount = amount; + } + + public void setPrice(double price) { + this.price = price; + } + + public void setOrderedAt(Date orderedAt) { + this.orderedAt = orderedAt; + } + + @Override + public String toString() { + return "Order{" + + "orderId='" + orderId + '\'' + + ", customerId='" + customerId + '\'' + + ", product='" + product + '\'' + + ", amount=" + amount + + ", price=" + price + + ", orderedAt=" + orderedAt + + '}'; + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/OrderJsonSerializer.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/OrderJsonSerializer.java new file mode 100644 index 000000000..f30a7e547 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/OrderJsonSerializer.java @@ -0,0 +1,21 @@ +package kafka_tutorial.exercise_04_ksql; + +import com.fasterxml.jackson.databind.ObjectMapper; +import org.apache.kafka.common.serialization.Serializer; + +public class OrderJsonSerializer implements Serializer { + + @Override + public byte[] serialize(String s, Object o) { + byte[] retVal = null; + ObjectMapper objectMapper = new ObjectMapper(); + try { + retVal = objectMapper.writeValueAsString(o).getBytes(); + } catch (Exception e) { + e.printStackTrace(); + } + return retVal; + } + +} + diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Producer.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Producer.java new file mode 100644 index 000000000..33f19f2be --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/Producer.java @@ -0,0 +1,42 @@ +package kafka_tutorial.exercise_04_ksql; + +import com.github.javafaker.Faker; +import org.apache.kafka.clients.producer.KafkaProducer; +import org.apache.kafka.clients.producer.ProducerRecord; + +import java.text.NumberFormat; +import java.util.Locale; +import java.util.Properties; +import java.util.UUID; +import java.util.concurrent.TimeUnit; + +public class Producer { + + public static void main(String[] args){ + Properties properties = new Properties(); + properties.put("bootstrap.servers", "localhost:9092"); + properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); + properties.put("value.serializer", "kafka_tutorial.exercise_04_ksql.OrderJsonSerializer"); + + KafkaProducer kafkaProducer = new KafkaProducer(properties); + Faker faker = new Faker(); + try{ + NumberFormat format = NumberFormat.getInstance(Locale.FRENCH); + while(true){ + Thread.sleep(1000); + Order order = new Order (UUID.randomUUID().toString(), + faker.number().numberBetween(1,5), + faker.commerce().productName(), + faker.number().numberBetween(1,100), + format.parse(faker.commerce().price()).doubleValue(), + faker.date().past(10, TimeUnit.DAYS)); + System.out.println(order); + kafkaProducer.send(new ProducerRecord("orders", order.getOrderId(), order )); + } + }catch (Exception e){ + e.printStackTrace(); + }finally { + kafkaProducer.close(); + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/README.md b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/README.md new file mode 100644 index 000000000..9507c6344 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_04_ksql/README.md @@ -0,0 +1,160 @@ +# KSQL Lab + +## Objectives + +### Requirements + +* Docker for Windows +* Docker Compose + +## Quick theoretical Kafka intro +Streams vs Tables. Below link explains the difference. Thouh in next exercises we will use a different use case. +https://developer.confluent.io/courses/ksqldb/streams-and-tables/#:~:text=Streams%20are%20unbounded%20series%20of,want%20to%20use%20the%20data. + +## Java App + +Compile and execute the Producer Java App. That will create a topic, and send messages to it. + +## Run + +Simple scenario: 1 zookeeper + 1 Kafka broker. + +Start the ZooKeeper and Kafka container. + +```sh +docker-compose up -d +``` + +```sh +docker-compose exec ksql-cli ksql http://host.docker.internal:8088 +``` + +docker-compose exec ksql-cli bash -c 'echo -e "\n\n⏳ Waiting for KSQL to be available before launching CLI\n"; while [ $(curl -s -o /dev/null -w %{http_code} http://ksql-server:8088/) -eq 000 ] ; do echo -e $(date) "KSQL Server HTTP state: " $(curl -s -o /dev/null -w %{http_code} http://ksql-server:8088/) " (waiting for 200)" ; sleep 5 ; done; ksql http://ksql-server:8088' + +⏳ Waiting for KSQL to be available before launching CLI + + =========================================== + = _ __ _____ ____ _ = + = | |/ // ____|/ __ \| | = + = | ' /| (___ | | | | | = + = | < \___ \| | | | | = + = | . \ ____) | |__| | |____ = + = |_|\_\_____/ \___\_\______| = + = = + = Streaming SQL Engine for Apache Kafka® = + =========================================== +Copyright 2017-2019 Confluent Inc. + +CLI v5.4.1, Server v located at http://ksql-server:8088 + +https://docs.confluent.io/current/ksql/docs/developer-guide/syntax-reference.html + +```sql +ksql> SET 'auto.offset.reset' = 'earliest'; +``` +```sql +ksql> SHOW TOPICS; +``` +``` +Kafka Topic | Partitions | Partition Replicas +------------------------------------------------ + orders | 1 | 1 +------------------------------------------------ +``` +```sql +ksql> PRINT 'orders' FROM BEGINNING; +``` +```sql +ksql> CREATE STREAM orders_stream + (orderedAt BIGINT, + customerId BIGINT, + amount BIGINT, + price DOUBLE, + product VARCHAR) + WITH (KAFKA_TOPIC='orders', + VALUE_FORMAT='JSON', + TIMESTAMP='orderedAt'); +``` +```sh + Message +---------------- + Stream created +---------------- +``` +```sql +ksql> SELECT * FROM orders_stream WHERE customerId = 1 EMIT CHANGES; +``` +```sql +ksql> SELECT customerId, + count(*) + FROM orders_stream + GROUP BY customerId + EMIT CHANGES; +``` +``` +------------------------------------------------------------------------------------- +| CUSTOMERID | KSQL_COL_1 | +------------------------------------------------------------------------------------- +| 2 | 704 | +| 4 | 1141 | +| 3 | 788 | +| 1 | 367 +``` +``` + Message +---------------------------------------------------------- + Source `ORDERS_STREAM` (topic: orders) was dropped. +---------------------------------------------------------- +``` +```sql +ksql> CREATE TABLE orders_by_customer AS + SELECT customerId, + count(*) + FROM orders_stream + GROUP BY customerId + EMIT CHANGES; +``` +``` + Message +--------------------------------------------------------------------------------------------------------- + Table ORDERS_BY_CUSTOMER created and running. Created by query with query ID: CTAS_ORDERS_BY_CUSTOMER_1 +--------------------------------------------------------------------------------------------------------- + + + Table Name | Kafka Topic | Format | Windowed +------------------------------------------------------------- + ORDERS_BY_CUSTOMER | ORDERS_BY_CUSTOMER | JSON | false +------------------------------------------------------------- + +``` +```sql +ksql> select * from orders_by_customer emit changes; +``` +``` +----------------------------------------------------------------------------------------------------------------------------------------------------- +|ROWTIME |ROWKEY |CUSTOMERID |KSQL_COL_1 | +----------------------------------------------------------------------------------------------------------------------------------------------------- +|1584199961874 |1 |1 |245 | +|1584209859996 |2 |2 |477 | +|1584211766419 |4 |4 |757 | +|1584211668855 |3 |3 |521 | + +Query terminated + +``` + +### If you want to know more +https://ksqldb.io/examples.html + +### Clean up + +```sh +ksql> DROP STREAM ORDERS +``` + +Shut down Docker Compose + +```sh +$ docker-compose down +``` + diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ConsumerCloud.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ConsumerCloud.java new file mode 100644 index 000000000..125e9a6ce --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ConsumerCloud.java @@ -0,0 +1,53 @@ +package kafka_tutorial.exercise_05_confluent_cloud; + +import org.apache.kafka.clients.consumer.*; +import java.time.Duration; +import java.util.Arrays; +import java.util.Properties; +import java.io.FileInputStream; +import java.io.IOException; +import java.io.InputStream; +import java.nio.file.Files; +import java.nio.file.Paths; + +public class ConsumerCloud { + + public static Properties loadConfig(final String configFile) throws IOException { + if (!Files.exists(Paths.get(configFile))) { + throw new IOException(configFile + " not found."); + } + final Properties cfg = new Properties(); + try (InputStream inputStream = new FileInputStream(configFile)) { + cfg.load(inputStream); + } + return cfg; + } + + public static void main(String[] args) { + Properties props = null; // Declare props here + try { + props = loadConfig("client.properties"); + props.put(ConsumerConfig.GROUP_ID_CONFIG, "kafka-java-getting-started"); + props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest"); + + KafkaConsumer kafkaConsumer = new KafkaConsumer<>(props); + kafkaConsumer.subscribe(Arrays.asList("topic_java")); + + while (true) { + ConsumerRecords records = kafkaConsumer.poll(Duration.ofMillis(100)); + for (ConsumerRecord record : records) { + System.out.printf("key = %s, value = %s%n", record.key(), record.value()); + } + } + } catch (IOException e) { + e.printStackTrace(); + System.err.println("Failed to load configuration file."); + } finally { + if (props != null) { // Check if props is not null before trying to create a KafkaConsumer + try (KafkaConsumer kafkaConsumer = new KafkaConsumer<>(props)) { + kafkaConsumer.close(); + } + } + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ProducerCloud.java b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ProducerCloud.java new file mode 100644 index 000000000..d35fc8254 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/ProducerCloud.java @@ -0,0 +1,47 @@ +package kafka_tutorial.exercise_05_confluent_cloud; + +import org.apache.kafka.clients.producer.KafkaProducer; +import org.apache.kafka.clients.producer.ProducerRecord; + +import java.io.FileInputStream; +import java.io.IOException; +import java.io.InputStream; +import java.nio.file.Files; +import java.nio.file.Paths; +import java.util.Properties; + +public class ProducerCloud { + + public static Properties loadConfig(final String configFile) throws IOException { + if (!Files.exists(Paths.get(configFile))) { + throw new IOException(configFile + " not found."); + } + final Properties cfg = new Properties(); + try (InputStream inputStream = new FileInputStream(configFile)) { + cfg.load(inputStream); + } + return cfg; + } + + public static void main(String[] args){ + try { + final Properties props = loadConfig("client.properties"); + + KafkaProducer kafkaProducer = new KafkaProducer<>(props); + try{ + for(int i = 0; i < 100; i++){ + int value = i*4; + System.out.println(i); + kafkaProducer.send(new ProducerRecord("topic_java", Integer.toString(i), "test message - " + value)); + } + } catch (Exception e){ + e.printStackTrace(); + } finally { + kafkaProducer.close(); + } + } catch (IOException e) { + e.printStackTrace(); + System.err.println("Failed to load configuration file."); + } + } +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/README_CLOUD.md b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/README_CLOUD.md new file mode 100644 index 000000000..261a881ed --- /dev/null +++ b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/README_CLOUD.md @@ -0,0 +1,159 @@ +# EXCERCISES KAFKA 2023 + +The following excercises cover the basic use of Kafka using Java. Since Kafka is designed to be used with Java, the Python API is not as complete as the Java one. For this reason, the excercise related to kafka streams will be done using Java. + +## EXCERCISE 1 + +This excercise can be done using two different environments: a docker-compose environment or the environment provided by Confluent Cloud. We will start by using the Confluent Cloud environment, as its configuration is not hardware dependent and it is easier to use. + +### Confluent Cloud + +**Steps:** + +1. Create a Confluent Cloud account. You can use the free trial, which will be enough for this excercise. You can create an account [here](https://confluent.cloud/signup). We will use the Sign Up with Google option, but you can choose whichever you prefer. + ![images/imgage1.png](images/image1.png) + +2. Provide your full name and as a company write "EDEM" or "GFT". Click on Next. + ![images/imgage1.png](images/image1.png) + +3. In the next screen, select the options that describe your expertise with Kafka best. Click on Next. + ![images/imgage3.png](images/image3.png) + +4. Now we can configure the cluster that will be used for this excercise. Choose Google Cloud as the cloud provided and Madrid as the region. Leave the name as "cluster_0". Click on Next. + ![images/imgage4.png](images/image4.png) + +5. The next screen is for configuring the billing. However, you **don't need to include any payment information**. Just click on Skip at the bottom left of the screen. + ![images/imgage5.png](images/image5.png) + +6. Now the environment is created and you can click on "keep exploring on my own". + ![images/imgage6.png](images/image6.png) + +7. We will now create the topic to which we will send the messages. Click on the "Topics" tab on the left side of the screen and then click on "Create Topic". + ![images/imgage7.png](images/image7.png) + +8. Change the Topic name to "topic_java" and the Partitions to 1. Click on Create with defaults. + ![images/imgage8.png](images/image8.png) + +9. Now the topic has been created. Confluent allows now to define a schema for the messages, but for this excercise we will not use it. Click on Skip for now. + ![images/imgage9.png](images/image9.png) + +After this step the environment is ready to be used. We will now create a producer and a consumer to send and receive messages. + +10. In case you want to try the Java excercise, you should also create a topic called "topic_java" following the same steps as before (steps 7-9). + +
+ +#### **Java** + +(An appropriate Java environment is required to run this excercise. Usually, IntelliJ is used for this purpose.) + +*Steps:* + +0. Create a Topic following the steps name to "topic_java" and the Partitions to 1. Click on Create with defaults. +1. Open the file `client.properties` located in the directory `ConfluentCloud/Java/1-kafka-producer-consuer-api`. Now, we have to replace the lines of that file with the ones generated for our Java connector by Confluent Cloud. To do so, click on the "Connectors" tab on the left side of the screen and then search "Java". Click on "Connect with Java". + +

+ +

+ +2. Click on "Create Kafka Cluster API key" to get the Key and secrets for your client.properties file. Then click on continue in the pop-up window that appears: + +

+ +

+ +3. Copy only the highlighted lines into the client.properties file: + +

+ +

+ +4. Now we will launch the message producer which code is implemented in the file called `producer.py`. You can open the file to have a look at it. To launch the producer you should first build the project in IntelliJ and then run the class `Producer`. + +You should see an output like: + +```sh +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 +94 +95 +96 +97 +98 +99 + +Process finished with exit code 0 +``` + +You can also see the messages in the Confluent Cloud UI. To do so just click on the Topics button on the left bar, click on the topic name and then select messages. You should sort them by "Jump to offset" and write 0 in the search bar to select partition 0 of the topic. + +

+ +

+ +5. To consume the message, there is a script called `consumer.py` that connects to the Kafka broker deployed on Confluent Cloud and gets the messages by the earliest offset. To run the consumer you can run the class `Consumer`. + +You should see an output like the following: + +```sh +key = 85, value = test message - 340 +key = 86, value = test message - 344 +key = 87, value = test message - 348 +key = 88, value = test message - 352 +key = 89, value = test message - 356 +key = 90, value = test message - 360 +key = 91, value = test message - 364 +key = 92, value = test message - 368 +key = 93, value = test message - 372 +key = 94, value = test message - 376 +key = 95, value = test message - 380 +key = 96, value = test message - 384 +key = 97, value = test message - 388 +key = 98, value = test message - 392 +key = 99, value = test message - 396 +``` + + +{ +"name": "sourcePostgresql", +"config":{ +"connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", +"task.max": "1", +"connection.url": "jdbc:postgresql://postgres:5432/postgres", +"connection.user": "postgres", +"connection.password": "Welcome01", +"topic.prefix": "xflight", +"validate.none.null": "false", +"mode": "incrementing", +"incrementing.column.name": "id", +"table.whitelist": "aeroplane" +} +} + + +{ +"name": "conectorSink", +"config":{ +"connector.class": "io.confluent.connect.jdbc.JdbcSinkConnector", +"connection.url": "jdbc:postgresql://localhost:5432/dvdrental", +"connection.user": "postgres", +"connection.password": "Welcome01", +"topics": "test1", +"insert.mode": "UPSERT", +"db.timezone": "UTC", +"auto.create": "true", +"auto.evolve": "true", +"pk.mode": "record_value", +"pk.fields": "customer_id", +"tasks.max": "1" +} +} diff --git a/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/images/image1.png b/PROFESORES/KAFKA/src/main/java/kafka_tutorial/exercise_05_confluent_cloud/images/image1.png new file mode 100644 index 0000000000000000000000000000000000000000..6e3d9b77b779bf4446e098f64055112a582469c1 GIT binary patch literal 109100 zcmeFYc{J30_&+QqMTI2Y$b^z5BxE0J$-eJfB0~18V@8W*qNvbBmMnv@?>kc>m5_ZM 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+ + + + diff --git a/PROFESORES/KAFKA/src/main/python/.vscode/settings.json b/PROFESORES/KAFKA/src/main/python/.vscode/settings.json new file mode 100644 index 000000000..b881eff7b --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.analysis.autoImportCompletions": true +} \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise1/README.md b/PROFESORES/KAFKA/src/main/python/exercise1/README.md new file mode 100644 index 000000000..3a7f0be29 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise1/README.md @@ -0,0 +1,25 @@ +# Exercise 1: Confluent Kafka Cloud - Login + +Create an account into Confluent Kafka Cloud. + +## Confluent Cloud + +**Steps:** + +1. Create a Confluent Cloud account. You can use the free trial, which will be enough for this excercise. You can create an account [here](https://confluent.cloud/signup). We will use the Sign Up with Google option, but you can choose whichever you prefer. + ![images/imgage1.png](images/image1.png) + +2. Provide your full name and as a company write "EDEM" or "GFT". Click on Next. + ![images/imgage1.png](images/image1.png) + +3. In the next screen, select the options that describe your expertise with Kafka best. Click on Next. + ![images/imgage3.png](images/image3.png) + +4. Now we can configure the cluster that will be used for this excercise. Choose Google Cloud as the cloud provided and Madrid as the region. Leave the name as "cluster_0". Click on Next. + ![images/imgage4.png](images/image4.png) + +5. The next screen is for configuring the billing. However, you **don't need to include any payment information**. Just click on Skip at the bottom left of the screen. + ![images/imgage5.png](images/image5.png) + +6. 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And create an automtic +radom data generator of messages, which are sent to this Kafka Topic. + +## Excersise +Click on Learn button and select first option "Confluent Cloud". 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b/PROFESORES/KAFKA/src/main/python/exercise3/README.md new file mode 100644 index 000000000..ed3436372 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise3/README.md @@ -0,0 +1,75 @@ +# Exercise 3: Python App to Cloud + +## Target +Send messages from your computer, from a Python App, messages to Confluent Kafka Cloud. + +## Steps + +We will now create the topic to which we will send the messages. Click on the "Topics" tab on the left side of the screen and then click on "Create Topic". + ![images/imgage1.png](images/image1.png) + +

    + +Change the Topic name to "topic_python" and the Partitions to 1. Click on Create with defaults. + ![images/imgage2.png](images/image2.png) + + +## Python App + +### Python libraries installation +Before running the example, you need to install the python kafka client library: + +```sh +$ pip install kafka-python +$ pip install confluent_kafka +``` +

    + +### Set up Python credentials from Confluent Cloud +Click "Learn" button. Then Click on Python image. +![images/imgage3.png](images/image3.png) + +

    + +Once you choose an environment/cluser. you will be shown a screen with instructions. You only need generate a new API +access key: +![images/imgage4.png](images/image4.png) + +

    +Enter a description and save the credentials in your computer. +![images/imgage5.png](images/image5.png) + + +

    + +### Modify Apo Python to use your new API credentials +Modify the file client.properties and put your credentials. +![img.png](images/image6.png) + +### Run the python scripts +Execute the Consumer: run the script consumer.py +Execute the Producer: run the script producer.py + + +#### More Exercises + +##### Exercise 3.1 +* Modify the python Producer script (producer.py) and send different messages. Verify in the Confluent topic's Cloud +* and in the Python Consumer App that the new messages arrive. + +##### Exercise 3.2 +* Use a messages more complex than a String. For example a JSON message like this one => {"name":"John", "age": 26} +where you can increment the message JSON attribute "age" number in each sent message. + +##### Exercise 3.3 +* Create a new topic in Confluen Cloud. Adapt the Produce and Consumer Python Apps to use this new topic. Test it +* end to end. + +##### Exercise 3.4 TEAM WORK :)!!!!! +* Make a Development Team composed by 2-3 colleagues. From each of your computer run the Python Producer Application to +* just ONLY ONE Cloud server. To do that, you will need to use the same API credentials. Verify that you can see in the +* Cloud how the messages from different computers are arriving. +* +*Then start in the different computers the Consumer, and verify you read messages in your computer produced by other +colleagues in their computers. + diff --git a/PROFESORES/KAFKA/src/main/python/exercise3/client.properties b/PROFESORES/KAFKA/src/main/python/exercise3/client.properties new file mode 100644 index 000000000..b4d63bb64 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise3/client.properties @@ -0,0 +1,13 @@ + +############# COPY BELOW THE LINES FROM CONFLUENT CLOUD UI ##################### + +# Required connection configs for Kafka producer, consumer, and admin +bootstrap.servers=pkc-mxqvx.europe-southwest1.gcp.confluent.cloud:9092 +security.protocol=SASL_SSL +sasl.mechanisms=PLAIN +sasl.username=ZXYB6NK4BP6UOWCL +sasl.password=HdfsYdnGmiEdhswCbAGc6TO1RfU7xy1ScJENyOC9CeSdYhEPmAO6gCxkrJuERfha + +################################################################################# + + diff --git a/PROFESORES/KAFKA/src/main/python/exercise3/consumer.py b/PROFESORES/KAFKA/src/main/python/exercise3/consumer.py new file mode 100644 index 000000000..b968741d4 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise3/consumer.py @@ -0,0 +1,33 @@ +from kafka import KafkaConsumer +from json import loads + +from confluent_kafka import Consumer + + +def read_ccloud_config(config_file): + conf = {} + with open(config_file) as fh: + for line in fh: + line = line.strip() + if len(line) != 0 and line[0] != "#": + parameter, value = line.strip().split('=', 1) + conf[parameter] = value.strip() + return conf + + +props = read_ccloud_config("exercise3/client.properties") +props["group.id"] = "python-group-1" +props["auto.offset.reset"] = "earliest" + +consumer = Consumer(props) +consumer.subscribe(["ARBOLES"]) + +try: + while True: + msg = consumer.poll(1.0) + if msg is not None and 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z9pQMOi_OA%cKLYbV=9l1kGfUXMds!MhX(y8r-xEyy!P|WQBB~T#w%9nCe9DnYiY?t zmyO8!(C)?=%I$Ux2Ym=l+Ujez(WD;(%P>B~(!q1BL;al2Z}{wI^2z=(8}04>#kjvZ zKpl}@+R2&>Y2r-lPTPv35`IAQMRu5z*N*(i#-*n4hD*)#U9a~`w-zV(jiYH&1$xWo zIQQF2s%*~aF=&}PF#Pn{7kSvB?_&J&qxnGOC`wl!$?f^f_}R#aOCeIpkds}i z6f9T|8rVv^qh=80fW4pH^MS`g5_X$s{JbveV0yuYBS5Cf==1Jq07*|(5L5CMxqt0p z+sM{UMgGc{QNHneCKQ3fNtgedL3v8ZE}4fV?+71l5L{zEz`$ Then, produce messages from one console and read the messages from the other two consoles. + +## Run the Producer Python App from VisualStudio +Open VisualStudio and run the producer.py App + +## Run the Consumer Python App from VisualStudio +Open VisualStudio and run the consumer.py App + + +#### More Exercises +##### Exercise 4.1 +Check that in the opened two consumer consoles, you are getting the messages produced by the Python Producer Application. + +Produce messages from the Producer console, and check that 1 ) you are getting the messages produced by the Python Producer Application. +and 2) you are getting the messages from the Python Consumer Application. + +##### Exercise 4.2 +Create a new topic from the command line. Then modify the Producer App and the Consumer App to use the new topic. + +##### Exercise 4.4 +Read the messages also with the Kafka Admin screen at http://localhost:9021/clusters +Check that this admin web console is very similar to the one we used in Confluent Cloud. + +### Clean up + +Shut down Docker Compose + +```sh +docker-compose down +``` \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise4/consumer.py b/PROFESORES/KAFKA/src/main/python/exercise4/consumer.py new file mode 100644 index 000000000..f022301d1 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise4/consumer.py @@ -0,0 +1,39 @@ +from confluent_kafka import Consumer, KafkaError + +# Configuración del consumidor +config = { + 'bootstrap.servers': 'localhost:9092', + 'group.id': 'python-consumer-group', + 'auto.offset.reset': 'earliest' +} + +# Crear un consumidor +consumer = Consumer(config) + +# Suscribirse a un tópico +topic = 'ventas' # El nombre del tópico +consumer.subscribe([topic]) + +# Loop infinito de consumo de mensajes del topic +try: + while True: + msg = consumer.poll(1.0) # Lee nuevos mensajes cada 1 segundo + + if msg is None: + continue + if msg.error(): + if msg.error().code() == KafkaError._PARTITION_EOF: + print("No hay más mensajes en esta partición.") + else: + print("Error al recibir mensaje: {}".format(msg.error())) + else: + # Procesar el mensaje recibido. Aqui solo lo mostramos por pantall. En una App real se pueden hacer cualquier + # cosa con un mensaje. Ejmplos: Filtrarlo, modificarlo, guardarlo en una base de datos, + #enviarlo a otra applicación, etc. + print('Nuevo mensaje: {}'.format(msg.value().decode('utf-8'))) + +except KeyboardInterrupt: + pass +finally: + # Cerrar el consumidor al parar la Applicacion Python + consumer.close() diff --git a/PROFESORES/KAFKA/src/main/python/exercise4/docker-compose.yml b/PROFESORES/KAFKA/src/main/python/exercise4/docker-compose.yml new file mode 100644 index 000000000..270a96a7d --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise4/docker-compose.yml @@ -0,0 +1,45 @@ +--- +version: '3.5' +services: + zookeeper: + image: confluentinc/cp-zookeeper:latest + environment: + ZOOKEEPER_CLIENT_PORT: 2181 + ZOOKEEPER_TICK_TIME: 2000 + ports: + - 2181:2181 + + kafka: + image: confluentinc/cp-kafka:7.3.3 + depends_on: + - zookeeper + ports: + - 9092:9092 + environment: + KAFKA_BROKER_ID: 1 + KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181 + KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092 + KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT + KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT + KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 + + control-center: + image: confluentinc/cp-enterprise-control-center:7.3.3 + hostname: control-center + container_name: control-center + depends_on: + - kafka + ports: + - "9021:9021" + environment: + CONTROL_CENTER_BOOTSTRAP_SERVERS: 'kafka:29092' + CONTROL_CENTER_CONNECT_CONNECT-DEFAULT_CLUSTER: 'kafka-connect:8083' + CONTROL_CENTER_KSQL_KSQLDB1_URL: "http://ksql-server:8088" + CONTROL_CENTER_KSQL_KSQLDB1_ADVERTISED_URL: "http://localhost:8088" + CONTROL_CENTER_SCHEMA_REGISTRY_URL: "http://schema-registry:8081" + CONTROL_CENTER_REPLICATION_FACTOR: 1 + CONTROL_CENTER_INTERNAL_TOPICS_PARTITIONS: 1 + CONTROL_CENTER_MONITORING_INTERCEPTOR_TOPIC_PARTITIONS: 1 + CONFLUENT_METRICS_TOPIC_REPLICATION: 1 + CONTROL_CENTER_CONNECT_HEALTHCHECK_ENDPOINT: '/connectors' + PORT: 9021 \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise4/producer.py b/PROFESORES/KAFKA/src/main/python/exercise4/producer.py new file mode 100644 index 000000000..ac5a952e0 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise4/producer.py @@ -0,0 +1,35 @@ +import time +from json import dumps +from confluent_kafka import Producer + +# Configuración del productor +config = { + 'bootstrap.servers': 'localhost:9092', # Cambia esto con la dirección de tu servidor Kafka + 'client.id': 'python-producer' +} + +# Crear un productor +producer = Producer(config) + + +# Send 100 messages where the key is the index and the message to send is "test message - index" +# the topic name is myTopic + +topic_kafka = 'ventas' + +for e in range(100): + data = {'Nueva Venta - ': e*4} + data_str = dumps(data) # Serialize dictionary to a string + data_bytes = data_str.encode('utf-8') # Encode string to bytes + key = str(e).encode('utf-8') + producer.produce(topic=topic_kafka, value=data_bytes, key=key) # Send bytes + print("Sending data: {} to topic {}".format(data, topic_kafka)) + time.sleep(1) + +# After your loop where you send messages: +producer.flush() + +# Optionally, you can check if there are any messages that failed to be delivered: +if producer.flush() != 0: + print("Some messages failed to be delivered") + diff --git a/PROFESORES/KAFKA/src/main/python/exercise5/README.md b/PROFESORES/KAFKA/src/main/python/exercise5/README.md new file mode 100644 index 000000000..4966ea333 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise5/README.md @@ -0,0 +1,145 @@ +# Exercise 5: KSQL + +## Objectives + +1) Produce messages from a Python application. +2) Use KSql to query the messages + + +## Run Kafka in your computer with Docker +Simple scenario: 1 zookeeper + 1 Kafka broker + 1 Ksql server + 1 Ksql CLI. + +Start the ZooKeeper and Kafka container. + +**To execute below command, make sure you open the terminal under the folder "exercise5/"** +```sh +$ docker-compose up -d +``` + +## Command Line , create a new Kafka topic + +Run the command line producer: + +```sh +docker-compose exec kafka kafka-topics --create --topic palabras --partitions 1 --replication-factor 1 --if-not-exists --bootstrap-server localhost:9092 +``` + +## Run the Producer Python App from VisualStudio +Execute the exercise5/producer.py Python Producer Application. Check that it shows El Quijote's phrases. + +Then ensure the messages (each word of El Quijote's book) is being sent to the topic "palabras". + +```sh +docker-compose exec kafka kafka-console-consumer --topic palabras --from-beginning --bootstrap-server localhost:9092 +``` + +If the messages are arriving you are OK. Now Exit with Control-C. You are ready to start to query the messages with KSQL in next steps :)!!! + +## Open KSql in a console +```sh +docker-compose exec ksql-cli ksql http://host.docker.internal:8088 +```` + +Check you can see an output like the below one: + + ⏳ Waiting for KSQL to be available before launching CLI + + =========================================== + = _ __ _____ ____ _ = + = | |/ // ____|/ __ \| | = + = | ' /| (___ | | | | | = + = | < \___ \| | | | | = + = | . \ ____) | |__| | |____ = + = |_|\_\_____/ \___\_\______| = + = = + = Streaming SQL Engine for Apache Kafka® = + =========================================== + Copyright 2017-2019 Confluent Inc. + + CLI v5.4.1, Server v located at http://ksql-server:8088 + + +## Query the messages from a topic with KSQL + +```sql +SET 'auto.offset.reset' = 'earliest'; +``` +```sql +SHOW TOPICS; +``` +``` +Kafka Topic | Partitions | Partition Replicas +------------------------------------------------ + palabras | 1 | 1 +------------------------------------------------ +``` +```sql +PRINT 'palabras' FROM BEGINNING; +``` + +Press Control-C some times to exit. Be patient, some times take time :)....If it does not stop, close the terminal and +open a new one under the same folder "exercise5". + +If you have data in an existing Apache Kafka topic, you can create a stream or a table backed by that topic and begin +streaming the data into ksqlDB: +Any subsequent data produced to the topic will be streamed into ksqlDB, and any data inserted into the new stream will +be written to the Kafka topic automagically. +```sql +CREATE STREAM palabras_stream + (palabra VARCHAR) + WITH (KAFKA_TOPIC='palabras', + VALUE_FORMAT='DELIMITED'); +``` + +Select all the messages from the stream (topic), and show what is each word's length. +```sql +SELECT palabra, LEN(palabra) FROM palabras_stream emit changes; +``` + +Select the messages from the stream where the message size is major than 7 +```sql +SELECT palabra AS mi_palabra, LEN(palabra) AS longitud FROM palabras_stream WHERE LEN(palabra) > 7 emit changes; +``` + +### Do this Exercise on your own +Select the messages from the stream where the message size is major than 10 + +### Keep going on +Filter words **starting** with the letter **"t"** in a stream named palabras_stream +```sql +SELECT palabra +FROM palabras_stream +WHERE palabra LIKE 't%' emit changes; +``` + +### Do this Exercise on your own +Filter words **ending** with the letters **"go"** in a stream named palabras_stream + +### Keep going on +Create a KTable, to check out how many times a word has appeared in the El Quijote's book. Notice that while the words +are arriving to the topic, the KTable gets updated in real time!!! +```sql +CREATE TABLE mi_ktable AS +SELECT palabra, +count(*) +FROM palabras_stream +GROUP BY palabra +EMIT CHANGES; +``` + +Read the KTable +```sql +SELECT * FROM mi_ktable EMIT CHANGES; +``` + +#### More Exercises +##### Exercise 5.1 +Find the words that starts with 'ca' **and** finishes with 'o' **and** the word is longer than 6 characters. + +##### Exercise 5.2 +Select all the words, but transformed in Uppercase. Hint: use a select and using the function UCASE(...) + +##### Exercise 5.3 ADVANCED +**Note:** If you are interested in learning more on KSQL you can find developer info here: https://docs.confluent.io/current/ksql/docs/developer-guide/syntax-reference.html +And you can try from this doc to performe another KSQL with the 'palabras' topic. + diff --git a/PROFESORES/KAFKA/src/main/python/exercise5/el_quijote_book.txt b/PROFESORES/KAFKA/src/main/python/exercise5/el_quijote_book.txt new file mode 100644 index 000000000..c83309667 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise5/el_quijote_book.txt @@ -0,0 +1,2186 @@ +DON QUIJOTE DE LA MANCHA +Miguel de Cervantes Saavedra + +PRIMERA PARTE +CAPÍTULO 1: Que trata de la condición y ejercicio del famoso hidalgo D. Quijote de la Mancha +En un lugar de la Mancha, de cuyo nombre no quiero acordarme, no ha mucho tiempo que vivía un hidalgo de los de lanza en astillero, adarga antigua, rocín flaco y galgo corredor. Una olla de algo más vaca que carnero, salpicón las más noches, duelos y quebrantos los sábados, lentejas los viernes, algún palomino de añadidura los domingos, consumían las tres partes de su hacienda. El resto della concluían sayo de velarte, calzas de velludo para las fiestas con sus pantuflos de lo mismo, los días de entre semana se honraba con su vellori de lo más fino. Tenía en su casa una ama que pasaba de los cuarenta, y una sobrina que no llegaba a los veinte, y un mozo de campo y plaza, que así ensillaba el rocín como tomaba la podadera. Frisaba la edad de nuestro hidalgo con los cincuenta años, era de complexión recia, seco de carnes, enjuto de rostro; gran madrugador y amigo de la caza. Quieren decir que tenía el sobrenombre de Quijada o Quesada (que en esto hay alguna diferencia en los autores que deste caso escriben), aunque por conjeturas verosímiles se deja entender que se llama Quijana; pero esto importa poco a nuestro cuento; basta que en la narración dél no se salga un punto de la verdad. Es, pues, de saber, que este sobredicho hidalgo, los ratos que estaba ocioso (que eran los más del año) se daba a leer libros de caballerías con tanta afición y gusto, que olvidó casi de todo punto el ejercicio de la caza, y aun la administración de su hacienda; y llegó a tanto su curiosidad y desatino en esto, que vendió muchas hanegas de tierra de sembradura, para comprar libros de caballerías en que leer; y así llevó a su casa todos cuantos pudo haber dellos; y de todos ningunos le parecían tan bien como los que compuso el famoso Feliciano de Silva: porque la claridad de su prosa, y aquellas intrincadas razones suyas, le parecían de perlas; y más cuando llegaba a leer aquellos requiebros y cartas de desafío, donde en muchas partes hallaba escrito: la razón de la sinrazón que a mi razón se hace, de tal manera mi razón enflaquece, que con razón me quejo de la vuestra fermosura, y también cuando leía: los altos cielos que de vuestra divinidad divinamente con las estrellas se fortifican, y os hacen merecedora del merecimiento que merece la vuestra grandeza. Con estas y semejantes razones perdía el pobre caballero el juicio, y desvelábase por entenderlas, y desentrañarles el sentido, que no se lo sacara, ni las entendiera el mismo Aristóteles, si resucitara para sólo ello. No estaba muy bien con las heridas que don Belianis daba y recibía, porque se imaginaba que por grandes maestros que le hubiesen curado, no dejaría de tener el rostro y todo el cuerpo lleno de cicatrices y señales; pero con todo alababa en su autor aquel acabar su libro con la promesa de aquella inacabable aventura, y muchas veces le vino deseo de tomar la pluma, y darle fin al pie de la letra como allí se promete; y sin duda alguna lo hiciera, y aun saliera con ello, si otros mayores y continuos pensamientos no se lo estorbaran. +Tuvo muchas veces competencia con el cura de su lugar (que era hombre docto graduado en Sigüenza), sobre cuál había sido mejor caballero, Palmerín de Inglaterra o Amadís de Gaula; mas maese Nicolás, barbero del mismo pueblo, decía que ninguno llegaba al caballero del Febo, y que si alguno se le podía comparar, era don Galaor, hermano de Amadís de Gaula, porque tenía muy acomodada condición para todo; que no era caballero melindroso, ni tan llorón como su hermano, y que en lo de la valentía no le iba en zaga. +En resolución, él se enfrascó tanto en su lectura, que se le pasaban las noches leyendo de claro en claro, y los días de turbio en turbio, y así, del poco dormir y del mucho leer, se le secó el cerebro, de manera que vino a perder el juicio. Llenósele la fantasía de todo aquello que leía en los libros, así de encantamientos, como de pendencias, batallas, desafíos, heridas, requiebros, amores, tormentas y disparates imposibles, y asentósele de tal modo en la imaginación que era verdad toda aquella máquina de aquellas soñadas invenciones que leía, que para él no había otra +historia más cierta en el mundo. +Decía él, que el Cid Ruy Díaz había sido muy buen caballero; pero que no tenía que ver con el caballero de la ardiente espada, que de sólo un revés había partido por medio dos fieros y descomunales gigantes. Mejor estaba con Bernardo del Carpio, porque en Roncesvalle había muerto a Roldán el encantado, valiéndose de la industria de Hércules, cuando ahogó a Anteo, el hijo de la Tierra, entre los brazos. Decía mucho bien del gigante Morgante, porque con ser de aquella generación gigantesca, que todos son soberbios y descomedidos, él solo era afable y bien criado; pero sobre todos estaba bien con Reinaldos de Montalbán, y más cuando le veía salir de su castillo y robar cuantos topaba, y cuando en Allende robó aquel ídolo de Mahoma, que era todo de oro, según dice su historia. Diera él, por dar una mano de coces al traidor de Galalón, al ama que tenía y aun a su sobrina de añadidura. +En efecto, rematado ya su juicio, vino a dar en el más extraño pensamiento que jamás dio loco en el mundo, y fue que le pareció convenible y necesario, así para el aumento de su honra, como para el servicio de su república, hacerse caballero andante, e irse por todo el mundo con sus armas y caballo a buscar las aventuras, y a ejercitarse en todo aquello que él había leído, que los caballeros andantes se ejercitaban, deshaciendo todo género de agravio, y poniéndose en ocasiones y peligros, donde acabándolos, cobrase eterno nombre y fama. +Imaginábase el pobre ya coronado por el valor de su brazo por lo menos del imperio de Trapisonda: y así con estos tan agradables pensamientos, llevado del estraño gusto que en ellos sentía, se dió priesa a poner en efecto lo que deseaba. Y lo primero que hizo, fue limpiar unas armas, que habían sido de sus bisabuelos, que, tomadas de orín y llenas de moho, luengos siglos había que estaban puestas y olvidadas en un rincón. Limpiólas y aderezólas lo mejor que pudo; pero vió que tenían una gran falta, y era que no tenía celada de encaje, sino morrión simple; mas a esto suplió su industria, porque de cartones hizo un modo de media celada, que encajada con el morrión, hacía una apariencia de celada entera. Es verdad que para probar si era fuerte, y podía estar al riesgo de una cuchillada, sacó su espada, y le dió dos golpes, y con el primero y en un punto deshizo lo que había hecho en una semana: y no dejó de parecerle mal la facilidad con que la había hecho pedazos, y por asegurarse de este peligro, lo tornó a hacer de nuevo, poniéndole unas barras de hierro por de dentro de tal manera, que él quedó satisfecho de su fortaleza; y, sin querer hacer nueva experiencia de ella, la diputó y tuvo por celada finísima de encaje. Fue luego a ver a su rocín, y aunque tenía más cuartos que un real, y más tachas que el caballo de Gonela, que tantum pellis, et ossa fuit, le pareció que ni el Bucéfalo de Alejandro, ni Babieca el del Cid con él se igualaban. Cuatro días se le pasaron en imaginar qué nombre le podría: porque, según se decía él a sí mismo, no era razón que caballo de caballero tan famoso, y tan bueno él por sí, estuviese sin nombre conocido; y así procuraba acomodársele, de manera que declarase quien había sido, antes que fuese de caballero andante, y lo que era entones: pues estaba muy puesto en razón, que mudando su señor estado, mudase él también el nombre; y le cobrase famoso y de estruendo, como convenía a la nueva orden y al nuevo ejercicio que ya profesaba: y así después de muchos nombres que formó, borró y quitó, añadió, deshizo y tornó a hacer en su memoria e imaginación, al fin le vino a llamar Rocinante, nombre a su parecer alto, sonoro y significativo de lo que había sido cuando fue rocín, antes de lo que ahora era, que era antes y primero de todos los rocines del mundo. Puesto nombre y tan a su gusto a su caballo, quiso ponérsele a sí mismo, y en este pensamiento, duró otros ocho días, y al cabo se vino a llamar don Quijote, de donde como queda dicho, tomaron ocasión los autores de esta tan verdadera historia, que sin duda se debía llamar Quijada, y no Quesada como otros quisieron decir. Pero acordándose que el valeroso Amadís, no sólo se había contentado con llamarse Amadís a secas, sino que añadió el nombre de su reino y patria, por hacerla famosa, y se llamó Amadís de Gaula, así quiso, como buen caballero, añadir al suyo el nombre de la suya, y llamarse don Quijote de la Mancha, con que a su parecer declaraba muy al vivo su +linaje y patria, y la honraba con tomar el sobrenombre della. +Limpias, pues, sus armas, hecho del morrión celada, puesto nombre a su rocín, y confirmándose a sí mismo, se dió a entender que no le faltaba otra cosa, sino buscar una dama de quien enamorarse, porque el caballero andante sin amores, era árbol sin hojas y sin fruto, y cuerpo sin alma. Decíase él: si yo por malos de mis pecados, por por mi buena suerte, me encuentro por ahí con algún gigante, como de ordinario les acontece a los caballeros andantes, y le derribo de un encuentro, o le parto por mitad del cuerpo, o finalmente, le venzo y le rindo, ¿no será bien tener a quién enviarle presentado, y que entre y se hinque de rodillas ante mi dulce señora, y diga con voz humilde y rendida: yo señora, soy el gigante Caraculiambro, señor de la ínsula Malindrania, a quien venció en singular batalla el jamás como se debe alabado caballero D. Quijote de la Mancha, el cual me mandó que me presentase ante la vuestra merced, para que la vuestra grandeza disponga de mí a su talante? ¡Oh, cómo se holgó nuestro buen caballero, cuando hubo hecho este discurso, y más cuando halló a quién dar nombre de su dama! Y fue, a lo que se cree, que en un lugar cerca del suyo había una moza labradora de muy buen parecer, de quien él un tiempo anduvo enamorado, aunque según se entiende, ella jamás lo supo ni se dió cata de ello. Llamábase Aldonza Lorenzo, y a esta le pareció ser bien darle título de señora de sus pensamientos; y buscándole nombre que no desdijese mucho del suyo, y que tirase y se encaminase al de princesa y gran señora, vino a llamarla Dulcinea del Toboso, porque era natural del Toboso, nombre a su parecer músico y peregrino y significativo, como todos los demás que a él y a sus cosas había puesto. +Capítulo 2: Que trata de la primera salida que de su tierra hizo el ingenioso D. Quijote +Hechas, pues, estas prevenciones, no quiso aguardar más tiempo a poner en efecto su pensamiento, apretándole a ello la falta que él pensaba que hacía en el mundo su tardanza, según eran los agravios que pensaba deshacer, tuertos que enderezar, sinrazones que enmendar, y abusos que mejorar, y deudas que satisfacer; y así, sin dar parte a persona alguna de su intención, y sin que nadie le viese, una mañana, antes del día (que era uno de los calurosos del mes de Julio), se armó de todas sus armas, subió sobre Rocinante, puesta su mal compuesta celada, embrazó su adarga, tomó su lanza, y por la puerta falsa de un corral, salió al campo con grandísimo contento y alborozo de ver con cuánta facilidad había dado principio a su buen deseo. Mas apenas se vió en el campo, cuando le asaltó un pensamiento terrible, y tal, que por poco le hiciera dejar la comenzada empresa: y fue que le vino a la memoria que no era armado caballero, y que, conforme a la ley de caballería, ni podía ni debía tomar armas con ningún caballero; y puesto qeu lo fuera, había de llevar armas blancas, como novel caballero, sin empresa en el escudo, hasta que por su esfuerzo la ganase. +Estos pensamientos le hicieron titubear en su propósito; mas pudiendo más su locura que otra razón alguna, propuso de hacerse armar caballero del primero que topase, a imitación de otros muchos que así lo hicieron, según él había leído en los libros que tal le tenían. En lo de las armas blancas pensaba limpiarlas de manera, en teniendo lugar, que lo fuesen más que un armiño: y con esto se quietó y prosiguió su camino, sin llevar otro que el que su caballo quería, creyendo que en aquello consistía la fuerza de las aventuras. Yendo, pues, caminando nuestro flamante aventurero, iba hablando consigo mismo, y diciendo: ¿Quién duda sino que en los venideros tiempos, ciando salga a luz la verdadera historia de mis famosos hechos, que el sabio que los escribiere, no ponga, cuando llegue a contar esta mi primera salida tan de mañana, de esta manera? "Apenas había el rubicundo Apolo tendido por la faz de la ancha y espaciosa tierra las doradas hebras de sus hermosos cabellos, y apenas los pequeños y pintados pajarillos con sus arpadas lenguas habían saludado con dulce y meliflua armonía la venida de la rosada aurora que dejando la blanda cama del celoso marido, por las puertas y balcones del manchego horizonte a los mortales se mostraba, cuando el famoso caballero D. Quijote de la Mancha, dejando las ociosas plumas, subió sobre su famoso caballo Rocinante, y comenzó a caminar por el antiguo y conocido campo de Montiel." (Y era la verdad que por él caminaba) y añadió diciendo: "dichosa edad, y siglo dichoso aquel adonde saldrán a luz las famosas hazañas mías, dignas de entallarse en bronce, esculpirse en mármoles y esculpirse en mármoles y pintarse en tablas para memoria en lo futuro. ¡Oh tú, sabio encantador, quienquiera que seas, a quien ha de tocar el ser coronista de esta peregrina historia! Ruégote que no te olvides de mi buen Rocinante compañero eterno mío en todos mis caminos y carreras." Luego volvía diciendo, como si verdaderamente fuera enamorado: "¡Oh, princesa Dulcinea, señora de este cautivo corazón! Mucho agravio me habedes fecho en despedirme y reprocharme con el riguroso afincamiento de mandarme no parecer ante la vuestra fermosura. Plégaos, señora, de membraros de este vuestro sujeto corazón, que tantas cuitas por vuestro amor padece." +Con estos iba ensartando otros disparates, todos al modo de los que sus libros le habían enseñado, imitando en cuanto podía su lenguaje; y con esto caminaba tan despaico, y el sol entraba tan apriesa y con tanto ardor, que fuera bastante a derretirle los sesos, si algunos tuviera. Casi todo aquel día caminó sin acontecerle cosa que de contar fuese, de lo cual se desesperaba, poerque quisiera topar luego, con quien hacer experiencia del valor de su fuerte brazo. +Autores hay que dicen que la primera aventura que le avino fue la de Puerto Lápice; otros dicen que la de los molinos de viento; pero lo que yo he podido averiguar en este caso, y lo que he hallado escrito en los anales de la Mancha, es que él anduvo todo aquel día, y al anochecer, su rocín y él se hallaron cansados y +muertos de hambre; y que mirando a todas partes, por ver si descubriría algún castillo o alguna majada de pastores donde recogerse, y adonde pudiese remediar su mucha necesidad, vió no lejos del camino por donde iba una venta, que fue como si viera una estrella, que a los portales, si no a los alcázares de su redención, le encaminaba. Dióse priesa a caminar, y llegó a ella a tiempo que anochecía. Estaban acaso a la puerta dos mujeres mozas, de estas que llaman del partido, las cuales iban a Sevilla con unos arrieros, que en la venta aquella noche acertaron a hacer jornada; y como a nuestro aventurero todo cuanto pensaba, veía o imaginaba, le parecía ser hecho y pasar al modo de lo que había leído, luego que vió la venta se le representó que era un castillo con sus cuatro torres y chapiteles de luciente plata, sin faltarle su puente levadizo y honda cava, con todos aquellos adherentes que semejantes castillos se pintan. +Fuese llegando a la venta (que a él le parecía castillo), y a poco trecho de ella detuvo las riendas a Rocinante, esperando que algún enano se pusiese entre las almenas a dar señal con alguna trompeta de que llegaba caballero al castillo; pero como vió que se tardaban, y que Rocinante se daba priesa por llegar a la caballeriza, se llegó a la puerta de la venta, y vió a las dos distraídas mozas que allí estaban, que a él le parecieron dos hermosas doncellas, o dos graciosas damas, que delante de la puerta del castillo se estaban solazando. En esto sucedió acaso que un porquero, que andaba recogiendo de unos rastrojos una manada de puercos (que sin perdón así se llaman), tocó un cuerno, a cuya señal ellos se recogen, y al instante se le representó a D. Quijote lo que deseaba, que era que algún enano hacía señal de su venida, y así con extraño contento llegó a la venta y a las damas, las cuales, como vieron venir un hombre de aquella suerte armado, y con lanza y adarga, llenas de miedo se iban a entrar en la venta; pero Don Quijote, coligiendo por su huida su miedo, alzándose la visera de papelón y descubriendo su seco y polvoso rostro, con gentil talante y voz reposada les dijo: non fuyan las vuestras mercedes, nin teman desaguisado alguno, ca a la órden de caballería que profeso non toca ni atañe facerle a ninguno, cuanto más a tan altas doncellas, como vuestras presencias demuestran. Mirábanle las mozas y andaban con los ojos buscándole el rostro que la mala visera le encubría; mas como se oyeron llamar doncellas, cosa tan fuera de su profesión, no pudieron tener la risa, y fue de manera, que Don Quijote vino a correrse y a decirles: Bien parece la mesura en las fermosas, y es mucha sandez además la risa que de leve causa procede; pero non vos lo digo porque os acuitedes ni mostredes mal talante, que el mío non es de al que de serviros. +El lenguaje no entendido de las señoras, y el mal talle de nuestro caballero, acrecentaba en ellas la risa y en él el enojo; y pasara muy adelante, si a aquel punto no saliera el ventero, hombre que por ser muy gordo era muy pacífico, el cual, viendo aquella figura contrahecha, armada de armas tan desiguales, como eran la brida, lanza, adarga y coselete, no estuvo en nada en acompañar a las doncellas en las muestras de su contento; mas, en efecto, temiendo la máquina de tantos pertrechos, determinó de hablarle comedidamente, y así le dijo: si vuestra merced, señor caballero, busca posada, amén del lecho (porque en esta venta no hay ninguno), todo lo demás se hallará en ella en mucha abundancia. Viendo Don Quijote la humildad del alcaide de la fortaleza (que tal le pareció a él el ventero y la venta), respondió: para mí, señor castellano, cualquiera cosa basta, porque mis arreos son las armas, mi descanso el pelear, etc. +Pensó el huésped que el haberle llamado castellano había sido por haberle parecido de los senos de Castilla, aunque él era andaluz y de los de la playa de Sanlúcar, no menos ladrón que Caco, ni menos maleante que estudiante o paje. Y así le respondió: según eso, las camas de vuestra merced serán duras peñas, y su dormir siempre velar; y siendo así, bien se puede apear con seguridad de hallar en esta choza ocasión y ocasiones para no dormir en todo un año, cuanto más en una noche. Y diciendo esto, fue a tener del estribo a D. Quijote, el cual se apeó con mucha dificultad y trabajo, como aquel que en todo aquel día no se había desayunado. Dijo luego al huésped que le tuviese mucho cuidad de su caballo, +porque era la mejor pieza que comía pan en el mundo. Miróle el ventero, y no le pareció tan bueno como Don Quijote decía, ni aun la mitad; y acomodándole en la caballeriza, volvió a ver lo que su huésped mandaba; al cual estaban desarmando las doncellas (que ya se habían reconciliado con él), las cuales, aunque le habían quitado el peto y el espaldar, jamás supieron ni pudieron desencajarle la gola, ni quitarle la contrahecha celada, que traía atada con unas cintas verdes, y era menester cortarlas, por no poderse queitar los nudos; mas él no lo quiso consentir en ninguna manera; y así se quedó toda aquella noche con la celada puesta, que era la más graciosa y extraña figura que se pudiera pensar; y al desarmarle (como él se imaginaba que aquellas traídas y llevadas que le desarmaban, eran algunas principales señoras y damas de aquel castillo), les dijo con mucho donaire: +Nunca fuera caballero de damas tan bien servido, como fuera D. Quijote cuando de su aldea vino; doncellas curaban dél, princesas de su Rocino. +O Rocinante, que este es el nombre, señoras mías, de mi caballo, y Don Quijote de la Mancha el mío; que puesto que no quisiera descubrirme fasta que las fazañas fechas en vuestro servicio y pro me descubrieran, la fuerza de acomodar al propósito presente este romance viejo de Lanzarote, ha sido causa que sepáis mi nombre antes de toda sazón; pero tiempo vendrá en que las vuestras señorías me manden, y yo obedezca, y el valor de mi brazo descubra el deseo que tengo de serviros. Las mozas, que no estaban hechas a oír semejantes retóricas, no respondían palabra; sólo le preguntaron si quería comer alguna cosa. Cualquiera yantaría yo, respondió D. Quijote, porque a lo que entiendo me haría mucho al caso. A dicha acertó a ser viernes aquél día, y no había en toda la venta sino unas raciones de un pescado, que en Castilla llaman abadejo, y en Andalucía bacalao, y en otras partes curadillo, y en otras truchuela. +Preguntáronle si por ventura comería su merced truchuela, que no había otro pescado que darle a comer. Como haya muchas truchuelas, respondió D. Quijote, podrán servir de una trueba; porque eso se me da que me den ocho reales en sencillos, que una pieza de a ocho. Cuanto más, que podría ser que fuesen estas truchuelas como la ternera, que es mejor que la vaca, y el cabrito que el cabrón. Pero sea lo que fuere, venga luego, que el trabajo y peso de las armas no se puede llevar sin el gobierno de las tripas. Pusiéronle la mesa a la puerta de la venta por el fresco, y trájole el huésped una porción de mal remojado, y peor cocido bacalao, y un pan tan negro y mugriento como sus armas. Pero era materia de grande risa verle comer, porque como tenía puesta la celada y alzada la visera, no podía poner nada en la boca con sus manos, si otro no se lo daba y ponía; y así una de aquellas señoras sería de este menester; mas el darle de beber no fue posible, ni lo fuera si el ventero no horadara una caña, y puesto el un cabo en la boca, por el otro, le iba echando el vino. Y todo esto lo recibía en paciencia, a trueco de no romper las cintas de la celada. +Estando en esto, llegó acaso a la venta un castrador de puercos, y así como llegó sonó su silbato de cañas cuatro o cinco veces, con lo cual acabó de confirmar Don Quijote que estaba en algún famoso castillo, y que le servían con música, y que el abadejo eran truchas, el pan candeal, y las rameras damas, y el ventero castellano del castillo; y con esto daba por bien empleada su determinación y salida. Mas lo que más le fatigaba era el no verse armado caballero, por parecerle que no se podría poner legítimamente en aventura alguna sin recibir la órden de caballería. +Capítulo 3: Donde se cuenta la graciosa manera que tuvo D. Quijote en armarse caballero +Y así, fatigado de este pensamiento, abrevió su venteril y limitada cena, la cual acabada llamó al ventero, y encerrándose con él en la caballeriza, se hincó de rodillas ante él, diciéndole, no me levantaré jamás de donde estoy, valeroso caballero, fasta que la vuestra cortesía, me otorgue un don que pedirle quiero, el cual redundará en alabanza vuestra y en pro del género humano. El ventero que vió a su huésped a sus pies, y oyó semejantes razones, estaba confuso mirándole, sin saber qué hacerse ni decirle, y porfiaba con él que se levantase; y jamás quiso, hasta que le hubo de decir que él le otorgaba el don que le pedía. No esperaba yo menos de la gran magnificencia vuestra, señor mío, respondió D. Quijote; y así os digo que el don que os he pedido, y de vuestra liberalidad me ha sido otorgado, es que mañana, en aquel día, me habéis de armar caballero, y esta noche en la capilla de este vuestro castillo velaré las armas; y mañana, como tengo dicho, se cumplirá lo que tanto deseo, para poder, como se debe, ir por todas las cuatro partes del mundo buscando las aventuras en pro de los menesterosos, como está a cargo de la caballería y de los caballeros andantes, como yo soy, cuyo deseo a semejantes fazañas es inclinado. +El ventero, que como está dicho, era un poco socarrón, y ya tenía algunos barruntos de la falta de juicio de su huésped, acabó de creerlo cuando acabó de oír semejantes razones, y por tener que reír aquella noche, determinó seguirle el humor; así le dijo que andaba muy acertado en lo qeu deseaba y pedía, y que tal prosupuesto era propio y natural de los caballeros tan principales como él parecía, y como su gallarda presencia mostraba, y que él ansimesmo, en los años de su mocedad se había dado a aquel honroso ejercicio, andando por diversas partes del mundo buscando sus aventuras, sin que hubiese dejado los percheles de Málaga, islas de Riarán, compás de Sevilla, azoguejo de Segovia, la olivera de Valencia, rondilla de Granada, playa de Sanlúcar, potro de Córdoba, y las ventillas de Toledo, y otras diversas partes donde había ejercitado la ligereza de sus pies y sutileza de sus manos, haciendo muchos tuertos, recuestando muchas viudas, deshaciendo algunas doncellas, y engañando a muchos pupilos, y finalmente, dándose a conocer por cuantas audiencias y tribunales hay casi en toda España; y que a lo último se había venido a recoger a aquel su castillo, donde vivía con toda su hacienda y con las ajenas, recogiendo en él a todos los caballeros andantes de cualquiera calidad y condición que fuesen, sólo por la mucha afición que les tenía, y porque partiesen con él de su shaberes en pago de su buen deseo. Díjole también que en aquel su castillo no había capilla alguna donde poder velar las armas, porque estaba derribada para hacerla de nuevo; pero en caso de necesidad él sabía que se podían velar donde quiera, y que aquella noche las podría velar en un patio del castillo; que a la mañana, siendo Dios servido, se harían las debidas ceremonias de manera que él quedase armado caballero, y tan caballero que no pudiese ser más en el mundo. +Preguntóle si traía dineros: respondió Don Quijote que no traía blanca, porque él nunca había leído en las historias de los caballeros andantes que ninguno los hubiese traído. A esto dijo el ventero que se engañaba: que puesto caso que en las historias no se escribía, por haberles parecido a los autores de ellas que no era menester escribir una cosa tan clara y tan necesaria de traerse, como eran dineros y camisas limpias, no por eso se había de creer que no los trajeron; y así tuviese por cierto y averiguado que todos los caballeros andantes (de que tantos libros están llenos y atestados) llevaban bien erradas las bolsas por lo que pudiese sucederles, y que asimismo llevaban camisas y una arqueta pequeña llena de ungüentos para curar las heridas que recibían, porque no todas veces en los campos y desiertos, donde se combatían y salían heridos, había quien los curase, si ya no era que tenían algún sabio encantador por amigo que luego los socorría, trayendo por el aire, en alguna nube, alguna doncella o enano con alguna redoma de agua de tal virtud, que en gustando alguna gota de ella, luego al punto +quedaban sanos de sus llagas y heridas, como si mal alguno no hubiesen tenido; mas que en tanto que esto no hubiese, tuvieron los pasados caballeros por cosa acertada que sus escuderos fuesen proveídos de dineros y de otras cosas necesarias, como eran hilas y ungüentos para curarse; y cuando sucedía que los tales caballeros no tenían escuderos (que eran pocas y raras veces), ellos mismos lo llevaban todo en unas alforjas muy sutiles, que casi no se parecían a las ancas del caballo, como que era otra cosa de más importancia; porque no siendo por ocasión semejante, esto de llevar alforjas no fue muy admitido entre los caballeros andantes; y por esto le daba por consejo (pues aún se lo podía mandar como a su ahijado, que tan presto lo había de ser), que no caminase de allí adelante sn dineros y sin las prevenciones referidas, y que vería cuán bien se hallaba con ellas cuando menos se pensase. Prometióle don Quijote de hacer lo que se le aconsejaba con toda puntualidad; y así se dió luego orden como velase las armas en un corral grande, que a un lado de la venta estaba, y recogiéndolas Don Quijote todas, las puso sobre una pila que junto a un pozo estaba, y embrazando su adarga, asió de su lanza, y con gentil continente se comenzó a pasear delante de la pila; y cuando comenzó el paseo, comenzaba a cerrar la noche. +Contó el ventero a todos cuantos estaban en la venta la locura de su huésped, la vela de las armas y la armazón de caballería que esperaba. Admirándose de tan extraño género de locura, fuéronselo a mirar desde lejos, y vieron que, con sosegado ademán, unas veces se paseaba, otras arrimado a su lanza ponía los ojos en las armas sin quitarlos por un buen espacio de ellas. Acabó de cerrar la noche; pero con tanta claridad de la luna, que podía competir con el que se le prestaba, de manera que cuanto el novel caballero hacía era bien visto de todos. +Antojósele en esto a uno de los arrieros que estaban en la venta ir a dar agua a su recua, y fue menester quitar las armas de Don Quijote, que estaban sobre la pila, el cual, viéndole llegar, en voz alta le dijo: ¡Oh tú, quienquiera que seas, atrevido caballero, que llegas a tocar las armas del más valeroso andante que jamás se ciñó espada, mira lo que haces, y no las toques, si no quieres dejar la vida en pago de tu atrevimiento! No se curó el arriero de estas razones (y fuera mejor que se curara, porque fuera curarse en salud); antes, trabando de las correas, las arrojó gran trecho de sí, lo cual visto por Don Quijote, alzó los ojos al cielo, y puesto el pensamiento (a lo que pareció) en su señora Dulcinea, dijo: acorredme, señora mía, en esta primera afrenta que a este vuestro avasallado pecho se le ofrece; no me desfallezca en este primero trance vuestro favor y amparo: y diciendo estas y otras semejantes razones, soltando la adarga, alzó la lanza a dos manos y dió con ella tan gran golpe al arriero en la cabeza, que le derribó en el suelo tan maltrecho, que, si secundara con otro, no tuviera necesidad de maestro que le curara. Hecho esto, recogió sus armas, y tornó a pasearse con el mismo reposo que primero. Desde allí a poco, sin saberse lo que había pasado (porque aún estaba aturdido el arriero), llegó otro con la misma intención de dar agua a sus mulos; y llegando a quitar las armas para desembarazar la pila, sin hablar Don Quijote palabra, y sin pedir favor a nadie, soltó otra vez la adarga, y alzó otra vez la lanza, y sin hacerla pedazos hizo más de tres la cabeza del segundo arriero, porque se la abrió por cuatro. Al ruido acudió toda la gente de la venta, y entre ellos el ventero. Viendo esto Don Quijote, embrazó su adarga, y puesta mano a su espada, dijo: ¡Oh, señora de la fermosura, esfuerzo y vigor del debilitado corazón mío, ahora es tiempo que vuelvas los ojos de tu grandeza a este tu cautivo caballero, que tamaña aventura está atendiendo! Con esto cobró a su parecer tanto ánimo, que si le acometieran todos los arrieros del mundo, no volviera el pie atrás. Los compañeros de los heridos que tales los vieron, comenzaron desde lejos a llover piedras sobre Don Quijote, el cual lo mejor que podía se reparaba con su adarga y no se osaba apartar de la pila por no desamparar las armas. El ventero daba voces que le dejasen, porque ya les había dicho como era loco, y que por loco se libraría, aunque los matase a todos. También Don Quijote las daba mayores, llamándolos de alevosos y traidores, y que el señor del castillo era un follón y mal nacido caballero, pues de tal manera consentía que se tratasen los andantes caballeros, y que si él +hubiera recibido la orden de caballería, que él le diera a entender su alevosía; pero de vosotros, soez y baja canalla, no hago caso alguno: tirad, llegad, venid y ofendedme en cuanto pudiéredes, que vosotros veréis el pago que lleváis de vuestra sandez y demasía. Decía esto con tanto brío y denuedo, que infundió un terrible temor en los que le acometían; y así por esto como por las persuasiones del ventero, le dejaron de tirar, y él dejó retirar a los heridos, y tornó a la vela de sus armas con la misma quietud y sosiego que primero. +No le parecieron bien al ventero las burlas de su huésped, y determinó abreviar y darle la negra orden de caballería luego, antes que otra desgracia sucediese; y así, llegándose a él se disculpó de la insolencia que aquella gente baja con él había usado, sin que él supiese cosa alguna; pero que bien castigado quedaban de su atrevimiento. Díjole, como ya le había dicho, que en aquel castillo no había capilla, y para lo que restaba de hacer tampoco era necesaria; que todo el toque de quedar armado caballero consistía en la pescozada y en el espaldarazo, según él tenía noticia del ceremonial de la orden, y que aquello en mitad de un campo se podía hacer; y que ya había cumplido con lo que tocaba al elar de las armas, que con solas dos horas de vela se cumplía, cuanto más que él había estado más de cuatro. Todo se lo creyó Don Quijote, y dijo que él estaba allí pronto para obedecerle, y que concluyese con la mayor brevedad que pudiese; porque si fuese otra vez acometido, y se viese armado caballero, no pensaba dejar persona viva en el castillo, excepto aquellas que él le mandase, a quien por su respeto dejaría. Advertido y medroso de esto el castellano, trajo luego un libro donde asentaba la paja y cebada que daba a los arrieros, y con un cabo de vela que le traía un muchacho, y con las dos ya dichas doncellas, se vino a donde Don Quijote estaba, al cual mandó hincar de rodillas, y leyendo en su manual como que decía alguna devota oración, en mitad de la leyenda alzó la mano, y dióle sobre el cuello un buen golpe, y tras él con su misma espada un gentil espaldarazo, siempre murmurando entre dientes como que rezaba. Hecho esto, mandó a una de aquellas damas que le ciñese la espada, la cual lo hizo con mucha desenvoltura y discreción, porque no fue menester poca para no reventar de risa a cada punto de las ceremonias; pero las proezas que ya habían visto del novel caballero les tenía la risa a raya. Al ceñirle la espada dijo la buena señora: Dios haga a vuestra merced muy venturoso caballero, y le dé ventura en lides. Don Quijote le preguntó como se llamaba, porque él supiese de allí adelante a quién quedaba obligado por la merced recibida, porque pensaba darle alguna parte de la honra que alcanzase por el valor de su brazo. Ella respondió con mucha humildad que se llamaba la Tolosa, y que era hija de un remendón, natural de Toledo, que vivía a las tendillas de Sancho Bienaya, y que donde quiera que ella estuviese le serviría y le tendría por señor. Don Quijote le replicó que por su amor le hiciese merced, que de allí en adelante se pusiese don, y se llamase doña Tolosa. Ella se lo prometió; y la otra le calzó la espuela, con la cual le pasó casi el mismo coloquio que con la de la espada. Preguntóle su nombre, y dijo que se llamaba la Molinera, y que era hija de un honrado molinero de Antequera; a la cual también rogó Don Quijote que se pusiese don, y se llamase doña Molinera, ofreciéndole nuevos servicios y mercedes. +Hechas, pues, de galope y aprisa las hasta allí nunca vistas ceremonias, no vió la hora Don Quijote de verse a caballo y salir buscando las aventuras; y ensillando luego a Rocinante, subió en él, y abrazando a su huésped, le dijo cosas tan extrañas, agradeciéndole la merced de haberle armado caballero, que no es posible acertar a referirlas. El ventero, por verle ya fuera de la venta, con no menos retóricas, aunque con más breves palabras, respondió a las suyas, y sin pedirle la costa de la posada, le dejó ir a la buena hora. +Capítulo 4: De lo que le sucedió a nuestro caballero cuando salió de la venta +La del alba sería cuando Don Quijote salió de la venta, tan contento, tan gallardo, tan alborozado por verse ya armado caballero, que el gozo le reventaba por las cinchas del caballo. Mas viniéndole a la memoria los consejos de su huésped acerca de las prevenciones tan necesarias que había de llevar consigo, en especial la de los dineros y camisas, determinó volver a su casa y acomodarse de todo, y de un escudero, haciendo cuenta de recibir a un labrador vecino suyo, que era pobre y con hijos, pero muy a propósito para el oficio escuderil de la caballería. Con este pensamiento guió a Rocinante hacia su aldea, el cual casi conociendo la querencia, con tanta gana comenzó a caminar, que parecía que no ponía los pies en el suelo. No había andado mucho, cuando le pareció que a su diestra mano, de la espesura de un bosque que allí estaba, salían unas voces delicadas, como de persona que se quejaba; y apenas las hubo oído, cuando dijo: gracias doy al cielo por la merced que me hace, pues tan presto me pone ocasiones delante, donde yo pueda cumplir con lo que debo a mi profesión, y donde pueda coger el fruto de mis buenos deseos: estas voces sin duda son de algún menesteroso o menesterosa, que ha menester mi favor y ayuda: y volviendo las riendas encaminó a Rocinante hacia donde le pareció que las voces salían; y a pocos pasos que entró por el bosque, vió atada una yegua a una encina, y atado en otra un muchacho desnudo de medio cuerpo arriba, de edad de quince años, que era el que las voces daba y no sin causa, porque le estaba dando con una pretina muchos azotes un labrador de buen talle, y cada azote le acompañaba con una reprensión y consejo, porque decía: la lengua queda y los ojos listos. Y el muchacho respondía: no lo haré otra vez, señor mío; por la pasión de Dios, que no lo haré otra vez, y yo prometo de tener de aquí adelante más cuidado con el hato. Y viendo Don Quijote lo que pasaba, con voz airada dijo: descortés caballero, mal parece tomaros con quien defender no se puede; subid sobre vuestro caballo y tomad vuestra lanza, (que también tenía una lanza arrimada a la encina, adonde estaba arrendada la yegua) que yo os haré conocer ser de cobardes lo que estáis haciendo. +El labrador, que vió sobre sí aquella figura llena de armas, blandiendo la lanza sobre su rostro, túvose por muerto, y con buenas palabras respondió: señor caballero, este muchacho que estoy castigando es un mi criado, que me sirve de guardar una manada de ovejas que tengo en estos contornos, el cual es tan descuidado que cada día me falta una, y porque castigo su descuido o bellaquería, dice que lo hago de miserable, por no pagarle la soldada que le debo, y en Dios y en mi ánima que miente. ¿Miente, delante de mí, ruin villano? dijo Don Quijote. Por el sol que nos alumbra, que estoy por pasaros de parte a parte con esta lanza: pagadle luego sin más réplica; si no, por el Dios que nos rige, que os concluya y aniquile en este punto: desatadlo luego. El labrador bajó la cabeza, y sin responder palabra desató a su criado, al cual preguntó Don Quijote que cuánto le debía su amo. El dijo que nueve meses, a siete reales cada mes. Hizo la cuenta Don Quijote, y halló que montaban sesenta y tres reales, y díjole al labrador que al momento los desembolsase, si no quería morir por ello. Respondió el medroso villano, que por el paso en que estaba y juramento que había hecho (y aún no había jurado nada), que no eran tantos, porque se le había de descontar y recibir en cuenta tres pares de zapatos que le había dado, y un real de dos sangrías que le habían hecho estando enfermo. Bien está todo eso, replicó Don Quijote; pero quédense los zapatos y las sangrías por los azotes que sin culpa le habéis dado, que si él rompió el cuero de los zapatos que vos pagásteis, vos le habéis rompido el de su cuerpo, y si le sacó el barbero sangre estando enfermo, vos en sanidad se la habéis sacado; así que por esta parte no os debe nada. El daño está, señor caballero, en que no tengo aquí dineros: véngase Andrés conmigo a mi casa, que yo se los pagaré un real sobre otro. +¿Irme yo con él, dijo el muchacho, más? ¡Mal año! No, señor, ni por pienso, porque en viéndose solo me desollará como a un San Bartolomé. No hará tal, replicó Don +Quijote; basta que yo se lo mande para que me tenga respeto, y con que él me lo jure por la ley de caballería que ha recibido, le dejaré ir libre y aseguraré la paga. Mire vuestra merced, señor, lo que dice, dijo el muchacho, que este mi amo no es caballero, ni ha recibido orden de caballería alguna, que es Juan Haldudo el rico, vecino del Quintanar. +Importa poco eso, respondió Don Quijote, que Haldudos puede haber caballeros, cuanto más que cada uno es hijo de sus obras. Así es verdad, dijo Andrés; pero este mi amo, ¿de qué obras es hijo, pues me niega mi soldada y mi sudor y trabajo? No niego, hermano Andrés, respondió el labrador, y hacedme placer de veniros conmigo, que yo juro, por todas las órdenes de caballerías hay en el mundo, de pagaros, como tengo dicho, un real sobre otro, y aun sahumados. Del sahumerio os hago gracia, dijo Don Quijote, dádselos en reales, que con esto me contento; y mirad que lo cumpláis como lo habéis jurado; si no, por el mismojuramento os juro de volver a buscaros y a castigaros, y que os tengo de hallar aunque os escondáis más que una lagartija. Y si queréis saber quién os manda esto, para quedar con más veras obligado a cumplirlo, sabed que yo soy el valeroso Don Quijote de la Mancha, el desfacedor de agravios y sinrazones; y a Dios quedad, y no se os parta de las mientes lo prometido y jurado, so pena de la pena pronunciada. +Y en diciendo esto picó a su Rocinante, y en breve espacio se apartó de ellos. Siguióle el labrador con los ojos, y cuando vió que había traspuesto el bosque y que ya no parecía, volvióse a su criado Andrés y díjole: Venid acá, hijo mío, que os quiero pagar lo que os debo, como aquel desfacedor de agravios me dejó mandado. Eso juro yo, dijo Andrés, y como que andará vuestra merced acertado en cumplir el mandamiento de aquel buen caballero, que mil años viva, que según es de valeroso y de buen jue, vive Roque, que si no me paga, que vuelva y ejecute lo que dijo. También lo juro yo, dijo el labrador; pero por lo mucho que os quiero, quiero acrecentar la deuda por acrecentar la paga. Y asiéndolo del brazo, le tornó a atar a la encina, donde le dió tantos azotes, que le dejó por muerto. Llamad, señor Andrés, ahora, decía el labrador, al desfacedor de agravios, veréis cómo no desface aqueste, aunque creo que no está acabado de hacer, porque me viene gana de desollaros vivo, como vos temíades.9092/tcp +lab1_zookeeper_1 /etc/confluent/docker/run Up 0.0.0.0:2181->2181/tcp, 2888/tcp, 3888/tcp +``` + +## Set up the Docker Connect image + +### Create a Postgresql database and table +Follow instruction at https://github.com/a10pepo/postgressetup to just create a new server with the a postgresql database. +Then create a new table named after "aeroplane". It must have an "id" int sequencer column and a "name" varchar column. +```sql +CREATE TABLE public.aeroplane +( +id bigint NOT NULL, +name character varying COLLATE pg_catalog."default", +CONSTRAINT aeroplane_pkey PRIMARY KEY (id) +) +```` + +### Install nano editor +From below command, change 5e1fee5e...by your Docker Coonect id process. You can find it with docker ps or with the Docker UI: +docker exec -u 0 -it 5e1fee5e286e9f75759780df8b35faa0b57cf0d7dd348a586b6768a1c0174999 bash +yum install nano +exit +![img_1.png](images/img_1.png) + +### Install and configure the JDBC Kafka connector to connect to postgresql (it could be also used to connnect to MySql or Oracle databases) +Perform below instructions inside your Docker Connect instance +![img_2.png](images/img_2.png) + +confluent-hub install confluentinc/kafka-connect-jdbc:10.7.4 +![img.png](images/img.png) + + +nano /etc/schema-registry/connect-avro-distributed.properties +Add /usr/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib/ as value of the key "plugin.path". Use the +separator ",". +When edited , type Control-X(in windows) or Command-X(in Mac) to save it. +![img_3.png](images/img_3.png) + + +Restart docker container + +Then upload the file postgresqlSourceConnectConfig.json + +![img_4.png](images/img_4.png) + +And Launch it: +![img_5.png](images/img_5.png) + +Check it is running OK: +![img_6.png](images/img_6.png) + + +Insert a new row into the database: +```sql +insert into aeroplane (id, name) values (0,'airbus 320'); +``` + +![img_8.png](images/img_8.png) + +Check the topic has been created and a new message inserted: +![img_7.png](images/img_7.png) + + \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise6/docker-compose.yml b/PROFESORES/KAFKA/src/main/python/exercise6/docker-compose.yml new file mode 100644 index 000000000..62ef4b4a0 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise6/docker-compose.yml @@ -0,0 +1,63 @@ +--- +version: '3.5' +services: + zookeeper: + image: confluentinc/cp-zookeeper:latest + environment: + ZOOKEEPER_CLIENT_PORT: 2181 + ZOOKEEPER_TICK_TIME: 2000 + ports: + - 2181:2181 + + kafka: + image: confluentinc/cp-kafka:7.3.3 + depends_on: + - zookeeper + ports: + - 9092:9092 + environment: + KAFKA_BROKER_ID: 1 + KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181 + KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:29092,PLAINTEXT_HOST://localhost:9092 + KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT + KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT + KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1 + + ksql-server: + image: confluentinc/cp-ksql-server:latest + depends_on: + - kafka + ports: + - 8088:8088 + environment: + KSQL_BOOTSTRAP_SERVERS: kafka:29092 + KSQL_LISTENERS: http://0.0.0.0:8088 + + ksql-cli: + image: confluentinc/cp-ksql-cli:latest + depends_on: + - ksql-server + entrypoint: /bin/sh + tty: true + + control-center: + image: confluentinc/cp-enterprise-control-center:7.3.3 + hostname: control-center + container_name: control-center + depends_on: + - kafka + - ksql-server + ports: + - "9021:9021" + environment: + CONTROL_CENTER_BOOTSTRAP_SERVERS: 'kafka:29092' + CONTROL_CENTER_CONNECT_CONNECT-DEFAULT_CLUSTER: 'kafka-connect:8083' + CONTROL_CENTER_KSQL_KSQLDB1_URL: "http://ksql-server:8088" + CONTROL_CENTER_KSQL_KSQLDB1_ADVERTISED_URL: "http://localhost:8088" + CONTROL_CENTER_SCHEMA_REGISTRY_URL: "http://schema-registry:8081" + CONTROL_CENTER_REPLICATION_FACTOR: 1 + CONTROL_CENTER_INTERNAL_TOPICS_PARTITIONS: 1 + CONTROL_CENTER_MONITORING_INTERCEPTOR_TOPIC_PARTITIONS: 1 + CONFLUENT_METRICS_TOPIC_REPLICATION: 1 + CONTROL_CENTER_CONNECT_HEALTHCHECK_ENDPOINT: '/connectors' + PORT: 9021 \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise6/images/img.png b/PROFESORES/KAFKA/src/main/python/exercise6/images/img.png new file mode 100644 index 0000000000000000000000000000000000000000..115402c0306ceaed44e4ee231f1f89168dd5f624 GIT binary patch literal 86753 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a/PROFESORES/KAFKA/src/main/python/exercise6/postgresqlSourceConnectConfig.json b/PROFESORES/KAFKA/src/main/python/exercise6/postgresqlSourceConnectConfig.json new file mode 100644 index 000000000..34e07b442 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise6/postgresqlSourceConnectConfig.json @@ -0,0 +1,15 @@ +{ + "name": "sourcePostgresql", + "config":{ + "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector", + "task.max": "1", + "connection.url": "jdbc:postgresql://postgres:5432/postgres", + "connection.user": "postgres", + "connection.password": "Welcome01", + "topic.prefix": "xflight", + "validate.none.null": "false", + "mode": "incrementing", + "incrementing.column.name": "id", + "table.whitelist": "aeroplane" + } +} \ No newline at end of file diff --git a/PROFESORES/KAFKA/src/main/python/exercise7/README.md b/PROFESORES/KAFKA/src/main/python/exercise7/README.md new file mode 100644 index 000000000..3d87a200e --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise7/README.md @@ -0,0 +1,53 @@ +# Ejercicios Prácticos con Kafka: Producer y Consumer + +Este repositorio contiene ejercicios prácticos para trabajar con **Kafka Producer** y **Kafka Consumer** utilizando un JSON con datos de transferencias bancarias. Todos los ejercicios se realizan exclusivamente por consola, sin necesidad de bases de datos. + +## Ejercicios + +### 1. Ejercicio de Producción de Datos + +- **Objetivo**: Configurar un productor de Kafka que lea el JSON de transferencias bancarias y lo envíe como mensajes a un tópico de Kafka. +- **Acción**: El productor envía cada transferencia como un mensaje JSON al tópico y muestra por consola cada mensaje enviado. + +### 2. Ejercicio de Consumo de Datos + +- **Objetivo**: Configurar un consumidor de Kafka que lea los mensajes desde el tópico donde se publican las transferencias. +- **Acción**: El consumidor deserializa los mensajes JSON y los muestra por consola, mostrando el detalle de cada transferencia. + +### 3. Filtrado de Transferencias por País + +- **Objetivo**: Filtrar y mostrar solo las transferencias de países específicos (ej. paraísos fiscales). +- **Acción**: El consumidor solo muestra las transferencias cuyo país de origen o destino sea **Islas Caimán** o **Singapur**, imprimiendo solo esos registros por consola. + +### 4. Transformación de Datos (Suma de Montos) + +- **Objetivo**: Transformar los datos y calcular el monto total de transferencias por país. +- **Acción**: El consumidor agrupa las transferencias por país de origen y muestra la suma total de los montos por país, todo en consola. + +### 5. Almacenamiento Temporal en Memoria (Solo por Consola) + +- **Objetivo**: Procesar solo las transferencias con estado **"Completada"**. +- **Acción**: El consumidor filtra las transferencias con estado "Completada" y las imprime por consola. + +### 6. Tópico de Transacciones Pendientes + +- **Objetivo**: Filtrar y mostrar solo las transferencias con estado **"Pendiente"**. +- **Acción**: El consumidor filtra las transferencias con estado "Pendiente" y las muestra por consola, indicando que están pendientes de procesar. + +## Requisitos + +- **Apache Kafka** instalado y corriendo. +- **Kafka Python (confluent-kafka)** para la implementación de Producer y Consumer. +- **JSON de Transferencias Bancarias** que contiene información como el ID de la transferencia, país de origen/destino, monto, etc. + +## Ejecución + +1. **Ejecutar el Productor**: + - El productor enviará los datos de las transferencias al tópico de Kafka. + +2. **Ejecutar el Consumidor**: + - El consumidor leerá los mensajes de Kafka y los procesará según el ejercicio. + +## Contribuciones + +Si deseas contribuir, por favor haz un fork del repositorio, realiza los cambios necesarios y crea un pull request. diff --git a/PROFESORES/KAFKA/src/main/python/exercise7/transferencias.txt b/PROFESORES/KAFKA/src/main/python/exercise7/transferencias.txt new file mode 100644 index 000000000..f14402d6e --- /dev/null +++ b/PROFESORES/KAFKA/src/main/python/exercise7/transferencias.txt @@ -0,0 +1,21 @@ +{"id_transferencia":"T001","fecha":"2024-11-21T10:15:00Z","cuenta_origen":"KY12345678901234567890","pais_origen":"Islas Caimán","cuenta_destino":"ES09876543210987654321","pais_destino":"España","monto":2500.75,"moneda":"EUR","concepto":"Pago de alquiler","estado":"Completada"} +{"id_transferencia":"T002","fecha":"2024-11-21T12:30:00Z","cuenta_origen":"SG11223344556677889900","pais_origen":"Singapur","cuenta_destino":"US99887766554433221100","pais_destino":"Estados Unidos","monto":150.00,"moneda":"USD","concepto":"Compra en línea","estado":"Completada"} +{"id_transferencia":"T003","fecha":"2024-11-22T08:45:00Z","cuenta_origen":"FR12345678901234567890","pais_origen":"Francia","cuenta_destino":"GB12345678901234567890","pais_destino":"Reino Unido","monto":5000.00,"moneda":"EUR","concepto":"Transferencia empresarial","estado":"Pendiente"} +{"id_transferencia":"T004","fecha":"2024-11-22T14:00:00Z","cuenta_origen":"DE22334455667788990011","pais_origen":"Alemania","cuenta_destino":"IT12345678901234567890","pais_destino":"Italia","monto":750.25,"moneda":"EUR","concepto":"Pago de proveedores","estado":"Completada"} +{"id_transferencia":"T005","fecha":"2024-11-23T09:20:00Z","cuenta_origen":"US99887766554433221100","pais_origen":"Estados Unidos","cuenta_destino":"ES11223344556677889900","pais_destino":"España","monto":500.00,"moneda":"USD","concepto":"Compra de tecnología","estado":"Completada"} +{"id_transferencia":"T006","fecha":"2024-11-23T11:00:00Z","cuenta_origen":"KY11223344556677889900","pais_origen":"Islas Caimán","cuenta_destino":"ES55667788990011223344","pais_destino":"España","monto":1250.00,"moneda":"EUR","concepto":"Pago de dividendos","estado":"Completada"} +{"id_transferencia":"T007","fecha":"2024-11-24T16:10:00Z","cuenta_origen":"SG22334455667788990011","pais_origen":"Singapur","cuenta_destino":"DE99887766554433221100","pais_destino":"Alemania","monto":350.00,"moneda":"EUR","concepto":"Transferencia comercial","estado":"Pendiente"} +{"id_transferencia":"T008","fecha":"2024-11-24T18:00:00Z","cuenta_origen":"DE33445566778899001122","pais_origen":"Alemania","cuenta_destino":"IT12345678901234567890","pais_destino":"Italia","monto":4000.75,"moneda":"USD","concepto":"Pago de contrato","estado":"Completada"} +{"id_transferencia":"T009","fecha":"2024-11-25T13:30:00Z","cuenta_origen":"SG99887766554433221100","pais_origen":"Singapur","cuenta_destino":"US22334455667788990011","pais_destino":"Estados Unidos","monto":750.50,"moneda":"USD","concepto":"Compra de acciones","estado":"Completada"} +{"id_transferencia":"T010","fecha":"2024-11-25T15:45:00Z","cuenta_origen":"KY55667788990011223344","pais_origen":"Islas Caimán","cuenta_destino":"IT44556677889900112233","pais_destino":"Italia","monto":2000.00,"moneda":"EUR","concepto":"Pago por consultoría","estado":"Completada"} +{"id_transferencia":"T011","fecha":"2024-11-26T08:10:00Z","cuenta_origen":"SG55667788990011223344","pais_origen":"Singapur","cuenta_destino":"GB33445566778899001122","pais_destino":"Reino Unido","monto":1000.00,"moneda":"GBP","concepto":"Préstamo personal","estado":"Completada"} +{"id_transferencia":"T012","fecha":"2024-11-26T11:45:00Z","cuenta_origen":"DE66778899001122334455","pais_origen":"Alemania","cuenta_destino":"ES55667788990011223344","pais_destino":"España","monto":300.00,"moneda":"EUR","concepto":"Pago de factura","estado":"Completada"} +{"id_transferencia":"T013","fecha":"2024-11-27T09:25:00Z","cuenta_origen":"US22334455667788990011","pais_origen":"Estados Unidos","cuenta_destino":"GB22334455667788990011","pais_destino":"Reino Unido","monto":1500.50,"moneda":"GBP","concepto":"Inversión inmobiliaria","estado":"Completada"} +{"id_transferencia":"T014","fecha":"2024-11-27T14:00:00Z","cuenta_origen":"SG22334455667788990011","pais_origen":"Singapur","cuenta_destino":"IT66778899001122334455","pais_destino":"Italia","monto":850.00,"moneda":"EUR","concepto":"Pago por servicios legales","estado":"Completada"} +{"id_transferencia":"T015","fecha":"2024-11-28T08:30:00Z","cuenta_origen":"FR11223344556677889900","pais_origen":"Francia","cuenta_destino":"ES66778899001122334455","pais_destino":"España","monto":1250.50,"moneda":"EUR","concepto":"Pago por software","estado":"Completada"} +{"id_transferencia":"T016","fecha":"2024-11-28T11:00:00Z","cuenta_origen":"US33445566778899001122","pais_origen":"Estados Unidos","cuenta_destino":"FR66778899001122334455","pais_destino":"Francia","monto":4500.00,"moneda":"USD","concepto":"Venta de propiedad","estado":"Pendiente"} +{"id_transferencia":"T017","fecha":"2024-11-29T10:15:00Z","cuenta_origen":"US22334455667788990011","pais_origen":"Estados Unidos","cuenta_destino":"GB55667788990011223344","pais_destino":"Reino Unido","monto":3200.00,"moneda":"GBP","concepto":"Transferencia de fondos","estado":"Completada"} +{"id_transferencia":"T018","fecha":"2024-11-29T12:40:00Z","cuenta_origen":"IT22334455667788990011","pais_origen":"Italia","cuenta_destino":"DE55667788990011223344","pais_destino":"Alemania","monto":800.75,"moneda":"EUR","concepto":"Pago de proveedores","estado":"Completada"} +{"id_transferencia":"T019","fecha":"2024-11-30T14:15:00Z","cuenta_origen":"US22334455667788990011","pais_origen":"Estados Unidos","cuenta_destino":"ES22334455667788990011","pais_destino":"España","monto":950.25,"moneda":"USD","concepto":"Pago de inversión","estado":"Completada"} +{"id_transferencia":"T020","fecha":"2024-11-30T17:20:00Z","cuenta_origen":"SG33445566778899001122","pais_origen":"Singapur","cuenta_destino":"IT33445566778899001122","pais_destino":"Italia","monto":720.00,"moneda":"EUR","concepto":"Pago de salario","estado":"Completada"} +{"id_transferencia":"T021","fecha":"2024-12-01T10:00:00Z","cuenta_origen":"KY11223344556677889900","pais_origen":"Islas Caimán","cuenta_destino":"IT11223344556677889900","pais_destino":"Italia","monto":1500.00,"moneda":"EUR","concepto":"Donación","estado":"Completada"} diff --git a/PROFESORES/KAFKA/src/main/resources/client.properties b/PROFESORES/KAFKA/src/main/resources/client.properties new file mode 100644 index 000000000..ae4889c61 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/resources/client.properties @@ -0,0 +1,30 @@ + +########## LINES TO BE MODIFIED ########## + +# Required connection configs for Kafka producer, consumer, and admin +bootstrap.servers=pkc-mxqvx.europe-southwest1.gcp.confluent.cloud:9092 +security.protocol=SASL_SSL +sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='PS24DDNH3WJY5JHH' password='J4arpeSN+ztKQhEy8K6AQHU8959YO6iu0eRxHvgK3tOa2dOK7+IKImNOrcYEvsuV'; +########################################## + + +sasl.mechanism=PLAIN +# Required for correctness in Apache Kafka clients prior to 2.6 +client.dns.lookup=use_all_dns_ips + +# Best practice for higher availability in Apache Kafka clients prior to 3.0 +session.timeout.ms=45000 + +# Best practice for Kafka producer to prevent data loss +acks=all + +# Serializer configurations +key.serializer=org.apache.kafka.common.serialization.StringSerializer +value.serializer=org.apache.kafka.common.serialization.StringSerializer + + +# Deserializer configurations + +key.deserializer=org.apache.kafka.common.serialization.StringDeserializer +value.deserializer=org.apache.kafka.common.serialization.StringDeserializer + diff --git a/PROFESORES/KAFKA/src/main/resources/confluent-cloud/README.md b/PROFESORES/KAFKA/src/main/resources/confluent-cloud/README.md new file mode 100644 index 000000000..1847ec8a6 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/resources/confluent-cloud/README.md @@ -0,0 +1 @@ +https://confluent.cloud/learn diff --git a/PROFESORES/KAFKA/src/main/resources/log4j.properties b/PROFESORES/KAFKA/src/main/resources/log4j.properties new file mode 100644 index 000000000..01f171120 --- /dev/null +++ b/PROFESORES/KAFKA/src/main/resources/log4j.properties @@ -0,0 +1,20 @@ +log4j.rootLogger=WARN, stdout + +log4j.appender.stdout=org.apache.log4j.ConsoleAppender +log4j.appender.stdout.layout=org.apache.log4j.PatternLayout +log4j.appender.stdout.layout.ConversionPattern=[%d] %p [%t] %m (%c)%n + +# Enable for debugging if need be +#log4j.logger.org.apache.kafka.streams=DEBUG, stdout +#log4j.additivity.org.apache.kafka.streams=false + +# Squelch expected error messages like: +# java.lang.IllegalStateException: This consumer has already been closed. +log4j.logger.org.apache.kafka.streams.processor.internals.StreamThread=FATAL, stdout +log4j.logger.org.apache.kafka.clients.consumer.internals.Fetcher=WARN +# adjusting logging for entire Kafka +log4j.logger.org.apache.kafka=INFO + +# Enable for debugging if need be +#log4j.logger.io.confluent=DEBUG, stdout +#log4j.additivity.io.confluent=false \ No newline at end of file diff --git a/PROFESORES/LINUX/README.md b/PROFESORES/LINUX/README.md new file mode 100644 index 000000000..250319e78 --- /dev/null +++ b/PROFESORES/LINUX/README.md @@ -0,0 +1,91 @@ +# Linux_Comandos + +Para entregar este ejercicio, debes copiar este archivo en tu carpeta de alumno y completar las respuestas a las preguntas que se formulan en el mismo. +Una vez completado, debes subirlo a vuestro repositorio remoto de GitHub y realizar una Pull Request poniendo a Pedro Nieto como reviewer. + + +Ejercicio de comandos en la consola de linux. + + 1.Listar todos los archivos del directorio bin. + + + + + 2.Listar todos los archivos del directorio tmp. + + + + 3.Listar todos los archivos del directorio etc que empiecen por t + + + + 4.Listar todos los archivos del directorio dev que empiecen por tty. + + + + 5.Listar todos los archivos del directorio dev que empiecen por tty y acaben en 3. + + + + 6.Listar todos los archivos del directorio dev que empiecen por t y acaben en C1. + + + + 7.Listar todos los archivos, incluidos los ocultos, del directorio raíz. + + + + 8.Listar todos los archivos del directorio etc que no empiecen por t. + + + + 9.Listar todos los archivos del directorio usr y sus subdirectorios. + + + + 10.Cambiarse al directorio tmp, crear directorio PRUEBA. + + + + 11.Verificar que el directorio actual ha cambiado. + + + + 12.Mostrar el día y la hora actual. + + + + 13.Con un solo comando posicionarse en el directorio $HOME. + + + + 14.Verificar que se está en él. + + + + 15.Listar todos los ficheros del directorio HOME mostrando sus permisos. + + + + 16.Borrar todos los archivos y directorios visibles de vuestro directorio PRUEBA. + + + + 17.Crear los directorios dir1, dir2 y dir3 en el directorio PRUEBA. Dentro de dir1 crear el directorio dir11. Dentro del directorio + dir3 crear el directorio dir31. Dentro del directorio dir31, crear los directorios dir311 y dir312. + + + + 18.Copiar el archivo /etc/motd a un archivo llamado mensaje de vuestro directorio PRUEBA. + + + + 19.Copiar mensaje en dir1, dir2 y dir3. + + + + 20.Comprobar el ejercicio anterior mediante un solo comando. + + + + diff --git a/PROFESORES/LINUX/script.md b/PROFESORES/LINUX/script.md new file mode 100644 index 000000000..ae1677736 --- /dev/null +++ b/PROFESORES/LINUX/script.md @@ -0,0 +1,86 @@ +# Desarrollar un script en Bash + +## Descripción + +En este ejercicio vamos a desarrollar un script en Bash que nos permita realizar una serie de tareas de forma automática. Para ello, vamos a utilizar los conocimientos adquiridos en el módulo de Linux. Para realizar este ejercicio separaremos la forma de probar dichos ejercicios en dos grupos de alumnos (Mac y Windows). +Antes de empezar recordemos las bases del ejercicio: + +1) Las carpetas deben crearse en la carpeta de Usuario de cada alumno dentro de LINUX (Nota: Directorio de Trabajo) +2) El script debe ser capaz de crear las carpetas y los archivos necesarios para realizar las tareas sin intervención del usuario más allá de su ejecución +3) El script debe ser reejecutable sin necesidad de modificarlo, por lo que debe borrar al inicio lo creado en la ejecución anterior +4) El script debe mostrar una leyenda al inicio de cada tarea indicando que tarea se va a realizar (Nota: echo) + +### Comunes + +Abre tu Visual Studio y crea un nuevo archivo llamado `script.sh`. En este archivo vamos a desarrollar el script que nos permita realizar las tareas a automatizar. +Recuerda que el script debe tener siempre la siguiente primera línea: + +```bash +#!/bin/bash +``` + + +### Mac + +Si ya has generado el script toca probarlo, en el caso de Mac debemos ir a la terminal de Visual Studio y ejecutar el siguiente comando: + +```bash +./script.sh +``` +En caso de recibir un error de permisos, debemos darle permisos de ejecución al script. Para ello, ejecutaremos el siguiente comando: + +```bash +chmod 777 script.sh +``` +Una vez ejecutado el script deberemos poder listar los contenidos de las carpetas creadas en el ejercicio y validar que el resultado es correcto. Si es así FEICIDADES, has terminado el ejercicio. + + +### Windows + +Si ya has generado el script toca probarlo, en el caso de Windows como ya hemos comentado en clase debido a la virtualización que usa no podemos ejecutar un script de Linux. Para solucionarlo realizaremos los siguientes pasos: + +1) Dentro de la terminal de Visual Studio Code, ejecutaremos el siguiente comando: + +```bash +docker create -it --name unix ubuntu:latest +docker start unix +docker exec -it unix /bin/bash +apt update +apt install vim +``` + +2) Una vez ejecutados nos aseguraremos que estamos dentro del contenedor, nuestro prompt debe ser algo similar a: + +```bash +root@f0b3c2c3f0b3:/# +``` + +3) Una vez abierto, abriremos la terminal y ejecutaremos los siguientes comandos: + +```bash +vi script.sh +``` + +4) Una vez dentro de Vi pulsaremos lo siguiente para entrar en modo edición: + +```bash +a +``` +Y se activará el modo edición: + +**-- INSERT --** + +5) Ahora copiaremos el contenido del script que hemos generado en Visual Studio Code y lo pegaremos en la terminal de Vi usando el botón derecho del ratón. Una vez pegado, pulsaremos la tecla `ESC` y escribiremos `:wq` para guardar y salir del editor. + +6) Una vez guardado el script, ejecutaremos el siguiente comando para darle permisos de ejecución: + +```bash +chmod 777 script.sh +``` + +7) Una vez ejecutado el script deberemos poder listar los contenidos de las carpetas creadas en el ejercicio y validar que el resultado es correcto. Si es así FEICIDADES, has terminado el ejercicio. Si no es así, puedes ejecutar el siguiente commando que borrará el script y volver al punto 3: + +```bash +rm script.sh +``` + diff --git a/PROFESORES/PYSPARK/1_introduction.pdf b/PROFESORES/PYSPARK/1_introduction.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d63bfe9bfb0ab62d3dd2b49e4c3eb9e612e00945 GIT binary patch literal 1036473 zcma&N1yEhhwk;go9fCUqmyKI+cXx-4ySux)yF<|6!QI{6CAb8)pM2-sckit_uin3^ z)~a5sWzL?n$LQL%dyvZui_!t;*$~Kg_on6$U;zLoW+Fx+TSE&39v%j1a~q(6gSiLL zm^M9r<7fDR0zRt8SMe|2Gy5Z2;h6Jla!;}m3O=U@h~GqEsp2(qvMM3@;F1=v`H zIfO)cc^H{kM45!yn1z@{*jR;FSOpnbg+*9J1pq<JrlFxRvcA5de)t#EC^>;3%s72XknmQ2bv6GW7pSmC zDDmSJ5EvZ{qI_d$GM-iJG%O8X5Dj?vDE06UUtC`>aI7ygLU5=Eu%AKw_n`j{ucMQJ zgOi&B&;$XN5doH*TtrS30rr1}oaOIq{+(7KA~sfz{}@psHjd9k|7*sqEUcfIW@BMu zQ2I(a0W&-;|4%=GFc6R+Kf_A?@tM5tqJFaB@nIsO?(S~N?jn7hmF{ki z9oS56Fe7fXqYb#2&ZykAXt29)mFYun{YUYjLi~0RiK2brb#7lFbz5~quMiwKX&tfO zS!m_;_1&H1^2Ug-LHe>&Sq=T+Nod*^H&;7Gk1moIC`?~$T(!F8fBq>pdT6(fX%o!q 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zhP(YW_YXnEHDH#GE(k%KJpfuRws7}ft8}#gE5Wb1f93OwApf&cQ3RI48tyLEURH2- z@n6R?MCSsi+FQEA-2D*!iw6wi0rMjC|Cf^g!2H*m{xs_RBFL|~h%pT@oC1FzPyZ_8 z@5=fAocNU`i~I(xxjzn`ZF5;G5ue-{v$+xF7RKt{*1Y8mAy$&b7<~})t-qV{@%}O8`OU_&YiOeZa(jk`s*BOh;^>|0_Wq2j{ULOAf0a4G{bu z&8*ZQO+j;ojoLLa!w%6ldUGNTRD)^?YdyHE=x%cir@n7dZW>QcG$J&o;mUl#oSuuX zWaM$Ko*lx^Ifu&~Z_8^NNfA-3Ee^tg$FR zNKlBMR~8JFh01`0_++Jo1i><38Gfj&v=EeEP(WN%P+k@!D`_=7KDPN zi;E{B|7X}dEugl&KOFJnA7|za zwBR;CePb{H0sx!-v4~)|U}=ej%qkxtl5k)3{{1-pkJc=iE%=N=AHt@uE$Tsu)&tk~ z$-bZ@IRd4awI!lR@-WH1BFDytPGd?IkKlr?$fH&#(Vr(`lEI8H$@CR^kA8$scUc^< zMXkDGCf#QMBp(ocGAkhk@K#a}U^Jz>kxdN_#9Q#o14dU^qpZVTHDN$;9m%g;sue}1 zRHMv4mVl3tI+k3xIH%+>1Aseq5&zpA#Y=~rFnx(+Mc611}eBvEA%Bv@($7B xMryhP#z@D2)&3Zvi1Q!^&shEcMt_KTo4cp4JKP2r8w`fxVzaQwsmtSH{~w#H0%`yN literal 0 HcmV?d00001 diff --git a/PROFESORES/PYSPARK/e2e_spark/README.md b/PROFESORES/PYSPARK/e2e_spark/README.md new file mode 100644 index 000000000..4b0faa479 --- /dev/null +++ b/PROFESORES/PYSPARK/e2e_spark/README.md @@ -0,0 +1,15 @@ +# Spark EDEM MDA 2324 + +In this repo we have the code to develop the deliverable Spark exercise. To raise the Spark containers go into the docker folder and launch: +```sh +docker-compose up -d +``` + +Also we leave here the command we will use to launch our Spark apps: +```sh +bash /spark/bin/spark-submit --master local opt/project/src/python/example.py +``` + +For more detailed info about the exercise please refer to the PDF included in this repo 4_diy_etl.pdf. + +Thanks :) \ No newline at end of file diff --git a/PROFESORES/PYSPARK/e2e_spark/docker/docker-compose.yml b/PROFESORES/PYSPARK/e2e_spark/docker/docker-compose.yml new file mode 100644 index 000000000..1e6dc0f26 --- /dev/null +++ b/PROFESORES/PYSPARK/e2e_spark/docker/docker-compose.yml @@ -0,0 +1,26 @@ +version: '3' +services: + # Spark containers + spark-master: + image: metabrainz/spark-master:latest + ports: + - "4040:4040" + - "9090:9090" + - "7077:7077" + volumes: + - ../project:/opt/project + environment: + - "SPARK_LOCAL_IP=spark-master" + + spark-worker: + image: metabrainz/spark-worker:latest + depends_on: + - spark-master + environment: + - SPARK_MASTER=spark://spark-master:7077 + - SPARK_WORKER_CORES=1 + - SPARK_WORKER_MEMORY=1G + - SPARK_DRIVER_MEMORY=128m + - SPARK_EXECUTOR_MEMORY=256m + volumes: + - ../project:/opt/project \ No newline at end of file diff --git a/PROFESORES/PYSPARK/e2e_spark/project/resources/example.csv b/PROFESORES/PYSPARK/e2e_spark/project/resources/example.csv new file mode 100644 index 000000000..192148b27 --- /dev/null +++ b/PROFESORES/PYSPARK/e2e_spark/project/resources/example.csv @@ -0,0 +1,3 @@ +example,no +ex1,1 +ex2,2 \ No newline at end of file diff --git a/PROFESORES/PYSPARK/e2e_spark/project/src/python/example.py b/PROFESORES/PYSPARK/e2e_spark/project/src/python/example.py new file mode 100644 index 000000000..d01b735eb --- /dev/null +++ b/PROFESORES/PYSPARK/e2e_spark/project/src/python/example.py @@ -0,0 +1,9 @@ +from pyspark.sql import SparkSession +spark = SparkSession.builder.getOrCreate() + +# Import sql functions +from pyspark.sql.functions import * + +exampleDF = spark.read.options(header='True', inferSchema='True').csv("/opt/project/resources/example.csv") + +exampleDF.show() \ No newline at end of file diff --git a/PROFESORES/PYSPARK/notebooks/01.DataFramesBascis.ipynb b/PROFESORES/PYSPARK/notebooks/01.DataFramesBascis.ipynb new file mode 100644 index 000000000..3782f4bfa --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/01.DataFramesBascis.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"u3WKxUMvQ9JW"},"source":["# DataFrames Basics"]},{"cell_type":"markdown","metadata":{"id":"IBsRl4yARA3x"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"0KID1ObYRDA4"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":105079,"status":"ok","timestamp":1670605221056,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-9EjQzqhRJSZ"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1670605221057,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"E4s6wcUsSPHQ","outputId":"feab5ebb-a7f9-4078-c85d-e112383efe5b"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 7 14:41 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":3869,"status":"ok","timestamp":1670605224920,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"hSA_T2q7SEt_"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4427,"status":"ok","timestamp":1670605229334,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"HpKEfJTeii2Y"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17123,"status":"ok","timestamp":1670605246445,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zwH-zC17SGnP","outputId":"1cd82f81-01c3-4283-ebdb-bba0318a4b52"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[K |████████████████████████████████| 200 kB 6.8 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n"]}],"source":["\n","!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"y1tk452JRjuY"},"source":["Define the environment"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":31,"status":"ok","timestamp":1670605246446,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vrMxCiuZRl7h"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"vk_3rL02Q9JZ"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":11208,"status":"ok","timestamp":1670605257627,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-HtQz6mfQ9JZ","outputId":"b1ed7644-4b47-49f4-89ca-310a9c173e65"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"DataFrames Basics\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2047,"status":"ok","timestamp":1670605259667,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"DrOjmfL9Q9Ja","outputId":"3a4dba91-ad21-4c19-8034-7526d4e19811"},"outputs":[{"data":{"text/html":["\n","

    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
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    v3.3.1
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    Master
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    local[*]
    \n","
    AppName
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    DataFrames Basics
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    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670605259668,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"IBZ8ufPAQ9Jb"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670605259668,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nJ-cdrAlQ9Jb"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"AIP7U3YYTDbw"},"source":["Download datasets"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":2655,"status":"ok","timestamp":1670605262320,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"d63cazZLTKTv"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/cars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/bank.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/vehicles.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1670605262320,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ml1O2OiQU6ah","outputId":"ad13f4aa-4fe6-47d7-a4c0-b9c210432a75"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 1784\n","-rw-r--r-- 1 root root 461474 Dec 9 17:01 bank.csv\n","-rw-r--r-- 1 root root 74910 Dec 9 17:00 cars.json\n","-rw-r--r-- 1 root root 1274347 Dec 9 17:01 movies.json\n","-rw-r--r-- 1 root root 4370 Dec 9 17:01 vehicles.csv\n"]}],"source":["ls -l /dataset"]},{"cell_type":"markdown","metadata":{"id":"94aRTBiPQ9Jc"},"source":["## Examples"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":593,"status":"ok","timestamp":1670605328886,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XxXiDT4XUgjl"},"outputs":[],"source":["from pyspark.sql.types import Row\n","from pyspark.sql.functions import *"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13116,"status":"ok","timestamp":1670605344595,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"p69TjjJBTrDF","outputId":"86dccaab-1eac-45ef-e965-6fe37dea7a3c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---+----------+-------+---------+-------+\n","|age| job|marital|education|balance|\n","+---+----------+-------+---------+-------+\n","| 30|unemployed|married| primary| 1787|\n","| 33| services|married|secondary| 4789|\n","| 35|management| single| tertiary| 1350|\n","+---+----------+-------+---------+-------+\n","only showing top 3 rows\n","\n"]}],"source":["bankText = spark.sparkContext.textFile(\"/dataset/bank.csv\")\n","\n","#we have to: remove firt row (headers), map the rest, and create DF\n","bank = bankText.map(lambda lineaCsv: lineaCsv.split(\";\"))\\\n",".filter(lambda s: s[0] != \"\\\"age\\\"\") \\\n",".map(lambda row: Row(int(row[0]), row[1].replace(\"\\\"\", \"\"), row[2].replace(\"\\\"\", \"\"), row[3].replace(\"\\\"\", \"\"), row[5].replace(\"\\\"\", \"\"))) \\\n",".toDF([\"age\", \"job\", \"marital\", \"education\", \"balance\"]) \\\n",".withColumn(\"age\", col(\"age\").cast(\"int\"))\n","\n","bank.show(3)"]},{"cell_type":"markdown","metadata":{"id":"DnCG9XHsTUfe"},"source":["Read directly from JSON file to a DF"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":1409,"status":"ok","timestamp":1670605345988,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"iBWBcsLDTYuF"},"outputs":[],"source":["carsDF = spark.read.option(\"inferSchema\", True).json(\"/dataset/cars.json\") # inferSchema requires one extra pass over the data\n","\n","# if None is set, it uses de default value (default = False) you can also pass the schema manually"]},{"cell_type":"markdown","metadata":{"id":"beN35oUrUo4c"},"source":["Read directly from csv"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1488,"status":"ok","timestamp":1670605379551,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"xaxDO13vUlSs","outputId":"117625e7-ad3e-47a3-f305-e1208b82faa8"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","|age| job|marital|education|default|balance|housing|loan| contact|day|month|duration|campaign|pdays|previous|poutcome| y|\n","+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","| 30|unemployed|married| primary| no| 1787| no| no|cellular| 19| oct| 79| 1| -1| 0| unknown| no|\n","| 33| services|married|secondary| no| 4789| yes| yes|cellular| 11| may| 220| 1| 339| 4| failure| no|\n","| 35|management| single| tertiary| no| 1350| yes| no|cellular| 16| apr| 185| 1| 330| 1| failure| no|\n","+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","only showing top 3 rows\n","\n"]}],"source":["bankDF = spark.read.option(\"header\", \"true\").option(\"delimiter\", \";\").csv(\"/dataset/bank.csv\")\n","bankDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"OOt52pbaQ9Jc"},"source":["Showing a DF and print schema"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":494,"status":"ok","timestamp":1670580334437,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"PmmsrRxaQ9Jd","outputId":"7016f8d3-376a-4ad3-b98e-2e5a0ce3e5aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 2 rows\n","\n","root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Cylinders: long (nullable = true)\n"," |-- Displacement: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight_in_lbs: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["carsDF.show(2)\n","carsDF.printSchema()"]},{"cell_type":"markdown","metadata":{"id":"ij4TDMZ0lkBZ"},"source":["Get Rows"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":614,"status":"ok","timestamp":1670605438909,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SB06W84wlnlY","outputId":"11894b33-d726-4577-ae57-4c0312429d5d"},"outputs":[{"data":{"text/plain":["[Row(Acceleration=12.0, Cylinders=8, Displacement=307.0, Horsepower=130, Miles_per_Gallon=18.0, Name='chevrolet chevelle malibu', Origin='USA', Weight_in_lbs=3504, Year='1970-01-01'),\n"," Row(Acceleration=11.5, Cylinders=8, Displacement=350.0, Horsepower=165, Miles_per_Gallon=15.0, Name='buick skylark 320', Origin='USA', Weight_in_lbs=3693, Year='1970-01-01')]"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["carsDF.take(2)"]},{"cell_type":"markdown","metadata":{"id":"Z89eHF1aQ9Jd"},"source":["Count"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1183,"status":"ok","timestamp":1670605451441,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"rtFilGlZWGyo","outputId":"3dad6a3c-7e6f-4d92-f7ed-5b9c049eb025"},"outputs":[{"data":{"text/plain":["406"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["carsDF.count()"]},{"cell_type":"markdown","metadata":{"id":"akmA70EPQ9Jd"},"source":["Schema"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670580335852,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Rp6hKanzQ9Je","outputId":"e57a56d7-fdb3-4007-8d30-c6a4d3d9de10"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","StructType([StructField('Acceleration', DoubleType(), True), StructField('Cylinders', LongType(), True), StructField('Displacement', DoubleType(), True), StructField('Horsepower', LongType(), True), StructField('Miles_per_Gallon', DoubleType(), True), StructField('Name', StringType(), True), StructField('Origin', StringType(), True), StructField('Weight_in_lbs', LongType(), True), StructField('Year', StringType(), True)])\n"]}],"source":["# obtain a schema\n","carsSchema = carsDF.schema\n","print(type(carsSchema))\n","print(carsSchema)"]},{"cell_type":"markdown","metadata":{"id":"v_XS0vB0Q9Je"},"source":["Custom Schemas"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":256,"status":"ok","timestamp":1670605490627,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ekQkJLT8Q9Je"},"outputs":[],"source":["example = spark.sparkContext.parallelize([(\"chevrolet chevelle malibu\",18,\"1970-01-01\",\"USA\"),\n"," (\"buick skylark 320\",15,\"1970-01-01\",\"USA\")])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":387,"status":"ok","timestamp":1670580336237,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nUzjX4kTQ9Je","outputId":"915707fc-a31f-4cc1-f7dd-a8ee013e82a5"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- _1: string (nullable = true)\n"," |-- _2: long (nullable = true)\n"," |-- _3: string (nullable = true)\n"," |-- _4: string (nullable = true)\n","\n"]}],"source":["exampleDF = spark.createDataFrame(example)\n","exampleDF.printSchema()"]},{"cell_type":"markdown","metadata":{"id":"GeTtjRirQ9Je"},"source":["With columns names"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uhY-f3j1Q9Jf"},"outputs":[],"source":["names = list([\"name\", \"weight\", \"date\", \"country\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670580336237,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nqxqPyr1Q9Jf","outputId":"60ef9d54-9365-4a2a-8ee7-c96b08cece92"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- weight: long (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example2DF = example.toDF(names)\n","example2DF.printSchema()"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":341,"status":"ok","timestamp":1670605542291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"toUIbosSQ9Jf"},"outputs":[],"source":["# importing sql types\n","from pyspark.sql.types import *"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KZNfLhFWQ9Jf"},"outputs":[],"source":["# custom schema\n","customSchema = StructType([ \\\n"," StructField('name', StringType(), True), \\\n"," StructField('weight', StringType(), True), \\\n"," StructField('date', StringType(), True), \\\n"," StructField('country', StringType(), True)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670580336238,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"x63bbTDYQ9Jf","outputId":"ac14e809-6a0a-4b2c-de40-bc471939d3f0"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- weight: string (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example3DF = spark.createDataFrame(example, customSchema)\n","example3DF.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1670580336238,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eRiqa-G5Q9Jf","outputId":"6b62a663-7334-4711-a716-898ce9f718af"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------------+------+----------+-------+\n","|name |weight|date |country|\n","+-------------------------+------+----------+-------+\n","|chevrolet chevelle malibu|18 |1970-01-01|USA |\n","|buick skylark 320 |15 |1970-01-01|USA |\n","+-------------------------+------+----------+-------+\n","\n"]}],"source":["example3DF.show(2, False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M3qToymLQ9Jg"},"outputs":[],"source":["# we can also specify schema with DDL (Data Definition Language)\n","customSchema2 = \"`name` STRING NOT NULL, `weight` INT, `date` STRING, `country` STRING\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670580336701,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"7k9X1gSSQ9Jg","outputId":"70a855ab-0961-43a9-8f48-9f04cfb757b3"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = false)\n"," |-- weight: integer (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example4DF = spark.createDataFrame(example, customSchema2)\n","example4DF.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670580336701,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"E5rayh3uQ9Jg","outputId":"0864a25e-c98f-4438-9bef-859ca9b2384c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n"]}],"source":["print(type(example2DF.collect()[0][\"weight\"]))\n","print(type(example3DF.collect()[0][\"weight\"]))"]},{"cell_type":"markdown","metadata":{"id":"6QnciaoWQ9Jg"},"source":["## Exercises\n","1) Create a manual DF describing smartphones\n"," - maker\n"," - model\n"," - screen dimension\n"," - camera megapixels\n"," \n","2) Read another file from the dataset/ folder, e.g. movies.json\n"," - print its schema\n"," - count the number of rows, call count()\n","\n","3) Take a look to vehicles.csv. Read the file to a DF but this time with your own schema"]},{"cell_type":"markdown","metadata":{"id":"kQR1cduXQ9Jg"},"source":["Exercise 1"]},{"cell_type":"markdown","metadata":{"id":"Zmg5kbOpl-s3"},"source":["Exercise 2"]},{"cell_type":"markdown","metadata":{"id":"JAqADV5jW00d"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JD2KQtXiYmVn"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":["94aRTBiPQ9Jc"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/02.ColumnsAndExpressions.ipynb b/PROFESORES/PYSPARK/notebooks/02.ColumnsAndExpressions.ipynb new file mode 100644 index 000000000..167f03dac --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/02.ColumnsAndExpressions.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"eyZfoGe_trKF"},"source":["# Columns and Expressions"]},{"cell_type":"markdown","metadata":{"id":"OF1B2ocetvbn"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"l_Vt3AVEt3uX"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":20309,"status":"ok","timestamp":1670786288520,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JrJqxyXZt9wn"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1670786288521,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"WBfjT1TouA1O","outputId":"849b3c81-2ca2-4da3-a114-c1839e6cb4e1"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 8 14:36 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":3624,"status":"ok","timestamp":1670786292141,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"bHueJOLRuD2H"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":3931,"status":"ok","timestamp":1670786296068,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"arpGbc-EuDlH"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12537,"status":"ok","timestamp":1670786308602,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Bc6eiLp0uKov","outputId":"8bf55c6e-f254-4c05-a04c-a8091df0c579"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[?25l\r\u001b[K |█▋ | 10 kB 19.9 MB/s eta 0:00:01\r\u001b[K |███▎ | 20 kB 16.3 MB/s eta 0:00:01\r\u001b[K |█████ 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19.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 200 kB 19.0 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n"]}],"source":["\n","!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"mfEdQvJLuP7Y"},"source":["Define the environment"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1670786308602,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zU_829Z5uPEv"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"GByUn4STtrKH"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9402,"status":"ok","timestamp":1670786317995,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"lW82io8xtrKI","outputId":"9b3ef234-8873-41b5-f12e-2abbcc9fc22f"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Columns and Expressions\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1898,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Xe0_i5fHtrKJ","outputId":"8a9f40c7-a480-487f-aef6-d1ef630204ef"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Columns and Expressions
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"mIXSyPeytrKJ"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SsEHbN1HtrKJ"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"Cc8OP9YSu3KY"},"source":["Download datasets"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":982,"status":"ok","timestamp":1670786320867,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vgd-xCVUu5e4"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/cars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/more_cars.json -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"2m7U4JG7trKK"},"source":["Read JSON file"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":9150,"status":"ok","timestamp":1670786330016,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"j40ajnajtrKK"},"outputs":[],"source":["carsDF = spark.read \\\n"," .option(\"inferSchema\", True) \\\n"," .json(\"/dataset/cars.json\")"]},{"cell_type":"markdown","metadata":{"id":"rBegVS5dtrKK"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"-j-DuH6xtrKL"},"source":["Select a column"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":665,"status":"ok","timestamp":1670786330672,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pJeRA5chtrKL","outputId":"bb2fc632-3e12-4093-bfb7-c697dcde2f61"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------------+\n","|Name |\n","+-------------------------+\n","|chevrolet chevelle malibu|\n","|buick skylark 320 |\n","|plymouth satellite |\n","+-------------------------+\n","only showing top 3 rows\n","\n"]}],"source":["carsDF.select(col(\"Name\")).show(3, False)"]},{"cell_type":"markdown","metadata":{"id":"1Wd_xjFOY9Tl"},"source":["We can use various metods to refer a column"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":496,"status":"ok","timestamp":1670786331165,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RJYKyUputrKL","outputId":"0996f33d-676e-41c9-c86e-19387132cb5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+------------+-------------+\n","| Name|Acceleration|Weight_in_lbs|\n","+--------------------+------------+-------------+\n","|chevrolet chevell...| 12.0| 3504|\n","| buick skylark 320| 11.5| 3693|\n","| plymouth satellite| 11.0| 3436|\n","+--------------------+------------+-------------+\n","only showing top 3 rows\n","\n"]}],"source":["# various select methods\n","carsDF.select(\n"," carsDF.Name,\n"," col(\"Acceleration\"),\n"," \"Weight_in_lbs\"\n",").show(3)"]},{"cell_type":"markdown","metadata":{"id":"1BHOu4KEtrKL"},"source":["Expressions. We can use SQL like expression inside select to make operations with a column"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":629,"status":"ok","timestamp":1670786331793,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-OGl-DU7trKM","outputId":"d8d00a01-8d31-4d82-c6ca-b9b1ba7e71ed"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Name: string (nullable = true)\n"," |-- Weight_in_lbs: long (nullable = true)\n"," |-- Weight_in_kg_2: integer (nullable = true)\n"," |-- Weight_in_T: string (nullable = true)\n","\n","+--------------------+-------------+--------------+-----------+\n","| Name|Weight_in_lbs|Weight_in_kg_2|Weight_in_T|\n","+--------------------+-------------+--------------+-----------+\n","|chevrolet chevell...| 3504| 1592| 3.504|\n","| buick skylark 320| 3693| 1678| 3.693|\n","| plymouth satellite| 3436| 1561| 3.436|\n","+--------------------+-------------+--------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["# \n","carsWithKgDF = carsDF.select(\n"," col(\"Name\"),\n"," col(\"Weight_in_lbs\"),\n"," (col(\"Weight_in_lbs\")/2.2).cast(\"int\").alias(\"Weight_in_kg_2\"), #cast result to int\n"," expr(\"Weight_in_lbs / 1000\").cast(\"string\").alias(\"Weight_in_T\") #cast result to str\n",")\n","carsWithKgDF.printSchema()\n","carsWithKgDF.show(3)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":367,"status":"ok","timestamp":1670786332158,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"NZYAC9A8trKM","outputId":"74f8fdac-8acc-44f1-caef-5ebab0458d11"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-------------+---------------------+\n","| Name|Weight_in_lbs|(Weight_in_lbs / 2.2)|\n","+--------------------+-------------+---------------------+\n","|chevrolet chevell...| 3504| 1592.727273|\n","| buick skylark 320| 3693| 1678.636364|\n","| plymouth satellite| 3436| 1561.818182|\n","+--------------------+-------------+---------------------+\n","only showing top 3 rows\n","\n"]}],"source":["# with expressions\n","carsWithSelectExprWeightsDF = carsDF.selectExpr(\n"," \"Name\",\n"," \"Weight_in_lbs\",\n"," \"Weight_in_lbs / 2.2\"\n"," )\n","carsWithSelectExprWeightsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"LDwREHXhtrKM"},"source":["### DF Processing"]},{"cell_type":"markdown","metadata":{"id":"D-12eNHDtrKM"},"source":["Add a column"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":698,"status":"ok","timestamp":1670786332854,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"S09Nw_iytrKM","outputId":"374e95f6-3d68-4bf5-f22a-2e866d5aa6db"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year| Weight_in_kg_3|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|1592.7272727272725|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|1678.6363636363635|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|1561.8181818181818|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","only showing top 3 rows\n","\n"]}],"source":["carsWithKg3DF = carsDF.withColumn(\"Weight_in_kg_3\", col(\"Weight_in_lbs\") / 2.2)\n","carsWithKg3DF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"SaYkSZ-AtrKN"},"source":["Rename a column"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":513,"status":"ok","timestamp":1670786333366,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"5m8EFdTBtrKN","outputId":"8dfff95c-47d9-4df2-f788-3e08209201b2"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight in pounds| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["carsWithColumnRenamed = carsDF.withColumnRenamed(\"Weight_in_lbs\", \"Weight in pounds\")\n","carsWithColumnRenamed.show(3)"]},{"cell_type":"code","execution_count":19,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1670786333366,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zzwPcSFqtrKN"},"outputs":[],"source":["# careful with column names\n","# carsWithColumnRenamed.selectExpr(\"Weight in pounds\")"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"8Y0Ir5jBtrKN","outputId":"7b9b4047-cf09-4da4-ee8e-9ba539df7569"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------+\n","|Weight in pounds|\n","+----------------+\n","| 3504|\n","| 3693|\n","| 3436|\n","+----------------+\n","only showing top 3 rows\n","\n"]}],"source":["# as we hace special characters (spaces) we have to use the ``\n","carsWithColumnRenamed.selectExpr(\"`Weight in pounds`\").show(3)"]},{"cell_type":"markdown","metadata":{"id":"mkqoz74TtrKN"},"source":["Remove a column"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"6qaH5KLhtrKO","outputId":"a3a1858b-b277-46e1-de67-09470093d1ba"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Cylinders: long (nullable = true)\n"," |-- Displacement: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight in pounds: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["carsWithColumnRenamed.printSchema()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SE_SvbNNtrKO","outputId":"5e4775bc-c6c8-4acc-ff3c-fc5a1a2dce4c"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight in pounds: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["dropColsDF = carsWithColumnRenamed.drop(\"Cylinders\", \"Displacement\")\n","dropColsDF.printSchema()\n"]},{"cell_type":"markdown","metadata":{"id":"QKtbVrvBtrKO"},"source":["Filtering"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1506,"status":"ok","timestamp":1670786334871,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pr2vC4_9trKO","outputId":"5a14fe5b-8038-4c73-bf6e-1a015d44174f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 17.5| 4| 133.0| 115| null|citroen ds-21 pallas|Europe| 3090|1970-01-01|\n","| 15.0| 4| 113.0| 95| 24.0|toyota corona mar...| Japan| 2372|1970-01-01|\n","| 14.5| 4| 97.0| 88| 27.0| datsun pl510| Japan| 2130|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 3 rows\n","\n","152 == 152\n"]}],"source":["nonUSCarsDF = carsDF.filter(col(\"Origin\") != \"USA\")\n","nonUSCarsDF2 = carsDF.where(col(\"Origin\") != \"USA\")\n","nonUSCarsDF.show(3)\n","print(f\"{nonUSCarsDF.count()} == {nonUSCarsDF2.count()}\")"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":700,"status":"ok","timestamp":1670786335570,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Cc58o5B1trKO","outputId":"89677fd2-6f2d-4244-f984-472a975d2635"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["# filtering with expression strings\n","americanCarsDF = carsDF.filter(\"Origin = 'USA'\")\n","americanCarsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"YvqWVJKmtrKO"},"source":["Chain filters"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":371,"status":"ok","timestamp":1670786335936,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"QSYv7uastrKO","outputId":"a00f6ff7-3ecb-4640-e6ed-42d74d9ee293"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","| 11.5| 8| 350.0| 165| 15.0|buick skylark 320| USA| 3693|1970-01-01|\n","| 10.0| 8| 429.0| 198| 15.0| ford galaxie 500| USA| 4341|1970-01-01|\n","| 9.0| 8| 454.0| 220| 14.0| chevrolet impala| USA| 4354|1970-01-01|\n","+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["americanPowerfulCarsDF = carsDF.filter(col(\"Origin\") == \"USA\").filter(col(\"Horsepower\") > 150)\n","americanPowerfulCarsDF2 = carsDF.filter((col(\"Origin\") == \"USA\") & (col(\"Horsepower\") > 150))\n","americanPowerfulCarsDF3 = carsDF.filter(\"Origin = 'USA' and Horsepower > 150\")\n","americanPowerfulCarsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"kVhkmxwZtrKP"},"source":["## Exercises\n","1. Read the movies DF and select 2 columns of your choice\n","2. Create another column summing up the total profit of the movies = US_Gross + Worldwide_Gross + DVD sales. Are you obtaining nulls? How you can solve it?\n","3. Select all COMEDY movies with IMDB rating above 6\n","Use as many versions as possible"]},{"cell_type":"markdown","metadata":{"id":"QgpQ09-wtrKP"},"source":["Exercise 1"]},{"cell_type":"markdown","metadata":{"id":"RgtGVWTrtrKQ"},"source":["Exercise 2"]},{"cell_type":"markdown","metadata":{"id":"9j710M_JQYwd"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"03-5sRApo571"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":["LDwREHXhtrKM"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/03.Aggregations.ipynb b/PROFESORES/PYSPARK/notebooks/03.Aggregations.ipynb new file mode 100644 index 000000000..167222ebb --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/03.Aggregations.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"s4YqddS_vlYH"},"source":["# Aggregations and Grouping"]},{"cell_type":"markdown","metadata":{"id":"WwNMmP6GvoDQ"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HvezyELEvqZH"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7b0-USnDvst_"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1670581376752,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RnfbBxRivvWX","outputId":"6d1dd47f-2eb6-469e-f505-67be729bcb9a"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 2 root root 4096 Dec 9 10:22 \u001b[0m\u001b[01;34mdataset\u001b[0m/\n","drwxr-xr-x 1 root root 4096 Dec 7 14:41 \u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IhVzl2vbvxhY"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8JuU0Aylvxeo"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13363,"status":"ok","timestamp":1670581398749,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"0hvlB3OlvzxX","outputId":"0aff085f-53a1-40ed-c742-89aeac594b9b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[K |████████████████████████████████| 200 kB 5.4 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n"]}],"source":["!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"O0C9EzZbv4x4"},"source":["Define the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rf4ibNTdv7fv"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"K90Ld_NkvlYI"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9353,"status":"ok","timestamp":1670581408100,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2Fg_PLR_vlYI","outputId":"2dc34c37-2e19-4b3b-ec66-ef29f99419a1"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Aggregations and Grouping\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1809,"status":"ok","timestamp":1670581409903,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RnamkNcgvlYJ","outputId":"f3286245-59e1-4b01-d40a-4f2b8cbc7c9b"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Aggregations and Grouping
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9AGniM-7vlYK"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e0DXUK1fvlYK"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"1mCPp9fQwEif"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5xTARwq9wLgH"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/vehicles.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/characters.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"yD4q57C_vlYK"},"source":["Read JSON file\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dZWORhHKvlYK"},"outputs":[],"source":["moviesDF = spark.read \\\n"," .option(\"inferSchema\", True) \\\n"," .json(\"/dataset/movies.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1067,"status":"ok","timestamp":1670581420751,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"4h2S0ponvlYK","outputId":"07cfbb5f-67f2-4815-d9f4-e70c47089d11"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","|Creative_Type|Director|Distributor|IMDB_Rating|IMDB_Votes|MPAA_Rating|Major_Genre|Production_Budget|Release_Date|Rotten_Tomatoes_Rating|Running_Time_min|Source|Title |US_DVD_Sales|US_Gross|Worldwide_Gross|\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","|null |null |Gramercy |6.1 |1071 |R |null |8000000 |12-Jun-98 |null |null |null |The Land Girls |null |146083 |146083 |\n","|null |null |Strand |6.9 |207 |R |Drama |300000 |7-Aug-98 |null |null |null |First Love, Last Rites|null |10876 |10876 |\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","only showing top 2 rows\n","\n","[StructField('Creative_Type', StringType(), True), StructField('Director', StringType(), True), StructField('Distributor', StringType(), True), StructField('IMDB_Rating', DoubleType(), True), StructField('IMDB_Votes', LongType(), True), StructField('MPAA_Rating', StringType(), True), StructField('Major_Genre', StringType(), True), StructField('Production_Budget', LongType(), True), StructField('Release_Date', StringType(), True), StructField('Rotten_Tomatoes_Rating', LongType(), True), StructField('Running_Time_min', LongType(), True), StructField('Source', StringType(), True), StructField('Title', StringType(), True), StructField('US_DVD_Sales', LongType(), True), StructField('US_Gross', LongType(), True), StructField('Worldwide_Gross', LongType(), True)]\n"]},{"data":{"text/plain":["['Creative_Type',\n"," 'Director',\n"," 'Distributor',\n"," 'IMDB_Rating',\n"," 'IMDB_Votes',\n"," 'MPAA_Rating',\n"," 'Major_Genre',\n"," 'Production_Budget',\n"," 'Release_Date',\n"," 'Rotten_Tomatoes_Rating',\n"," 'Running_Time_min',\n"," 'Source',\n"," 'Title',\n"," 'US_DVD_Sales',\n"," 'US_Gross',\n"," 'Worldwide_Gross']"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["moviesDF.show(2, False)\n","print(moviesDF.schema.fields)\n","moviesDF.columns"]},{"cell_type":"markdown","metadata":{"id":"M7Ab75vzvlYK"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"3L-vJzo5vlYK"},"source":["Count"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1175,"status":"ok","timestamp":1670581421925,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eWoq50okvlYL","outputId":"2765af46-4f08-4e2a-eb4c-f9b9c561ead9"},"outputs":[{"data":{"text/plain":["3201"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# df rows counting, including NULLS\n","moviesDF.count()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":813,"status":"ok","timestamp":1670581422733,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JwFlAfygvlYL","outputId":"f5901338-7177-4d72-f0f6-b631112a7280"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------------+\n","|count(Major_Genre)|\n","+------------------+\n","| 2926|\n","+------------------+\n","\n"]}],"source":["# using sql functions, NOT including NULLS\n","genresCountDF = moviesDF.select(count(col(\"Major_Genre\")))\n","genresCountDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":267,"status":"ok","timestamp":1670581422999,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XOnzyw5pvlYL","outputId":"ed582d04-e104-4816-e363-afa12ad9406c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n"]}],"source":["directorsCountDF = moviesDF.select(count(moviesDF.Director))\n","directorsCountDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":266,"status":"ok","timestamp":1670581423264,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"I6zWJ2x8vlYL","outputId":"3d570765-2630-4dfb-d75b-6ff9b2d8f5e1"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+---------------+\n","|countMajor|count(Director)|\n","+----------+---------------+\n","| 2926| 1870|\n","+----------+---------------+\n","\n"]}],"source":["moviesDF.select(count(moviesDF.Major_Genre).alias(\"countMajor\"), count(moviesDF.Director)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":767,"status":"ok","timestamp":1670581424029,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nWOO2IBsvlYL","outputId":"cc36949a-5f6c-4e45-a26f-5b57aa04bd03"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n","+-----+\n","|count|\n","+-----+\n","| 1870|\n","+-----+\n","\n"]}],"source":["#using SQL syntax\n","moviesDF.select(expr(\"count(Director)\")).show()\n","moviesDF.selectExpr(\"count(Director) as count\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QOje34ZavlYL"},"outputs":[],"source":["# using SQL\n","moviesDF.createOrReplaceTempView(\"movies\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":801,"status":"ok","timestamp":1670581424829,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-UTqpjafvlYM","outputId":"4129c709-8bed-4c6c-d5c4-c405b3a89cdd"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n"]}],"source":["spark.sql(\"select count(Director) from movies\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":225,"status":"ok","timestamp":1670581425052,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-bsgNq42vlYM","outputId":"54d942af-b340-44db-c175-5e45cd681494"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------+------------------+\n","|countDirector|count(Major_Genre)|\n","+-------------+------------------+\n","| 1870| 2926|\n","+-------------+------------------+\n","\n"]}],"source":["spark.sql(\"select count(Director) as countDirector, count(Major_Genre) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"7tD9smRhvlYM"},"source":["Count Distinct"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":515,"status":"ok","timestamp":1670581425565,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ngR6WFUovlYM","outputId":"bdd9d3c8-9b6c-4fc4-91b3-659ee17f55f8"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|count(DISTINCT Major_Genre)|\n","+---------------------------+\n","| 12|\n","+---------------------------+\n","\n"]}],"source":["moviesDF.select(countDistinct(moviesDF.Major_Genre)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":606,"status":"ok","timestamp":1670581426170,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"65m9Ev4MvlYM","outputId":"36e25661-5573-43c3-fcf3-68f0db17ad77"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|count(DISTINCT Major_Genre)|\n","+---------------------------+\n","| 12|\n","+---------------------------+\n","\n"]}],"source":["spark.sql(\"select count(distinct Major_Genre) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"wpwLzjsovlYM"},"source":["Min and max"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":691,"status":"ok","timestamp":1670581426859,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"FPOzkXPEvlYN","outputId":"e01067da-310d-4816-9712-bb1be6e6c35a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+----------------------+\n","|min(Production_Budget)|max(Production_Budget)|\n","+----------------------+----------------------+\n","| 218| 300000000|\n","+----------------------+----------------------+\n","\n"]}],"source":["moviesDF.select(min(moviesDF.Production_Budget), max(moviesDF.Production_Budget)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":245,"status":"ok","timestamp":1670581427103,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pY6U_O43vlYN","outputId":"dd249a1d-b87f-4107-a91d-86f28e1bf312"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+\n","|min(Production_Budget)|\n","+----------------------+\n","| 218|\n","+----------------------+\n","\n"]}],"source":["spark.sql(\"select min(Production_Budget) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"ID9Q9X7EvlYN"},"source":["Sum"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":504,"status":"ok","timestamp":1670581427606,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RHKzZO-6vlYN","outputId":"28db29ba-d97b-4ed2-8368-705f43a5af51"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+\n","| salesUS|\n","+-----------+\n","|19684472405|\n","+-----------+\n","\n","+-----------+\n","| sales|\n","+-----------+\n","|19684472405|\n","+-----------+\n","\n"]}],"source":["moviesDF.select(sum(moviesDF.US_DVD_Sales).alias(\"salesUS\")).show()\n","moviesDF.selectExpr(\"sum(US_DVD_Sales) as sales\").show()"]},{"cell_type":"markdown","metadata":{"id":"Yq5pF3mLvlYN"},"source":["Average"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":502,"status":"ok","timestamp":1670581428107,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"fDVmzYTsvlYN","outputId":"f33c15c5-05e4-4f51-9278-dccc0547ad65"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+\n","|avg(Production_Budget)|\n","+----------------------+\n","| 3.10691714484375E7|\n","+----------------------+\n","\n","+----------------------+\n","|avg(Production_Budget)|\n","+----------------------+\n","| 3.10691714484375E7|\n","+----------------------+\n","\n"]}],"source":["moviesDF.select(avg(moviesDF.Production_Budget)).show()\n","spark.sql(\"select avg(Production_Budget) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"4RLegmspvlYN"},"source":["Stats"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":713,"status":"ok","timestamp":1670581428815,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"WTJQxCazvlYO","outputId":"074f42ff-9506-4235-a284-d890e1da873c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|avg(Rotten_Tomatoes_Rating)|\n","+---------------------------+\n","| 54.33692373976734|\n","+---------------------------+\n","\n","+-----------------------------------+\n","|stddev_samp(Rotten_Tomatoes_Rating)|\n","+-----------------------------------+\n","| 28.07659263787602|\n","+-----------------------------------+\n","\n"]}],"source":["moviesDF.select(mean(moviesDF.Rotten_Tomatoes_Rating)).show()\n","moviesDF.select(stddev(moviesDF.Rotten_Tomatoes_Rating)).show()"]},{"cell_type":"markdown","metadata":{"id":"BrbK9dgcvlYO"},"source":["### Grouping\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":713,"status":"ok","timestamp":1670581429523,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"yYrN8U1ZvlYO","outputId":"6d8ef70e-3866-4ad3-cc4d-b5190771de6a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+-----+\n","| Major_Genre|count|\n","+-------------------+-----+\n","|Concert/Performance| 5|\n","| Black Comedy| 36|\n","| Western| 36|\n","| Documentary| 43|\n","| Musical| 53|\n","| Romantic Comedy| 137|\n","| Horror| 219|\n","| Thriller/Suspense| 239|\n","| Adventure| 274|\n","| null| 275|\n","| Action| 420|\n","| Comedy| 675|\n","| Drama| 789|\n","+-------------------+-----+\n","\n"]}],"source":["countByGenreGF = moviesDF.groupBy(moviesDF.Major_Genre).count().orderBy(\"count\")\n","countByGenreGF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":365,"status":"ok","timestamp":1670581429886,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ZZO4-ux9vlYO","outputId":"0cfd68a8-c3e1-459c-f700-b8b1cfd729b0"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+-----+\n","| Major_Genre|count|\n","+-------------------+-----+\n","|Concert/Performance| 5|\n","| Black Comedy| 36|\n","| Western| 36|\n","| Documentary| 43|\n","| Musical| 53|\n","| Romantic Comedy| 137|\n","| Horror| 219|\n","| Thriller/Suspense| 239|\n","| Adventure| 274|\n","| Action| 420|\n","| Comedy| 675|\n","| Drama| 789|\n","+-------------------+-----+\n","\n"]}],"source":["spark.sql(\"select Major_Genre, count(Major_Genre) as count from movies where Major_Genre is not null group by Major_Genre order by count\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":592,"status":"ok","timestamp":1670581430473,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eITO9H-gvlYO","outputId":"5abf6fce-9f3a-4329-8a95-9a7ba882fbaa"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+------------------+\n","| Major_Genre| avg(IMDB_Rating)|\n","+-------------------+------------------+\n","| Documentary| 6.997297297297298|\n","| Western| 6.842857142857142|\n","| Black Comedy|6.8187500000000005|\n","| Drama| 6.773441734417339|\n","| null| 6.50082644628099|\n","| Musical| 6.448|\n","| Thriller/Suspense| 6.360944206008582|\n","| Adventure| 6.345019920318729|\n","|Concert/Performance| 6.325|\n","| Action| 6.114795918367349|\n","| Romantic Comedy| 5.873076923076922|\n","| Comedy| 5.853858267716529|\n","| Horror|5.6760765550239185|\n","+-------------------+------------------+\n","\n"]}],"source":["avgRatingByGenreDF = moviesDF.groupBy(col(\"Major_Genre\")).avg(\"IMDB_Rating\").orderBy(col(\"avg(IMDB_Rating)\").desc())\n","avgRatingByGenreDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":448,"status":"ok","timestamp":1670581430919,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vnmu7fuovlYO","outputId":"80432633-aaa0-4bc0-ac8d-bd272a10b1b5"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+------------------+\n","| Major_Genre| avg|\n","+-------------------+------------------+\n","| Documentary| 6.997297297297298|\n","| Western| 6.842857142857142|\n","| Black Comedy|6.8187500000000005|\n","| Drama| 6.773441734417339|\n","| null| 6.50082644628099|\n","| Musical| 6.448|\n","| Thriller/Suspense| 6.360944206008582|\n","| Adventure| 6.345019920318729|\n","|Concert/Performance| 6.325|\n","| Action| 6.114795918367349|\n","| Romantic Comedy| 5.873076923076922|\n","| Comedy| 5.853858267716529|\n","| Horror|5.6760765550239185|\n","+-------------------+------------------+\n","\n"]}],"source":["moviesDF.groupBy(col(\"Major_Genre\")).agg(avg(\"IMDB_Rating\") \\\n"," .alias(\"avg\")).orderBy(col(\"avg\").desc()).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":367,"status":"ok","timestamp":1670581431285,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2fk-AjXIvlYO","outputId":"4cdea3f6-b1e3-4db5-bfc2-231d0de2241f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+--------+------------------+\n","| Major_Genre|N_Movies| rating|\n","+-------------------+--------+------------------+\n","| Documentary| 43| 6.997297297297298|\n","| Western| 36| 6.842857142857142|\n","| Black Comedy| 36|6.8187500000000005|\n","| Drama| 789| 6.773441734417339|\n","| null| 275| 6.50082644628099|\n","| Musical| 53| 6.448|\n","| Thriller/Suspense| 239| 6.360944206008582|\n","| Adventure| 274| 6.345019920318729|\n","|Concert/Performance| 5| 6.325|\n","| Action| 420| 6.114795918367349|\n","| Romantic Comedy| 137| 5.873076923076922|\n","| Comedy| 675| 5.853858267716529|\n","| Horror| 219|5.6760765550239185|\n","+-------------------+--------+------------------+\n","\n"]}],"source":["aggregationsByGenreDF = moviesDF.groupBy(\"Major_Genre\") \\\n"," .agg(\n"," count(\"*\").alias(\"N_Movies\"),\n"," avg(\"IMDB_Rating\").alias(\"rating\")\n"," ) \\\n"," .orderBy(col(\"rating\").desc()).show()"]},{"cell_type":"markdown","metadata":{"id":"cQ3xjlDxvlYP"},"source":["## Exercises\n"," 1. Sum up all the worldwide profits of ALL the movies in the DF. Then sum the worldwide profits per director\n"," 2. Count how many distinct directors we have\n"," 3. Show the mean and standard deviation of US gross revenue for the movies (all the movies)\n"," 4. Compute the average IMDB rating and the average US gross revenue PER DIRECTOR\n"," 5. Sum up ALL the profits of ALL the movies in the DF. Then sum ALL the profits per director. Can you see null values? Why? How you can solve it? \n"]},{"cell_type":"markdown","metadata":{"id":"dzh5hcajvlYP"},"source":["Exercise 1"]},{"cell_type":"markdown","metadata":{"id":"x_KjX-qxvlYP"},"source":["Exercise 2"]},{"cell_type":"markdown","metadata":{"id":"x6iJ5GWlvlYQ"},"source":["Exercise 3"]},{"cell_type":"markdown","metadata":{"id":"qTyt8CotvlYQ"},"source":["Exercise 4"]},{"cell_type":"markdown","metadata":{"id":"HOs_Y_y0vlYQ"},"source":["Exercise 5"]}],"metadata":{"colab":{"collapsed_sections":["M7Ab75vzvlYK","BrbK9dgcvlYO","cQ3xjlDxvlYP"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/04.Joins.ipynb b/PROFESORES/PYSPARK/notebooks/04.Joins.ipynb new file mode 100644 index 000000000..4cb2db4b7 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/04.Joins.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"h-3AXSJMxWcj"},"source":["# Joins"]},{"cell_type":"markdown","metadata":{"id":"NqSTh04rxXpg"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"alWDpL2TxZt3"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mpinu0eRxcmO"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":255,"status":"ok","timestamp":1670436579804,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Jz43HkUGxg_3","outputId":"ac5c6ea2-0832-4c9b-a2f5-a40a3ea9d40d"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 1 root root 4096 Dec 6 14:35 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FIMyGT7Axg9G"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BdJz2vonxjDe"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10739,"status":"ok","timestamp":1670436621663,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vLrv7k6cxqVY","outputId":"335a57a9-43f9-4cba-a4f9-70ff84625d77"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: py4j in /usr/local/lib/python3.8/dist-packages (0.10.9.7)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n"]}],"source":["!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"uYSsCnv5xoPq"},"source":["Define the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ueuKeDHqxorP"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"77nvOyd1xWck"},"source":["Start Spark Session \n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":7803,"status":"ok","timestamp":1670436701150,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"u9zEr_35xWck","outputId":"6b19ccb2-446a-469f-eec0-2784973bc406"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Joins\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1905,"status":"ok","timestamp":1670436706003,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"oS7E2-87xWcl","outputId":"849af19b-7917-4d4c-d665-e18d972ac53a"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Joins
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"75QZiN7GxWcl"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"4Cx-_TA5xxVw"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IFP9OP4fxzGw"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/guitars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/guitarPlayers.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/bands.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/employees.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/salaries.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/deptmanagers.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/titles.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"CPNyqoE8xWcm"},"source":["Read JSONs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k9_4mNbqxWcm"},"outputs":[],"source":["guitarsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/guitars.json\")\n","\n","guitaristsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/guitarPlayers.json\")\n","\n","bandsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/bands.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1999,"status":"ok","timestamp":1670436771444,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Aww2pe7sxWcm","outputId":"1800739a-870e-40bc-be06-67bb8b95d637"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+---+------+------------+\n","| guitarType| id| make| model|\n","+--------------------+---+------+------------+\n","|Electric double-n...| 0|Gibson| EDS-1275|\n","| Electric| 5|Fender|Stratocaster|\n","| Electric| 1|Gibson| SG|\n","+--------------------+---+------+------------+\n","only showing top 3 rows\n","\n","+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 0| [0]| 0| Jimmy Page|\n","| 1| [1]| 1| Angus Young|\n","| 2| [1, 5]| 2|Eric Clapton|\n","+----+-------+---+------------+\n","only showing top 3 rows\n","\n","+-----------+---+------------+----+\n","| hometown| id| name|year|\n","+-----------+---+------------+----+\n","| Sydney| 1| AC/DC|1973|\n","| London| 0|Led Zeppelin|1968|\n","|Los Angeles| 3| Metallica|1981|\n","+-----------+---+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitarsDF.show(3)\n","guitaristsDF.show(3)\n","bandsDF.show(3)\n"]},{"cell_type":"markdown","metadata":{"id":"opA3NFOfxWcm"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"GmN9q_ltxWcm"},"source":["Inner Join = everything from BOTH DFs for which there is a row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1263,"status":"ok","timestamp":1670436775322,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"voyAapmexWcm","outputId":"0b70a609-66df-47ee-8381-a997f9857e81"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+---+------------+----+\n","|band|guitars| id| name| hometown| id| name|year|\n","+----+-------+---+------------+-----------+---+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","+----+-------+---+------------+-----------+---+------------+----+\n","\n"]}],"source":["joinCondition = guitaristsDF.band == bandsDF.id\n","guitaristsBandsDF = guitaristsDF.join(bandsDF, joinCondition, \"inner\")\n","guitaristsBandsDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ce_yS-kVxWcn"},"outputs":[],"source":["# things to bear in mind\n","# guitaristsBandsDF.select(\"id\", \"band\").show(3) # will crash because there are two columns named \"id\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":914,"status":"ok","timestamp":1670436778566,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ZD_tC8GKxWcn","outputId":"99519fb6-224d-4f93-9150-fb561b40e4d7"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+---+------------+-----------+------+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","+----+-------+---+------------+-----------+------+------------+----+\n","\n"]}],"source":["# you can rename the column\n","bandsDFMod = bandsDF.withColumnRenamed(\"id\", \"bandId\") \\\n"," .withColumnRenamed(\"name\", \"bandName\")\n","guitaristsBandsDF2 = guitaristsDF.join(bandsDFMod,\n"," guitaristsDF.band == bandsDFMod.bandId, \"inner\").orderBy(\"name\")\n","guitaristsBandsDF2.show(3)\n","\n","# there are other options but it is the best practice if it is possible"]},{"cell_type":"markdown","metadata":{"id":"WyFeqM1yxWcn"},"source":["Left Outer = everything in the inner join + all the rows in the LEFT DF, with nulls in where the data is missing"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":917,"status":"ok","timestamp":1670436780904,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"wRt01JY-xWcn","outputId":"0fd6dff7-42d4-4ae2-febe-f71876426f5f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+--------+------+------------+----+\n","|band|guitars| id| name|hometown|bandId| bandName|year|\n","+----+-------+---+------------+--------+------+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| null| null| null|null|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","+----+-------+---+------------+--------+------+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["joinCondition2 = guitaristsDF.band == bandsDFMod.bandId\n","guitaristsDF.join(bandsDFMod, joinCondition2, \"left_outer\").orderBy(\"name\").show(3)\n","# see nulls on Eric Calpton"]},{"cell_type":"markdown","metadata":{"id":"jx7loGmOxWcn"},"source":[" Right Outer = everything in the inner join + all the rows in the RIGHT DF, with nulls in where the data is missing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":487,"status":"ok","timestamp":1670436782239,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"H0t5qxmDxWcn","outputId":"c268f3f6-19c1-4aff-f182-f0cd80b040d3"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+----+-----------+---------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+----+-----------+---------+------+------------+----+\n","|null| null|null| null|Liverpool| 4| The Beatles|1960|\n","| 1| [1]| 1|Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","+----+-------+----+-----------+---------+------+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"right_outer\").orderBy(\"name\").show(3)\n","# see nulls for The Beatles"]},{"cell_type":"markdown","metadata":{"id":"-YP3UfTaxWco"},"source":[" Outer Join = everything in the inner join + all the rows in BOTH DFs, with nulls in where the data is missing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1614,"status":"ok","timestamp":1670436785314,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"08o47mejxWco","outputId":"37cee475-2da2-4d72-aa7b-c818c4972e0a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+----+------------+---------+------+-----------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+----+------------+---------+------+-----------+----+\n","|null| null|null| null|Liverpool| 4|The Beatles|1960|\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| null| null| null|null|\n","+----+-------+----+------------+---------+------+-----------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"outer\").orderBy(\"name\").show(3)\n","# see nulls for The Beatles and Eric Clapton"]},{"cell_type":"markdown","metadata":{"id":"tMlhMqhExWco"},"source":[" Semi-joins = everything in the LEFT DF for which there is a row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":481,"status":"ok","timestamp":1670436786026,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XwLKWOASxWco","outputId":"faa1b781-84a6-4fab-b2b7-32688ed1c65a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 1| [1]| 1| Angus Young|\n","| 0| [0]| 0| Jimmy Page|\n","| 3| [3]| 3|Kirk Hammett|\n","+----+-------+---+------------+\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"left_semi\").orderBy(\"name\").show(3)\n"]},{"cell_type":"markdown","metadata":{"id":"aiNQKxV5xWco"},"source":["Anti-joins = everything in the LEFT DF for which there is NO row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":736,"status":"ok","timestamp":1670436787511,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"9jYq8v1exWco","outputId":"de110140-c691-4043-c240-e17af16da907"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 2| [1, 5]| 2|Eric Clapton|\n","+----+-------+---+------------+\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"anti\").orderBy(\"name\").show(3)"]},{"cell_type":"markdown","metadata":{"id":"J0Gz1XFuxWcp"},"source":["Cross-join = takes every instance in the LEFT DF and create a new one for every intance in the right DF (not very common)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":896,"status":"ok","timestamp":1670436789370,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"wfrjSgZlxWcp","outputId":"2cb1037a-b7f7-4342-fc83-12a51de0476a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+---+------------+-----------+------+------------+----+\n","| 0| [0]| 0| Jimmy Page| Sydney| 1| AC/DC|1973|\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| Sydney| 1| AC/DC|1973|\n","| 3| [3]| 3|Kirk Hammett| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 1| [1]| 1| Angus Young| London| 0|Led Zeppelin|1968|\n","| 2| [1, 5]| 2|Eric Clapton| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett| London| 0|Led Zeppelin|1968|\n","| 0| [0]| 0| Jimmy Page|Los Angeles| 3| Metallica|1981|\n","| 1| [1]| 1| Angus Young|Los Angeles| 3| Metallica|1981|\n","| 2| [1, 5]| 2|Eric Clapton|Los Angeles| 3| Metallica|1981|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","| 0| [0]| 0| Jimmy Page| Liverpool| 4| The Beatles|1960|\n","| 1| [1]| 1| Angus Young| Liverpool| 4| The Beatles|1960|\n","| 2| [1, 5]| 2|Eric Clapton| Liverpool| 4| The Beatles|1960|\n","| 3| [3]| 3|Kirk Hammett| Liverpool| 4| The Beatles|1960|\n","+----+-------+---+------------+-----------+------+------------+----+\n","\n"]}],"source":["guitaristsDF.crossJoin(bandsDFMod).show(20)"]},{"cell_type":"markdown","metadata":{"id":"h85qdkxcxWcp"},"source":["*Union Join is just an Union (note it only works if both DFs have the same schema)"]},{"cell_type":"markdown","metadata":{"id":"SB3QfnzsxWcp"},"source":["## Exercises\n","1. Read employees.csv, deptmanagers.csv, salaries.csv and titles.csv to a DFs.\n","2. Show all employees and their max salary (there can be different salaries registered for the same employee)\n","3. Show all employees who were never managers. Check it by getting all the managers and checking that they are not in the table\n","4. Find the job titles of the best paid 10 employees in the company (note that there can be different titles registered for the same employee)"]},{"cell_type":"markdown","metadata":{"id":"QqKb-sgFxWcp"},"source":["Exercise 1"]},{"cell_type":"markdown","metadata":{"id":"PYtLrSQ2xWcq"},"source":["Exercise 2"]},{"cell_type":"markdown","metadata":{"id":"RntnapY5xWcq"},"source":["Exercise 3"]},{"cell_type":"markdown","metadata":{"id":"LSokNABIxWcr"},"source":["Exercise 4"]}],"metadata":{"colab":{"collapsed_sections":["opA3NFOfxWcm","SB3QfnzsxWcp"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/05.WindowPartitioning_UDFs.ipynb b/PROFESORES/PYSPARK/notebooks/05.WindowPartitioning_UDFs.ipynb new file mode 100644 index 000000000..e1ac07736 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/05.WindowPartitioning_UDFs.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"BNkZfzfxGZ0z"},"source":["# Windows Partitioning"]},{"cell_type":"markdown","metadata":{"id":"AQieQ5pkGfNm"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HelxRmCPGpql"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9Cn3c-ywGtDV"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1670442099775,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"D95sNcJfGvyV","outputId":"2653eff1-49ca-4320-9751-cf5ad29bdc3a"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 1 root root 4096 Dec 6 14:35 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qtBMGi7EGvwN"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6JO331NrGvtt"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"markdown","metadata":{"id":"02epIDkbG24d"},"source":["Defining the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qmON5zHJG4-m"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"WgvNJQOAGZ00"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9098,"status":"ok","timestamp":1670442128571,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"siaPZq4XGZ00","outputId":"525e7ed9-c4cd-4387-cf51-fc2a6069f972"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Window Partitioning\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2863,"status":"ok","timestamp":1670442153333,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nsBkpLh6GZ01","outputId":"7c3c6f13-7451-4a44-91e3-305c9978419c"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Windows Partitioning
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Bqu4fQnNGZ02"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-9DDmYQKGZ02"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"NYrtXWZIHKMt"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2lkKBm3CHL-l"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/characters.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/employees.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/salaries.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/deptmanagers.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/titles.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"sxWVtHu5GZ02"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{},"source":["### Window Partitioning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JWgnuYZ8GZ02"},"outputs":[],"source":["employeesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/employees.csv\")\n","salariesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/salaries.csv\")\n","deptManagersDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/deptmanagers.csv\")\n","titlesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/titles.csv\")\n","charactersDF = spark.read.option(\"inferSchema\", \"true\").option(\"header\", \"true\").csv(\"/dataset/characters.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":673,"status":"ok","timestamp":1670442251373,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"m26N92p-GZ02","outputId":"d0ee0fc4-6213-43ff-dc9e-1557814b990b"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+--------+----------+----------+\n","|emp_no| title| from_date| to_date|\n","+------+--------+----------+----------+\n","| 10010|Engineer|1996-11-24|9999-01-01|\n","| 10020|Engineer|1997-12-30|9999-01-01|\n","+------+--------+----------+----------+\n","only showing top 2 rows\n","\n"]}],"source":["titlesDF.show(2)"]},{"cell_type":"markdown","metadata":{"id":"O9KiG0PHGZ03"},"source":["Get the last title for the employees"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1183,"status":"ok","timestamp":1670442253888,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"8W89HFrvGZ03","outputId":"06339219-7d05-4739-9889-b26b4d3cc171"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+----------+\n","|emp_no| title| from_date| to_date|\n","+------+---------------+----------+----------+\n","| 10040| Engineer|1993-02-14|1999-02-14|\n","| 10040|Senior Engineer|1999-02-14|9999-01-01|\n","+------+---------------+----------+----------+\n","\n"]}],"source":["titlesDF.filter(col(\"emp_no\") == 10040).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vJzQSe5pGZ03"},"outputs":[],"source":["# import library\n","from pyspark.sql.window import Window"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3221,"status":"ok","timestamp":1670442257825,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"smIYb3ogGZ03","outputId":"1ad92b06-8204-49f6-ac3d-48eaf05e6e84"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+\n","|emp_no| title| to_date|\n","+------+---------------+----------+\n","| 10040|Senior Engineer|9999-01-01|\n","+------+---------------+----------+\n","\n"]}],"source":["# get last title by date for each employee (similar to previous exercise in Joins)\n","byEmployee = Window.partitionBy(\"emp_no\").orderBy(col(\"to_date\").desc())\n","mostRecentJobTitlesDF = titlesDF.withColumn(\"datesOrder\", row_number().over(byEmployee)) \\\n"," .filter(col(\"datesOrder\") == 1) \\\n"," .select(\"emp_no\", \"title\", \"to_date\")\n","\n","mostRecentJobTitlesDF.filter(col(\"emp_no\") == 10040).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2fcF-z4WGZ03"},"outputs":[],"source":["# for the previous example we saw in joins, if we use windogs partitioning we do not need to do the previous step (filtering max date and then do the join over the same table)"]},{"cell_type":"markdown","metadata":{"id":"Y2uP3Mo-GZ04"},"source":["Get the max salaries (three for example) for job title"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5092,"status":"ok","timestamp":1670442263272,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"LUTvHq4EGZ04","outputId":"6d56f043-b362-44d9-af50-3a990e6b279f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+\n","|salary| title|\n","+------+---------------+\n","| 72668|Senior Engineer|\n","+------+---------------+\n","\n","+------+---------------+\n","|salary| title|\n","+------+---------------+\n","| 80324| Engineer|\n","| 47017| Engineer|\n","| 88806|Senior Engineer|\n","+------+---------------+\n","only showing top 3 rows\n","\n"]}],"source":["# we need salary data with title data joined first\n","bestPaidPerTitlerawDF = salariesDF.withColumn(\"salary\", col(\"salary\").cast(\"long\")) \\\n"," .join(mostRecentJobTitlesDF, (salariesDF.emp_no == mostRecentJobTitlesDF.emp_no) & (salariesDF.to_date == mostRecentJobTitlesDF.to_date)).drop(\"emp_no\", \"from_date\", \"to_date\")\n","\n","bestPaidPerTitlerawDF.filter(salariesDF.emp_no == 10040).show()\n","bestPaidPerTitlerawDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4259,"status":"ok","timestamp":1670442267529,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Y7-8h-0nGZ04","outputId":"6639ac14-ee81-4949-cfb4-e9a2c05737f4"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+------------------+-----------+\n","|salary| title|rank_salary|\n","+------+------------------+-----------+\n","|101622|Assistant Engineer| 1|\n","| 92674|Assistant Engineer| 2|\n","| 92034|Assistant Engineer| 3|\n","|130939| Engineer| 1|\n","|121819| Engineer| 2|\n","|120417| Engineer| 3|\n","+------+------------------+-----------+\n","only showing top 6 rows\n","\n"]}],"source":["# now we apply the window partitioning\n","byTitle = Window.partitionBy(\"title\").orderBy(col(\"salary\").desc())\n","\n","bestPaidPerTitleDF = bestPaidPerTitlerawDF.withColumn(\"rank_salary\", row_number().over(byTitle)).filter(col(\"rank_salary\") <= 3)\n","\n","bestPaidPerTitleDF.show(6)"]},{"cell_type":"markdown","metadata":{},"source":["### UDFs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["charactersDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# we define a function to remove the BBY from the birth_year\n","year = udf(lambda s: s[:-3])\n","\n","charactersDF.withColumn(\"year\", year(col(\"birth_year\"))).show(3)"]},{"cell_type":"markdown","metadata":{"id":"IYWyYjXPGZ04"},"source":["## Exercises Window Partitioning\n","1) Load characters.csv to a DataFrame. Then, get the (two) tallest characters per species and per homeworld planet. Select the name, height, and species/homeworld in their case.\n","2) Get the height difference for each character with respect to the smallest one in the same homeworld."]},{"cell_type":"markdown","metadata":{"id":"ndRe6jVQGZ04"},"source":["Exercise 1"]},{"cell_type":"markdown","metadata":{"id":"7uz7mRJwGZ05"},"source":["Exercise 2"]},{"cell_type":"markdown","metadata":{},"source":["## Exercises UDFs\n","1. Choose one of the DFs we have, define two UDFs of your own, and apply them (with a withColumn) on the DF. Show the results "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EdA3Q2K3GZ05"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/06.Partitioning.ipynb b/PROFESORES/PYSPARK/notebooks/06.Partitioning.ipynb new file mode 100644 index 000000000..22a1d07b7 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/06.Partitioning.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"BNkZfzfxGZ0z"},"source":["# Partitioning"]},{"cell_type":"markdown","metadata":{"id":"AQieQ5pkGfNm"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HelxRmCPGpql"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":57405,"status":"ok","timestamp":1670666888439,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"9Cn3c-ywGtDV"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1670666888440,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"D95sNcJfGvyV","outputId":"7b823201-ab1c-4452-d091-bb995fd23e6e"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 8 14:36 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":2865,"status":"ok","timestamp":1670666891295,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qtBMGi7EGvwN"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4334,"status":"ok","timestamp":1670666895619,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"6JO331NrGvtt"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"markdown","metadata":{"id":"02epIDkbG24d"},"source":["Defining the environment"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":19,"status":"ok","timestamp":1670666895620,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qmON5zHJG4-m"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"WgvNJQOAGZ00"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":6346,"status":"ok","timestamp":1670666901951,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"siaPZq4XGZ00","outputId":"517fa3d4-7f52-4b7b-f33a-b27ecef9a830"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Window Partitioning Exercises\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2348,"status":"ok","timestamp":1670666904291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nsBkpLh6GZ01","outputId":"8b85e798-6e00-4c6f-c200-9f257dec4f76"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Window Partitioning Exercises
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1670666904291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Bqu4fQnNGZ02"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1670666904292,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-9DDmYQKGZ02"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"NYrtXWZIHKMt"},"source":["Download datasets"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2526,"status":"ok","timestamp":1670666906811,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2lkKBm3CHL-l","outputId":"df5bac53-e576-4134-8560-d1fe2b81e002"},"outputs":[{"name":"stdout","output_type":"stream","text":["bank.csv characters.csv planets.csv species.csv vehicles.csv\n"]}],"source":["!mkdir -p /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/bank.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/vehicles.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/characters.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/planets.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/species.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"d1tEe9JCILAe"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"HWlZ5puMLqvX"},"source":["### Patitioning"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":8328,"status":"ok","timestamp":1670666915136,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"B10rScPeIOCn"},"outputs":[],"source":["# Load characters CSV\n","charactersDF = spark.read.option(\"inferSchema\", \"true\").option(\"header\", \"true\").csv(\"/dataset/characters.csv\")"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1489,"status":"ok","timestamp":1670666916608,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"_z47cLpVJQgV","outputId":"3df2be0d-f74d-4118-8b7a-e82fc900c949"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 87|\n","+-----------+-----+\n","\n"]}],"source":["# Show how the data is partitioned now\n","charactersDF \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"count\").desc()) \\\n"," .show()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1525,"status":"ok","timestamp":1670666918132,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"NGfdi2WhJQeH","outputId":"4c872e9c-45ef-47e1-c092-87cd3e206764"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 4|\n","| 1| 4|\n","| 2| 4|\n","| 3| 4|\n","| 4| 4|\n","| 5| 4|\n","| 6| 4|\n","| 7| 4|\n","| 8| 4|\n","| 9| 5|\n","| 10| 5|\n","| 11| 5|\n","| 12| 5|\n","| 13| 5|\n","| 14| 5|\n","| 15| 5|\n","| 16| 4|\n","| 17| 4|\n","| 18| 4|\n","| 19| 4|\n","+-----------+-----+\n","\n"]}],"source":["# We will now repartition the DF to 20 partitions\n","charactersRepDF = charactersDF.repartition(20)\n","charactersRepDF \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"partitionId\")) \\\n"," .show()"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":706,"status":"ok","timestamp":1670666918833,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"sEwePVEfJvMN","outputId":"ab472f88-1eb1-4d87-bdcc-f4cbf77268f9"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 17|\n","| 1| 18|\n","| 2| 18|\n","| 3| 17|\n","| 4| 17|\n","+-----------+-----+\n","\n"]}],"source":["# Now we can use coalesce to reduce the number of partitions\n","charactersRepDF \\\n"," .coalesce(5) \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"partitionId\")) \\\n"," .show()"]},{"cell_type":"markdown","metadata":{"id":"sxWVtHu5GZ02"},"source":["## Partitioning Exercises"]},{"cell_type":"markdown","metadata":{"id":"rZt5nAVLRmeF"},"source":["1. Try repartition/colaesce yourself"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DF-WHVfjRq49"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks/07.TaxiExercise.ipynb b/PROFESORES/PYSPARK/notebooks/07.TaxiExercise.ipynb new file mode 100644 index 000000000..d33f3eb2a --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks/07.TaxiExercise.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"P0VSNugGxYj4"},"source":["# Taxi Exercise"]},{"cell_type":"markdown","metadata":{"id":"fhqHAQWiyLZA"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"O-nP6sc6xgbg"},"source":["Install Java and Spark in VM"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":73671,"status":"ok","timestamp":1675880362086,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JRywFs7DxgOQ"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":6533,"status":"ok","timestamp":1675880368614,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"w8zwZ4GWxtMI"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":22,"metadata":{"executionInfo":{"elapsed":4962,"status":"ok","timestamp":1675880373536,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"PAG_MdchxuxI"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1675880373536,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"BM0GAnLCxwkP"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"6AnypwdzxYj7"},"source":["Start Spark Session \n","\n","---"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"CIJ4mkL-xYj7","outputId":"b1103e23-09a8-4c4e-d4ac-11cc01fd3305"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Joins\") \\\n"," .master(\"local[*]\") \\\n"," .config(\"spark.ui.port\", \"4500\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"L2BsUYftxYj8","outputId":"60dfc7c8-1e9d-4180-fc37-025c710d22fc"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Joins
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":26,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"R6uQC9cJxYj9"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":1424,"status":"ok","timestamp":1675880374953,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"VegBGDs3yIIH"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/taxi_data.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/taxi_zones.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"Jsnl4Q2NxYj9"},"source":["Load the datasets"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6842,"status":"ok","timestamp":1675880381794,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"KBbu7DMqxYj9","outputId":"39d9a256-0a4f-407a-de0b-62a7ff428d3a"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- VendorID: integer (nullable = true)\n"," |-- tpep_pickup_datetime: timestamp (nullable = true)\n"," |-- tpep_dropoff_datetime: timestamp (nullable = true)\n"," |-- passenger_count: integer (nullable = true)\n"," |-- trip_distance: double (nullable = true)\n"," |-- RatecodeID: integer (nullable = true)\n"," |-- store_and_fwd_flag: string (nullable = true)\n"," |-- PULocationID: integer (nullable = true)\n"," |-- DOLocationID: integer (nullable = true)\n"," |-- payment_type: integer (nullable = true)\n"," |-- fare_amount: double (nullable = true)\n"," |-- extra: double (nullable = true)\n"," |-- mta_tax: double (nullable = true)\n"," |-- tip_amount: double (nullable = true)\n"," |-- tolls_amount: double (nullable = true)\n"," |-- improvement_surcharge: double (nullable = true)\n"," |-- total_amount: double (nullable = true)\n","\n"]}],"source":["taxiDF = spark.read \\\n"," .option(\"header\", \"true\") \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .csv(\"/dataset/taxi_data.csv\")\n","\n","taxiDF.printSchema()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":347,"status":"ok","timestamp":1675880382138,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"yOnQ1DjhxYj-","outputId":"aa7807d4-9b62-4d23-d164-30c0c49ec496"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","|VendorID|tpep_pickup_datetime|tpep_dropoff_datetime|passenger_count|trip_distance|RatecodeID|store_and_fwd_flag|PULocationID|DOLocationID|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","| 2| 2018-01-24 23:02:56| 2018-01-24 23:10:58| 1| 2.02| 1| N| 48| 107| 2| 8.5| 0.5| 0.5| 0.0| 0.0| 0.3| 9.8|\n","| 2| 2018-01-24 23:57:13| 2018-01-25 00:21:17| 1| 10.13| 1| N| 79| 244| 2| 28.5| 0.5| 0.5| 0.0| 0.0| 0.3| 29.8|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","only showing top 2 rows\n","\n"]}],"source":["taxiDF.show(2)"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":576,"status":"ok","timestamp":1675880382711,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"UtysOEnoxYj-","outputId":"13f2b048-5823-4471-ba4a-9416446e48a9"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- LocationID: integer (nullable = true)\n"," |-- Borough: string (nullable = true)\n"," |-- Zone: string (nullable = true)\n"," |-- service_zone: string (nullable = true)\n","\n"]}],"source":["taxiZonesDF = spark.read \\\n"," .option(\"header\", \"true\") \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .csv(\"/dataset/taxi_zones.csv\")\n","\n","taxiZonesDF.printSchema()"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":966,"status":"ok","timestamp":1675880383675,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qCYJZpiXxYj-","outputId":"10842c3b-b922-4332-84be-d942246b01a5"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+-------+--------------------+------------+\n","|LocationID|Borough| Zone|service_zone|\n","+----------+-------+--------------------+------------+\n","| 1| EWR| Newark Airport| EWR|\n","| 2| Queens| Jamaica Bay| Boro Zone|\n","| 3| Bronx|Allerton/Pelham G...| Boro Zone|\n","+----------+-------+--------------------+------------+\n","only showing top 3 rows\n","\n"]}],"source":["taxiZonesDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"EjGzj1Rsx3Ns"},"source":["## Exercise"]},{"cell_type":"markdown","metadata":{"id":"JOH-iRShx1cw"},"source":["In this exercise we will be working with two DFs. The first one, taxiDf holds info about taxi rides per 2018 year. And the second, taxiZonesDF, have info about the Zones. Please load the DFs and print the schemas and two (or more) rows for more detailed info.\n","\n","The aim of the exercise is to answer the questions listed below."]},{"cell_type":"markdown","metadata":{"id":"tQaqDr3hxYj_"},"source":["**Questions:**\n","\n"," 1. Which zones have the most pickups/dropoffs overall? Note there are many PULocationIDs per Zone?\n"," 2. What are the peak hours for taxi?\n"," 3. How are the trips distributed by length? Show stats like mean, max, min, etc. \n"," Then get the total trips for less/more than 30 km. Why are people taking the cab? For long or short trips?\n"," You can also try the same with different distances. Which is the expected value for threshold is we want to obtain more or less the same trips in long/short counting?\n"," 4. What are the peak hours for long/short trips?\n"," 5. What are the top 3 pickup/dropoff zones for long/short trips?\n"," 6. How are people paying for the ride, on long/short trips? Hint: the information about how good is the payment is in RatecodeID column.\n"," 7. How is the payment type (RatecodeId) evolving with time (in days)? Hint: use the column with pickup time info.\n"," Get the same info but with avg of ratecode and total trips per day."]},{"cell_type":"markdown","metadata":{"id":"NvllEugwxYj_"},"source":["### Question 1"]},{"cell_type":"markdown","metadata":{"id":"-um1G1zKxYkA"},"source":["### Question 2"]},{"cell_type":"markdown","metadata":{"id":"Hn_hf8VOxYkB"},"source":["### Question 3"]},{"cell_type":"markdown","metadata":{"id":"ty8IZ-C6xYkC"},"source":["### Question 4"]},{"cell_type":"markdown","metadata":{"id":"ChmRlWgIxYkD"},"source":["### Question 5"]},{"cell_type":"markdown","metadata":{"id":"vEGDhOIpxYkE"},"source":["### Question 6"]},{"cell_type":"markdown","metadata":{"id":"tLf0Rms-xYkF"},"source":["### Question 7"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/01.DataFramesBascis_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/01.DataFramesBascis_solved.ipynb new file mode 100644 index 000000000..0dec7117d --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/01.DataFramesBascis_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"u3WKxUMvQ9JW"},"source":["# DataFrames Basics"]},{"cell_type":"markdown","metadata":{"id":"IBsRl4yARA3x"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"0KID1ObYRDA4"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":105079,"status":"ok","timestamp":1670605221056,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-9EjQzqhRJSZ"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1670605221057,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"E4s6wcUsSPHQ","outputId":"feab5ebb-a7f9-4078-c85d-e112383efe5b"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 7 14:41 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":3869,"status":"ok","timestamp":1670605224920,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"hSA_T2q7SEt_"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4427,"status":"ok","timestamp":1670605229334,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"HpKEfJTeii2Y"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17123,"status":"ok","timestamp":1670605246445,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zwH-zC17SGnP","outputId":"1cd82f81-01c3-4283-ebdb-bba0318a4b52"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[K |████████████████████████████████| 200 kB 6.8 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n"]}],"source":["\n","!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"y1tk452JRjuY"},"source":["Define the environment"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":31,"status":"ok","timestamp":1670605246446,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vrMxCiuZRl7h"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"vk_3rL02Q9JZ"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":11208,"status":"ok","timestamp":1670605257627,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-HtQz6mfQ9JZ","outputId":"b1ed7644-4b47-49f4-89ca-310a9c173e65"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"DataFrames Basics\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2047,"status":"ok","timestamp":1670605259667,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"DrOjmfL9Q9Ja","outputId":"3a4dba91-ad21-4c19-8034-7526d4e19811"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    DataFrames Basics
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670605259668,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"IBZ8ufPAQ9Jb"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670605259668,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nJ-cdrAlQ9Jb"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"AIP7U3YYTDbw"},"source":["Download datasets"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":2655,"status":"ok","timestamp":1670605262320,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"d63cazZLTKTv"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/cars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/bank.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/vehicles.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1670605262320,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ml1O2OiQU6ah","outputId":"ad13f4aa-4fe6-47d7-a4c0-b9c210432a75"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 1784\n","-rw-r--r-- 1 root root 461474 Dec 9 17:01 bank.csv\n","-rw-r--r-- 1 root root 74910 Dec 9 17:00 cars.json\n","-rw-r--r-- 1 root root 1274347 Dec 9 17:01 movies.json\n","-rw-r--r-- 1 root root 4370 Dec 9 17:01 vehicles.csv\n"]}],"source":["ls -l /dataset"]},{"cell_type":"markdown","metadata":{"id":"94aRTBiPQ9Jc"},"source":["## Examples"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":593,"status":"ok","timestamp":1670605328886,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XxXiDT4XUgjl"},"outputs":[],"source":["from pyspark.sql.types import Row\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"DnCG9XHsTUfe"},"source":["Read directly from JSON file to a DF"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":1409,"status":"ok","timestamp":1670605345988,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"iBWBcsLDTYuF"},"outputs":[],"source":["carsDF = spark.read.option(\"inferSchema\", True).json(\"/dataset/cars.json\") # inferSchema requires one extra pass over the data\n","\n","# if None is set, it uses de default value (default = False) you can also pass the schema manually"]},{"cell_type":"markdown","metadata":{"id":"beN35oUrUo4c"},"source":["Read directly from csv"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1488,"status":"ok","timestamp":1670605379551,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"xaxDO13vUlSs","outputId":"117625e7-ad3e-47a3-f305-e1208b82faa8"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","|age| job|marital|education|default|balance|housing|loan| contact|day|month|duration|campaign|pdays|previous|poutcome| y|\n","+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","| 30|unemployed|married| primary| no| 1787| no| no|cellular| 19| oct| 79| 1| -1| 0| unknown| no|\n","| 33| services|married|secondary| no| 4789| yes| yes|cellular| 11| may| 220| 1| 339| 4| failure| no|\n","| 35|management| single| tertiary| no| 1350| yes| no|cellular| 16| apr| 185| 1| 330| 1| failure| no|\n","+---+----------+-------+---------+-------+-------+-------+----+--------+---+-----+--------+--------+-----+--------+--------+---+\n","only showing top 3 rows\n","\n"]}],"source":["bankDF = spark.read.option(\"header\", \"true\").option(\"delimiter\", \";\").csv(\"/dataset/bank.csv\")\n","bankDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"OOt52pbaQ9Jc"},"source":["Showing a DF and print schema"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":494,"status":"ok","timestamp":1670580334437,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"PmmsrRxaQ9Jd","outputId":"7016f8d3-376a-4ad3-b98e-2e5a0ce3e5aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 2 rows\n","\n","root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Cylinders: long (nullable = true)\n"," |-- Displacement: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight_in_lbs: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["carsDF.show(2)\n","carsDF.printSchema()"]},{"cell_type":"markdown","metadata":{"id":"ij4TDMZ0lkBZ"},"source":["Get Rows"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":614,"status":"ok","timestamp":1670605438909,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SB06W84wlnlY","outputId":"11894b33-d726-4577-ae57-4c0312429d5d"},"outputs":[{"data":{"text/plain":["[Row(Acceleration=12.0, Cylinders=8, Displacement=307.0, Horsepower=130, Miles_per_Gallon=18.0, Name='chevrolet chevelle malibu', Origin='USA', Weight_in_lbs=3504, Year='1970-01-01'),\n"," Row(Acceleration=11.5, Cylinders=8, Displacement=350.0, Horsepower=165, Miles_per_Gallon=15.0, Name='buick skylark 320', Origin='USA', Weight_in_lbs=3693, Year='1970-01-01')]"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["carsDF.take(2)"]},{"cell_type":"markdown","metadata":{"id":"Z89eHF1aQ9Jd"},"source":["Count"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1183,"status":"ok","timestamp":1670605451441,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"rtFilGlZWGyo","outputId":"3dad6a3c-7e6f-4d92-f7ed-5b9c049eb025"},"outputs":[{"data":{"text/plain":["406"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["carsDF.count()"]},{"cell_type":"markdown","metadata":{"id":"akmA70EPQ9Jd"},"source":["Schema"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670580335852,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Rp6hKanzQ9Je","outputId":"e57a56d7-fdb3-4007-8d30-c6a4d3d9de10"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","StructType([StructField('Acceleration', DoubleType(), True), StructField('Cylinders', LongType(), True), StructField('Displacement', DoubleType(), True), StructField('Horsepower', LongType(), True), StructField('Miles_per_Gallon', DoubleType(), True), StructField('Name', StringType(), True), StructField('Origin', StringType(), True), StructField('Weight_in_lbs', LongType(), True), StructField('Year', StringType(), True)])\n"]}],"source":["# obtain a schema\n","carsSchema = carsDF.schema\n","print(type(carsSchema))\n","print(carsSchema)"]},{"cell_type":"markdown","metadata":{"id":"v_XS0vB0Q9Je"},"source":["Custom Schemas"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":256,"status":"ok","timestamp":1670605490627,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ekQkJLT8Q9Je"},"outputs":[],"source":["example = spark.sparkContext.parallelize([(\"chevrolet chevelle malibu\",18,\"1970-01-01\",\"USA\"),\n"," (\"buick skylark 320\",15,\"1970-01-01\",\"USA\")])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":387,"status":"ok","timestamp":1670580336237,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nUzjX4kTQ9Je","outputId":"915707fc-a31f-4cc1-f7dd-a8ee013e82a5"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- _1: string (nullable = true)\n"," |-- _2: long (nullable = true)\n"," |-- _3: string (nullable = true)\n"," |-- _4: string (nullable = true)\n","\n"]}],"source":["exampleDF = spark.createDataFrame(example)\n","exampleDF.printSchema()"]},{"cell_type":"markdown","metadata":{"id":"GeTtjRirQ9Je"},"source":["With columns names"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uhY-f3j1Q9Jf"},"outputs":[],"source":["names = list([\"name\", \"weight\", \"date\", \"country\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670580336237,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nqxqPyr1Q9Jf","outputId":"60ef9d54-9365-4a2a-8ee7-c96b08cece92"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- weight: long (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example2DF = example.toDF(names)\n","example2DF.printSchema()"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":341,"status":"ok","timestamp":1670605542291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"toUIbosSQ9Jf"},"outputs":[],"source":["# importing sql types\n","from pyspark.sql.types import *"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KZNfLhFWQ9Jf"},"outputs":[],"source":["# custom schema\n","customSchema = StructType([ \\\n"," StructField('name', StringType(), True), \\\n"," StructField('weight', StringType(), True), \\\n"," StructField('date', StringType(), True), \\\n"," StructField('country', StringType(), True)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670580336238,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"x63bbTDYQ9Jf","outputId":"ac14e809-6a0a-4b2c-de40-bc471939d3f0"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- weight: string (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example3DF = spark.createDataFrame(example, customSchema)\n","example3DF.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1670580336238,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eRiqa-G5Q9Jf","outputId":"6b62a663-7334-4711-a716-898ce9f718af"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------------+------+----------+-------+\n","|name |weight|date |country|\n","+-------------------------+------+----------+-------+\n","|chevrolet chevelle malibu|18 |1970-01-01|USA |\n","|buick skylark 320 |15 |1970-01-01|USA |\n","+-------------------------+------+----------+-------+\n","\n"]}],"source":["example3DF.show(2, False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M3qToymLQ9Jg"},"outputs":[],"source":["# we can also specify schema with DDL (Data Definition Language)\n","customSchema2 = \"`name` STRING NOT NULL, `weight` INT, `date` STRING, `country` STRING\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670580336701,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"7k9X1gSSQ9Jg","outputId":"70a855ab-0961-43a9-8f48-9f04cfb757b3"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = false)\n"," |-- weight: integer (nullable = true)\n"," |-- date: string (nullable = true)\n"," |-- country: string (nullable = true)\n","\n"]}],"source":["example4DF = spark.createDataFrame(example, customSchema2)\n","example4DF.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670580336701,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"E5rayh3uQ9Jg","outputId":"0864a25e-c98f-4438-9bef-859ca9b2384c"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","\n"]}],"source":["print(type(example2DF.collect()[0][\"weight\"]))\n","print(type(example3DF.collect()[0][\"weight\"]))"]},{"cell_type":"markdown","metadata":{"id":"6QnciaoWQ9Jg"},"source":["## Exercises\n","1) Create a manual DF describing smartphones\n"," - maker\n"," - model\n"," - screen dimension\n"," - camera megapixels\n"," \n","2) Read another file from the dataset/ folder, e.g. movies.json\n"," - print its schema\n"," - count the number of rows, call count()\n","\n","3) Take a look to vehicles.csv. Read the file to a DF but this time with your own schema"]},{"cell_type":"markdown","metadata":{"id":"kQR1cduXQ9Jg"},"source":["Exercise 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4pqCIeTwQ9Jh"},"outputs":[],"source":["smartphones = spark.sparkContext.parallelize([\n"," (\"Samsung\", \"Galaxy S10\", \"Android\", 12),\n"," (\"Apple\", \"iPhone X\", \"iOS\", 13),\n"," (\"Nokia\", \"3310\", \"THE BEST\", 0)])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2Ngd1MWOQ9Jh"},"outputs":[],"source":["smartphonesDF = smartphones.toDF([\"Make\", \"Model\", \"Platform\", \"CameraMegapixels\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":237,"status":"ok","timestamp":1670580337187,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"8XI6GmuJQ9Jh","outputId":"8794a9ae-524c-4195-a71d-352a18c1eaf2"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------+----------+--------+----------------+\n","| Make| Model|Platform|CameraMegapixels|\n","+-------+----------+--------+----------------+\n","|Samsung|Galaxy S10| Android| 12|\n","| Apple| iPhone X| iOS| 13|\n","| Nokia| 3310|THE BEST| 0|\n","+-------+----------+--------+----------------+\n","\n"]}],"source":["smartphonesDF.show()"]},{"cell_type":"markdown","metadata":{"id":"Zmg5kbOpl-s3"},"source":["Exercise 2"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KX8JcDENQ9Jh"},"outputs":[],"source":["moviesDF = spark.read \\\n"," .format(\"json\") \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .load(\"/dataset/movies.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670580338388,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"dpj8cry1Q9Jh","outputId":"7186242b-4080-4bbb-8ee8-812e0d1f43fa"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Creative_Type: string (nullable = true)\n"," |-- Director: string (nullable = true)\n"," |-- Distributor: string (nullable = true)\n"," |-- IMDB_Rating: double (nullable = true)\n"," |-- IMDB_Votes: long (nullable = true)\n"," |-- MPAA_Rating: string (nullable = true)\n"," |-- Major_Genre: string (nullable = true)\n"," |-- Production_Budget: long (nullable = true)\n"," |-- Release_Date: string (nullable = true)\n"," |-- Rotten_Tomatoes_Rating: long (nullable = true)\n"," |-- Running_Time_min: long (nullable = true)\n"," |-- Source: string (nullable = true)\n"," |-- Title: string (nullable = true)\n"," |-- US_DVD_Sales: long (nullable = true)\n"," |-- US_Gross: long (nullable = true)\n"," |-- Worldwide_Gross: long (nullable = true)\n","\n","The Movies DF has 3201 rows\n"]}],"source":["moviesDF.printSchema()\n","print(f\"The Movies DF has {moviesDF.count()} rows\")"]},{"cell_type":"markdown","metadata":{"id":"JAqADV5jW00d"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":375,"status":"ok","timestamp":1670580516328,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"oU8EyulZQ9Jh","outputId":"31de6d21-2295-4f35-90ee-1736d566038a"},"outputs":[{"name":"stdout","output_type":"stream","text":["name,model,manufacturer,cost_in_credits,length,max_atmosphering_speed,crew,passengers,cargo_capacity,consumables,vehicle_class\n","Sand Crawler,Digger Crawler,Corellia Mining Corporation,150000,36.8,30,46,30,50000,2 months,wheeled\n","T-16 skyhopper,T-16 skyhopper,Incom Corporation,14500,10.4,1200,1,1,50,0,repulsorcraft\n","X-34 landspeeder,X-34 landspeeder,SoroSuub Corporation,10550,3.4,250,1,1,5,NA,repulsorcraft\n","TIE/LN starfighter,Twin Ion Engine/Ln Starfighter,Sienar Fleet Systems,NA,6.4,1200,1,0,65,2 days,starfighter\n","Snowspeeder,t-47 airspeeder,Incom corporation,NA,4.5,650,2,0,10,none,airspeeder\n","TIE bomber,TIE/sa bomber,Sienar Fleet Systems,NA,7.8,850,1,0,none,2 days,space/planetary bomber\n","AT-AT,All Terrain Armored Transport,\"Kuat Drive Yards, Imperial Department of Military Research\",NA,20,60,5,40,1000,NA,assault walker\n","AT-ST,All Terrain Scout Transport,\"Kuat Drive Yards, Imperial Department of Military Research\",NA,2,90,2,0,200,none,walker\n","Storm IV Twin-Pod cloud car,Storm IV Twin-Pod,Bespin Motors,75000,7,1500,2,0,10,1 day,repulsorcraft\n"]}],"source":["!head /dataset/vehicles.csv"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":606,"status":"ok","timestamp":1670580981465,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"EtCK7D0yXfbk","outputId":"3aa263d0-e07d-46fe-fce0-53388e0c656f"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- model: string (nullable = true)\n"," |-- manufacturer: string (nullable = true)\n"," |-- cost_in_credits: string (nullable = true)\n"," |-- length: string (nullable = true)\n"," |-- max_atmosphering_speed: string (nullable = true)\n"," |-- crew: integer (nullable = true)\n"," |-- passengers: string (nullable = true)\n"," |-- cargo_capacity: string (nullable = true)\n"," |-- consumables: string (nullable = true)\n"," |-- vehicle_class: string (nullable = true)\n","\n"]}],"source":["vehicles = spark.read.option(\"header\", \"true\").option(\"inferSchema\", \"true\").csv(\"/dataset/vehicles.csv\")\n","vehicles.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BpNYLQOtW5yR"},"outputs":[],"source":["vehiclesSchema = StructType([StructField('name', StringType(), True), StructField('model', StringType(), True), StructField('manufacturer', StringType(), True), \n"," StructField('cost_in_credits', LongType(), True), StructField('length', DoubleType(), True), StructField('max_atmosphering_speed', DoubleType(), True), \n"," StructField('crew', IntegerType(), True), StructField('passengers', IntegerType(), True), \n"," StructField('cargo_capacity', LongType(), True), StructField('consumables', StringType(), True), StructField('vehicle_class', StringType(), True)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670580970691,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"L_UsBd8mYFtl","outputId":"6062d159-b821-4bba-dc3b-94b92286dfa5"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- name: string (nullable = true)\n"," |-- model: string (nullable = true)\n"," |-- manufacturer: string (nullable = true)\n"," |-- cost_in_credits: long (nullable = true)\n"," |-- length: double (nullable = true)\n"," |-- max_atmosphering_speed: double (nullable = true)\n"," |-- crew: integer (nullable = true)\n"," |-- passengers: integer (nullable = true)\n"," |-- cargo_capacity: long (nullable = true)\n"," |-- consumables: string (nullable = true)\n"," |-- vehicle_class: string (nullable = true)\n","\n"]}],"source":["vehiclesDF = spark.read.option(\"header\", \"true\").schema(vehiclesSchema).csv(\"/dataset/vehicles.csv\")\n","vehiclesDF.printSchema()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JD2KQtXiYmVn"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":["94aRTBiPQ9Jc"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/02.ColumnsAndExpressions_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/02.ColumnsAndExpressions_solved.ipynb new file mode 100644 index 000000000..eba103b55 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/02.ColumnsAndExpressions_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"eyZfoGe_trKF"},"source":["# Columns and Expressions"]},{"cell_type":"markdown","metadata":{"id":"OF1B2ocetvbn"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"l_Vt3AVEt3uX"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":20309,"status":"ok","timestamp":1670786288520,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JrJqxyXZt9wn"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1670786288521,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"WBfjT1TouA1O","outputId":"849b3c81-2ca2-4da3-a114-c1839e6cb4e1"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 8 14:36 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":3624,"status":"ok","timestamp":1670786292141,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"bHueJOLRuD2H"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":3931,"status":"ok","timestamp":1670786296068,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"arpGbc-EuDlH"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12537,"status":"ok","timestamp":1670786308602,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Bc6eiLp0uKov","outputId":"8bf55c6e-f254-4c05-a04c-a8091df0c579"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[?25l\r\u001b[K |█▋ | 10 kB 19.9 MB/s eta 0:00:01\r\u001b[K |███▎ | 20 kB 16.3 MB/s eta 0:00:01\r\u001b[K |█████ 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19.0 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 200 kB 19.0 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n"]}],"source":["\n","!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"mfEdQvJLuP7Y"},"source":["Define the environment"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1670786308602,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zU_829Z5uPEv"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"GByUn4STtrKH"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9402,"status":"ok","timestamp":1670786317995,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"lW82io8xtrKI","outputId":"9b3ef234-8873-41b5-f12e-2abbcc9fc22f"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Columns and Expressions\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1898,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Xe0_i5fHtrKJ","outputId":"8a9f40c7-a480-487f-aef6-d1ef630204ef"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

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    SparkContext

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    Spark UI

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    Version
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    v3.3.1
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    Master
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    local[*]
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    AppName
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    Columns and Expressions
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    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"mIXSyPeytrKJ"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786319889,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SsEHbN1HtrKJ"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"Cc8OP9YSu3KY"},"source":["Download datasets"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":982,"status":"ok","timestamp":1670786320867,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vgd-xCVUu5e4"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/cars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/more_cars.json -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"2m7U4JG7trKK"},"source":["Read JSON file"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":9150,"status":"ok","timestamp":1670786330016,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"j40ajnajtrKK"},"outputs":[],"source":["carsDF = spark.read \\\n"," .option(\"inferSchema\", True) \\\n"," .json(\"/dataset/cars.json\")"]},{"cell_type":"markdown","metadata":{"id":"rBegVS5dtrKK"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"-j-DuH6xtrKL"},"source":["Select a column"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":665,"status":"ok","timestamp":1670786330672,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pJeRA5chtrKL","outputId":"bb2fc632-3e12-4093-bfb7-c697dcde2f61"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------------+\n","|Name |\n","+-------------------------+\n","|chevrolet chevelle malibu|\n","|buick skylark 320 |\n","|plymouth satellite |\n","+-------------------------+\n","only showing top 3 rows\n","\n"]}],"source":["carsDF.select(col(\"Name\")).show(3, False)"]},{"cell_type":"markdown","metadata":{"id":"1Wd_xjFOY9Tl"},"source":["We can use various metods to refer a column"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":496,"status":"ok","timestamp":1670786331165,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RJYKyUputrKL","outputId":"0996f33d-676e-41c9-c86e-19387132cb5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+------------+-------------+\n","| Name|Acceleration|Weight_in_lbs|\n","+--------------------+------------+-------------+\n","|chevrolet chevell...| 12.0| 3504|\n","| buick skylark 320| 11.5| 3693|\n","| plymouth satellite| 11.0| 3436|\n","+--------------------+------------+-------------+\n","only showing top 3 rows\n","\n"]}],"source":["# various select methods\n","carsDF.select(\n"," carsDF.Name,\n"," col(\"Acceleration\"),\n"," \"Weight_in_lbs\"\n",").show(3)"]},{"cell_type":"markdown","metadata":{"id":"1BHOu4KEtrKL"},"source":["Expressions. We can use SQL like expression inside select to make operations with a column"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":629,"status":"ok","timestamp":1670786331793,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-OGl-DU7trKM","outputId":"d8d00a01-8d31-4d82-c6ca-b9b1ba7e71ed"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Name: string (nullable = true)\n"," |-- Weight_in_lbs: long (nullable = true)\n"," |-- Weight_in_kg_2: integer (nullable = true)\n"," |-- Weight_in_T: string (nullable = true)\n","\n","+--------------------+-------------+--------------+-----------+\n","| Name|Weight_in_lbs|Weight_in_kg_2|Weight_in_T|\n","+--------------------+-------------+--------------+-----------+\n","|chevrolet chevell...| 3504| 1592| 3.504|\n","| buick skylark 320| 3693| 1678| 3.693|\n","| plymouth satellite| 3436| 1561| 3.436|\n","+--------------------+-------------+--------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["# \n","carsWithKgDF = carsDF.select(\n"," col(\"Name\"),\n"," col(\"Weight_in_lbs\"),\n"," (col(\"Weight_in_lbs\")/2.2).cast(\"int\").alias(\"Weight_in_kg_2\"), #cast result to int\n"," expr(\"Weight_in_lbs / 1000\").cast(\"string\").alias(\"Weight_in_T\") #cast result to str\n",")\n","carsWithKgDF.printSchema()\n","carsWithKgDF.show(3)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":367,"status":"ok","timestamp":1670786332158,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"NZYAC9A8trKM","outputId":"74f8fdac-8acc-44f1-caef-5ebab0458d11"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-------------+---------------------+\n","| Name|Weight_in_lbs|(Weight_in_lbs / 2.2)|\n","+--------------------+-------------+---------------------+\n","|chevrolet chevell...| 3504| 1592.727273|\n","| buick skylark 320| 3693| 1678.636364|\n","| plymouth satellite| 3436| 1561.818182|\n","+--------------------+-------------+---------------------+\n","only showing top 3 rows\n","\n"]}],"source":["# with expressions\n","carsWithSelectExprWeightsDF = carsDF.selectExpr(\n"," \"Name\",\n"," \"Weight_in_lbs\",\n"," \"Weight_in_lbs / 2.2\"\n"," )\n","carsWithSelectExprWeightsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"LDwREHXhtrKM"},"source":["### DF Processing"]},{"cell_type":"markdown","metadata":{"id":"D-12eNHDtrKM"},"source":["Add a column"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":698,"status":"ok","timestamp":1670786332854,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"S09Nw_iytrKM","outputId":"374e95f6-3d68-4bf5-f22a-2e866d5aa6db"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year| Weight_in_kg_3|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|1592.7272727272725|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|1678.6363636363635|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|1561.8181818181818|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+------------------+\n","only showing top 3 rows\n","\n"]}],"source":["carsWithKg3DF = carsDF.withColumn(\"Weight_in_kg_3\", col(\"Weight_in_lbs\") / 2.2)\n","carsWithKg3DF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"SaYkSZ-AtrKN"},"source":["Rename a column"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":513,"status":"ok","timestamp":1670786333366,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"5m8EFdTBtrKN","outputId":"8dfff95c-47d9-4df2-f788-3e08209201b2"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight in pounds| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+----------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["carsWithColumnRenamed = carsDF.withColumnRenamed(\"Weight_in_lbs\", \"Weight in pounds\")\n","carsWithColumnRenamed.show(3)"]},{"cell_type":"code","execution_count":19,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1670786333366,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"zzwPcSFqtrKN"},"outputs":[],"source":["# careful with column names\n","# carsWithColumnRenamed.selectExpr(\"Weight in pounds\")"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"8Y0Ir5jBtrKN","outputId":"7b9b4047-cf09-4da4-ee8e-9ba539df7569"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------+\n","|Weight in pounds|\n","+----------------+\n","| 3504|\n","| 3693|\n","| 3436|\n","+----------------+\n","only showing top 3 rows\n","\n"]}],"source":["# as we hace special characters (spaces) we have to use the ``\n","carsWithColumnRenamed.selectExpr(\"`Weight in pounds`\").show(3)"]},{"cell_type":"markdown","metadata":{"id":"mkqoz74TtrKN"},"source":["Remove a column"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"6qaH5KLhtrKO","outputId":"a3a1858b-b277-46e1-de67-09470093d1ba"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Cylinders: long (nullable = true)\n"," |-- Displacement: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight in pounds: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["carsWithColumnRenamed.printSchema()"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786333367,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SE_SvbNNtrKO","outputId":"5e4775bc-c6c8-4acc-ff3c-fc5a1a2dce4c"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Acceleration: double (nullable = true)\n"," |-- Horsepower: long (nullable = true)\n"," |-- Miles_per_Gallon: double (nullable = true)\n"," |-- Name: string (nullable = true)\n"," |-- Origin: string (nullable = true)\n"," |-- Weight in pounds: long (nullable = true)\n"," |-- Year: string (nullable = true)\n","\n"]}],"source":["dropColsDF = carsWithColumnRenamed.drop(\"Cylinders\", \"Displacement\")\n","dropColsDF.printSchema()\n"]},{"cell_type":"markdown","metadata":{"id":"QKtbVrvBtrKO"},"source":["Filtering"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1506,"status":"ok","timestamp":1670786334871,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pr2vC4_9trKO","outputId":"5a14fe5b-8038-4c73-bf6e-1a015d44174f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 17.5| 4| 133.0| 115| null|citroen ds-21 pallas|Europe| 3090|1970-01-01|\n","| 15.0| 4| 113.0| 95| 24.0|toyota corona mar...| Japan| 2372|1970-01-01|\n","| 14.5| 4| 97.0| 88| 27.0| datsun pl510| Japan| 2130|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 3 rows\n","\n","152 == 152\n"]}],"source":["nonUSCarsDF = carsDF.filter(col(\"Origin\") != \"USA\")\n","nonUSCarsDF2 = carsDF.where(col(\"Origin\") != \"USA\")\n","nonUSCarsDF.show(3)\n","print(f\"{nonUSCarsDF.count()} == {nonUSCarsDF2.count()}\")"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":700,"status":"ok","timestamp":1670786335570,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Cc58o5B1trKO","outputId":"89677fd2-6f2d-4244-f984-472a975d2635"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","| 12.0| 8| 307.0| 130| 18.0|chevrolet chevell...| USA| 3504|1970-01-01|\n","| 11.5| 8| 350.0| 165| 15.0| buick skylark 320| USA| 3693|1970-01-01|\n","| 11.0| 8| 318.0| 150| 18.0| plymouth satellite| USA| 3436|1970-01-01|\n","+------------+---------+------------+----------+----------------+--------------------+------+-------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["# filtering with expression strings\n","americanCarsDF = carsDF.filter(\"Origin = 'USA'\")\n","americanCarsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"YvqWVJKmtrKO"},"source":["Chain filters"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":371,"status":"ok","timestamp":1670786335936,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"QSYv7uastrKO","outputId":"a00f6ff7-3ecb-4640-e6ed-42d74d9ee293"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","|Acceleration|Cylinders|Displacement|Horsepower|Miles_per_Gallon| Name|Origin|Weight_in_lbs| Year|\n","+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","| 11.5| 8| 350.0| 165| 15.0|buick skylark 320| USA| 3693|1970-01-01|\n","| 10.0| 8| 429.0| 198| 15.0| ford galaxie 500| USA| 4341|1970-01-01|\n","| 9.0| 8| 454.0| 220| 14.0| chevrolet impala| USA| 4354|1970-01-01|\n","+------------+---------+------------+----------+----------------+-----------------+------+-------------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["americanPowerfulCarsDF = carsDF.filter(col(\"Origin\") == \"USA\").filter(col(\"Horsepower\") > 150)\n","americanPowerfulCarsDF2 = carsDF.filter((col(\"Origin\") == \"USA\") & (col(\"Horsepower\") > 150))\n","americanPowerfulCarsDF3 = carsDF.filter(\"Origin = 'USA' and Horsepower > 150\")\n","americanPowerfulCarsDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"kVhkmxwZtrKP"},"source":["## Exercises\n","1. Read the movies DF and select 2 columns of your choice\n","2. Create another column summing up the total profit of the movies = US_Gross + Worldwide_Gross + DVD sales. Are you obtaining nulls? How you can solve it?\n","3. Select all COMEDY movies with IMDB rating above 6\n","Use as many versions as possible"]},{"cell_type":"markdown","metadata":{"id":"QgpQ09-wtrKP"},"source":["Exercise 1"]},{"cell_type":"code","execution_count":29,"metadata":{"executionInfo":{"elapsed":353,"status":"ok","timestamp":1670786337998,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"d9aYXiA6trKQ"},"outputs":[],"source":["moviesDF = spark.read.option(\"inferSchema\", \"true\").json(\"/dataset/movies.json\")"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":272,"status":"ok","timestamp":1670786338266,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"e64DWN0CtrKQ","outputId":"660c9183-421a-4ddc-ac48-d45d3a391f65"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+--------------------+------------+--------+---------------+\n","|Creative_Type|Director|Distributor|IMDB_Rating|IMDB_Votes|MPAA_Rating|Major_Genre|Production_Budget|Release_Date|Rotten_Tomatoes_Rating|Running_Time_min|Source| Title|US_DVD_Sales|US_Gross|Worldwide_Gross|\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+--------------------+------------+--------+---------------+\n","| null| null| Gramercy| 6.1| 1071| R| null| 8000000| 12-Jun-98| null| null| null| The Land Girls| null| 146083| 146083|\n","| null| null| Strand| 6.9| 207| R| Drama| 300000| 7-Aug-98| null| null| null|First Love, Last ...| null| 10876| 10876|\n","| null| null| Lionsgate| 6.8| 865| null| Comedy| 250000| 28-Aug-98| null| null| null|I Married a Stran...| null| 203134| 203134|\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+--------------------+------------+--------+---------------+\n","only showing top 3 rows\n","\n"]}],"source":["moviesDF.show(3)"]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1670786338267,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JDixyg2wtrKQ"},"outputs":[],"source":["moviesReleaseDF = moviesDF.select(\"Title\", \"Release_Date\")\n","\n","moviesReleaseDF2 = moviesDF.select(\n"," moviesDF.Title,\n"," col(\"Release_Date\"),\n"," \"Major_Genre\",\n"," expr(\"IMDB_Rating\"))\n"," \n","moviesReleaseDF3 = moviesDF.selectExpr(\n"," \"Title\", \"Release_Date\")"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1670786338515,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XYrPvINstrKQ","outputId":"fc2ba9a1-2a73-4acd-82be-f5b90d5e7093"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- Title: string (nullable = true)\n"," |-- Release_Date: string (nullable = true)\n"," |-- Major_Genre: string (nullable = true)\n"," |-- IMDB_Rating: double (nullable = true)\n","\n"]}],"source":["moviesReleaseDF2.printSchema()"]},{"cell_type":"markdown","metadata":{"id":"RgtGVWTrtrKQ"},"source":["Exercise 2"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":330,"status":"ok","timestamp":1670786338838,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-CnhhLgItrKQ","outputId":"5ddd85dd-d746-4238-9a1b-563174625d79"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------+---------------+------------+-----------+\n","| Title|US_Gross|Worldwide_Gross|US_DVD_Sales|Total_Gross|\n","+--------------------+--------+---------------+------------+-----------+\n","| The Land Girls| 146083| 146083| null| null|\n","|First Love, Last ...| 10876| 10876| null| null|\n","|I Married a Stran...| 203134| 203134| null| null|\n","+--------------------+--------+---------------+------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["moviesProfitDF = moviesDF.select(\n"," col(\"Title\"),\n"," col(\"US_Gross\"),\n"," col(\"Worldwide_Gross\"),\n"," col(\"US_DVD_Sales\"),\n"," (col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\"))\n","\n","moviesProfitDF.show(3)"]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786338838,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ed9GePFJtrKQ","outputId":"aeac6866-08e8-4fa1-b1be-b8add7feff1c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Title|Total_Gross|\n","+--------------------+-----------+\n","| The Land Girls| 292166|\n","|First Love, Last ...| 21752|\n","|I Married a Stran...| 406268|\n","+--------------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["# we can replace null, or make our sum null same (via udf, filter, sql statement, ect) we will not describe it in detail now\n","moviesProfitDF2 = moviesDF.fillna(0).select(\n"," col(\"Title\"),\n"," (col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\"))\n","\n","moviesProfitDF2.show(3)"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":252,"status":"ok","timestamp":1670786339089,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"KhDM4I12trKR","outputId":"11e7f1ab-2f2b-455c-c156-2f58b9048c9a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------+---------------+------------+-----------+\n","| Title|US_Gross|Worldwide_Gross|US_DVD_Sales|Total_Gross|\n","+--------------------+--------+---------------+------------+-----------+\n","| The Land Girls| 146083| 146083| 0| 292166|\n","|First Love, Last ...| 10876| 10876| 0| 21752|\n","|I Married a Stran...| 203134| 203134| 0| 406268|\n","+--------------------+--------+---------------+------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["moviesProfitDF3 = moviesDF.fillna(0).selectExpr(\n"," \"Title\",\n"," \"US_Gross\",\n"," \"Worldwide_Gross\",\n"," \"US_DVD_Sales\",\n"," \"US_Gross + Worldwide_Gross + US_DVD_Sales as Total_Gross\")\n","\n","moviesProfitDF3.show(3)"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":404,"status":"ok","timestamp":1670786339490,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"WhGynG4NtrKR","outputId":"70b9450c-13af-4c00-804a-dfe656f19dc6"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------+---------------+------------+-----------+\n","| Title|US_Gross|Worldwide_Gross|US_DVD_Sales|Total_Gross|\n","+--------------------+--------+---------------+------------+-----------+\n","| The Land Girls| 146083| 146083| 0| 292166|\n","|First Love, Last ...| 10876| 10876| 0| 21752|\n","|I Married a Stran...| 203134| 203134| 0| 406268|\n","+--------------------+--------+---------------+------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["moviesProfitDF4 = moviesDF.fillna(0).select(\"Title\", \"US_Gross\", \"Worldwide_Gross\", \"US_DVD_Sales\") \\\n"," .withColumn(\"Total_Gross\", col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\"))\n","\n","moviesProfitDF4.show(3)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670786339737,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"syGNtFuJtrKR","outputId":"87c6c7f3-8800-4138-8b74-b31091c34079"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------+---------------+------------+-----------+\n","| Title|US_Gross|Worldwide_Gross|US_DVD_Sales|Total_Gross|\n","+--------------------+--------+---------------+------------+-----------+\n","| The Land Girls| 146083| 146083| 0| 292166|\n","|First Love, Last ...| 10876| 10876| 0| 21752|\n","|I Married a Stran...| 203134| 203134| 0| 406268|\n","+--------------------+--------+---------------+------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["# using conditions over columns to handle with nulls (harder but sometimes necessary)\n","moviesProfitDF5 = moviesDF.select(\"Title\", \"US_Gross\", \"Worldwide_Gross\", \"US_DVD_Sales\") \\\n"," .withColumn(\"US_DVD_Sales\", when(col(\"US_DVD_Sales\").isNotNull(), col(\"US_DVD_Sales\")).otherwise(0)) \\\n"," .withColumn(\"Total_Gross\", col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\"))\n","\n","moviesProfitDF5.show(3)"]},{"cell_type":"markdown","metadata":{"id":"9j710M_JQYwd"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":243,"status":"ok","timestamp":1670786339978,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"IbdYbj32trKR","outputId":"164fad6e-c31a-4e90-a87a-0226dad94ccd"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Title|IMDB_Rating|\n","+--------------------+-----------+\n","|I Married a Stran...| 6.8|\n","|24 7: Twenty Four...| 6.9|\n","| Four Rooms| 6.4|\n","+--------------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["comediesDF = moviesDF.select(\"Title\", \"IMDB_Rating\") \\\n"," .where((col(\"Major_Genre\") == \"Comedy\") & (col(\"IMDB_Rating\") > 6))\n","\n","comediesDF.show(3)"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":285,"status":"ok","timestamp":1670786340262,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"N0pDUe70trKR","outputId":"1d537398-cdeb-4f46-87ae-85b2284a5761"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Title|IMDB_Rating|\n","+--------------------+-----------+\n","|I Married a Stran...| 6.8|\n","|24 7: Twenty Four...| 6.9|\n","| Four Rooms| 6.4|\n","+--------------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["\n","comediesDF2 = moviesDF.select(\"Title\", \"IMDB_Rating\") \\\n"," .where(col(\"Major_Genre\") == \"Comedy\") \\\n"," .where(col(\"IMDB_Rating\") > 6)\n","\n","comediesDF2.show(3)"]},{"cell_type":"code","execution_count":41,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":263,"status":"ok","timestamp":1670786427897,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Km6rZ26ztrKR","outputId":"713b909d-b00f-410b-b731-b949f1653a01"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Title|IMDB_Rating|\n","+--------------------+-----------+\n","|I Married a Stran...| 6.8|\n","|24 7: Twenty Four...| 6.9|\n","| Four Rooms| 6.4|\n","+--------------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["comediesDF3 = moviesDF.select(\"Title\", \"IMDB_Rating\") \\\n"," .where(\"Major_Genre = 'Comedy' and IMDB_Rating > 6\")\n","\n","comediesDF3.show(3)"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":270,"status":"ok","timestamp":1670786581476,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Z9gaeIs2obTI","outputId":"aaaf9ba3-894f-4c94-b94c-399c032d3a2a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Title|IMDB_Rating|\n","+--------------------+-----------+\n","|I Married a Stran...| 6.8|\n","|24 7: Twenty Four...| 6.9|\n","| Four Rooms| 6.4|\n","+--------------------+-----------+\n","only showing top 3 rows\n","\n"]}],"source":["moviesDF.createOrReplaceTempView(\"comedies\")\n","\n","spark.sql(\"select Title, IMDB_Rating from comedies where (Major_Genre = 'Comedy' and IMDB_Rating > 6)\").show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"03-5sRApo571"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":["LDwREHXhtrKM"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/03.Aggregations_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/03.Aggregations_solved.ipynb new file mode 100644 index 000000000..94cd6c391 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/03.Aggregations_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"s4YqddS_vlYH"},"source":["# Aggregations and Grouping"]},{"cell_type":"markdown","metadata":{"id":"WwNMmP6GvoDQ"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HvezyELEvqZH"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7b0-USnDvst_"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1670581376752,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RnfbBxRivvWX","outputId":"6d1dd47f-2eb6-469e-f505-67be729bcb9a"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 2 root root 4096 Dec 9 10:22 \u001b[0m\u001b[01;34mdataset\u001b[0m/\n","drwxr-xr-x 1 root root 4096 Dec 7 14:41 \u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IhVzl2vbvxhY"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8JuU0Aylvxeo"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13363,"status":"ok","timestamp":1670581398749,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"0hvlB3OlvzxX","outputId":"0aff085f-53a1-40ed-c742-89aeac594b9b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting py4j\n"," Downloading py4j-0.10.9.7-py2.py3-none-any.whl (200 kB)\n","\u001b[K |████████████████████████████████| 200 kB 5.4 MB/s \n","\u001b[?25hInstalling collected packages: py4j\n","Successfully installed py4j-0.10.9.7\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n"]}],"source":["!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"O0C9EzZbv4x4"},"source":["Define the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rf4ibNTdv7fv"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"K90Ld_NkvlYI"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9353,"status":"ok","timestamp":1670581408100,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2Fg_PLR_vlYI","outputId":"2dc34c37-2e19-4b3b-ec66-ef29f99419a1"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Aggregations and Grouping\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1809,"status":"ok","timestamp":1670581409903,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RnamkNcgvlYJ","outputId":"f3286245-59e1-4b01-d40a-4f2b8cbc7c9b"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Aggregations and Grouping
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9AGniM-7vlYK"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e0DXUK1fvlYK"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"1mCPp9fQwEif"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5xTARwq9wLgH"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/movies.json -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/vehicles.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/characters.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"yD4q57C_vlYK"},"source":["Read JSON file\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dZWORhHKvlYK"},"outputs":[],"source":["moviesDF = spark.read \\\n"," .option(\"inferSchema\", True) \\\n"," .json(\"/dataset/movies.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1067,"status":"ok","timestamp":1670581420751,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"4h2S0ponvlYK","outputId":"07cfbb5f-67f2-4815-d9f4-e70c47089d11"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","|Creative_Type|Director|Distributor|IMDB_Rating|IMDB_Votes|MPAA_Rating|Major_Genre|Production_Budget|Release_Date|Rotten_Tomatoes_Rating|Running_Time_min|Source|Title |US_DVD_Sales|US_Gross|Worldwide_Gross|\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","|null |null |Gramercy |6.1 |1071 |R |null |8000000 |12-Jun-98 |null |null |null |The Land Girls |null |146083 |146083 |\n","|null |null |Strand |6.9 |207 |R |Drama |300000 |7-Aug-98 |null |null |null |First Love, Last Rites|null |10876 |10876 |\n","+-------------+--------+-----------+-----------+----------+-----------+-----------+-----------------+------------+----------------------+----------------+------+----------------------+------------+--------+---------------+\n","only showing top 2 rows\n","\n","[StructField('Creative_Type', StringType(), True), StructField('Director', StringType(), True), StructField('Distributor', StringType(), True), StructField('IMDB_Rating', DoubleType(), True), StructField('IMDB_Votes', LongType(), True), StructField('MPAA_Rating', StringType(), True), StructField('Major_Genre', StringType(), True), StructField('Production_Budget', LongType(), True), StructField('Release_Date', StringType(), True), StructField('Rotten_Tomatoes_Rating', LongType(), True), StructField('Running_Time_min', LongType(), True), StructField('Source', StringType(), True), StructField('Title', StringType(), True), StructField('US_DVD_Sales', LongType(), True), StructField('US_Gross', LongType(), True), StructField('Worldwide_Gross', LongType(), True)]\n"]},{"data":{"text/plain":["['Creative_Type',\n"," 'Director',\n"," 'Distributor',\n"," 'IMDB_Rating',\n"," 'IMDB_Votes',\n"," 'MPAA_Rating',\n"," 'Major_Genre',\n"," 'Production_Budget',\n"," 'Release_Date',\n"," 'Rotten_Tomatoes_Rating',\n"," 'Running_Time_min',\n"," 'Source',\n"," 'Title',\n"," 'US_DVD_Sales',\n"," 'US_Gross',\n"," 'Worldwide_Gross']"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["moviesDF.show(2, False)\n","print(moviesDF.schema.fields)\n","moviesDF.columns"]},{"cell_type":"markdown","metadata":{"id":"M7Ab75vzvlYK"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"3L-vJzo5vlYK"},"source":["Count"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1175,"status":"ok","timestamp":1670581421925,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eWoq50okvlYL","outputId":"2765af46-4f08-4e2a-eb4c-f9b9c561ead9"},"outputs":[{"data":{"text/plain":["3201"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# df rows counting, including NULLS\n","moviesDF.count()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":813,"status":"ok","timestamp":1670581422733,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JwFlAfygvlYL","outputId":"f5901338-7177-4d72-f0f6-b631112a7280"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------------+\n","|count(Major_Genre)|\n","+------------------+\n","| 2926|\n","+------------------+\n","\n"]}],"source":["# using sql functions, NOT including NULLS\n","genresCountDF = moviesDF.select(count(col(\"Major_Genre\")))\n","genresCountDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":267,"status":"ok","timestamp":1670581422999,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XOnzyw5pvlYL","outputId":"ed582d04-e104-4816-e363-afa12ad9406c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n"]}],"source":["directorsCountDF = moviesDF.select(count(moviesDF.Director))\n","directorsCountDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":266,"status":"ok","timestamp":1670581423264,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"I6zWJ2x8vlYL","outputId":"3d570765-2630-4dfb-d75b-6ff9b2d8f5e1"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+---------------+\n","|countMajor|count(Director)|\n","+----------+---------------+\n","| 2926| 1870|\n","+----------+---------------+\n","\n"]}],"source":["moviesDF.select(count(moviesDF.Major_Genre).alias(\"countMajor\"), count(moviesDF.Director)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":767,"status":"ok","timestamp":1670581424029,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nWOO2IBsvlYL","outputId":"cc36949a-5f6c-4e45-a26f-5b57aa04bd03"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n","+-----+\n","|count|\n","+-----+\n","| 1870|\n","+-----+\n","\n"]}],"source":["#using SQL syntax\n","moviesDF.select(expr(\"count(Director)\")).show()\n","moviesDF.selectExpr(\"count(Director) as count\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QOje34ZavlYL"},"outputs":[],"source":["# using SQL\n","moviesDF.createOrReplaceTempView(\"movies\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":801,"status":"ok","timestamp":1670581424829,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-UTqpjafvlYM","outputId":"4129c709-8bed-4c6c-d5c4-c405b3a89cdd"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------+\n","|count(Director)|\n","+---------------+\n","| 1870|\n","+---------------+\n","\n"]}],"source":["spark.sql(\"select count(Director) from movies\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":225,"status":"ok","timestamp":1670581425052,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-bsgNq42vlYM","outputId":"54d942af-b340-44db-c175-5e45cd681494"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------+------------------+\n","|countDirector|count(Major_Genre)|\n","+-------------+------------------+\n","| 1870| 2926|\n","+-------------+------------------+\n","\n"]}],"source":["spark.sql(\"select count(Director) as countDirector, count(Major_Genre) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"7tD9smRhvlYM"},"source":["Count Distinct"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":515,"status":"ok","timestamp":1670581425565,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ngR6WFUovlYM","outputId":"bdd9d3c8-9b6c-4fc4-91b3-659ee17f55f8"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|count(DISTINCT Major_Genre)|\n","+---------------------------+\n","| 12|\n","+---------------------------+\n","\n"]}],"source":["moviesDF.select(countDistinct(moviesDF.Major_Genre)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":606,"status":"ok","timestamp":1670581426170,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"65m9Ev4MvlYM","outputId":"36e25661-5573-43c3-fcf3-68f0db17ad77"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|count(DISTINCT Major_Genre)|\n","+---------------------------+\n","| 12|\n","+---------------------------+\n","\n"]}],"source":["spark.sql(\"select count(distinct Major_Genre) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"wpwLzjsovlYM"},"source":["Min and max"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":691,"status":"ok","timestamp":1670581426859,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"FPOzkXPEvlYN","outputId":"e01067da-310d-4816-9712-bb1be6e6c35a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+----------------------+\n","|min(Production_Budget)|max(Production_Budget)|\n","+----------------------+----------------------+\n","| 218| 300000000|\n","+----------------------+----------------------+\n","\n"]}],"source":["moviesDF.select(min(moviesDF.Production_Budget), max(moviesDF.Production_Budget)).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":245,"status":"ok","timestamp":1670581427103,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"pY6U_O43vlYN","outputId":"dd249a1d-b87f-4107-a91d-86f28e1bf312"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+\n","|min(Production_Budget)|\n","+----------------------+\n","| 218|\n","+----------------------+\n","\n"]}],"source":["spark.sql(\"select min(Production_Budget) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"ID9Q9X7EvlYN"},"source":["Sum"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":504,"status":"ok","timestamp":1670581427606,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RHKzZO-6vlYN","outputId":"28db29ba-d97b-4ed2-8368-705f43a5af51"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+\n","| salesUS|\n","+-----------+\n","|19684472405|\n","+-----------+\n","\n","+-----------+\n","| sales|\n","+-----------+\n","|19684472405|\n","+-----------+\n","\n"]}],"source":["moviesDF.select(sum(moviesDF.US_DVD_Sales).alias(\"salesUS\")).show()\n","moviesDF.selectExpr(\"sum(US_DVD_Sales) as sales\").show()"]},{"cell_type":"markdown","metadata":{"id":"Yq5pF3mLvlYN"},"source":["Average"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":502,"status":"ok","timestamp":1670581428107,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"fDVmzYTsvlYN","outputId":"f33c15c5-05e4-4f51-9278-dccc0547ad65"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------------+\n","|avg(Production_Budget)|\n","+----------------------+\n","| 3.10691714484375E7|\n","+----------------------+\n","\n","+----------------------+\n","|avg(Production_Budget)|\n","+----------------------+\n","| 3.10691714484375E7|\n","+----------------------+\n","\n"]}],"source":["moviesDF.select(avg(moviesDF.Production_Budget)).show()\n","spark.sql(\"select avg(Production_Budget) from movies\").show()"]},{"cell_type":"markdown","metadata":{"id":"4RLegmspvlYN"},"source":["Stats"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":713,"status":"ok","timestamp":1670581428815,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"WTJQxCazvlYO","outputId":"074f42ff-9506-4235-a284-d890e1da873c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+---------------------------+\n","|avg(Rotten_Tomatoes_Rating)|\n","+---------------------------+\n","| 54.33692373976734|\n","+---------------------------+\n","\n","+-----------------------------------+\n","|stddev_samp(Rotten_Tomatoes_Rating)|\n","+-----------------------------------+\n","| 28.07659263787602|\n","+-----------------------------------+\n","\n"]}],"source":["moviesDF.select(mean(moviesDF.Rotten_Tomatoes_Rating)).show()\n","moviesDF.select(stddev(moviesDF.Rotten_Tomatoes_Rating)).show()"]},{"cell_type":"markdown","metadata":{"id":"BrbK9dgcvlYO"},"source":["### Grouping\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":713,"status":"ok","timestamp":1670581429523,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"yYrN8U1ZvlYO","outputId":"6d8ef70e-3866-4ad3-cc4d-b5190771de6a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+-----+\n","| Major_Genre|count|\n","+-------------------+-----+\n","|Concert/Performance| 5|\n","| Black Comedy| 36|\n","| Western| 36|\n","| Documentary| 43|\n","| Musical| 53|\n","| Romantic Comedy| 137|\n","| Horror| 219|\n","| Thriller/Suspense| 239|\n","| Adventure| 274|\n","| null| 275|\n","| Action| 420|\n","| Comedy| 675|\n","| Drama| 789|\n","+-------------------+-----+\n","\n"]}],"source":["countByGenreGF = moviesDF.groupBy(moviesDF.Major_Genre).count().orderBy(\"count\")\n","countByGenreGF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":365,"status":"ok","timestamp":1670581429886,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ZZO4-ux9vlYO","outputId":"0cfd68a8-c3e1-459c-f700-b8b1cfd729b0"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+-----+\n","| Major_Genre|count|\n","+-------------------+-----+\n","|Concert/Performance| 5|\n","| Black Comedy| 36|\n","| Western| 36|\n","| Documentary| 43|\n","| Musical| 53|\n","| Romantic Comedy| 137|\n","| Horror| 219|\n","| Thriller/Suspense| 239|\n","| Adventure| 274|\n","| Action| 420|\n","| Comedy| 675|\n","| Drama| 789|\n","+-------------------+-----+\n","\n"]}],"source":["spark.sql(\"select Major_Genre, count(Major_Genre) as count from movies where Major_Genre is not null group by Major_Genre order by count\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":592,"status":"ok","timestamp":1670581430473,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"eITO9H-gvlYO","outputId":"5abf6fce-9f3a-4329-8a95-9a7ba882fbaa"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+------------------+\n","| Major_Genre| avg(IMDB_Rating)|\n","+-------------------+------------------+\n","| Documentary| 6.997297297297298|\n","| Western| 6.842857142857142|\n","| Black Comedy|6.8187500000000005|\n","| Drama| 6.773441734417339|\n","| null| 6.50082644628099|\n","| Musical| 6.448|\n","| Thriller/Suspense| 6.360944206008582|\n","| Adventure| 6.345019920318729|\n","|Concert/Performance| 6.325|\n","| Action| 6.114795918367349|\n","| Romantic Comedy| 5.873076923076922|\n","| Comedy| 5.853858267716529|\n","| Horror|5.6760765550239185|\n","+-------------------+------------------+\n","\n"]}],"source":["avgRatingByGenreDF = moviesDF.groupBy(col(\"Major_Genre\")).avg(\"IMDB_Rating\").orderBy(col(\"avg(IMDB_Rating)\").desc())\n","avgRatingByGenreDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":448,"status":"ok","timestamp":1670581430919,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vnmu7fuovlYO","outputId":"80432633-aaa0-4bc0-ac8d-bd272a10b1b5"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+------------------+\n","| Major_Genre| avg|\n","+-------------------+------------------+\n","| Documentary| 6.997297297297298|\n","| Western| 6.842857142857142|\n","| Black Comedy|6.8187500000000005|\n","| Drama| 6.773441734417339|\n","| null| 6.50082644628099|\n","| Musical| 6.448|\n","| Thriller/Suspense| 6.360944206008582|\n","| Adventure| 6.345019920318729|\n","|Concert/Performance| 6.325|\n","| Action| 6.114795918367349|\n","| Romantic Comedy| 5.873076923076922|\n","| Comedy| 5.853858267716529|\n","| Horror|5.6760765550239185|\n","+-------------------+------------------+\n","\n"]}],"source":["moviesDF.groupBy(col(\"Major_Genre\")).agg(avg(\"IMDB_Rating\") \\\n"," .alias(\"avg\")).orderBy(col(\"avg\").desc()).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":367,"status":"ok","timestamp":1670581431285,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2fk-AjXIvlYO","outputId":"4cdea3f6-b1e3-4db5-bfc2-231d0de2241f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+--------+------------------+\n","| Major_Genre|N_Movies| rating|\n","+-------------------+--------+------------------+\n","| Documentary| 43| 6.997297297297298|\n","| Western| 36| 6.842857142857142|\n","| Black Comedy| 36|6.8187500000000005|\n","| Drama| 789| 6.773441734417339|\n","| null| 275| 6.50082644628099|\n","| Musical| 53| 6.448|\n","| Thriller/Suspense| 239| 6.360944206008582|\n","| Adventure| 274| 6.345019920318729|\n","|Concert/Performance| 5| 6.325|\n","| Action| 420| 6.114795918367349|\n","| Romantic Comedy| 137| 5.873076923076922|\n","| Comedy| 675| 5.853858267716529|\n","| Horror| 219|5.6760765550239185|\n","+-------------------+--------+------------------+\n","\n"]}],"source":["aggregationsByGenreDF = moviesDF.groupBy(\"Major_Genre\") \\\n"," .agg(\n"," count(\"*\").alias(\"N_Movies\"),\n"," avg(\"IMDB_Rating\").alias(\"rating\")\n"," ) \\\n"," .orderBy(col(\"rating\").desc()).show()"]},{"cell_type":"markdown","metadata":{"id":"cQ3xjlDxvlYP"},"source":["## Exercises\n"," 1. Sum up all the worldwide profits of ALL the movies in the DF. Then sum the worldwide profits per director\n"," 2. Count how many distinct directors we have\n"," 3. Show the mean and standard deviation of US gross revenue for the movies (all the movies)\n"," 4. Compute the average IMDB rating and the average US gross revenue PER DIRECTOR\n"," 5. Sum up ALL the profits of ALL the movies in the DF. Then sum ALL the profits per director. Can you see null values? Why? How you can solve it? \n"]},{"cell_type":"markdown","metadata":{"id":"dzh5hcajvlYP"},"source":["Exercise 1"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":247,"status":"ok","timestamp":1670581431531,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"X4qMkSZhvlYP","outputId":"827b80a8-dfa2-42a0-cd2d-3fffa09d2def"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+\n","|sum(Worldwide_Gross)|\n","+--------------------+\n","| 272586820052|\n","+--------------------+\n","\n"]}],"source":["moviesDF.select(sum(\"Worldwide_Gross\")).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":616,"status":"ok","timestamp":1670581432146,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"x-Ofo8AwvlYP","outputId":"746821ce-6fe8-4777-eeff-5a536d476e4b"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------------------+\n","| Director|sum(Worldwide_Gross)|\n","+--------------------+--------------------+\n","| null| 47605151241|\n","| Abel Ferrara| 1412799|\n","| Adam McKay| 252379731|\n","| Adam Shankman| 841310147|\n","| Adrian Lyne| 648560696|\n","| Adrienne Shelly| 22202180|\n","| Akira Kurosawa| 320592|\n","| Alan Alda| 42488161|\n","| Alan J. Pakula| 140900000|\n","| Alan Parker| 193906027|\n","| Alan Rudolph| 178287|\n","| Albert Brooks| 11614954|\n","| Albert Hughes| 59329835|\n","|Alejandro Gonzale...| 216635372|\n","| Alex Kendrick| 43629810|\n","| Alex Proyas| 563716726|\n","| Alexander Payne| 230472709|\n","| Alexandre Aja| 69623713|\n","| Alfonso Cuaron| 898639035|\n","| Alfred Hitchcock| 98264742|\n","+--------------------+--------------------+\n","only showing top 20 rows\n","\n"]}],"source":["moviesDF.select(col(\"Director\"), col(\"Worldwide_Gross\")).groupBy(\"Director\").sum(\"Worldwide_Gross\").orderBy(col(\"Director\")).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":431,"status":"ok","timestamp":1670581432575,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ZxFG7UbivlYP","outputId":"21dd75c0-0107-4a37-bcc0-6a7ee39d6ce2"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------+---------------+\n","| Director|Worldwide_Gross|\n","+--------------+---------------+\n","|Akira Kurosawa| 48856|\n","|Akira Kurosawa| 271736|\n","+--------------+---------------+\n","\n"]}],"source":["moviesDF.filter(col(\"Director\") == \"Akira Kurosawa\").select(col(\"Director\"), col(\"Worldwide_Gross\")).show()"]},{"cell_type":"markdown","metadata":{"id":"x_KjX-qxvlYP"},"source":["Exercise 2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":261,"status":"ok","timestamp":1670581432833,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vagKxk9rvlYP","outputId":"b1ee3a40-6fa9-493c-8855-d509064c95e8"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------------------+\n","|count(DISTINCT Director)|\n","+------------------------+\n","| 550|\n","+------------------------+\n","\n"]}],"source":["moviesDF.select(countDistinct(\"Director\")).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1670581432833,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"RwsBfHQivlYP","outputId":"db07c08a-b5a5-4565-b4f8-d7ee1e878602"},"outputs":[{"data":{"text/plain":["['Creative_Type',\n"," 'Director',\n"," 'Distributor',\n"," 'IMDB_Rating',\n"," 'IMDB_Votes',\n"," 'MPAA_Rating',\n"," 'Major_Genre',\n"," 'Production_Budget',\n"," 'Release_Date',\n"," 'Rotten_Tomatoes_Rating',\n"," 'Running_Time_min',\n"," 'Source',\n"," 'Title',\n"," 'US_DVD_Sales',\n"," 'US_Gross',\n"," 'Worldwide_Gross']"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["moviesDF.columns"]},{"cell_type":"markdown","metadata":{"id":"x6iJ5GWlvlYQ"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":841,"status":"ok","timestamp":1670581433673,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"cMWSCLigvlYQ","outputId":"2b771f29-a52e-4b41-c95f-1dbf747ed35b"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+---------------------+\n","| avg(US_Gross)|stddev_samp(US_Gross)|\n","+--------------------+---------------------+\n","|4.4002085163744524E7| 6.255531139066214E7|\n","+--------------------+---------------------+\n","\n"]}],"source":["moviesDF.select(mean(\"US_Gross\"), stddev(\"US_Gross\")).show()"]},{"cell_type":"markdown","metadata":{"id":"qTyt8CotvlYQ"},"source":["Exercise 4"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1670581433674,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"s4jC3p6ivlYQ","outputId":"6f1fc683-7f79-416d-d539-fdd5cfa4d842"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+--------------------+-----------------+\n","| Director| avg(US_Gross)| avg(IMDB_Rating)|\n","+--------------------+--------------------+-----------------+\n","| null| 2.082582007471698E7|5.924237427864797|\n","| Abel Ferrara| 1212799.0| 6.4|\n","| Adam McKay| 1.16175143E8| 6.7|\n","| Adam Shankman| 9.145074516666667E7|5.833333333333333|\n","| Adrian Lyne| 6.1548874E7|6.219999999999999|\n","| Adrienne Shelly| 1.909755E7| 7.2|\n","| Akira Kurosawa| 160296.0|8.100000000000001|\n","| Alan Alda| 4.2488161E7| 7.0|\n","| Alan J. Pakula| 4.2885593E7| 5.8|\n","| Alan Parker|2.7593491333333332E7| 6.8|\n","| Alan Rudolph| 178287.0| 4.3|\n","| Albert Brooks| 1.1614954E7| 5.5|\n","| Albert Hughes| 2.96649175E7| 7.1|\n","|Alejandro Gonzale...| 1.8645124E7|7.866666666666667|\n","| Alex Kendrick| 2.1814905E7| 5.8|\n","| Alex Proyas| 7.973124433333333E7| 7.1|\n","| Alexander Payne|5.0483700666666664E7| 7.5|\n","| Alexandre Aja| 4.1778863E7| 6.5|\n","| Alfonso Cuaron| 9.958040533333333E7| 7.9|\n","| Alfred Hitchcock| 9826474.2| 7.25|\n","+--------------------+--------------------+-----------------+\n","only showing top 20 rows\n","\n"]}],"source":["moviesDF.groupBy(\"Director\").agg(avg(\"US_Gross\"), avg(\"IMDB_Rating\")).orderBy(\"Director\").show()"]},{"cell_type":"markdown","metadata":{"id":"HOs_Y_y0vlYQ"},"source":["Exercise 5"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":361,"status":"ok","timestamp":1670581434031,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"r9P2HCEwvlYQ","outputId":"71ce0e5a-d806-4e08-befb-25946092ca3a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------+\n","|sum(Total_Gross)|\n","+----------------+\n","| 139190135783|\n","+----------------+\n","\n"]}],"source":["moviesDF.select((col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\")).select(sum(\"Total_Gross\")).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":272,"status":"ok","timestamp":1670581434302,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"T3YnTJn6vlYQ","outputId":"66e58054-ffdb-40ae-e3ea-a6e273d71b8c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Director|Total_Gross|\n","+--------------------+-----------+\n","| null|23161808950|\n","| Abel Ferrara| null|\n","| Adam McKay| 396065126|\n","| Adam Shankman| 669875496|\n","| Adrian Lyne| null|\n","| Adrienne Shelly| 64427279|\n","| Akira Kurosawa| null|\n","| Alan Alda| null|\n","| Alan J. Pakula| null|\n","| Alan Parker| null|\n","| Alan Rudolph| null|\n","| Albert Brooks| null|\n","| Albert Hughes| null|\n","|Alejandro Gonzale...| 201064882|\n","| Alex Kendrick| 139250136|\n","| Alex Proyas| 291267207|\n","| Alexander Payne| null|\n","| Alexandre Aja| 131979381|\n","| Alfonso Cuaron| 130347856|\n","| Alfred Hitchcock| null|\n","+--------------------+-----------+\n","only showing top 20 rows\n","\n"]}],"source":["moviesDF.groupBy(\"Director\").agg(sum(col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\")).orderBy(\"Director\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":486,"status":"ok","timestamp":1670581434784,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"jRLPJvUvvlYQ","outputId":"a4783406-2167-47cf-b7b6-a16060842c05"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------+---------------+------------+\n","|US_Gross|Worldwide_Gross|US_DVD_Sales|\n","+--------+---------------+------------+\n","| 48856| 48856| null|\n","| 271736| 271736| null|\n","+--------+---------------+------------+\n","\n"]}],"source":["moviesDF.filter(col(\"Director\") == \"Akira Kurosawa\").select(col(\"US_Gross\"), col(\"Worldwide_Gross\"), col(\"US_DVD_Sales\")).show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":620,"status":"ok","timestamp":1670581435402,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"kH40vP5KvlYR","outputId":"e50c6e97-ee1b-4400-8e41-d69d2f604624"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+-----------+\n","| Director|Total_Gross|\n","+--------------------+-----------+\n","| null|79073427920|\n","| Abel Ferrara| 2625598|\n","| Adam McKay| 569568389|\n","| Adam Shankman| 1520657429|\n","| Adrian Lyne| 956305066|\n","| Adrienne Shelly| 64427279|\n","| Akira Kurosawa| 641184|\n","| Alan Alda| 84976322|\n","| Alan J. Pakula| 183785593|\n","| Alan Parker| 276686501|\n","| Alan Rudolph| 356574|\n","| Albert Brooks| 23229908|\n","| Albert Hughes| 118659670|\n","|Alejandro Gonzale...| 304029952|\n","| Alex Kendrick| 139250136|\n","| Alex Proyas| 826361390|\n","| Alexander Payne| 381923811|\n","| Alexandre Aja| 131979381|\n","| Alfonso Cuaron| 1222725522|\n","| Alfred Hitchcock| 196529484|\n","+--------------------+-----------+\n","only showing top 20 rows\n","\n"]}],"source":["moviesDF.fillna(0).groupBy(\"Director\").agg(sum(col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\")).orderBy(\"Director\").show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":537,"status":"ok","timestamp":1670581435934,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"BSUecA9AvlYR","outputId":"58659287-fe85-48f7-c70e-b290bd9bd4b4"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------------+\n","|sum(Total_Gross)|\n","+----------------+\n","| 139190135783|\n","+----------------+\n","\n","+----------------+\n","|sum(Total_Gross)|\n","+----------------+\n","| 432813952470|\n","+----------------+\n","\n"]}],"source":["moviesDF.select((col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\")).select(sum(\"Total_Gross\")).show()\n","moviesDF.fillna(0).select((col(\"US_Gross\") + col(\"Worldwide_Gross\") + col(\"US_DVD_Sales\")).alias(\"Total_Gross\")).select(sum(\"Total_Gross\")).show()"]},{"cell_type":"markdown","metadata":{"id":"cJzr-2AhvlYR"},"source":["Rememeber SQL expressions are NOT null safe!!!"]}],"metadata":{"colab":{"collapsed_sections":["M7Ab75vzvlYK","BrbK9dgcvlYO","cQ3xjlDxvlYP"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/04.Joins_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/04.Joins_solved.ipynb new file mode 100644 index 000000000..99d521dc7 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/04.Joins_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"h-3AXSJMxWcj"},"source":["# Joins"]},{"cell_type":"markdown","metadata":{"id":"NqSTh04rxXpg"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"alWDpL2TxZt3"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Mpinu0eRxcmO"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":255,"status":"ok","timestamp":1670436579804,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Jz43HkUGxg_3","outputId":"ac5c6ea2-0832-4c9b-a2f5-a40a3ea9d40d"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 1 root root 4096 Dec 6 14:35 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FIMyGT7Axg9G"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BdJz2vonxjDe"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10739,"status":"ok","timestamp":1670436621663,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"vLrv7k6cxqVY","outputId":"335a57a9-43f9-4cba-a4f9-70ff84625d77"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: py4j in /usr/local/lib/python3.8/dist-packages (0.10.9.7)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: folium in /usr/local/lib/python3.8/dist-packages (0.12.1.post1)\n","Requirement already satisfied: branca>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from folium) (0.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.23.0)\n","Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.8/dist-packages (from folium) (2.11.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.6)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.8/dist-packages (from jinja2>=2.9->folium) (2.0.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2.10)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: plotly in /usr/local/lib/python3.8/dist-packages (5.5.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly) (1.15.0)\n","Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly) (8.1.0)\n"]}],"source":["!pip install py4j\n","\n","# For maps\n","!pip install folium\n","!pip install plotly"]},{"cell_type":"markdown","metadata":{"id":"uYSsCnv5xoPq"},"source":["Define the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ueuKeDHqxorP"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"77nvOyd1xWck"},"source":["Start Spark Session \n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":7803,"status":"ok","timestamp":1670436701150,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"u9zEr_35xWck","outputId":"6b19ccb2-446a-469f-eec0-2784973bc406"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Joins\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":1905,"status":"ok","timestamp":1670436706003,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"oS7E2-87xWcl","outputId":"849af19b-7917-4d4c-d665-e18d972ac53a"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Joins
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"75QZiN7GxWcl"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"4Cx-_TA5xxVw"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IFP9OP4fxzGw"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/guitars.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/guitarPlayers.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/bands.json -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/employees.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/salaries.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/deptmanagers.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/titles.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"CPNyqoE8xWcm"},"source":["Read JSONs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k9_4mNbqxWcm"},"outputs":[],"source":["guitarsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/guitars.json\")\n","\n","guitaristsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/guitarPlayers.json\")\n","\n","bandsDF = spark.read \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .json(\"/dataset/bands.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1999,"status":"ok","timestamp":1670436771444,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Aww2pe7sxWcm","outputId":"1800739a-870e-40bc-be06-67bb8b95d637"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+---+------+------------+\n","| guitarType| id| make| model|\n","+--------------------+---+------+------------+\n","|Electric double-n...| 0|Gibson| EDS-1275|\n","| Electric| 5|Fender|Stratocaster|\n","| Electric| 1|Gibson| SG|\n","+--------------------+---+------+------------+\n","only showing top 3 rows\n","\n","+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 0| [0]| 0| Jimmy Page|\n","| 1| [1]| 1| Angus Young|\n","| 2| [1, 5]| 2|Eric Clapton|\n","+----+-------+---+------------+\n","only showing top 3 rows\n","\n","+-----------+---+------------+----+\n","| hometown| id| name|year|\n","+-----------+---+------------+----+\n","| Sydney| 1| AC/DC|1973|\n","| London| 0|Led Zeppelin|1968|\n","|Los Angeles| 3| Metallica|1981|\n","+-----------+---+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitarsDF.show(3)\n","guitaristsDF.show(3)\n","bandsDF.show(3)\n"]},{"cell_type":"markdown","metadata":{"id":"opA3NFOfxWcm"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"GmN9q_ltxWcm"},"source":["Inner Join = everything from BOTH DFs for which there is a row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1263,"status":"ok","timestamp":1670436775322,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"voyAapmexWcm","outputId":"0b70a609-66df-47ee-8381-a997f9857e81"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+---+------------+----+\n","|band|guitars| id| name| hometown| id| name|year|\n","+----+-------+---+------------+-----------+---+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","+----+-------+---+------------+-----------+---+------------+----+\n","\n"]}],"source":["joinCondition = guitaristsDF.band == bandsDF.id\n","guitaristsBandsDF = guitaristsDF.join(bandsDF, joinCondition, \"inner\")\n","guitaristsBandsDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ce_yS-kVxWcn"},"outputs":[],"source":["# things to bear in mind\n","# guitaristsBandsDF.select(\"id\", \"band\").show(3) # will crash because there are two columns named \"id\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":914,"status":"ok","timestamp":1670436778566,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"ZD_tC8GKxWcn","outputId":"99519fb6-224d-4f93-9150-fb561b40e4d7"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+---+------------+-----------+------+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","+----+-------+---+------------+-----------+------+------------+----+\n","\n"]}],"source":["# you can rename the column\n","bandsDFMod = bandsDF.withColumnRenamed(\"id\", \"bandId\") \\\n"," .withColumnRenamed(\"name\", \"bandName\")\n","guitaristsBandsDF2 = guitaristsDF.join(bandsDFMod,\n"," guitaristsDF.band == bandsDFMod.bandId, \"inner\").orderBy(\"name\")\n","guitaristsBandsDF2.show(3)\n","\n","# there are other options but it is the best practice if it is possible"]},{"cell_type":"markdown","metadata":{"id":"WyFeqM1yxWcn"},"source":["Left Outer = everything in the inner join + all the rows in the LEFT DF, with nulls in where the data is missing"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":917,"status":"ok","timestamp":1670436780904,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"wRt01JY-xWcn","outputId":"0fd6dff7-42d4-4ae2-febe-f71876426f5f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+--------+------+------------+----+\n","|band|guitars| id| name|hometown|bandId| bandName|year|\n","+----+-------+---+------------+--------+------+------------+----+\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| null| null| null|null|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","+----+-------+---+------------+--------+------+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["joinCondition2 = guitaristsDF.band == bandsDFMod.bandId\n","guitaristsDF.join(bandsDFMod, joinCondition2, \"left_outer\").orderBy(\"name\").show(3)\n","# see nulls on Eric Calpton"]},{"cell_type":"markdown","metadata":{"id":"jx7loGmOxWcn"},"source":[" Right Outer = everything in the inner join + all the rows in the RIGHT DF, with nulls in where the data is missing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":487,"status":"ok","timestamp":1670436782239,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"H0t5qxmDxWcn","outputId":"c268f3f6-19c1-4aff-f182-f0cd80b040d3"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+----+-----------+---------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+----+-----------+---------+------+------------+----+\n","|null| null|null| null|Liverpool| 4| The Beatles|1960|\n","| 1| [1]| 1|Angus Young| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","+----+-------+----+-----------+---------+------+------------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"right_outer\").orderBy(\"name\").show(3)\n","# see nulls for The Beatles"]},{"cell_type":"markdown","metadata":{"id":"-YP3UfTaxWco"},"source":[" Outer Join = everything in the inner join + all the rows in BOTH DFs, with nulls in where the data is missing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1614,"status":"ok","timestamp":1670436785314,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"08o47mejxWco","outputId":"37cee475-2da2-4d72-aa7b-c818c4972e0a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+----+------------+---------+------+-----------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+----+------------+---------+------+-----------+----+\n","|null| null|null| null|Liverpool| 4|The Beatles|1960|\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| null| null| null|null|\n","+----+-------+----+------------+---------+------+-----------+----+\n","only showing top 3 rows\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"outer\").orderBy(\"name\").show(3)\n","# see nulls for The Beatles and Eric Clapton"]},{"cell_type":"markdown","metadata":{"id":"tMlhMqhExWco"},"source":[" Semi-joins = everything in the LEFT DF for which there is a row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":481,"status":"ok","timestamp":1670436786026,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"XwLKWOASxWco","outputId":"faa1b781-84a6-4fab-b2b7-32688ed1c65a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 1| [1]| 1| Angus Young|\n","| 0| [0]| 0| Jimmy Page|\n","| 3| [3]| 3|Kirk Hammett|\n","+----+-------+---+------------+\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"left_semi\").orderBy(\"name\").show(3)\n"]},{"cell_type":"markdown","metadata":{"id":"aiNQKxV5xWco"},"source":["Anti-joins = everything in the LEFT DF for which there is NO row in the right DF satisfying the condition\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":736,"status":"ok","timestamp":1670436787511,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"9jYq8v1exWco","outputId":"de110140-c691-4043-c240-e17af16da907"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+\n","|band|guitars| id| name|\n","+----+-------+---+------------+\n","| 2| [1, 5]| 2|Eric Clapton|\n","+----+-------+---+------------+\n","\n"]}],"source":["guitaristsDF.join(bandsDFMod, joinCondition2, \"anti\").orderBy(\"name\").show(3)"]},{"cell_type":"markdown","metadata":{"id":"J0Gz1XFuxWcp"},"source":["Cross-join = takes every instance in the LEFT DF and create a new one for every intance in the right DF (not very common)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":896,"status":"ok","timestamp":1670436789370,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"wfrjSgZlxWcp","outputId":"2cb1037a-b7f7-4342-fc83-12a51de0476a"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----+-------+---+------------+-----------+------+------------+----+\n","|band|guitars| id| name| hometown|bandId| bandName|year|\n","+----+-------+---+------------+-----------+------+------------+----+\n","| 0| [0]| 0| Jimmy Page| Sydney| 1| AC/DC|1973|\n","| 1| [1]| 1| Angus Young| Sydney| 1| AC/DC|1973|\n","| 2| [1, 5]| 2|Eric Clapton| Sydney| 1| AC/DC|1973|\n","| 3| [3]| 3|Kirk Hammett| Sydney| 1| AC/DC|1973|\n","| 0| [0]| 0| Jimmy Page| London| 0|Led Zeppelin|1968|\n","| 1| [1]| 1| Angus Young| London| 0|Led Zeppelin|1968|\n","| 2| [1, 5]| 2|Eric Clapton| London| 0|Led Zeppelin|1968|\n","| 3| [3]| 3|Kirk Hammett| London| 0|Led Zeppelin|1968|\n","| 0| [0]| 0| Jimmy Page|Los Angeles| 3| Metallica|1981|\n","| 1| [1]| 1| Angus Young|Los Angeles| 3| Metallica|1981|\n","| 2| [1, 5]| 2|Eric Clapton|Los Angeles| 3| Metallica|1981|\n","| 3| [3]| 3|Kirk Hammett|Los Angeles| 3| Metallica|1981|\n","| 0| [0]| 0| Jimmy Page| Liverpool| 4| The Beatles|1960|\n","| 1| [1]| 1| Angus Young| Liverpool| 4| The Beatles|1960|\n","| 2| [1, 5]| 2|Eric Clapton| Liverpool| 4| The Beatles|1960|\n","| 3| [3]| 3|Kirk Hammett| Liverpool| 4| The Beatles|1960|\n","+----+-------+---+------------+-----------+------+------------+----+\n","\n"]}],"source":["guitaristsDF.crossJoin(bandsDFMod).show(20)"]},{"cell_type":"markdown","metadata":{"id":"h85qdkxcxWcp"},"source":["*Union Join is just an Union (note it only works if both DFs have the same schema)"]},{"cell_type":"markdown","metadata":{"id":"SB3QfnzsxWcp"},"source":["## Exercises\n","1. Read employees.csv, deptmanagers.csv, salaries.csv and titles.csv to a DFs.\n","2. Show all employees and their max salary (there can be different salaries registered for the same employee)\n","3. Show all employees who were never managers. Check it by getting all the managers and checking that they are not in the table\n","4. Find the job titles of the best paid 10 employees in the company (note that there can be different titles registered for the same employee)"]},{"cell_type":"markdown","metadata":{"id":"QqKb-sgFxWcp"},"source":["Exercise 1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UxuwHDkyxWcp"},"outputs":[],"source":["employeesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/employees.csv\")\n","salariesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/salaries.csv\")\n","deptManagersDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/deptmanagers.csv\")\n","titlesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/titles.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9dvzFpTBxWcp","outputId":"c3eb56a6-e9eb-4809-9d28-0f12cee2ab1f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|\n","+------+----------+----------+---------+------+----------+\n","| 10010|1963-06-01| Duangkaew| Piveteau| F|1989-08-24|\n","| 10020|1952-12-24| Mayuko| Warwick| M|1991-01-26|\n","| 10030|1958-07-14| Elvis| Demeyer| M|1994-02-17|\n","+------+----------+----------+---------+------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["employeesDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":711,"status":"ok","timestamp":1670436913257,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"LzHb4bX-xWcp","outputId":"22e31af0-2df4-4e75-8e30-dd6ede1657ef"},"outputs":[{"data":{"text/plain":["30003"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["employeesDF.count()"]},{"cell_type":"markdown","metadata":{"id":"PYtLrSQ2xWcq"},"source":["Exercise 2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1018,"status":"ok","timestamp":1670436915623,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"7GAjjKMZxWcq","outputId":"69983799-0e5e-4ef9-eb51-6d20babc4a23"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+------+----------+----------+\n","|emp_no|salary| from_date| to_date|\n","+------+------+----------+----------+\n","|100000| 41408|1991-07-02|1992-07-01|\n","|100000| 43119|1992-07-01|1993-07-01|\n","|100000| 46991|1993-07-01|1994-07-01|\n","|100000| 48751|1994-07-01|1995-07-01|\n","+------+------+----------+----------+\n","only showing top 4 rows\n","\n"]}],"source":["salariesDF.filter(col(\"emp_no\") == 100000).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2450,"status":"ok","timestamp":1670436919549,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"jONdeKMbxWcq","outputId":"4e9c66d4-42ef-4e11-ec36-7a9cef0593e0"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------+\n","|emp_no|maxSalary|\n","+------+---------+\n","|100000| 64961|\n","+------+---------+\n","\n"]}],"source":["maxSalariesPerEmpNoDF = salariesDF.groupBy(\"emp_no\").agg(max(\"salary\").cast(\"long\").alias(\"maxSalary\"))\n","maxSalariesPerEmpNoDF.filter(col(\"emp_no\") == 100000).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2876,"status":"ok","timestamp":1670436923263,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"mP_OSDoHxWcq","outputId":"7d3d67a4-fc06-4cac-d80e-2abc7bc42019"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+------+---------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|emp_no|maxSalary|\n","+------+----------+----------+---------+------+----------+------+---------+\n","|100000|1956-01-11| Hiroyasu| Emden| M|1991-07-02|100000| 64961|\n","|100010|1959-11-06| Youpyo| Aamodt| M|1991-10-04|100010| 71957|\n","|100020|1956-05-20| Pramod| Pelc| M|1987-03-01|100020| 70286|\n","+------+----------+----------+---------+------+----------+------+---------+\n","only showing top 3 rows\n","\n"]}],"source":["employeesSalariesDF = employeesDF.join(maxSalariesPerEmpNoDF, \n"," employeesDF.emp_no == maxSalariesPerEmpNoDF.emp_no, \"inner\")\n","employeesSalariesDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1817,"status":"ok","timestamp":1670436925945,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Yc87lz-pxWcq","outputId":"d9b70d4d-a8fa-45ba-f8a2-dda1c74cfb70"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+---------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|maxSalary|\n","+------+----------+----------+---------+------+----------+---------+\n","|100000|1956-01-11| Hiroyasu| Emden| M|1991-07-02| 64961|\n","|100010|1959-11-06| Youpyo| Aamodt| M|1991-10-04| 71957|\n","|100020|1956-05-20| Pramod| Pelc| M|1987-03-01| 70286|\n","+------+----------+----------+---------+------+----------+---------+\n","only showing top 3 rows\n","\n"]}],"source":["# note it is equivalent to\n","employeesSalariesDF2 = employeesDF.join(maxSalariesPerEmpNoDF, \"emp_no\")\n","employeesSalariesDF2.show(3)"]},{"cell_type":"markdown","metadata":{"id":"RntnapY5xWcq"},"source":["Exercise 3"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":386,"status":"ok","timestamp":1670436927155,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Cx6GhiPnxWcq","outputId":"9f429be9-3d1a-417d-c52f-2575660ec407"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|\n","+------+----------+----------+---------+------+----------+\n","| 10010|1963-06-01| Duangkaew| Piveteau| F|1989-08-24|\n","| 10020|1952-12-24| Mayuko| Warwick| M|1991-01-26|\n","| 10030|1958-07-14| Elvis| Demeyer| M|1994-02-17|\n","+------+----------+----------+---------+------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["empNeverManagersDF = employeesDF.join(\n"," deptManagersDF,\n"," employeesDF.emp_no == deptManagersDF.emp_no,\n"," \"left_anti\")\n","\n","empNeverManagersDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":617,"status":"ok","timestamp":1670436928936,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"teOO3XjgxWcq","outputId":"d8028035-5646-4465-e132-61e48ed23a28"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|\n","+------+----------+----------+---------+------+----------+\n","| 10010|1963-06-01| Duangkaew| Piveteau| F|1989-08-24|\n","| 10020|1952-12-24| Mayuko| Warwick| M|1991-01-26|\n","| 10030|1958-07-14| Elvis| Demeyer| M|1994-02-17|\n","+------+----------+----------+---------+------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["# alternatively\n","empNeverManagersDF2 = employeesDF.join(\n"," deptManagersDF,\n"," employeesDF.emp_no != deptManagersDF.emp_no,\n"," \"left_semi\")\n","\n","empNeverManagersDF2.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":895,"status":"ok","timestamp":1670436931249,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"chSByVN4xWcr","outputId":"54db6846-9c82-4c96-fd4a-e75ae2b02cdc"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+-------+------+----------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|dept_no|emp_no| from_date| to_date|\n","+------+----------+----------+---------+------+----------+-------+------+----------+----------+\n","|110420|1963-07-27| Oscar| Ghazalie| M|1992-02-05| d004|110420|1996-08-30|9999-01-01|\n","|110800|1963-02-07| Sanjoy| Quadeer| F|1986-08-12| d006|110800|1991-09-12|1994-06-28|\n","|111400|1959-11-09| Arie| Staelin| M|1985-01-01| d008|111400|1985-01-01|1991-04-08|\n","+------+----------+----------+---------+------+----------+-------+------+----------+----------+\n","\n"]}],"source":["# managers\n","managersDF = employeesDF.join(\n"," deptManagersDF,\n"," employeesDF.emp_no == deptManagersDF.emp_no,\n"," \"inner\")\n","\n","managersDF.show(6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":540,"status":"ok","timestamp":1670436932770,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"7L7QXliixWcr","outputId":"53d71d46-b4d9-4260-9694-44eef687eb1f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|\n","+------+----------+----------+---------+------+----------+\n","+------+----------+----------+---------+------+----------+\n","\n"]}],"source":["empNeverManagersDF.filter((col(\"first_name\") == \"Oscar\") & (col(\"emp_no\") == 110420)).show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"16hcNitfxWcr"},"outputs":[],"source":["# it is fine"]},{"cell_type":"markdown","metadata":{"id":"LSokNABIxWcr"},"source":["Exercise 4"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":477,"status":"ok","timestamp":1670436935341,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"NpXMVC9mxWcr","outputId":"75e9a816-32e5-4aa5-e1ae-9540bbf18bce"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+----------+\n","|emp_no| title| from_date| to_date|\n","+------+---------------+----------+----------+\n","| 10040| Engineer|1993-02-14|1999-02-14|\n","| 10040|Senior Engineer|1999-02-14|9999-01-01|\n","+------+---------------+----------+----------+\n","\n"]}],"source":["titlesDF.filter(col(\"emp_no\") == 10040).show(4)\n","# windowing will be useful for this cases"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1001,"status":"ok","timestamp":1670436937655,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"liHeXAJNxWcr","outputId":"25ef6078-bc95-47ea-8e78-65824b384869"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+-----------+\n","|emp_no|latest_date|\n","+------+-----------+\n","| 10040| 9999-01-01|\n","+------+-----------+\n","\n"]}],"source":["mostRecentJobTitlesDateDF = titlesDF.groupBy(\"emp_no\").agg(max(\"to_date\").alias(\"latest_date\"))\n","mostRecentJobTitlesDateDF.filter(col(\"emp_no\") == 10040).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1249,"status":"ok","timestamp":1670436939162,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"u5iKRDqaxWcr","outputId":"a0be0a65-35c9-4f1c-d704-945dad0bb56c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+\n","|emp_no| title| to_date|\n","+------+---------------+----------+\n","| 10010| Engineer|9999-01-01|\n","| 10020| Engineer|9999-01-01|\n","| 10030|Senior Engineer|9999-01-01|\n","| 10040|Senior Engineer|9999-01-01|\n","| 10050| Senior Staff|9999-01-01|\n","| 10060| Senior Staff|9999-01-01|\n","+------+---------------+----------+\n","only showing top 6 rows\n","\n"]}],"source":["mostRecentJobTitlesDF = titlesDF.join(mostRecentJobTitlesDateDF, (titlesDF.emp_no == mostRecentJobTitlesDateDF.emp_no) \n"," & (titlesDF.to_date == mostRecentJobTitlesDateDF.latest_date), \"semi\").drop(\"from_date\")\n","mostRecentJobTitlesDF.show(6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1687,"status":"ok","timestamp":1670436948154,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qOA_AuZ2xWcr","outputId":"f665154c-7111-48d3-af40-6ede16f326a6"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+------+---------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|emp_no|maxSalary|\n","+------+----------+----------+---------+------+----------+------+---------+\n","|205000|1956-01-14| Charmane| Griswold| M|1990-06-23|205000| 153715|\n","|246120|1961-05-26| Arnd| Junot| F|1985-04-07|246120| 146292|\n","|257360|1955-10-30| Honglan| Otillio| F|1985-12-09|257360| 144748|\n","|107140|1960-05-05| Ortrud|Diderrich| M|1987-12-08|107140| 142506|\n","|282030|1963-09-24| Sergi| Leslie| F|1989-10-09|282030| 142184|\n","|282370|1964-05-29| Cedric| Rossi| F|1987-06-06|282370| 141488|\n","|447240|1953-12-25| Maja| Riefers| M|1987-11-06|447240| 140332|\n","|295800|1952-07-14| Jingling| Weisert| F|1986-08-17|295800| 139973|\n","|296250|1961-04-30| Krisda| Papsdorf| M|1989-09-21|296250| 138716|\n","|411990|1955-02-23| Luigi| Renear| M|1987-05-02|411990| 138273|\n","+------+----------+----------+---------+------+----------+------+---------+\n","\n"]}],"source":["bestPaidEmployeesDF = employeesSalariesDF.orderBy(col(\"maxSalary\").desc()).limit(10)\n","bestPaidEmployeesDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2511,"status":"ok","timestamp":1670436951013,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"9a-o6iYPxWcs","outputId":"07c02083-e824-4677-85e8-76b6f2a7ff1c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+----------+----------+---------+------+----------+------+---------+------------+----------+\n","|emp_no|birth_date|first_name|last_name|gender| hire_date|emp_no|maxSalary| title| to_date|\n","+------+----------+----------+---------+------+----------+------+---------+------------+----------+\n","|107140|1960-05-05| Ortrud|Diderrich| M|1987-12-08|107140| 142506|Senior Staff|9999-01-01|\n","|205000|1956-01-14| Charmane| Griswold| M|1990-06-23|205000| 153715|Senior Staff|9999-01-01|\n","|246120|1961-05-26| Arnd| Junot| F|1985-04-07|246120| 146292|Senior Staff|9999-01-01|\n","|257360|1955-10-30| Honglan| Otillio| F|1985-12-09|257360| 144748|Senior Staff|9999-01-01|\n","|282030|1963-09-24| Sergi| Leslie| F|1989-10-09|282030| 142184|Senior Staff|9999-01-01|\n","|282370|1964-05-29| Cedric| Rossi| F|1987-06-06|282370| 141488|Senior Staff|9999-01-01|\n","|295800|1952-07-14| Jingling| Weisert| F|1986-08-17|295800| 139973|Senior Staff|9999-01-01|\n","|296250|1961-04-30| Krisda| Papsdorf| M|1989-09-21|296250| 138716|Senior Staff|9999-01-01|\n","|411990|1955-02-23| Luigi| Renear| M|1987-05-02|411990| 138273|Senior Staff|9999-01-01|\n","|447240|1953-12-25| Maja| Riefers| M|1987-11-06|447240| 140332|Senior Staff|9999-01-01|\n","+------+----------+----------+---------+------+----------+------+---------+------------+----------+\n","\n"]}],"source":["bestPaidJobsDF = bestPaidEmployeesDF.join(mostRecentJobTitlesDF, \"emp_no\")\n","bestPaidJobsDF.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aBku8MCExWcs"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":["opA3NFOfxWcm","SB3QfnzsxWcp"],"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/05.WindowPartitioning_UDFs_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/05.WindowPartitioning_UDFs_solved.ipynb new file mode 100644 index 000000000..97a382e51 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/05.WindowPartitioning_UDFs_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"BNkZfzfxGZ0z"},"source":["# Windows Partitioning"]},{"cell_type":"markdown","metadata":{"id":"AQieQ5pkGfNm"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HelxRmCPGpql"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9Cn3c-ywGtDV"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1670442099775,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"D95sNcJfGvyV","outputId":"2653eff1-49ca-4320-9751-cf5ad29bdc3a"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267684\n","drwxr-xr-x 1 root root 4096 Dec 6 14:35 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qtBMGi7EGvwN"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6JO331NrGvtt"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"markdown","metadata":{"id":"02epIDkbG24d"},"source":["Defining the environment"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qmON5zHJG4-m"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"WgvNJQOAGZ00"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":9098,"status":"ok","timestamp":1670442128571,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"siaPZq4XGZ00","outputId":"525e7ed9-c4cd-4387-cf51-fc2a6069f972"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Window Partitioning\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2863,"status":"ok","timestamp":1670442153333,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nsBkpLh6GZ01","outputId":"7c3c6f13-7451-4a44-91e3-305c9978419c"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Windows Partitioning
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Bqu4fQnNGZ02"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-9DDmYQKGZ02"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"NYrtXWZIHKMt"},"source":["Download datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2lkKBm3CHL-l"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/characters.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/employees.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/salaries.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/deptmanagers.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/titles.csv -P /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/characters.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"sxWVtHu5GZ02"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{},"source":["### Window Partitioning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JWgnuYZ8GZ02"},"outputs":[],"source":["employeesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/employees.csv\")\n","salariesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/salaries.csv\")\n","deptManagersDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/deptmanagers.csv\")\n","titlesDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/titles.csv\")\n","charactersDF = spark.read.option(\"inferSchema\", \"true\").option(\"header\", \"true\").csv(\"/dataset/characters.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":673,"status":"ok","timestamp":1670442251373,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"m26N92p-GZ02","outputId":"d0ee0fc4-6213-43ff-dc9e-1557814b990b"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+--------+----------+----------+\n","|emp_no| title| from_date| to_date|\n","+------+--------+----------+----------+\n","| 10010|Engineer|1996-11-24|9999-01-01|\n","| 10020|Engineer|1997-12-30|9999-01-01|\n","+------+--------+----------+----------+\n","only showing top 2 rows\n","\n"]}],"source":["titlesDF.show(2)"]},{"cell_type":"markdown","metadata":{"id":"O9KiG0PHGZ03"},"source":["Get the last title for the employees"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1183,"status":"ok","timestamp":1670442253888,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"8W89HFrvGZ03","outputId":"06339219-7d05-4739-9889-b26b4d3cc171"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+----------+\n","|emp_no| title| from_date| to_date|\n","+------+---------------+----------+----------+\n","| 10040| Engineer|1993-02-14|1999-02-14|\n","| 10040|Senior Engineer|1999-02-14|9999-01-01|\n","+------+---------------+----------+----------+\n","\n"]}],"source":["titlesDF.filter(col(\"emp_no\") == 10040).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vJzQSe5pGZ03"},"outputs":[],"source":["# import library\n","from pyspark.sql.window import Window"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3221,"status":"ok","timestamp":1670442257825,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"smIYb3ogGZ03","outputId":"1ad92b06-8204-49f6-ac3d-48eaf05e6e84"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+----------+\n","|emp_no| title| to_date|\n","+------+---------------+----------+\n","| 10040|Senior Engineer|9999-01-01|\n","+------+---------------+----------+\n","\n"]}],"source":["# get last title by date for each employee (similar to previous exercise in Joins)\n","byEmployee = Window.partitionBy(\"emp_no\").orderBy(col(\"to_date\").desc())\n","mostRecentJobTitlesDF = titlesDF.withColumn(\"datesOrder\", row_number().over(byEmployee)) \\\n"," .filter(col(\"datesOrder\") == 1) \\\n"," .select(\"emp_no\", \"title\", \"to_date\")\n","\n","mostRecentJobTitlesDF.filter(col(\"emp_no\") == 10040).show(4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2fcF-z4WGZ03"},"outputs":[],"source":["# for the previous example we saw in joins, if we use windogs partitioning we do not need to do the previous step (filtering max date and then do the join over the same table)"]},{"cell_type":"markdown","metadata":{"id":"Y2uP3Mo-GZ04"},"source":["Get the max salaries (three for example) for job title"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5092,"status":"ok","timestamp":1670442263272,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"LUTvHq4EGZ04","outputId":"6d56f043-b362-44d9-af50-3a990e6b279f"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------------+\n","|salary| title|\n","+------+---------------+\n","| 72668|Senior Engineer|\n","+------+---------------+\n","\n","+------+---------------+\n","|salary| title|\n","+------+---------------+\n","| 80324| Engineer|\n","| 47017| Engineer|\n","| 88806|Senior Engineer|\n","+------+---------------+\n","only showing top 3 rows\n","\n"]}],"source":["# we need salary data with title data joined first\n","bestPaidPerTitlerawDF = salariesDF.withColumn(\"salary\", col(\"salary\").cast(\"long\")) \\\n"," .join(mostRecentJobTitlesDF, (salariesDF.emp_no == mostRecentJobTitlesDF.emp_no) & (salariesDF.to_date == mostRecentJobTitlesDF.to_date)).drop(\"emp_no\", \"from_date\", \"to_date\")\n","\n","bestPaidPerTitlerawDF.filter(salariesDF.emp_no == 10040).show()\n","bestPaidPerTitlerawDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4259,"status":"ok","timestamp":1670442267529,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Y7-8h-0nGZ04","outputId":"6639ac14-ee81-4949-cfb4-e9a2c05737f4"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+------------------+-----------+\n","|salary| title|rank_salary|\n","+------+------------------+-----------+\n","|101622|Assistant Engineer| 1|\n","| 92674|Assistant Engineer| 2|\n","| 92034|Assistant Engineer| 3|\n","|130939| Engineer| 1|\n","|121819| Engineer| 2|\n","|120417| Engineer| 3|\n","+------+------------------+-----------+\n","only showing top 6 rows\n","\n"]}],"source":["# now we apply the window partitioning\n","byTitle = Window.partitionBy(\"title\").orderBy(col(\"salary\").desc())\n","\n","bestPaidPerTitleDF = bestPaidPerTitlerawDF.withColumn(\"rank_salary\", row_number().over(byTitle)).filter(col(\"rank_salary\") <= 3)\n","\n","bestPaidPerTitleDF.show(6)"]},{"cell_type":"markdown","metadata":{},"source":["### UDFs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["charactersDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# we define a function to remove the BBY from the birth_year\n","year = udf(lambda s: s[:-3])\n","\n","charactersDF.withColumn(\"year\", year(col(\"birth_year\"))).show(3)"]},{"cell_type":"markdown","metadata":{"id":"IYWyYjXPGZ04"},"source":["## Exercises Window Partitioning\n","1) Load characters.csv to a DataFrame. Then, get the (two) tallest characters per species and per homeworld planet. Select the name, height, and species/homeworld in their case.\n","2) Get the height difference for each character with respect to the smallest one in the same homeworld."]},{"cell_type":"markdown","metadata":{"id":"ndRe6jVQGZ04"},"source":["Exercise 1"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":914,"status":"ok","timestamp":1670442309384,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"O_pDCBgbGZ04","outputId":"c20ad7b6-aa46-4735-862b-8bd66ecebe5c"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------+------+----+----------+-----------+---------+----------+------+---------+-------+\n","| name|height|mass|hair_color| skin_color|eye_color|birth_year|gender|homeworld|species|\n","+--------------+------+----+----------+-----------+---------+----------+------+---------+-------+\n","|Luke Skywalker| 172| 77| blond| fair| blue| 19BBY| male| Tatooine| Human|\n","| C-3PO| 167| 75| NA| gold| yellow| 112BBY| NA| Tatooine| Droid|\n","| R2-D2| 96| 32| NA|white, blue| red| 33BBY| NA| Naboo| Droid|\n","+--------------+------+----+----------+-----------+---------+----------+------+---------+-------+\n","only showing top 3 rows\n","\n"]}],"source":["charactersDF = spark.read.option(\"header\", \"true\").csv(\"/dataset/characters.csv\")\n","\n","charactersDF.show(3)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y6fs1GeaGZ05"},"outputs":[],"source":["charactersDF = charactersDF.withColumn(\"height\", col(\"height\").cast(\"long\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1111,"status":"ok","timestamp":1670442313458,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"0FeesWjgGZ05","outputId":"a0e0e398-6c39-49b8-b971-40d200488b8d"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+------+-----------+---------+\n","| name|height| homeworld| species|\n","+--------------------+------+-----------+---------+\n","| Ratts Tyerell| 79|Aleen Minor| Aleena|\n","| Dexter Jettster| 198| Ojom| Besalisk|\n","| Ki-Adi-Mundi| 198| Cerea| Cerean|\n","| Mas Amedda| 196| Champala| Chagrian|\n","| Zam Wesell| 168| Zolan| Clawdite|\n","| IG-88| 200| NA| Droid|\n","| C-3PO| 167| Tatooine| Droid|\n","| Sebulba| 112| Malastare| Dug|\n","|Wicket Systri War...| 88| Endor| Ewok|\n","| Poggle the Lesser| 183| Geonosis|Geonosian|\n","+--------------------+------+-----------+---------+\n","only showing top 10 rows\n","\n"]}],"source":["bySpecies = Window.partitionBy(\"species\").orderBy(col(\"height\").desc())\n","tallestPerSpeciesDF = charactersDF.withColumn(\"tallest\", row_number().over(bySpecies)).filter(col(\"tallest\") <= 2).select(\"name\", \"height\", \"homeworld\", \"species\")\n","\n","tallestPerSpeciesDF.show(10)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":937,"status":"ok","timestamp":1670442318035,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JaQV09u2GZ05","outputId":"c6158935-83f5-42e2-abc8-52203e0c2f51"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-------------------+------+--------------+---------+\n","| name|height| homeworld| species|\n","+-------------------+------+--------------+---------+\n","|Bail Prestor Organa| 191| Alderaan| Human|\n","| Raymus Antilles| 188| Alderaan| Human|\n","| Ratts Tyerell| 79| Aleen Minor| Aleena|\n","| Lobot| 175| Bespin| Human|\n","| Jek Tono Porkins| 180| Bestine IV| Human|\n","| Nute Gunray| 191|Cato Neimoidia|Neimodian|\n","| Ki-Adi-Mundi| 198| Cerea| Cerean|\n","| Mas Amedda| 196| Champala| Chagrian|\n","| Mon Mothma| 150| Chandrila| Human|\n","| Jango Fett| 183| Concord Dawn| Human|\n","+-------------------+------+--------------+---------+\n","only showing top 10 rows\n","\n"]}],"source":["byHomeworld = Window.partitionBy(\"homeworld\").orderBy(col(\"height\").desc())\n","tallestPerHomeDF = charactersDF.withColumn(\"tallest\", row_number().over(byHomeworld)).filter(col(\"tallest\") <= 2).select(\"name\", \"height\", \"homeworld\", \"species\")\n","\n","tallestPerHomeDF.show(10)"]},{"cell_type":"markdown","metadata":{"id":"7uz7mRJwGZ05"},"source":["Exercise 2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1574,"status":"ok","timestamp":1670442324492,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"gfjsgoLXGZ05","outputId":"b9219e27-414e-4841-b29b-d4f56cff04a3"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------------------+------+----+------------+-------------+---------+----------+------+--------------+----------+-----------+\n","| name|height|mass| hair_color| skin_color|eye_color|birth_year|gender| homeworld| species|height_diff|\n","+--------------------+------+----+------------+-------------+---------+----------+------+--------------+----------+-----------+\n","| Bail Prestor Organa| 191| NA| black| tan| brown| 67BBY| male| Alderaan| Human| 41|\n","| Raymus Antilles| 188| 79| brown| light| brown| NA| male| Alderaan| Human| 38|\n","| Leia Organa| 150| 49| brown| light| brown| 19BBY|female| Alderaan| Human| 0|\n","| Ratts Tyerell| 79| 15| none| grey, blue| NA| NA| male| Aleen Minor| Aleena| 0|\n","| Lobot| 175| 79| none| light| blue| 37BBY| male| Bespin| Human| 0|\n","| Jek Tono Porkins| 180| 110| brown| fair| blue| NA| male| Bestine IV| Human| 0|\n","| Nute Gunray| 191| 90| none|mottled green| red| NA| male|Cato Neimoidia| Neimodian| 0|\n","| Ki-Adi-Mundi| 198| 82| white| pale| yellow| 92BBY| male| Cerea| Cerean| 0|\n","| Mas Amedda| 196| NA| none| blue| blue| NA| male| Champala| Chagrian| 0|\n","| Mon Mothma| 150| NA| auburn| fair| blue| 48BBY|female| Chandrila| Human| 0|\n","| Jango Fett| 183| 79| black| tan| brown| 66BBY| male| Concord Dawn| Human| 0|\n","| Han Solo| 180| 80| brown| fair| brown| 29BBY| male| Corellia| Human| 10|\n","| Wedge Antilles| 170| 77| brown| fair| hazel| 21BBY| male| Corellia| Human| 0|\n","| Adi Gallia| 184| 50| none| dark| blue| NA|female| Coruscant|Tholothian| 17|\n","| Finis Valorum| 170| NA| blond| fair| blue| 91BBY| male| Coruscant| Human| 3|\n","| Jocasta Nu| 167| NA| white| fair| blue| NA|female| Coruscant| Human| 0|\n","| Darth Maul| 175| 80| none| red| yellow| 54BBY| male| Dathomir| Zabrak| 0|\n","| Plo Koon| 188| 80| none| orange| black| 22BBY| male| Dorin| Kel Dor| 0|\n","|Wicket Systri War...| 88| 20| brown| brown| brown| 8BBY| male| Endor| Ewok| 0|\n","| Wilhuff Tarkin| 180| NA|auburn, grey| fair| blue| 64BBY| male| Eriadu| Human| 0|\n","+--------------------+------+----+------------+-------------+---------+----------+------+--------------+----------+-----------+\n","only showing top 20 rows\n","\n"]}],"source":["byHomeworldAsc = Window.partitionBy(\"homeworld\").orderBy(col(\"height\").asc())\n","\n","\n","diffHeightDF = charactersDF \\\n"," .withColumn(\"height_diff\", col(\"height\") - min(col(\"height\")).over(byHomeworldAsc)) \\\n"," .orderBy(col(\"homeworld\"), col(\"height\").desc()) \\\n"," .show(20)"]},{"cell_type":"markdown","metadata":{},"source":["## Exercises UDFs\n","1. Choose one of the DFs we have, define two UDFs of your own, and apply them (with a withColumn) on the DF. Show the results "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EdA3Q2K3GZ05"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/06.Partitioning_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/06.Partitioning_solved.ipynb new file mode 100644 index 000000000..002cc47cb --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/06.Partitioning_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"BNkZfzfxGZ0z"},"source":["# Partitioning"]},{"cell_type":"markdown","metadata":{"id":"AQieQ5pkGfNm"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"HelxRmCPGpql"},"source":["Install Spark and Java in VM"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":57405,"status":"ok","timestamp":1670666888439,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"9Cn3c-ywGtDV"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1670666888440,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"D95sNcJfGvyV","outputId":"7b823201-ab1c-4452-d091-bb995fd23e6e"},"outputs":[{"name":"stdout","output_type":"stream","text":["total 267680\n","drwxr-xr-x 1 root root 4096 Dec 8 14:36 \u001b[0m\u001b[01;34msample_data\u001b[0m/\n","-rw-r--r-- 1 root root 274099817 Oct 15 10:53 spark-3.3.1-bin-hadoop2.tgz\n"]}],"source":["ls -l # check the .tgz is there"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":2865,"status":"ok","timestamp":1670666891295,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qtBMGi7EGvwN"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4334,"status":"ok","timestamp":1670666895619,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"6JO331NrGvtt"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"markdown","metadata":{"id":"02epIDkbG24d"},"source":["Defining the environment"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":19,"status":"ok","timestamp":1670666895620,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qmON5zHJG4-m"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"WgvNJQOAGZ00"},"source":["Start Spark Session\n","\n","---"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":6346,"status":"ok","timestamp":1670666901951,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"siaPZq4XGZ00","outputId":"517fa3d4-7f52-4b7b-f33a-b27ecef9a830"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Window Partitioning Exercises\") \\\n"," .master(\"local[*]\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":2348,"status":"ok","timestamp":1670666904291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"nsBkpLh6GZ01","outputId":"8b85e798-6e00-4c6f-c200-9f257dec4f76"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Window Partitioning Exercises
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1670666904291,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Bqu4fQnNGZ02"},"outputs":[],"source":["# For Pandas conversion optimization\n","spark.conf.set(\"spark.sql.execution.arrow.enabled\", \"true\")"]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1670666904292,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"-9DDmYQKGZ02"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"markdown","metadata":{"id":"NYrtXWZIHKMt"},"source":["Download datasets"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2526,"status":"ok","timestamp":1670666906811,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"2lkKBm3CHL-l","outputId":"df5bac53-e576-4134-8560-d1fe2b81e002"},"outputs":[{"name":"stdout","output_type":"stream","text":["bank.csv characters.csv planets.csv species.csv vehicles.csv\n"]}],"source":["!mkdir -p /dataset\n","!wget -q https://github.com/masfworld/datahack_docker/raw/master/zeppelin/data/characters.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"d1tEe9JCILAe"},"source":["## Examples"]},{"cell_type":"markdown","metadata":{"id":"HWlZ5puMLqvX"},"source":["### Patitioning"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":8328,"status":"ok","timestamp":1670666915136,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"B10rScPeIOCn"},"outputs":[],"source":["# Load characters CSV\n","charactersDF = spark.read.option(\"inferSchema\", \"true\").option(\"header\", \"true\").csv(\"/dataset/characters.csv\")"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1489,"status":"ok","timestamp":1670666916608,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"_z47cLpVJQgV","outputId":"3df2be0d-f74d-4118-8b7a-e82fc900c949"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 87|\n","+-----------+-----+\n","\n"]}],"source":["# Show how the data is partitioned now\n","charactersDF \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"count\").desc()) \\\n"," .show()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1525,"status":"ok","timestamp":1670666918132,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"NGfdi2WhJQeH","outputId":"4c872e9c-45ef-47e1-c092-87cd3e206764"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 4|\n","| 1| 4|\n","| 2| 4|\n","| 3| 4|\n","| 4| 4|\n","| 5| 4|\n","| 6| 4|\n","| 7| 4|\n","| 8| 4|\n","| 9| 5|\n","| 10| 5|\n","| 11| 5|\n","| 12| 5|\n","| 13| 5|\n","| 14| 5|\n","| 15| 5|\n","| 16| 4|\n","| 17| 4|\n","| 18| 4|\n","| 19| 4|\n","+-----------+-----+\n","\n"]}],"source":["# We will now repartition the DF to 20 partitions\n","charactersRepDF = charactersDF.repartition(20)\n","charactersRepDF \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"partitionId\")) \\\n"," .show()"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":706,"status":"ok","timestamp":1670666918833,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"sEwePVEfJvMN","outputId":"ab472f88-1eb1-4d87-bdcc-f4cbf77268f9"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+-----+\n","|partitionId|count|\n","+-----------+-----+\n","| 0| 17|\n","| 1| 18|\n","| 2| 18|\n","| 3| 17|\n","| 4| 17|\n","+-----------+-----+\n","\n"]}],"source":["# Now we can use coalesce to reduce the number of partitions\n","charactersRepDF \\\n"," .coalesce(5) \\\n"," .withColumn(\"partitionId\", spark_partition_id()) \\\n"," .groupBy(\"partitionId\") \\\n"," .count() \\\n"," .orderBy(col(\"partitionId\")) \\\n"," .show()"]},{"cell_type":"markdown","metadata":{},"source":["## Partitioning Exercises"]},{"cell_type":"markdown","metadata":{},"source":["1. Try repartition/colaesce yourself"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.10.0 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/notebooks_solved/07.TaxiExercise_solved.ipynb b/PROFESORES/PYSPARK/notebooks_solved/07.TaxiExercise_solved.ipynb new file mode 100644 index 000000000..a792effb1 --- /dev/null +++ b/PROFESORES/PYSPARK/notebooks_solved/07.TaxiExercise_solved.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"P0VSNugGxYj4"},"source":["# Taxi Exercise"]},{"cell_type":"markdown","metadata":{"id":"fhqHAQWiyLZA"},"source":["## Prerrequisites"]},{"cell_type":"markdown","metadata":{"id":"O-nP6sc6xgbg"},"source":["Install Java and Spark in VM"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":73671,"status":"ok","timestamp":1675880362086,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"JRywFs7DxgOQ"},"outputs":[],"source":["# install Java8\n","!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n","# download spark 3.5.0\n","!wget -q https://apache.osuosl.org/spark/spark-3.5.0/spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":6533,"status":"ok","timestamp":1675880368614,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"w8zwZ4GWxtMI"},"outputs":[],"source":["# unzip it\n","!tar xf spark-3.5.0-bin-hadoop3.tgz"]},{"cell_type":"code","execution_count":22,"metadata":{"executionInfo":{"elapsed":4962,"status":"ok","timestamp":1675880373536,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"PAG_MdchxuxI"},"outputs":[],"source":["!pip install -q findspark"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1675880373536,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"BM0GAnLCxwkP"},"outputs":[],"source":["import os\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"SPARK_HOME\"] = \"/content/spark-3.5.0-bin-hadoop3\"\n","os.environ[\"PYSPARK_SUBMIT_ARGS\"] = \"--master local[*] pyspark-shell\""]},{"cell_type":"markdown","metadata":{"id":"6AnypwdzxYj7"},"source":["Start Spark Session \n","\n","---"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"CIJ4mkL-xYj7","outputId":"b1103e23-09a8-4c4e-d4ac-11cc01fd3305"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'3.3.1'"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["import findspark\n","findspark.init(\"spark-3.5.0-bin-hadoop3\")# SPARK_HOME\n","\n","from pyspark.sql import SparkSession\n","\n","# create the session\n","spark = SparkSession \\\n"," .builder \\\n"," .appName(\"Joins\") \\\n"," .master(\"local[*]\") \\\n"," .config(\"spark.ui.port\", \"4500\") \\\n"," .getOrCreate()\n","\n","spark.version"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"L2BsUYftxYj8","outputId":"60dfc7c8-1e9d-4180-fc37-025c710d22fc"},"outputs":[{"data":{"text/html":["\n","
    \n","

    SparkSession - in-memory

    \n"," \n","
    \n","

    SparkContext

    \n","\n","

    Spark UI

    \n","\n","
    \n","
    Version
    \n","
    v3.3.1
    \n","
    Master
    \n","
    local[*]
    \n","
    AppName
    \n","
    Joins
    \n","
    \n","
    \n"," \n","
    \n"," "],"text/plain":[""]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["spark"]},{"cell_type":"code","execution_count":26,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1675880373537,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"R6uQC9cJxYj9"},"outputs":[],"source":["# Import sql functions\n","from pyspark.sql.functions import *"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":1424,"status":"ok","timestamp":1675880374953,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"VegBGDs3yIIH"},"outputs":[],"source":["!mkdir -p dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/taxi_data.csv -P /dataset\n","!wget -q https://raw.githubusercontent.com/paponsro/spark_edem_2324/master/dataset/taxi_zones.csv -P /dataset\n","!ls /dataset"]},{"cell_type":"markdown","metadata":{"id":"Jsnl4Q2NxYj9"},"source":["Load the datasets"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6842,"status":"ok","timestamp":1675880381794,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"KBbu7DMqxYj9","outputId":"39d9a256-0a4f-407a-de0b-62a7ff428d3a"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- VendorID: integer (nullable = true)\n"," |-- tpep_pickup_datetime: timestamp (nullable = true)\n"," |-- tpep_dropoff_datetime: timestamp (nullable = true)\n"," |-- passenger_count: integer (nullable = true)\n"," |-- trip_distance: double (nullable = true)\n"," |-- RatecodeID: integer (nullable = true)\n"," |-- store_and_fwd_flag: string (nullable = true)\n"," |-- PULocationID: integer (nullable = true)\n"," |-- DOLocationID: integer (nullable = true)\n"," |-- payment_type: integer (nullable = true)\n"," |-- fare_amount: double (nullable = true)\n"," |-- extra: double (nullable = true)\n"," |-- mta_tax: double (nullable = true)\n"," |-- tip_amount: double (nullable = true)\n"," |-- tolls_amount: double (nullable = true)\n"," |-- improvement_surcharge: double (nullable = true)\n"," |-- total_amount: double (nullable = true)\n","\n"]}],"source":["taxiDF = spark.read \\\n"," .option(\"header\", \"true\") \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .csv(\"/dataset/taxi_data.csv\")\n","\n","taxiDF.printSchema()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":347,"status":"ok","timestamp":1675880382138,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"yOnQ1DjhxYj-","outputId":"aa7807d4-9b62-4d23-d164-30c0c49ec496"},"outputs":[{"name":"stdout","output_type":"stream","text":["+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","|VendorID|tpep_pickup_datetime|tpep_dropoff_datetime|passenger_count|trip_distance|RatecodeID|store_and_fwd_flag|PULocationID|DOLocationID|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","| 2| 2018-01-24 23:02:56| 2018-01-24 23:10:58| 1| 2.02| 1| N| 48| 107| 2| 8.5| 0.5| 0.5| 0.0| 0.0| 0.3| 9.8|\n","| 2| 2018-01-24 23:57:13| 2018-01-25 00:21:17| 1| 10.13| 1| N| 79| 244| 2| 28.5| 0.5| 0.5| 0.0| 0.0| 0.3| 29.8|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+\n","only showing top 2 rows\n","\n"]}],"source":["taxiDF.show(2)"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":576,"status":"ok","timestamp":1675880382711,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"UtysOEnoxYj-","outputId":"13f2b048-5823-4471-ba4a-9416446e48a9"},"outputs":[{"name":"stdout","output_type":"stream","text":["root\n"," |-- LocationID: integer (nullable = true)\n"," |-- Borough: string (nullable = true)\n"," |-- Zone: string (nullable = true)\n"," |-- service_zone: string (nullable = true)\n","\n"]}],"source":["taxiZonesDF = spark.read \\\n"," .option(\"header\", \"true\") \\\n"," .option(\"inferSchema\", \"true\") \\\n"," .csv(\"/dataset/taxi_zones.csv\")\n","\n","taxiZonesDF.printSchema()"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":966,"status":"ok","timestamp":1675880383675,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"qCYJZpiXxYj-","outputId":"10842c3b-b922-4332-84be-d942246b01a5"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+-------+--------------------+------------+\n","|LocationID|Borough| Zone|service_zone|\n","+----------+-------+--------------------+------------+\n","| 1| EWR| Newark Airport| EWR|\n","| 2| Queens| Jamaica Bay| Boro Zone|\n","| 3| Bronx|Allerton/Pelham G...| Boro Zone|\n","+----------+-------+--------------------+------------+\n","only showing top 3 rows\n","\n"]}],"source":["taxiZonesDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"EjGzj1Rsx3Ns"},"source":["## Exercise"]},{"cell_type":"markdown","metadata":{"id":"JOH-iRShx1cw"},"source":["In this exercise we will be working with two DFs. The first one, taxiDf holds info about taxi rides per 2018 year. And the second, taxiZonesDF, have info about the Zones. Please load the DFs and print the schemas and two (or more) rows for more detailed info.\n","\n","The aim of the exercise is to answer the questions listed below."]},{"cell_type":"markdown","metadata":{"id":"tQaqDr3hxYj_"},"source":["**Questions:**\n","\n"," 1. Which zones have the most pickups/dropoffs overall? Note there are many PULocationIDs per Zone?\n"," 2. What are the peak hours for taxi?\n"," 3. How are the trips distributed by length? Show stats like mean, max, min, etc. \n"," Then get the total trips for less/more than 30 km. Why are people taking the cab? For long or short trips?\n"," You can also try the same with different distances. Which is the expected value for threshold is we want to obtain more or less the same trips in long/short counting?\n"," 4. What are the peak hours for long/short trips?\n"," 5. What are the top 3 pickup/dropoff zones for long/short trips?\n"," 6. How are people paying for the ride, on long/short trips? Hint: the information about how good is the payment is in RatecodeID column.\n"," 7. How is the payment type (RatecodeId) evolving with time (in days)? Hint: use the column with pickup time info.\n"," Get the same info but with avg of ratecode and total trips per day."]},{"cell_type":"markdown","metadata":{"id":"NvllEugwxYj_"},"source":["### Question 1"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7371,"status":"ok","timestamp":1675880391045,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"QuLkCpnQG8t7","outputId":"1a50021d-4ea7-41a8-dd3e-2b0dfda2d140"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------------+----------+---------+--------------------+\n","|PULocationID|totalTrips| Borough| Zone|\n","+------------+----------+---------+--------------------+\n","| 237| 15945|Manhattan|Upper East Side S...|\n","| 161| 15255|Manhattan| Midtown Center|\n","| 236| 13767|Manhattan|Upper East Side N...|\n","+------------+----------+---------+--------------------+\n","only showing top 3 rows\n","\n","+------------+-------------+\n","|PULocationID|ratecodeTotal|\n","+------------+-------------+\n","| 148| 2920|\n","| 243| 42|\n","| 137| 4103|\n","+------------+-------------+\n","only showing top 3 rows\n","\n","+---------+----------+\n","| Borough|totalTrips|\n","+---------+----------+\n","|Manhattan| 304266|\n","| Queens| 17712|\n","+---------+----------+\n","only showing top 2 rows\n","\n","+------------+----------+---------+--------------------+\n","|DOLocationID|totalTrips| Borough| Zone|\n","+------------+----------+---------+--------------------+\n","| 161| 15099|Manhattan| Midtown Center|\n","| 236| 14261|Manhattan|Upper East Side N...|\n","| 237| 13754|Manhattan|Upper East Side S...|\n","+------------+----------+---------+--------------------+\n","only showing top 3 rows\n","\n","+---------+----------+\n","| Borough|totalTrips|\n","+---------+----------+\n","|Manhattan| 296527|\n","| Queens| 13819|\n","+---------+----------+\n","only showing top 2 rows\n","\n"]}],"source":["\"\"\"\n","Question 1: Which zones have the most pickups/dropoffs overall? Note there are many PULocationIDs per Zone?\n","\"\"\"\n","\n","\"\"\"\n","We can understand the question as getting the most popular zones for PU/DO, each one separately. \n","Or understand it as getting the most popular zones for PU/DO together. In the first case we will have to make two separate .groupBy() + .agg(count()), \n","in the second case, we will do just one. In this exercise we will do it with the first approach only.\n","\"\"\"\n","\n","# First approach\n","# First, we'll do the groupBy per PU, and then, we'll count the totalTrips. We need the join to have the zones info. And we join by PULocationID == LocationID because\n","# these are the columns with \"same\" info in our DFs\n","\n","pickupsByTaxiZonePUDF = taxiDF.groupBy(\"PULocationID\") \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .join(taxiZonesDF, col(\"PULocationID\") == col(\"LocationID\")) \\\n"," .drop(\"LocationID\", \"service_zone\") \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByTaxiZonePUDF.show(3)\n","\n","ratesByTaxiZonePUDF = taxiDF.groupBy(\"PULocationID\") \\\n"," .agg(count(\"RatecodeID\").alias(\"ratecodeTotal\")) \\\n","\n","ratesByTaxiZonePUDF.show(3) \n","\n","# 1b - Now we can group by borough. That's because one Borough have several Zones, and Borough info is more relevant (we think)\n","pickupsByBoroughPUDF = pickupsByTaxiZonePUDF.groupBy(col(\"Borough\")) \\\n"," .agg(sum(col(\"totalTrips\")).alias(\"totalTrips\")) \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByBoroughPUDF.show(2)\n","\n","# Now we have to do the same for DOLocationIDs:\n","pickupsByTaxiZoneDODF = taxiDF.groupBy(\"DOLocationID\") \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .join(taxiZonesDF, col(\"DOLocationID\") == col(\"LocationID\")) \\\n"," .drop(\"LocationID\", \"service_zone\") \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByTaxiZoneDODF.show(3)\n","\n","# 1b - Now we can group by borough.\n","pickupsByBoroughDODF = pickupsByTaxiZoneDODF.groupBy(col(\"Borough\")) \\\n"," .agg(sum(col(\"totalTrips\")).alias(\"totalTrips\")) \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByBoroughDODF.show(2)\n","\n","# Tips: drop and orderBy is just for make it more readable. The logic is groupBy -> agg -> join\n","# We make th count(\"*\") because we want to know EVERY different trips with no nulls in all columns. Depending on what we want, the logic may change at this point."]},{"cell_type":"markdown","metadata":{"id":"-um1G1zKxYkA"},"source":["### Question 2"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4409,"status":"ok","timestamp":1675880395438,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"SbjPIK6cG8t8","outputId":"4c003f62-7a96-492b-b6d7-a1abf9075010"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+----------+\n","|hour_of_day|totalTrips|\n","+-----------+----------+\n","| 16| 22121|\n","| 17| 21598|\n","| 19| 20884|\n","+-----------+----------+\n","only showing top 3 rows\n","\n"]}],"source":["\"\"\"\n","Question 2: What are the peak hours for taxi?\n","\"\"\"\n","\n","\"\"\"\n","This is similar to the previous exercise. The only difference will be we have to group by hour this time. As we don't have the hour info directly in our DF, we have\n","to process the tpep_pickup_datetime column first. The rest of the logic will be the same.\n","\"\"\"\n","\n","pickupsByHourDF = taxiDF \\\n"," .withColumn(\"hour_of_day\", hour(col(\"tpep_pickup_datetime\"))) \\\n"," .groupBy(\"hour_of_day\") \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByHourDF.show(3)"]},{"cell_type":"markdown","metadata":{"id":"Hn_hf8VOxYkB"},"source":["### Question 3"]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2257,"status":"ok","timestamp":1675880397690,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"q58mLZ1zG8t8","outputId":"85ae766b-88e3-4203-c476-4a190f074861"},"outputs":[{"name":"stdout","output_type":"stream","text":["+------+---------+------------------+------------------+---+----+\n","| count|threshold| mean| stddev|min| max|\n","+------+---------+------------------+------------------+---+----+\n","|331893| 30|2.7179894423805155|3.4851522248851214|0.0|66.0|\n","+------+---------+------------------+------------------+---+----+\n","\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+------+\n","|VendorID|tpep_pickup_datetime|tpep_dropoff_datetime|passenger_count|trip_distance|RatecodeID|store_and_fwd_flag|PULocationID|DOLocationID|payment_type|fare_amount|extra|mta_tax|tip_amount|tolls_amount|improvement_surcharge|total_amount|isLong|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+------+\n","| 2| 2018-01-24 23:02:56| 2018-01-24 23:10:58| 1| 2.02| 1| N| 48| 107| 2| 8.5| 0.5| 0.5| 0.0| 0.0| 0.3| 9.8| false|\n","| 2| 2018-01-24 23:57:13| 2018-01-25 00:21:17| 1| 10.13| 1| N| 79| 244| 2| 28.5| 0.5| 0.5| 0.0| 0.0| 0.3| 29.8| false|\n","+--------+--------------------+---------------------+---------------+-------------+----------+------------------+------------+------------+------------+-----------+-----+-------+----------+------------+---------------------+------------+------+\n","only showing top 2 rows\n","\n","+------+------+\n","|isLong| count|\n","+------+------+\n","| true| 83|\n","| false|331810|\n","+------+------+\n","\n"]}],"source":["\"\"\"\n","Question 3: How are the trips distributed by length? Show stats like mean, max, min, etc.\n","Then get the total trips for less/more than 30 km. Why are people taking the cab? For long or short trips?\n","You can also try the same with different distances. Which is the expected value for threshold is we want to obtain more or less the same trips in long/short counting?\n","\"\"\"\n","\n","\"\"\"\n","In this case we want to obtain data about one of the attributes of the df, the distance (trip_distance). \n","So we will make a select of that particular column and then apply the corresponding aggregations (count, average, etc).\n","\"\"\"\n","\n","# Select stats for taxiDF\n","tripDistanceDF = taxiDF.select(col(\"trip_distance\").alias(\"distance\"))\n","tripDistanceStatsDF = tripDistanceDF.select(\n"," count(\"*\").alias(\"count\"),\n"," lit(30).alias(\"threshold\"),\n"," mean(\"distance\").alias(\"mean\"),\n"," stddev(\"distance\").alias(\"stddev\"),\n"," min(\"distance\").alias(\"min\"),\n"," max(\"distance\").alias(\"max\"))\n","tripDistanceStatsDF.show(3)\n","\n","\"\"\"\n","In the second part we want to group by long vs. short trips. But we don't have this data in the DF, so we will first add a flag, \n","to determine if it is long/short. And then we can group by that flag.\n","\"\"\"\n","# We will add a isLong column (flag) with the true/false for long/short rides\n","tripsWithLengthDF = taxiDF.withColumn(\"isLong\", col(\"trip_distance\") >= 30)\n","tripsWithLengthDF.show(2)\n","\n","# As we want to know how many long/short trip are we have to groupBy islong and count()\n","tripsByLengthDF = tripsWithLengthDF.groupBy(\"isLong\").count()\n","tripsByLengthDF.show()"]},{"cell_type":"markdown","metadata":{"id":"ty8IZ-C6xYkC"},"source":["### Question 4"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5897,"status":"ok","timestamp":1675880403579,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"wtgxAuATG8t9","outputId":"d90a2fdd-1cee-48d6-e218-1586e414fd6d"},"outputs":[{"name":"stdout","output_type":"stream","text":["+-----------+------+----------+\n","|hour_of_day|isLong|totalTrips|\n","+-----------+------+----------+\n","| 19| true| 10|\n","| 17| true| 9|\n","| 7| true| 6|\n","| 6| true| 5|\n","| 12| true| 5|\n","| 14| true| 5|\n","| 11| true| 5|\n","| 21| true| 5|\n","| 5| true| 4|\n","| 18| true| 4|\n","| 10| true| 3|\n","| 23| true| 3|\n","| 13| true| 3|\n","| 20| true| 3|\n","| 9| true| 3|\n","| 15| true| 2|\n","| 8| true| 2|\n","| 16| true| 2|\n","| 0| true| 2|\n","| 1| true| 1|\n","+-----------+------+----------+\n","only showing top 20 rows\n","\n","+-----------+------+----------+\n","|hour_of_day|isLong|totalTrips|\n","+-----------+------+----------+\n","| 16| false| 22119|\n","| 17| false| 21589|\n","| 19| false| 20874|\n","| 18| false| 20314|\n","| 20| false| 19525|\n","| 6| false| 18862|\n","| 15| false| 18662|\n","| 13| false| 17840|\n","| 12| false| 17478|\n","| 7| false| 16834|\n","| 14| false| 16155|\n","| 11| false| 16077|\n","| 10| false| 15998|\n","| 9| false| 15561|\n","| 5| false| 15441|\n","| 8| false| 15346|\n","| 21| false| 14647|\n","| 4| false| 8600|\n","| 22| false| 7049|\n","| 23| false| 3975|\n","+-----------+------+----------+\n","only showing top 20 rows\n","\n"]}],"source":["\"\"\"\n","Question 4: What are the peak hours for long/short trips?\n","\"\"\"\n","\n","\"\"\"\n","In this exercise the logic is something like a mix of the two previous ones. On the one hand we want to group by long/short trips (the previous flag), \n","and on the other hand by hours. So we will use the DF that we have created previously with the flag (e.g. 3), modify the time column as we have done in e.g. 2; \n","and then we can group by (hours, long/short). Note that in this case the groupBy will be done by both columns.\n","\"\"\"\n","\n","# We will use the DF created before with isLong flag. \n","# Then, we'll groupBy either islong and hour columns and make a count.\n","\n","pickupsByHourByLengthDF = tripsWithLengthDF \\\n"," .withColumn(\"hour_of_day\", hour(col(\"tpep_pickup_datetime\"))) \\\n"," .groupBy(\"hour_of_day\", \"isLong\") \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","pickupsByHourByLengthDF.filter(col(\"isLong\") == True).show()\n","pickupsByHourByLengthDF.filter(col(\"isLong\") == False).show()"]},{"cell_type":"markdown","metadata":{"id":"ChmRlWgIxYkD"},"source":["### Question 5"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4559,"status":"ok","timestamp":1675880408136,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"Tuag3XtIG8t9","outputId":"1f1ba25b-659e-4eff-8b4b-daa507f89ff3"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+--------------------+\n","|totalTrips| Pickup_Zone|\n","+----------+--------------------+\n","| 15945|Upper East Side S...|\n","| 15255| Midtown Center|\n","| 13767|Upper East Side N...|\n","| 13715| Midtown East|\n","| 11702| Murray Hill|\n","| 11488| Union Sq|\n","| 11455|Times Sq/Theatre ...|\n","| 10319|Penn Station/Madi...|\n","| 10091| Clinton East|\n","| 9845| Midtown North|\n","+----------+--------------------+\n","only showing top 10 rows\n","\n","+----------+--------------------+\n","|totalTrips| Dropoff_Zone|\n","+----------+--------------------+\n","| 15099| Midtown Center|\n","| 14261|Upper East Side N...|\n","| 13754|Upper East Side S...|\n","| 11239| Murray Hill|\n","| 11090| Midtown East|\n","| 10054|Times Sq/Theatre ...|\n","| 9929| Union Sq|\n","| 8666| Lincoln Square East|\n","| 8594| Midtown North|\n","| 8258| Clinton East|\n","+----------+--------------------+\n","only showing top 10 rows\n","\n","+----------+--------------------+--------------------+\n","|totalTrips| Pickup_Zone| Dropoff_Zone|\n","+----------+--------------------+--------------------+\n","| 5561| NV| NV|\n","| 2425|Upper East Side S...|Upper East Side N...|\n","| 1962|Upper East Side N...|Upper East Side S...|\n","| 1944|Upper East Side N...|Upper East Side N...|\n","| 1928|Upper East Side S...|Upper East Side S...|\n","| 1052|Upper East Side S...| Midtown Center|\n","| 1012|Upper East Side S...| Midtown East|\n","| 987| Midtown Center|Upper East Side S...|\n","| 965|Upper West Side S...|Upper West Side N...|\n","| 882| Midtown Center| Midtown Center|\n","+----------+--------------------+--------------------+\n","only showing top 10 rows\n","\n"]}],"source":["\"\"\"\n","Question 5: What are the top 3 pickup/dropoff zones for long/short trips?\n","\"\"\"\n","\n","\"\"\"\n","Again, we are going to use the DF we created with the long/short flag. But in this case we also want to group by zones. \n","Similar to what we have seen in exercise 1, we can see it as PU/Do zones separately, or at the same time.\n","\"\"\"\n","\n","\"\"\"\n","Let's see the first approach (separately).\n","\"\"\"\n","# We will use again the DF with isLong info.\n","# If we understand the question as PU and DO popularity seprated, we will have two DFs, one per each.\n","\n","PUPopularDF = tripsWithLengthDF.groupBy(\"PULocationID\").agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .join(taxiZonesDF, col(\"PULocationID\") == col(\"LocationID\")) \\\n"," .withColumnRenamed(\"Zone\", \"Pickup_Zone\") \\\n"," .drop(\"LocationID\", \"Borough\", \"service_zone\", \"PULocationID\") \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","PUPopularDF.show(10)\n","\n","DOPopularDF = tripsWithLengthDF.groupBy(\"DOLocationID\").agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .join(taxiZonesDF, col(\"DOLocationID\") == col(\"LocationID\")) \\\n"," .withColumnRenamed(\"Zone\", \"Dropoff_Zone\") \\\n"," .drop(\"LocationID\", \"Borough\", \"service_zone\") \\\n"," .drop(\"DOLocationID\") \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","DOPopularDF.show(10)\n","\n","# If we want just the three first Rows we can also do a .limit(3) instead of .show()\n","\n","\"\"\"\n","For the second approach, we have to do the groupBy for the two columns and two joins, one for PU and one for DO.\n","\"\"\"\n","\n","PUandDOPopularDF = tripsWithLengthDF.groupBy(\"PULocationID\", \"DOLocationID\").agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .join(taxiZonesDF, col(\"PULocationID\") == col(\"LocationID\")) \\\n"," .withColumnRenamed(\"Zone\", \"Pickup_Zone\") \\\n"," .drop(\"LocationID\", \"Borough\", \"service_zone\") \\\n"," .join(taxiZonesDF, col(\"DOLocationID\") == col(\"LocationID\")) \\\n"," .withColumnRenamed(\"Zone\", \"Dropoff_Zone\") \\\n"," .drop(\"LocationID\", \"Borough\", \"service_zone\") \\\n"," .drop(\"PULocationID\", \"DOLocationID\") \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","PUandDOPopularDF.show(10)"]},{"cell_type":"markdown","metadata":{"id":"vEGDhOIpxYkE"},"source":["### Question 6"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1978,"status":"ok","timestamp":1675880410105,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"fnkGVElvG8t9","outputId":"0c576cf8-3867-4816-e13c-924b000f5ba7"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+\n","|RatecodeID|\n","+----------+\n","| 1|\n","| 6|\n","| 3|\n","| 5|\n","| 4|\n","| 2|\n","| 99|\n","+----------+\n","\n","+----------+----------+\n","|RatecodeID|totalTrips|\n","+----------+----------+\n","| 1| 324387|\n","| 2| 5878|\n","| 5| 895|\n","| 3| 530|\n","| 4| 193|\n","| 99| 7|\n","| 6| 3|\n","+----------+----------+\n","\n"]}],"source":["\"\"\"\n","Question 6: How are people paying for the ride, on long/short trips? Hint: the information about how good is the payment is in RatecodeID column.\n","\"\"\"\n","\n","\"\"\"\n","We can first inspect the RatecodeID column to see what information it provides. And then we simply do a .groupBy() + .agg(count()) as usual.\n","\"\"\"\n","\n","# The RatecodeID columns has the info about how good is the payment.\n","taxiDF.select(\"RatecodeID\").distinct().show()\n","\n","ratecodeDistributionDF = taxiDF \\\n"," .groupBy(col(\"RatecodeID\")).agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .orderBy(col(\"totalTrips\").desc())\n","\n","ratecodeDistributionDF.show()"]},{"cell_type":"markdown","metadata":{"id":"tLf0Rms-xYkF"},"source":["### Question 7"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6861,"status":"ok","timestamp":1675880416965,"user":{"displayName":"Pablo Pons","userId":"14943288678703205331"},"user_tz":-60},"id":"UwPTJgO7xYkG","outputId":"84c2bbed-1d56-455b-9252-649360a0e016"},"outputs":[{"name":"stdout","output_type":"stream","text":["+----------+----------+----------+\n","|pickup_day|RatecodeID|totalTrips|\n","+----------+----------+----------+\n","|2018-01-24| 1| 10760|\n","|2018-01-24| 2| 174|\n","|2018-01-24| 5| 80|\n","|2018-01-24| 3| 4|\n","|2018-01-24| 4| 9|\n","|2018-01-24| 6| 1|\n","|2018-01-25| 99| 7|\n","|2018-01-25| 3| 526|\n","|2018-01-25| 5| 815|\n","|2018-01-25| 2| 5704|\n","|2018-01-25| 1| 313627|\n","|2018-01-25| 4| 184|\n","|2018-01-25| 6| 2|\n","+----------+----------+----------+\n","\n","+----------+----------+------------------+\n","|pickup_day|totalTrips| avgRate|\n","+----------+----------+------------------+\n","|2018-01-25| 320865|17.142857142857142|\n","|2018-01-24| 11028| 3.5|\n","+----------+----------+------------------+\n","\n","+-----------+----------+----------+\n","|pickup_hour|RatecodeID|totalTrips|\n","+-----------+----------+----------+\n","| 0| 1| 2514|\n","| 0| 5| 20|\n","| 0| 2| 2|\n","| 0| 3| 1|\n","| 0| 4| 1|\n","| 1| 2| 15|\n","| 1| 5| 19|\n","| 1| 1| 1572|\n","| 1| 4| 1|\n","| 1| 3| 3|\n","| 2| 3| 11|\n","| 2| 5| 17|\n","| 2| 1| 1498|\n","| 2| 4| 1|\n","| 2| 2| 59|\n","| 3| 1| 2894|\n","| 3| 3| 27|\n","| 3| 2| 200|\n","| 3| 5| 12|\n","| 4| 3| 23|\n","+-----------+----------+----------+\n","only showing top 20 rows\n","\n","+-----------+----------+------------------+\n","|pickup_hour|totalTrips| avgRate|\n","+-----------+----------+------------------+\n","| 0| 2538| 3.0|\n","| 1| 1610| 3.0|\n","| 2| 1586| 3.0|\n","| 3| 3133| 2.75|\n","| 4| 8600| 3.0|\n","| 5| 15445| 3.0|\n","| 6| 18867| 3.0|\n","| 7| 16840| 3.0|\n","| 8| 15348| 3.0|\n","| 9| 15564| 19.0|\n","| 10| 16001| 19.0|\n","| 11| 16082|17.142857142857142|\n","| 12| 17483| 3.0|\n","| 13| 17843| 3.5|\n","| 14| 16160| 3.0|\n","| 15| 18664| 3.0|\n","| 16| 22121| 3.0|\n","| 17| 21598| 19.0|\n","| 18| 20318| 3.0|\n","| 19| 20884| 19.0|\n","+-----------+----------+------------------+\n","only showing top 20 rows\n","\n"]}],"source":["\"\"\"\n","Question 7: How is the payment type (RatecodeId) evolving with time (in days)? Hint: use the column with pickup time info.\n","Get the same info but with avg of ratecode and total trips per day.\n","\"\"\"\n","\n","\"\"\"\n","For the day we will use tpep_pickup_datetime column. We have to remove the time info from it in order to do the groupBy, \n","so we will do a to_date.\n","\"\"\"\n","\n","# We have to group by pickup time and ratecode this time.\n","ratecodeEvolution = taxiDF \\\n"," .withColumn(\"pickup_day\", to_date(col(\"tpep_pickup_datetime\"))) \\\n"," .groupBy(col(\"pickup_day\"), col(\"RatecodeID\")) \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .orderBy(col(\"pickup_day\"))\n","\n","ratecodeEvolution.show()\n","\n","# Now we can get the avg ratecode per day.\n","ratecodeEvolutionAvg = ratecodeEvolution.groupBy(\"pickup_day\").agg(sum(\"totalTrips\").alias(\"totalTrips\"), avg(\"RatecodeID\").alias(\"avgRate\"))\n","\n","ratecodeEvolutionAvg.show()\n","\n","\n","\"\"\"\n","We can do the same for hours (just fyi, not in question). First we'll do the same as in exercise 2. And then, we'll do the average.\n","\"\"\"\n","# And the same for hours\n","ratecodeEvolutionPerHour = taxiDF \\\n"," .withColumn(\"hour_of_day\", hour(col(\"tpep_pickup_datetime\"))) \\\n"," .groupBy(col(\"hour_of_day\").alias(\"pickup_hour\"), col(\"RatecodeID\")) \\\n"," .agg(count(\"*\").alias(\"totalTrips\")) \\\n"," .orderBy(col(\"pickup_hour\"))\n","\n","ratecodeEvolutionPerHour.show()\n","\n","# Now we agg in the same way to obtain avg rate and total trips.\n","ratecodeEvolutionPerHourAvg = ratecodeEvolutionPerHour.groupBy(\"pickup_hour\") \\\n"," .agg(sum(\"totalTrips\").alias(\"totalTrips\"), avg(\"RatecodeID\").alias(\"avgRate\")).orderBy(\"pickup_hour\")\n","\n","ratecodeEvolutionPerHourAvg.show()"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.0"},"orig_nbformat":4,"vscode":{"interpreter":{"hash":"ff1af5cda0bea4fe5c4ebc1f94ab9f13d8998f98d08e16d8aba48673b9d00116"}}},"nbformat":4,"nbformat_minor":0} diff --git a/PROFESORES/PYSPARK/spark-scala/.bsp/sbt.json b/PROFESORES/PYSPARK/spark-scala/.bsp/sbt.json new file mode 100644 index 000000000..031da5b8e --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.bsp/sbt.json @@ -0,0 +1 @@ +{"name":"sbt","version":"1.9.7","bspVersion":"2.1.0-M1","languages":["scala"],"argv":["C:\\Program Files\\Java\\jdk-11.0.17/bin/java","-Xms100m","-Xmx100m","-classpath","C:/Users/papon/AppData/Roaming/JetBrains/IdeaIC2021.2/plugins/Scala/launcher/sbt-launch.jar","xsbt.boot.Boot","-bsp","--sbt-launch-jar=C:/Users/papon/AppData/Roaming/JetBrains/IdeaIC2021.2/plugins/Scala/launcher/sbt-launch.jar"]} \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/.gitignore b/PROFESORES/PYSPARK/spark-scala/.idea/.gitignore new file mode 100644 index 000000000..26d33521a --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/codeStyles/codeStyleConfig.xml b/PROFESORES/PYSPARK/spark-scala/.idea/codeStyles/codeStyleConfig.xml new file mode 100644 index 000000000..a55e7a179 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/codeStyles/codeStyleConfig.xml @@ -0,0 +1,5 @@ + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_clearspring_analytics_stream_2_9_6_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_clearspring_analytics_stream_2_9_6_jar.xml new file mode 100644 index 000000000..b14323d31 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_clearspring_analytics_stream_2_9_6_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_kryo_shaded_4_0_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_kryo_shaded_4_0_2_jar.xml new file mode 100644 index 000000000..00abaebb1 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_kryo_shaded_4_0_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_minlog_1_3_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_minlog_1_3_0_jar.xml new file mode 100644 index 000000000..cf830b382 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_esotericsoftware_minlog_1_3_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_annotations_2_12_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_annotations_2_12_3_jar.xml new file mode 100644 index 000000000..c2df276d6 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_annotations_2_12_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_core_2_12_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_core_2_12_3_jar.xml new file mode 100644 index 000000000..3412ee0f8 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_core_2_12_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_databind_2_12_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_databind_2_12_3_jar.xml new file mode 100644 index 000000000..b0d6ad839 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_core_jackson_databind_2_12_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_module_jackson_module_scala_2_12_2_12_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_module_jackson_module_scala_2_12_2_12_3_jar.xml new file mode 100644 index 000000000..5d6d6b380 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_fasterxml_jackson_module_jackson_module_scala_2_12_2_12_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_github_luben_zstd_jni_1_5_0_4_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_github_luben_zstd_jni_1_5_0_4_jar.xml new file mode 100644 index 000000000..8ade4a8cb --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_github_luben_zstd_jni_1_5_0_4_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_code_findbugs_jsr305_3_0_2_jar.xml 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000000000..0ee3df407 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_crypto_tink_tink_1_6_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_flatbuffers_flatbuffers_java_1_9_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_flatbuffers_flatbuffers_java_1_9_0_jar.xml new file mode 100644 index 000000000..465341cdf --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_flatbuffers_flatbuffers_java_1_9_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_guava_guava_16_0_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_guava_guava_16_0_1_jar.xml new file mode 100644 index 000000000..557d67aae --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_guava_guava_16_0_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_protobuf_protobuf_java_3_14_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_protobuf_protobuf_java_3_14_0_jar.xml new file mode 100644 index 000000000..bb0da0bed --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_google_protobuf_protobuf_java_3_14_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_ning_compress_lzf_1_0_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_ning_compress_lzf_1_0_3_jar.xml new file mode 100644 index 000000000..246e66b5a --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_ning_compress_lzf_1_0_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_thoughtworks_paranamer_paranamer_2_8_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_thoughtworks_paranamer_paranamer_2_8_jar.xml new file mode 100644 index 000000000..054a5c9ec --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_thoughtworks_paranamer_paranamer_2_8_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_2_12_0_10_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_2_12_0_10_0_jar.xml new file mode 100644 index 000000000..cb79665c4 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_2_12_0_10_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_java_0_10_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_java_0_10_0_jar.xml new file mode 100644 index 000000000..a2ea971d9 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_twitter_chill_java_0_10_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_univocity_univocity_parsers_2_9_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_univocity_univocity_parsers_2_9_1_jar.xml new file mode 100644 index 000000000..6af85efed --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__com_univocity_univocity_parsers_2_9_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_codec_commons_codec_1_15_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_codec_commons_codec_1_15_jar.xml new file mode 100644 index 000000000..1cd58a373 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_codec_commons_codec_1_15_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_collections_commons_collections_3_2_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_collections_commons_collections_3_2_2_jar.xml new file mode 100644 index 000000000..db1085157 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_collections_commons_collections_3_2_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_io_commons_io_2_8_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_io_commons_io_2_8_0_jar.xml new file mode 100644 index 000000000..645f33022 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_io_commons_io_2_8_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_lang_commons_lang_2_6_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_lang_commons_lang_2_6_jar.xml new file mode 100644 index 000000000..985857d94 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_lang_commons_lang_2_6_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_logging_commons_logging_1_1_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_logging_commons_logging_1_1_3_jar.xml new file mode 100644 index 000000000..732c92e97 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_logging_commons_logging_1_1_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_net_commons_net_3_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_net_commons_net_3_1_jar.xml new file mode 100644 index 000000000..3c2144019 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__commons_net_commons_net_3_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_airlift_aircompressor_0_21_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_airlift_aircompressor_0_21_jar.xml new file mode 100644 index 000000000..651bc8d85 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_airlift_aircompressor_0_21_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_core_4_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_core_4_2_0_jar.xml new file mode 100644 index 000000000..76405f056 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_core_4_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_graphite_4_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_graphite_4_2_0_jar.xml new file mode 100644 index 000000000..585f2b321 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_graphite_4_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jmx_4_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jmx_4_2_0_jar.xml new file mode 100644 index 000000000..273ff235c --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jmx_4_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_json_4_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_json_4_2_0_jar.xml new file mode 100644 index 000000000..d127f73fc --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_json_4_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jvm_4_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jvm_4_2_0_jar.xml new file mode 100644 index 000000000..8f6dfe485 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_dropwizard_metrics_metrics_jvm_4_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_all_4_1_68_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_all_4_1_68_Final_jar.xml new file mode 100644 index 000000000..f43fe6823 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_all_4_1_68_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_buffer_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_buffer_4_1_50_Final_jar.xml new file mode 100644 index 000000000..a81061dce --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_buffer_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_codec_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_codec_4_1_50_Final_jar.xml new file mode 100644 index 000000000..d308fbbbd --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_codec_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_common_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_common_4_1_50_Final_jar.xml new file mode 100644 index 000000000..83eb77f80 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_common_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_handler_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_handler_4_1_50_Final_jar.xml new file mode 100644 index 000000000..e671cadc1 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_handler_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_resolver_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_resolver_4_1_50_Final_jar.xml new file mode 100644 index 000000000..664a8afc8 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_resolver_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_4_1_50_Final_jar.xml new file mode 100644 index 000000000..364f4ec8f --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_epoll_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_epoll_4_1_50_Final_jar.xml new file mode 100644 index 000000000..e7ed5495f --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_epoll_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_unix_common_4_1_50_Final_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_unix_common_4_1_50_Final_jar.xml new file mode 100644 index 000000000..a59129125 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__io_netty_netty_transport_native_unix_common_4_1_50_Final_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_annotation_jakarta_annotation_api_1_3_5_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_annotation_jakarta_annotation_api_1_3_5_jar.xml new file mode 100644 index 000000000..bddadc66b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_annotation_jakarta_annotation_api_1_3_5_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_servlet_jakarta_servlet_api_4_0_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_servlet_jakarta_servlet_api_4_0_3_jar.xml new file mode 100644 index 000000000..60a62a85f --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_servlet_jakarta_servlet_api_4_0_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_validation_jakarta_validation_api_2_0_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_validation_jakarta_validation_api_2_0_2_jar.xml new file mode 100644 index 000000000..10c8c0e1d --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_validation_jakarta_validation_api_2_0_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_ws_rs_jakarta_ws_rs_api_2_1_6_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_ws_rs_jakarta_ws_rs_api_2_1_6_jar.xml new file mode 100644 index 000000000..d1cb1df8b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__jakarta_ws_rs_jakarta_ws_rs_api_2_1_6_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_activation_activation_1_1_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_activation_activation_1_1_1_jar.xml new file mode 100644 index 000000000..389226bbd --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_activation_activation_1_1_1_jar.xml @@ -0,0 +1,11 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_annotation_javax_annotation_api_1_3_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_annotation_javax_annotation_api_1_3_2_jar.xml new file mode 100644 index 000000000..de4eb76f9 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_annotation_javax_annotation_api_1_3_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_xml_bind_jaxb_api_2_2_11_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_xml_bind_jaxb_api_2_2_11_jar.xml new file mode 100644 index 000000000..7e0d722ae --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__javax_xml_bind_jaxb_api_2_2_11_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__log4j_log4j_1_2_17_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__log4j_log4j_1_2_17_jar.xml new file mode 100644 index 000000000..4ea4213df --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__log4j_log4j_1_2_17_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_razorvine_pyrolite_4_30_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_razorvine_pyrolite_4_30_jar.xml new file mode 100644 index 000000000..42236f5c0 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_razorvine_pyrolite_4_30_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_sf_py4j_py4j_0_10_9_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_sf_py4j_py4j_0_10_9_3_jar.xml new file mode 100644 index 000000000..56cdd8c26 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__net_sf_py4j_py4j_0_10_9_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_antlr_antlr4_runtime_4_8_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_antlr_antlr4_runtime_4_8_jar.xml new file mode 100644 index 000000000..102ccc2ab --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_antlr_antlr4_runtime_4_8_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_format_2_0_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_format_2_0_0_jar.xml new file mode 100644 index 000000000..cbb4e3049 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_format_2_0_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_memory_core_2_0_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_memory_core_2_0_0_jar.xml new file mode 100644 index 000000000..9815cdce9 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_memory_core_2_0_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_memory_netty_2_0_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_arrow_arrow_memory_netty_2_0_0_jar.xml new file mode 100644 index 000000000..bc9d9f928 --- /dev/null +++ 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+ + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_ipc_1_10_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_ipc_1_10_2_jar.xml new file mode 100644 index 000000000..54e8d1946 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_ipc_1_10_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_mapred_1_10_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_mapred_1_10_2_jar.xml new file mode 100644 index 000000000..41a3df3f7 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_avro_avro_mapred_1_10_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_compress_1_20_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_compress_1_20_jar.xml new file mode 100644 index 000000000..f2c1096a3 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_compress_1_20_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_crypto_1_1_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_crypto_1_1_0_jar.xml new file mode 100644 index 000000000..69583a615 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_crypto_1_1_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_lang3_3_12_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_lang3_3_12_0_jar.xml new file mode 100644 index 000000000..52d528f7b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_lang3_3_12_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_math3_3_4_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_math3_3_4_1_jar.xml new file mode 100644 index 000000000..cc2509562 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_math3_3_4_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_text_1_6_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_text_1_6_jar.xml new file mode 100644 index 000000000..2fbe52f21 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_commons_commons_text_1_6_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_client_2_13_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_client_2_13_0_jar.xml new file mode 100644 index 000000000..a9545d1ca --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_client_2_13_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_framework_2_13_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_framework_2_13_0_jar.xml new file mode 100644 index 000000000..fe73505d2 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_framework_2_13_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_recipes_2_13_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_recipes_2_13_0_jar.xml new file mode 100644 index 000000000..27d931921 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_curator_curator_recipes_2_13_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git 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b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_logging_log4j_log4j_core_2_4_1_jar.xml new file mode 100644 index 000000000..232a49b9c --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_logging_log4j_log4j_core_2_4_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_orc_orc_core_1_6_12_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_orc_orc_core_1_6_12_jar.xml new file mode 100644 index 000000000..7fc33a466 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_orc_orc_core_1_6_12_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_orc_orc_mapreduce_1_6_12_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_orc_orc_mapreduce_1_6_12_jar.xml new file mode 100644 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b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_kvstore_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_launcher_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_launcher_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..3ba94733b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_launcher_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_common_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_common_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..170d9fa0e --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_common_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_shuffle_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_shuffle_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..95608800b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_network_shuffle_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sketch_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sketch_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..d1f2f9670 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sketch_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sql_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sql_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..aa5fd7501 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_sql_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_tags_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_tags_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..5652b3641 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_tags_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_unsafe_2_12_3_2_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_unsafe_2_12_3_2_1_jar.xml new file mode 100644 index 000000000..c7e53d28a --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_spark_spark_unsafe_2_12_3_2_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_xbean_xbean_asm9_shaded_4_20_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_xbean_xbean_asm9_shaded_4_20_jar.xml new file mode 100644 index 000000000..69bd2aefc --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_xbean_xbean_asm9_shaded_4_20_jar.xml @@ -0,0 +1,11 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_yetus_audience_annotations_0_12_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_yetus_audience_annotations_0_12_0_jar.xml new file mode 100644 index 000000000..fb30b5394 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_yetus_audience_annotations_0_12_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_3_6_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_3_6_2_jar.xml new file mode 100644 index 000000000..9752893b5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_3_6_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_jute_3_6_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_jute_3_6_2_jar.xml new file mode 100644 index 000000000..4f20e692b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_apache_zookeeper_zookeeper_jute_3_6_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_commons_compiler_3_0_16_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_commons_compiler_3_0_16_jar.xml new file mode 100644 index 000000000..fb33e3533 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_commons_compiler_3_0_16_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_janino_3_0_16_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_janino_3_0_16_jar.xml new file mode 100644 index 000000000..aea1eb735 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_codehaus_janino_janino_3_0_16_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_fusesource_leveldbjni_leveldbjni_all_1_8_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_fusesource_leveldbjni_leveldbjni_all_1_8_jar.xml new file mode 100644 index 000000000..fa1a9c37b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_fusesource_leveldbjni_leveldbjni_all_1_8_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_aopalliance_repackaged_2_6_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_aopalliance_repackaged_2_6_1_jar.xml new file mode 100644 index 000000000..353c2ff21 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_aopalliance_repackaged_2_6_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_jakarta_inject_2_6_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_jakarta_inject_2_6_1_jar.xml new file mode 100644 index 000000000..b399cd145 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_external_jakarta_inject_2_6_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_api_2_6_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_api_2_6_1_jar.xml new file mode 100644 index 000000000..036ec2900 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_api_2_6_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_locator_2_6_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_locator_2_6_1_jar.xml new file mode 100644 index 000000000..46e488cee --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_locator_2_6_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_utils_2_6_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_utils_2_6_1_jar.xml new file mode 100644 index 000000000..94e3f805d --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_hk2_utils_2_6_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_osgi_resource_locator_1_0_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_osgi_resource_locator_1_0_3_jar.xml new file mode 100644 index 000000000..b856ec706 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_hk2_osgi_resource_locator_1_0_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_2_34_jar.xml new file mode 100644 index 000000000..6bb9ccd41 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_core_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_core_2_34_jar.xml new file mode 100644 index 000000000..36977d634 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_containers_jersey_container_servlet_core_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_client_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_client_2_34_jar.xml new file mode 100644 index 000000000..abd0a8187 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_client_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_common_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_common_2_34_jar.xml new file mode 100644 index 000000000..23542fb82 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_common_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_server_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_server_2_34_jar.xml new file mode 100644 index 000000000..4a0fb5b39 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_core_jersey_server_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_inject_jersey_hk2_2_34_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_inject_jersey_hk2_2_34_jar.xml new file mode 100644 index 000000000..08531f16a --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_glassfish_jersey_inject_jersey_hk2_2_34_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_javassist_javassist_3_25_0_GA_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_javassist_javassist_3_25_0_GA_jar.xml new file mode 100644 index 000000000..062625e24 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_javassist_javassist_3_25_0_GA_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_jetbrains_annotations_17_0_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_jetbrains_annotations_17_0_0_jar.xml new file mode 100644 index 000000000..743d8a804 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_jetbrains_annotations_17_0_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_ast_2_12_3_7_0_M11_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_ast_2_12_3_7_0_M11_jar.xml new file mode 100644 index 000000000..aed5021d9 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_ast_2_12_3_7_0_M11_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_core_2_12_3_7_0_M11_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_core_2_12_3_7_0_M11_jar.xml new file mode 100644 index 000000000..a700e170d --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_core_2_12_3_7_0_M11_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_jackson_2_12_3_7_0_M11_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_jackson_2_12_3_7_0_M11_jar.xml new file mode 100644 index 000000000..027762ad5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_jackson_2_12_3_7_0_M11_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_scalap_2_12_3_7_0_M11_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_scalap_2_12_3_7_0_M11_jar.xml new file mode 100644 index 000000000..a1dcf6e78 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_json4s_json4s_scalap_2_12_3_7_0_M11_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_lz4_lz4_java_1_7_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_lz4_lz4_java_1_7_1_jar.xml new file mode 100644 index 000000000..0f4256232 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_lz4_lz4_java_1_7_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_objenesis_objenesis_2_5_1_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_objenesis_objenesis_2_5_1_jar.xml new file mode 100644 index 000000000..dbd9b52e2 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_objenesis_objenesis_2_5_1_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_postgresql_postgresql_42_2_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_postgresql_postgresql_42_2_2_jar.xml new file mode 100644 index 000000000..41857541d --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_postgresql_postgresql_42_2_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_RoaringBitmap_0_9_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_RoaringBitmap_0_9_0_jar.xml new file mode 100644 index 000000000..d7e057d8b --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_RoaringBitmap_0_9_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_shims_0_9_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_shims_0_9_0_jar.xml new file mode 100644 index 000000000..a5734d479 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_roaringbitmap_shims_0_9_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_rocksdb_rocksdbjni_6_20_3_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_rocksdb_rocksdbjni_6_20_3_jar.xml new file mode 100644 index 000000000..2771a66d7 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_rocksdb_rocksdbjni_6_20_3_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_parser_combinators_2_12_1_1_2_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_parser_combinators_2_12_1_1_2_jar.xml new file mode 100644 index 000000000..7b5dd4798 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_parser_combinators_2_12_1_1_2_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_xml_2_12_1_2_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_xml_2_12_1_2_0_jar.xml new file mode 100644 index 000000000..91601d537 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_modules_scala_xml_2_12_1_2_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_scala_library_2_12_10_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_scala_library_2_12_10_jar.xml new file mode 100644 index 000000000..40ffa1d67 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_scala_library_2_12_10_jar.xml @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_scala_lang_scala_reflect_2_12_10_jar.xml 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+ + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_spark_project_spark_unused_1_0_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_spark_project_spark_unused_1_0_0_jar.xml new file mode 100644 index 000000000..bdfccb510 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_spark_project_spark_unused_1_0_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_threeten_threeten_extra_1_5_0_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_threeten_threeten_extra_1_5_0_jar.xml new file mode 100644 index 000000000..4d745fc8f --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_threeten_threeten_extra_1_5_0_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_tukaani_xz_1_8_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_tukaani_xz_1_8_jar.xml new file mode 100644 index 000000000..fb83dda75 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_tukaani_xz_1_8_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_xerial_snappy_snappy_java_1_1_8_4_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_xerial_snappy_snappy_java_1_1_8_4_jar.xml new file mode 100644 index 000000000..bf9c38326 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__org_xerial_snappy_snappy_java_1_1_8_4_jar.xml @@ -0,0 +1,13 @@ + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__oro_oro_2_0_8_jar.xml b/PROFESORES/PYSPARK/spark-scala/.idea/libraries/sbt__oro_oro_2_0_8_jar.xml new file 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b/PROFESORES/PYSPARK/spark-scala/.idea/modules/spark-scala-build.iml @@ -0,0 +1,114 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/modules/spark-scala.iml b/PROFESORES/PYSPARK/spark-scala/.idea/modules/spark-scala.iml new file mode 100644 index 000000000..2f233ef58 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/modules/spark-scala.iml @@ -0,0 +1,152 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/runConfigurations.xml b/PROFESORES/PYSPARK/spark-scala/.idea/runConfigurations.xml new file mode 100644 index 000000000..797acea53 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/runConfigurations.xml @@ -0,0 +1,10 @@ + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/sbt.xml b/PROFESORES/PYSPARK/spark-scala/.idea/sbt.xml new file mode 100644 index 000000000..9e734e2ba --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/sbt.xml @@ -0,0 +1,17 @@ + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/.idea/scala_compiler.xml b/PROFESORES/PYSPARK/spark-scala/.idea/scala_compiler.xml new file mode 100644 index 000000000..59bacb804 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/.idea/scala_compiler.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/README.md b/PROFESORES/PYSPARK/spark-scala/README.md new file mode 100644 index 000000000..1d0c3d446 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/README.md @@ -0,0 +1,25 @@ +# EDEM MDA Spark Scala + +This repository contains the code in order to be able to do the class exercises also in Scala. + +## How to install + +- install [Docker](https://docker.com) +- either clone the repo or download as zip +- open with IntelliJ as an SBT project +- the datasets are available in https://github.com/paponsro/spark_edem_2324/tree/master/dataset + + +### A Note For Windows users: Adding Winutils + +By default, Spark will be unable to write files using the local Spark executor. To write files, you will need to install the Windows Hadoop binaries, aka [winutils](https://github.com/cdarlint/winutils). You can take the latest binary (Hadoop 3.2 as of June 2022), or use Hadoop 2.7 as a fallback. + +After you download winutils.exe, create a directory anywhere (e.g. `C:\\winutils`), then create a `bin` directory under that, then place the winutils executable there. + +You will also need to set the `HADOOP_HOME` environment variable to your directory where you added `bin\winutils.exe`. In the example above, that would be `C:\\winutils`. + +An alternative to setting the environment variable is to add this line at the beginning of every Spark application we write: + +```scala +System.setProperty("hadoop.home.dir","C:\\hadoop") // replace C:\\hadoop with your actual directory +``` \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/build.sbt b/PROFESORES/PYSPARK/spark-scala/build.sbt new file mode 100644 index 000000000..73abbdc7e --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/build.sbt @@ -0,0 +1,25 @@ +name := "spark-scala" + +version := "0.1" + +scalaVersion := "2.12.10" + +val sparkVersion = "3.2.1" +val postgresVersion = "42.2.2" + +resolvers ++= Seq( + "bintray-spark-packages" at "https://dl.bintray.com/spark-packages/maven", + "Typesafe Simple Repository" at "https://repo.typesafe.com/typesafe/simple/maven-releases", + "MavenRepository" at "https://mvnrepository.com" +) + + +libraryDependencies ++= Seq( + "org.apache.spark" %% "spark-core" 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/exportedProducts/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/exportedProducts/_global/streams/export new file mode 100644 index 000000000..2d3c6fe05 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/exportedProducts/_global/streams/export @@ -0,0 +1 @@ +C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/externalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/externalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..a89fbc8da --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/externalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/incOptions/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/incOptions/_global/streams/out new file mode 100644 index 000000000..d2b471e9e --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/incOptions/_global/streams/out @@ -0,0 +1,5 @@ +[debug] Created transactional ClassFileManager with tempDir = C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes.bak +[debug] About to delete class files: +[debug] We backup class files: +[debug] Created transactional ClassFileManager with tempDir = C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes.bak +[debug] Removing the temporary directory used for backing up class files: C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes.bak diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/internalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/internalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/internalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/internalDependencyClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/internalDependencyClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/managedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/managedClasspath/_global/streams/export new file mode 100644 index 000000000..a89fbc8da --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/managedClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/scalacOptions/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/scalacOptions/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedJars/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedJars/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/compile/unmanagedJars/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/dependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/dependencyClasspath/_global/streams/export new file mode 100644 index 000000000..8ed03c369 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/dependencyClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/exportedProducts/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/exportedProducts/_global/streams/export new file mode 100644 index 000000000..2d3c6fe05 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/exportedProducts/_global/streams/export @@ -0,0 +1 @@ +C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/externalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/externalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..5ac6f32f4 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/externalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/fullClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/fullClasspath/_global/streams/export new file mode 100644 index 000000000..8ed03c369 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/fullClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/internalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/internalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..2d3c6fe05 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/internalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ +C:\Users\papon\OneDrive\Documentos\SalvaDatos_Pablo\EDEM\2023_24_sessions\spark-scala\project\target\scala-2.12\sbt-1.0\classes diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/internalDependencyClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/internalDependencyClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/managedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/managedClasspath/_global/streams/export new file mode 100644 index 000000000..5ac6f32f4 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/managedClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedJars/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedJars/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/project/target/streams/runtime/unmanagedJars/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/src/main/resources/dataset/example.csv b/PROFESORES/PYSPARK/spark-scala/src/main/resources/dataset/example.csv new file mode 100644 index 000000000..192148b27 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/src/main/resources/dataset/example.csv @@ -0,0 +1,3 @@ +example,no +ex1,1 +ex2,2 \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/src/main/scala/Example.scala b/PROFESORES/PYSPARK/spark-scala/src/main/scala/Example.scala new file mode 100644 index 000000000..8cca7a0bb --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/src/main/scala/Example.scala @@ -0,0 +1,16 @@ +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.functions._ + +object Example extends App { + + val spark = SparkSession.builder() + .appName("Example") + .config("spark.master", "local") + .getOrCreate() + + val exampleDF = spark.read + .option("inferSchema", "true") + .csv("src/main/resources/dataset/example.csv") + + exampleDF.show() +} diff --git a/PROFESORES/PYSPARK/spark-scala/target/.history3 b/PROFESORES/PYSPARK/spark-scala/target/.history3 new file mode 100644 index 000000000..b4c7b8a42 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/.history3 @@ -0,0 +1 @@ +1704659756149:;set _root_.scala.collection.Seq(historyPath := None,shellPrompt := { _ => "" },SettingKey[_root_.scala.Option[_root_.sbt.File]]("sbtStructureOutputFile") in _root_.sbt.Global := _root_.scala.Some(_root_.sbt.file("C:/Users/papon/AppData/Local/Temp/sbt-structure.xml")),SettingKey[_root_.java.lang.String]("sbtStructureOptions") in _root_.sbt.Global := "download, resolveClassifiers");apply -cp "C:/Users/papon/AppData/Roaming/JetBrains/IdeaIC2021.2/plugins/Scala/repo/org/jetbrains/scala/sbt-structure-extractor_2.12_1.0/2021.1.1/sbt-structure-extractor-2021.1.1.jar" org.jetbrains.sbt.CreateTasks;*/*:dumpStructure 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b/PROFESORES/PYSPARK/spark-scala/target/scala-2.12/update/update_cache_2.12/inputs new file mode 100644 index 000000000..755094028 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/scala-2.12/update/update_cache_2.12/inputs @@ -0,0 +1 @@ +-1553174179 \ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/target/scala-2.12/update/update_cache_2.12/output b/PROFESORES/PYSPARK/spark-scala/target/scala-2.12/update/update_cache_2.12/output new file mode 100644 index 000000000..9f021152c --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/scala-2.12/update/update_cache_2.12/output @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ivyConfiguration/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ivyConfiguration/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ivySbt/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ivySbt/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/moduleSettings/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/moduleSettings/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/projectDescriptors/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/projectDescriptors/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ssExtractDependencies/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ssExtractDependencies/_global/streams/out new file mode 100644 index 000000000..9cbf66671 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/ssExtractDependencies/_global/streams/out @@ -0,0 +1,50 @@ +[error] sbt.librarymanagement.ResolveException: Error downloading org.apache.spark:spark-sql_2.13:3.1.2 +[error] Not found +[error] Not found +[error] not found: C:\Users\papon\.ivy2\localorg.apache.spark\spark-sql_2.13\3.1.2\ivys\ivy.xml +[error] not found: https://repo1.maven.org/maven2/org/apache/spark/spark-sql_2.13/3.1.2/spark-sql_2.13-3.1.2.pom +[error] Error downloading org.apache.spark:spark-core_2.13:3.1.2 +[error] Not found +[error] Not found +[error] not found: C:\Users\papon\.ivy2\localorg.apache.spark\spark-core_2.13\3.1.2\ivys\ivy.xml +[error] not found: https://repo1.maven.org/maven2/org/apache/spark/spark-core_2.13/3.1.2/spark-core_2.13-3.1.2.pom +[error] at lmcoursier.CoursierDependencyResolution.unresolvedWarningOrThrow(CoursierDependencyResolution.scala:344) +[error] at lmcoursier.CoursierDependencyResolution.$anonfun$update$38(CoursierDependencyResolution.scala:313) +[error] at scala.util.Either$LeftProjection.map(Either.scala:573) +[error] at lmcoursier.CoursierDependencyResolution.update(CoursierDependencyResolution.scala:313) +[error] at sbt.librarymanagement.DependencyResolution.update(DependencyResolution.scala:60) +[error] at sbt.internal.LibraryManagement$.resolve$1(LibraryManagement.scala:60) +[error] at sbt.internal.LibraryManagement$.$anonfun$cachedUpdate$12(LibraryManagement.scala:134) +[error] at sbt.util.Tracked$.$anonfun$lastOutput$1(Tracked.scala:74) +[error] at sbt.internal.LibraryManagement$.$anonfun$cachedUpdate$20(LibraryManagement.scala:147) +[error] at scala.util.control.Exception$Catch.apply(Exception.scala:228) +[error] at sbt.internal.LibraryManagement$.$anonfun$cachedUpdate$11(LibraryManagement.scala:147) +[error] at sbt.internal.LibraryManagement$.$anonfun$cachedUpdate$11$adapted(LibraryManagement.scala:128) +[error] at sbt.util.Tracked$.$anonfun$inputChangedW$1(Tracked.scala:220) +[error] at sbt.internal.LibraryManagement$.cachedUpdate(LibraryManagement.scala:161) +[error] at sbt.Classpaths$.$anonfun$updateTask0$1(Defaults.scala:3801) +[error] at scala.Function1.$anonfun$compose$1(Function1.scala:49) +[error] at sbt.internal.util.$tilde$greater.$anonfun$$u2219$1(TypeFunctions.scala:63) +[error] at sbt.std.Transform$$anon$4.work(Transform.scala:69) +[error] at sbt.Execute.$anonfun$submit$2(Execute.scala:283) +[error] at sbt.internal.util.ErrorHandling$.wideConvert(ErrorHandling.scala:24) +[error] at sbt.Execute.work(Execute.scala:292) +[error] at sbt.Execute.$anonfun$submit$1(Execute.scala:283) +[error] at sbt.ConcurrentRestrictions$$anon$4.$anonfun$submitValid$1(ConcurrentRestrictions.scala:265) +[error] at sbt.CompletionService$$anon$2.call(CompletionService.scala:65) +[error] at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264) +[error] at java.base/java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:515) +[error] at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264) +[error] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128) +[error] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628) +[error] at java.base/java.lang.Thread.run(Thread.java:834) +[error] (ssExtractDependencies) sbt.librarymanagement.ResolveException: Error downloading org.apache.spark:spark-sql_2.13:3.1.2 +[error] Not found +[error] Not found +[error] not found: C:\Users\papon\.ivy2\localorg.apache.spark\spark-sql_2.13\3.1.2\ivys\ivy.xml +[error] not found: https://repo1.maven.org/maven2/org/apache/spark/spark-sql_2.13/3.1.2/spark-sql_2.13-3.1.2.pom +[error] Error downloading org.apache.spark:spark-core_2.13:3.1.2 +[error] Not found +[error] Not found +[error] not found: C:\Users\papon\.ivy2\localorg.apache.spark\spark-core_2.13\3.1.2\ivys\ivy.xml +[error] not found: https://repo1.maven.org/maven2/org/apache/spark/spark-core_2.13/3.1.2/spark-core_2.13-3.1.2.pom diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/update/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/update/_global/streams/out new file mode 100644 index 000000000..dad971a5d --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/update/_global/streams/out @@ -0,0 +1,3 @@ +[debug] not up to date. inChanged = true, force = false +[debug] Updating ... +[debug] Done updating diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/out new file mode 100644 index 000000000..57115fa7c --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/out @@ -0,0 +1,3 @@ +[debug] not up to date. inChanged = true, force = false +[debug] Updating ProjectRef(uri("file:/C:/Users/papon/OneDrive/Escritorio/spark-scala/"), "spark-scala")... +[debug] Done updating ProjectRef(uri("file:/C:/Users/papon/OneDrive/Escritorio/spark-scala/"), "spark-scala") diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/update_cache_2.12/inputs b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/update_cache_2.12/inputs new file mode 100644 index 000000000..b7ba7d96e --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/_global/updateClassifiers/_global/streams/update_cache_2.12/inputs @@ -0,0 +1 @@ +-1189860260 \ No 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/compile/managedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/managedClasspath/_global/streams/export new file mode 100644 index 000000000..f5b848ce5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/managedClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/compile/scalacOptions/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/scalacOptions/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedJars/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedJars/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/compile/unmanagedJars/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/externalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/externalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..f5b848ce5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/externalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/managedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/managedClasspath/_global/streams/export new file mode 100644 index 000000000..f5b848ce5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/managedClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedJars/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedJars/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/runtime/unmanagedJars/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/test/externalDependencyClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/test/externalDependencyClasspath/_global/streams/export new file mode 100644 index 000000000..f5b848ce5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/test/externalDependencyClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/test/managedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/test/managedClasspath/_global/streams/export new file mode 100644 index 000000000..f5b848ce5 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/test/managedClasspath/_global/streams/export @@ -0,0 +1 @@ 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diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedClasspath/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedClasspath/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedClasspath/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedClasspath/_global/streams/out b/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedClasspath/_global/streams/out new file mode 100644 index 000000000..e69de29bb diff --git a/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedJars/_global/streams/export b/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedJars/_global/streams/export new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/PROFESORES/PYSPARK/spark-scala/target/streams/test/unmanagedJars/_global/streams/export @@ -0,0 +1 @@ + diff --git a/PROFESORES/PYTHON/Datos/pokemon_data.csv b/PROFESORES/PYTHON/Datos/pokemon_data.csv new file mode 100644 index 000000000..136cd7c01 --- /dev/null +++ b/PROFESORES/PYTHON/Datos/pokemon_data.csv @@ -0,0 +1,801 @@ +#,Name,Type 1,Type 2,HP,Attack,Defense,Sp. Atk,Sp. Def,Speed,Generation,Legendary +1,Bulbasaur,Grass,Poison,45,49,49,65,65,45,1,FALSE +2,Ivysaur,Grass,Poison,60,62,63,80,80,60,1,FALSE +3,Venusaur,Grass,Poison,80,82,83,100,100,80,1,FALSE +3,VenusaurMega Venusaur,Grass,Poison,80,100,123,122,120,80,1,FALSE +4,Charmander,Fire,,39,52,43,60,50,65,1,FALSE +5,Charmeleon,Fire,,58,64,58,80,65,80,1,FALSE +6,Charizard,Fire,Flying,78,84,78,109,85,100,1,FALSE +6,CharizardMega Charizard X,Fire,Dragon,78,130,111,130,85,100,1,FALSE +6,CharizardMega Charizard Y,Fire,Flying,78,104,78,159,115,100,1,FALSE +7,Squirtle,Water,,44,48,65,50,64,43,1,FALSE +8,Wartortle,Water,,59,63,80,65,80,58,1,FALSE +9,Blastoise,Water,,79,83,100,85,105,78,1,FALSE +9,BlastoiseMega Blastoise,Water,,79,103,120,135,115,78,1,FALSE +10,Caterpie,Bug,,45,30,35,20,20,45,1,FALSE +11,Metapod,Bug,,50,20,55,25,25,30,1,FALSE +12,Butterfree,Bug,Flying,60,45,50,90,80,70,1,FALSE +13,Weedle,Bug,Poison,40,35,30,20,20,50,1,FALSE +14,Kakuna,Bug,Poison,45,25,50,25,25,35,1,FALSE +15,Beedrill,Bug,Poison,65,90,40,45,80,75,1,FALSE +15,BeedrillMega Beedrill,Bug,Poison,65,150,40,15,80,145,1,FALSE +16,Pidgey,Normal,Flying,40,45,40,35,35,56,1,FALSE +17,Pidgeotto,Normal,Flying,63,60,55,50,50,71,1,FALSE +18,Pidgeot,Normal,Flying,83,80,75,70,70,101,1,FALSE +18,PidgeotMega Pidgeot,Normal,Flying,83,80,80,135,80,121,1,FALSE +19,Rattata,Normal,,30,56,35,25,35,72,1,FALSE +20,Raticate,Normal,,55,81,60,50,70,97,1,FALSE +21,Spearow,Normal,Flying,40,60,30,31,31,70,1,FALSE +22,Fearow,Normal,Flying,65,90,65,61,61,100,1,FALSE +23,Ekans,Poison,,35,60,44,40,54,55,1,FALSE +24,Arbok,Poison,,60,85,69,65,79,80,1,FALSE +25,Pikachu,Electric,,35,55,40,50,50,90,1,FALSE +26,Raichu,Electric,,60,90,55,90,80,110,1,FALSE +27,Sandshrew,Ground,,50,75,85,20,30,40,1,FALSE +28,Sandslash,Ground,,75,100,110,45,55,65,1,FALSE +29,Nidoran (Female),Poison,,55,47,52,40,40,41,1,FALSE +30,Nidorina,Poison,,70,62,67,55,55,56,1,FALSE +31,Nidoqueen,Poison,Ground,90,92,87,75,85,76,1,FALSE +32,Nidoran (Male),Poison,,46,57,40,40,40,50,1,FALSE +33,Nidorino,Poison,,61,72,57,55,55,65,1,FALSE +34,Nidoking,Poison,Ground,81,102,77,85,75,85,1,FALSE +35,Clefairy,Fairy,,70,45,48,60,65,35,1,FALSE +36,Clefable,Fairy,,95,70,73,95,90,60,1,FALSE +37,Vulpix,Fire,,38,41,40,50,65,65,1,FALSE +38,Ninetales,Fire,,73,76,75,81,100,100,1,FALSE +39,Jigglypuff,Normal,Fairy,115,45,20,45,25,20,1,FALSE +40,Wigglytuff,Normal,Fairy,140,70,45,85,50,45,1,FALSE +41,Zubat,Poison,Flying,40,45,35,30,40,55,1,FALSE +42,Golbat,Poison,Flying,75,80,70,65,75,90,1,FALSE +43,Oddish,Grass,Poison,45,50,55,75,65,30,1,FALSE +44,Gloom,Grass,Poison,60,65,70,85,75,40,1,FALSE +45,Vileplume,Grass,Poison,75,80,85,110,90,50,1,FALSE +46,Paras,Bug,Grass,35,70,55,45,55,25,1,FALSE +47,Parasect,Bug,Grass,60,95,80,60,80,30,1,FALSE +48,Venonat,Bug,Poison,60,55,50,40,55,45,1,FALSE +49,Venomoth,Bug,Poison,70,65,60,90,75,90,1,FALSE +50,Diglett,Ground,,10,55,25,35,45,95,1,FALSE +51,Dugtrio,Ground,,35,80,50,50,70,120,1,FALSE +52,Meowth,Normal,,40,45,35,40,40,90,1,FALSE +53,Persian,Normal,,65,70,60,65,65,115,1,FALSE +54,Psyduck,Water,,50,52,48,65,50,55,1,FALSE +55,Golduck,Water,,80,82,78,95,80,85,1,FALSE +56,Mankey,Fighting,,40,80,35,35,45,70,1,FALSE +57,Primeape,Fighting,,65,105,60,60,70,95,1,FALSE +58,Growlithe,Fire,,55,70,45,70,50,60,1,FALSE +59,Arcanine,Fire,,90,110,80,100,80,95,1,FALSE +60,Poliwag,Water,,40,50,40,40,40,90,1,FALSE +61,Poliwhirl,Water,,65,65,65,50,50,90,1,FALSE +62,Poliwrath,Water,Fighting,90,95,95,70,90,70,1,FALSE +63,Abra,Psychic,,25,20,15,105,55,90,1,FALSE +64,Kadabra,Psychic,,40,35,30,120,70,105,1,FALSE +65,Alakazam,Psychic,,55,50,45,135,95,120,1,FALSE +65,AlakazamMega Alakazam,Psychic,,55,50,65,175,95,150,1,FALSE +66,Machop,Fighting,,70,80,50,35,35,35,1,FALSE +67,Machoke,Fighting,,80,100,70,50,60,45,1,FALSE +68,Machamp,Fighting,,90,130,80,65,85,55,1,FALSE +69,Bellsprout,Grass,Poison,50,75,35,70,30,40,1,FALSE +70,Weepinbell,Grass,Poison,65,90,50,85,45,55,1,FALSE +71,Victreebel,Grass,Poison,80,105,65,100,70,70,1,FALSE +72,Tentacool,Water,Poison,40,40,35,50,100,70,1,FALSE +73,Tentacruel,Water,Poison,80,70,65,80,120,100,1,FALSE +74,Geodude,Rock,Ground,40,80,100,30,30,20,1,FALSE +75,Graveler,Rock,Ground,55,95,115,45,45,35,1,FALSE +76,Golem,Rock,Ground,80,120,130,55,65,45,1,FALSE +77,Ponyta,Fire,,50,85,55,65,65,90,1,FALSE +78,Rapidash,Fire,,65,100,70,80,80,105,1,FALSE +79,Slowpoke,Water,Psychic,90,65,65,40,40,15,1,FALSE +80,Slowbro,Water,Psychic,95,75,110,100,80,30,1,FALSE +80,SlowbroMega Slowbro,Water,Psychic,95,75,180,130,80,30,1,FALSE +81,Magnemite,Electric,Steel,25,35,70,95,55,45,1,FALSE +82,Magneton,Electric,Steel,50,60,95,120,70,70,1,FALSE +83,Farfetch'd,Normal,Flying,52,65,55,58,62,60,1,FALSE +84,Doduo,Normal,Flying,35,85,45,35,35,75,1,FALSE +85,Dodrio,Normal,Flying,60,110,70,60,60,100,1,FALSE +86,Seel,Water,,65,45,55,45,70,45,1,FALSE +87,Dewgong,Water,Ice,90,70,80,70,95,70,1,FALSE +88,Grimer,Poison,,80,80,50,40,50,25,1,FALSE +89,Muk,Poison,,105,105,75,65,100,50,1,FALSE +90,Shellder,Water,,30,65,100,45,25,40,1,FALSE +91,Cloyster,Water,Ice,50,95,180,85,45,70,1,FALSE +92,Gastly,Ghost,Poison,30,35,30,100,35,80,1,FALSE +93,Haunter,Ghost,Poison,45,50,45,115,55,95,1,FALSE +94,Gengar,Ghost,Poison,60,65,60,130,75,110,1,FALSE +94,GengarMega Gengar,Ghost,Poison,60,65,80,170,95,130,1,FALSE +95,Onix,Rock,Ground,35,45,160,30,45,70,1,FALSE +96,Drowzee,Psychic,,60,48,45,43,90,42,1,FALSE +97,Hypno,Psychic,,85,73,70,73,115,67,1,FALSE +98,Krabby,Water,,30,105,90,25,25,50,1,FALSE +99,Kingler,Water,,55,130,115,50,50,75,1,FALSE +100,Voltorb,Electric,,40,30,50,55,55,100,1,FALSE +101,Electrode,Electric,,60,50,70,80,80,140,1,FALSE +102,Exeggcute,Grass,Psychic,60,40,80,60,45,40,1,FALSE +103,Exeggutor,Grass,Psychic,95,95,85,125,65,55,1,FALSE +104,Cubone,Ground,,50,50,95,40,50,35,1,FALSE +105,Marowak,Ground,,60,80,110,50,80,45,1,FALSE +106,Hitmonlee,Fighting,,50,120,53,35,110,87,1,FALSE +107,Hitmonchan,Fighting,,50,105,79,35,110,76,1,FALSE +108,Lickitung,Normal,,90,55,75,60,75,30,1,FALSE +109,Koffing,Poison,,40,65,95,60,45,35,1,FALSE +110,Weezing,Poison,,65,90,120,85,70,60,1,FALSE +111,Rhyhorn,Ground,Rock,80,85,95,30,30,25,1,FALSE +112,Rhydon,Ground,Rock,105,130,120,45,45,40,1,FALSE +113,Chansey,Normal,,250,5,5,35,105,50,1,FALSE +114,Tangela,Grass,,65,55,115,100,40,60,1,FALSE +115,Kangaskhan,Normal,,105,95,80,40,80,90,1,FALSE +115,KangaskhanMega Kangaskhan,Normal,,105,125,100,60,100,100,1,FALSE +116,Horsea,Water,,30,40,70,70,25,60,1,FALSE +117,Seadra,Water,,55,65,95,95,45,85,1,FALSE +118,Goldeen,Water,,45,67,60,35,50,63,1,FALSE +119,Seaking,Water,,80,92,65,65,80,68,1,FALSE +120,Staryu,Water,,30,45,55,70,55,85,1,FALSE +121,Starmie,Water,Psychic,60,75,85,100,85,115,1,FALSE +122,Mr. Mime,Psychic,Fairy,40,45,65,100,120,90,1,FALSE +123,Scyther,Bug,Flying,70,110,80,55,80,105,1,FALSE +124,Jynx,Ice,Psychic,65,50,35,115,95,95,1,FALSE +125,Electabuzz,Electric,,65,83,57,95,85,105,1,FALSE +126,Magmar,Fire,,65,95,57,100,85,93,1,FALSE +127,Pinsir,Bug,,65,125,100,55,70,85,1,FALSE +127,PinsirMega Pinsir,Bug,Flying,65,155,120,65,90,105,1,FALSE +128,Tauros,Normal,,75,100,95,40,70,110,1,FALSE +129,Magikarp,Water,,20,10,55,15,20,80,1,FALSE +130,Gyarados,Water,Flying,95,125,79,60,100,81,1,FALSE +130,GyaradosMega Gyarados,Water,Dark,95,155,109,70,130,81,1,FALSE +131,Lapras,Water,Ice,130,85,80,85,95,60,1,FALSE +132,Ditto,Normal,,48,48,48,48,48,48,1,FALSE +133,Eevee,Normal,,55,55,50,45,65,55,1,FALSE +134,Vaporeon,Water,,130,65,60,110,95,65,1,FALSE +135,Jolteon,Electric,,65,65,60,110,95,130,1,FALSE +136,Flareon,Fire,,65,130,60,95,110,65,1,FALSE +137,Porygon,Normal,,65,60,70,85,75,40,1,FALSE +138,Omanyte,Rock,Water,35,40,100,90,55,35,1,FALSE +139,Omastar,Rock,Water,70,60,125,115,70,55,1,FALSE +140,Kabuto,Rock,Water,30,80,90,55,45,55,1,FALSE +141,Kabutops,Rock,Water,60,115,105,65,70,80,1,FALSE +142,Aerodactyl,Rock,Flying,80,105,65,60,75,130,1,FALSE +142,AerodactylMega Aerodactyl,Rock,Flying,80,135,85,70,95,150,1,FALSE +143,Snorlax,Normal,,160,110,65,65,110,30,1,FALSE +144,Articuno,Ice,Flying,90,85,100,95,125,85,1,TRUE +145,Zapdos,Electric,Flying,90,90,85,125,90,100,1,TRUE +146,Moltres,Fire,Flying,90,100,90,125,85,90,1,TRUE +147,Dratini,Dragon,,41,64,45,50,50,50,1,FALSE +148,Dragonair,Dragon,,61,84,65,70,70,70,1,FALSE +149,Dragonite,Dragon,Flying,91,134,95,100,100,80,1,FALSE +150,Mewtwo,Psychic,,106,110,90,154,90,130,1,TRUE +150,MewtwoMega Mewtwo X,Psychic,Fighting,106,190,100,154,100,130,1,TRUE +150,MewtwoMega Mewtwo Y,Psychic,,106,150,70,194,120,140,1,TRUE +151,Mew,Psychic,,100,100,100,100,100,100,1,FALSE +152,Chikorita,Grass,,45,49,65,49,65,45,2,FALSE +153,Bayleef,Grass,,60,62,80,63,80,60,2,FALSE +154,Meganium,Grass,,80,82,100,83,100,80,2,FALSE +155,Cyndaquil,Fire,,39,52,43,60,50,65,2,FALSE +156,Quilava,Fire,,58,64,58,80,65,80,2,FALSE +157,Typhlosion,Fire,,78,84,78,109,85,100,2,FALSE +158,Totodile,Water,,50,65,64,44,48,43,2,FALSE +159,Croconaw,Water,,65,80,80,59,63,58,2,FALSE +160,Feraligatr,Water,,85,105,100,79,83,78,2,FALSE +161,Sentret,Normal,,35,46,34,35,45,20,2,FALSE +162,Furret,Normal,,85,76,64,45,55,90,2,FALSE +163,Hoothoot,Normal,Flying,60,30,30,36,56,50,2,FALSE +164,Noctowl,Normal,Flying,100,50,50,76,96,70,2,FALSE +165,Ledyba,Bug,Flying,40,20,30,40,80,55,2,FALSE +166,Ledian,Bug,Flying,55,35,50,55,110,85,2,FALSE +167,Spinarak,Bug,Poison,40,60,40,40,40,30,2,FALSE +168,Ariados,Bug,Poison,70,90,70,60,60,40,2,FALSE +169,Crobat,Poison,Flying,85,90,80,70,80,130,2,FALSE +170,Chinchou,Water,Electric,75,38,38,56,56,67,2,FALSE +171,Lanturn,Water,Electric,125,58,58,76,76,67,2,FALSE +172,Pichu,Electric,,20,40,15,35,35,60,2,FALSE +173,Cleffa,Fairy,,50,25,28,45,55,15,2,FALSE +174,Igglybuff,Normal,Fairy,90,30,15,40,20,15,2,FALSE +175,Togepi,Fairy,,35,20,65,40,65,20,2,FALSE +176,Togetic,Fairy,Flying,55,40,85,80,105,40,2,FALSE +177,Natu,Psychic,Flying,40,50,45,70,45,70,2,FALSE +178,Xatu,Psychic,Flying,65,75,70,95,70,95,2,FALSE +179,Mareep,Electric,,55,40,40,65,45,35,2,FALSE +180,Flaaffy,Electric,,70,55,55,80,60,45,2,FALSE +181,Ampharos,Electric,,90,75,85,115,90,55,2,FALSE +181,AmpharosMega Ampharos,Electric,Dragon,90,95,105,165,110,45,2,FALSE +182,Bellossom,Grass,,75,80,95,90,100,50,2,FALSE +183,Marill,Water,Fairy,70,20,50,20,50,40,2,FALSE +184,Azumarill,Water,Fairy,100,50,80,60,80,50,2,FALSE +185,Sudowoodo,Rock,,70,100,115,30,65,30,2,FALSE +186,Politoed,Water,,90,75,75,90,100,70,2,FALSE +187,Hoppip,Grass,Flying,35,35,40,35,55,50,2,FALSE +188,Skiploom,Grass,Flying,55,45,50,45,65,80,2,FALSE +189,Jumpluff,Grass,Flying,75,55,70,55,95,110,2,FALSE +190,Aipom,Normal,,55,70,55,40,55,85,2,FALSE +191,Sunkern,Grass,,30,30,30,30,30,30,2,FALSE +192,Sunflora,Grass,,75,75,55,105,85,30,2,FALSE +193,Yanma,Bug,Flying,65,65,45,75,45,95,2,FALSE +194,Wooper,Water,Ground,55,45,45,25,25,15,2,FALSE +195,Quagsire,Water,Ground,95,85,85,65,65,35,2,FALSE +196,Espeon,Psychic,,65,65,60,130,95,110,2,FALSE +197,Umbreon,Dark,,95,65,110,60,130,65,2,FALSE +198,Murkrow,Dark,Flying,60,85,42,85,42,91,2,FALSE +199,Slowking,Water,Psychic,95,75,80,100,110,30,2,FALSE +200,Misdreavus,Ghost,,60,60,60,85,85,85,2,FALSE +201,Unown,Psychic,,48,72,48,72,48,48,2,FALSE +202,Wobbuffet,Psychic,,190,33,58,33,58,33,2,FALSE +203,Girafarig,Normal,Psychic,70,80,65,90,65,85,2,FALSE +204,Pineco,Bug,,50,65,90,35,35,15,2,FALSE +205,Forretress,Bug,Steel,75,90,140,60,60,40,2,FALSE +206,Dunsparce,Normal,,100,70,70,65,65,45,2,FALSE +207,Gligar,Ground,Flying,65,75,105,35,65,85,2,FALSE +208,Steelix,Steel,Ground,75,85,200,55,65,30,2,FALSE +208,SteelixMega Steelix,Steel,Ground,75,125,230,55,95,30,2,FALSE +209,Snubbull,Fairy,,60,80,50,40,40,30,2,FALSE +210,Granbull,Fairy,,90,120,75,60,60,45,2,FALSE +211,Qwilfish,Water,Poison,65,95,75,55,55,85,2,FALSE +212,Scizor,Bug,Steel,70,130,100,55,80,65,2,FALSE +212,ScizorMega Scizor,Bug,Steel,70,150,140,65,100,75,2,FALSE +213,Shuckle,Bug,Rock,20,10,230,10,230,5,2,FALSE +214,Heracross,Bug,Fighting,80,125,75,40,95,85,2,FALSE +214,HeracrossMega Heracross,Bug,Fighting,80,185,115,40,105,75,2,FALSE +215,Sneasel,Dark,Ice,55,95,55,35,75,115,2,FALSE +216,Teddiursa,Normal,,60,80,50,50,50,40,2,FALSE +217,Ursaring,Normal,,90,130,75,75,75,55,2,FALSE +218,Slugma,Fire,,40,40,40,70,40,20,2,FALSE +219,Magcargo,Fire,Rock,50,50,120,80,80,30,2,FALSE +220,Swinub,Ice,Ground,50,50,40,30,30,50,2,FALSE +221,Piloswine,Ice,Ground,100,100,80,60,60,50,2,FALSE +222,Corsola,Water,Rock,55,55,85,65,85,35,2,FALSE +223,Remoraid,Water,,35,65,35,65,35,65,2,FALSE +224,Octillery,Water,,75,105,75,105,75,45,2,FALSE +225,Delibird,Ice,Flying,45,55,45,65,45,75,2,FALSE +226,Mantine,Water,Flying,65,40,70,80,140,70,2,FALSE +227,Skarmory,Steel,Flying,65,80,140,40,70,70,2,FALSE +228,Houndour,Dark,Fire,45,60,30,80,50,65,2,FALSE +229,Houndoom,Dark,Fire,75,90,50,110,80,95,2,FALSE +229,HoundoomMega Houndoom,Dark,Fire,75,90,90,140,90,115,2,FALSE +230,Kingdra,Water,Dragon,75,95,95,95,95,85,2,FALSE +231,Phanpy,Ground,,90,60,60,40,40,40,2,FALSE +232,Donphan,Ground,,90,120,120,60,60,50,2,FALSE +233,Porygon2,Normal,,85,80,90,105,95,60,2,FALSE +234,Stantler,Normal,,73,95,62,85,65,85,2,FALSE +235,Smeargle,Normal,,55,20,35,20,45,75,2,FALSE +236,Tyrogue,Fighting,,35,35,35,35,35,35,2,FALSE +237,Hitmontop,Fighting,,50,95,95,35,110,70,2,FALSE +238,Smoochum,Ice,Psychic,45,30,15,85,65,65,2,FALSE +239,Elekid,Electric,,45,63,37,65,55,95,2,FALSE +240,Magby,Fire,,45,75,37,70,55,83,2,FALSE +241,Miltank,Normal,,95,80,105,40,70,100,2,FALSE +242,Blissey,Normal,,255,10,10,75,135,55,2,FALSE +243,Raikou,Electric,,90,85,75,115,100,115,2,TRUE +244,Entei,Fire,,115,115,85,90,75,100,2,TRUE +245,Suicune,Water,,100,75,115,90,115,85,2,TRUE +246,Larvitar,Rock,Ground,50,64,50,45,50,41,2,FALSE +247,Pupitar,Rock,Ground,70,84,70,65,70,51,2,FALSE +248,Tyranitar,Rock,Dark,100,134,110,95,100,61,2,FALSE +248,TyranitarMega Tyranitar,Rock,Dark,100,164,150,95,120,71,2,FALSE +249,Lugia,Psychic,Flying,106,90,130,90,154,110,2,TRUE +250,Ho-oh,Fire,Flying,106,130,90,110,154,90,2,TRUE +251,Celebi,Psychic,Grass,100,100,100,100,100,100,2,FALSE +252,Treecko,Grass,,40,45,35,65,55,70,3,FALSE +253,Grovyle,Grass,,50,65,45,85,65,95,3,FALSE +254,Sceptile,Grass,,70,85,65,105,85,120,3,FALSE +254,SceptileMega Sceptile,Grass,Dragon,70,110,75,145,85,145,3,FALSE +255,Torchic,Fire,,45,60,40,70,50,45,3,FALSE +256,Combusken,Fire,Fighting,60,85,60,85,60,55,3,FALSE +257,Blaziken,Fire,Fighting,80,120,70,110,70,80,3,FALSE +257,BlazikenMega Blaziken,Fire,Fighting,80,160,80,130,80,100,3,FALSE +258,Mudkip,Water,,50,70,50,50,50,40,3,FALSE +259,Marshtomp,Water,Ground,70,85,70,60,70,50,3,FALSE +260,Swampert,Water,Ground,100,110,90,85,90,60,3,FALSE +260,SwampertMega Swampert,Water,Ground,100,150,110,95,110,70,3,FALSE +261,Poochyena,Dark,,35,55,35,30,30,35,3,FALSE +262,Mightyena,Dark,,70,90,70,60,60,70,3,FALSE +263,Zigzagoon,Normal,,38,30,41,30,41,60,3,FALSE +264,Linoone,Normal,,78,70,61,50,61,100,3,FALSE +265,Wurmple,Bug,,45,45,35,20,30,20,3,FALSE +266,Silcoon,Bug,,50,35,55,25,25,15,3,FALSE +267,Beautifly,Bug,Flying,60,70,50,100,50,65,3,FALSE +268,Cascoon,Bug,,50,35,55,25,25,15,3,FALSE +269,Dustox,Bug,Poison,60,50,70,50,90,65,3,FALSE +270,Lotad,Water,Grass,40,30,30,40,50,30,3,FALSE +271,Lombre,Water,Grass,60,50,50,60,70,50,3,FALSE +272,Ludicolo,Water,Grass,80,70,70,90,100,70,3,FALSE +273,Seedot,Grass,,40,40,50,30,30,30,3,FALSE +274,Nuzleaf,Grass,Dark,70,70,40,60,40,60,3,FALSE +275,Shiftry,Grass,Dark,90,100,60,90,60,80,3,FALSE +276,Taillow,Normal,Flying,40,55,30,30,30,85,3,FALSE +277,Swellow,Normal,Flying,60,85,60,50,50,125,3,FALSE +278,Wingull,Water,Flying,40,30,30,55,30,85,3,FALSE +279,Pelipper,Water,Flying,60,50,100,85,70,65,3,FALSE +280,Ralts,Psychic,Fairy,28,25,25,45,35,40,3,FALSE +281,Kirlia,Psychic,Fairy,38,35,35,65,55,50,3,FALSE +282,Gardevoir,Psychic,Fairy,68,65,65,125,115,80,3,FALSE +282,GardevoirMega Gardevoir,Psychic,Fairy,68,85,65,165,135,100,3,FALSE +283,Surskit,Bug,Water,40,30,32,50,52,65,3,FALSE +284,Masquerain,Bug,Flying,70,60,62,80,82,60,3,FALSE +285,Shroomish,Grass,,60,40,60,40,60,35,3,FALSE +286,Breloom,Grass,Fighting,60,130,80,60,60,70,3,FALSE +287,Slakoth,Normal,,60,60,60,35,35,30,3,FALSE +288,Vigoroth,Normal,,80,80,80,55,55,90,3,FALSE +289,Slaking,Normal,,150,160,100,95,65,100,3,FALSE +290,Nincada,Bug,Ground,31,45,90,30,30,40,3,FALSE +291,Ninjask,Bug,Flying,61,90,45,50,50,160,3,FALSE +292,Shedinja,Bug,Ghost,1,90,45,30,30,40,3,FALSE +293,Whismur,Normal,,64,51,23,51,23,28,3,FALSE +294,Loudred,Normal,,84,71,43,71,43,48,3,FALSE +295,Exploud,Normal,,104,91,63,91,73,68,3,FALSE +296,Makuhita,Fighting,,72,60,30,20,30,25,3,FALSE +297,Hariyama,Fighting,,144,120,60,40,60,50,3,FALSE +298,Azurill,Normal,Fairy,50,20,40,20,40,20,3,FALSE +299,Nosepass,Rock,,30,45,135,45,90,30,3,FALSE +300,Skitty,Normal,,50,45,45,35,35,50,3,FALSE +301,Delcatty,Normal,,70,65,65,55,55,70,3,FALSE +302,Sableye,Dark,Ghost,50,75,75,65,65,50,3,FALSE +302,SableyeMega Sableye,Dark,Ghost,50,85,125,85,115,20,3,FALSE +303,Mawile,Steel,Fairy,50,85,85,55,55,50,3,FALSE +303,MawileMega Mawile,Steel,Fairy,50,105,125,55,95,50,3,FALSE +304,Aron,Steel,Rock,50,70,100,40,40,30,3,FALSE +305,Lairon,Steel,Rock,60,90,140,50,50,40,3,FALSE +306,Aggron,Steel,Rock,70,110,180,60,60,50,3,FALSE +306,AggronMega Aggron,Steel,,70,140,230,60,80,50,3,FALSE +307,Meditite,Fighting,Psychic,30,40,55,40,55,60,3,FALSE +308,Medicham,Fighting,Psychic,60,60,75,60,75,80,3,FALSE +308,MedichamMega Medicham,Fighting,Psychic,60,100,85,80,85,100,3,FALSE +309,Electrike,Electric,,40,45,40,65,40,65,3,FALSE +310,Manectric,Electric,,70,75,60,105,60,105,3,FALSE +310,ManectricMega Manectric,Electric,,70,75,80,135,80,135,3,FALSE +311,Plusle,Electric,,60,50,40,85,75,95,3,FALSE +312,Minun,Electric,,60,40,50,75,85,95,3,FALSE +313,Volbeat,Bug,,65,73,55,47,75,85,3,FALSE +314,Illumise,Bug,,65,47,55,73,75,85,3,FALSE +315,Roselia,Grass,Poison,50,60,45,100,80,65,3,FALSE +316,Gulpin,Poison,,70,43,53,43,53,40,3,FALSE +317,Swalot,Poison,,100,73,83,73,83,55,3,FALSE +318,Carvanha,Water,Dark,45,90,20,65,20,65,3,FALSE +319,Sharpedo,Water,Dark,70,120,40,95,40,95,3,FALSE +319,SharpedoMega Sharpedo,Water,Dark,70,140,70,110,65,105,3,FALSE +320,Wailmer,Water,,130,70,35,70,35,60,3,FALSE +321,Wailord,Water,,170,90,45,90,45,60,3,FALSE +322,Numel,Fire,Ground,60,60,40,65,45,35,3,FALSE +323,Camerupt,Fire,Ground,70,100,70,105,75,40,3,FALSE +323,CameruptMega Camerupt,Fire,Ground,70,120,100,145,105,20,3,FALSE +324,Torkoal,Fire,,70,85,140,85,70,20,3,FALSE +325,Spoink,Psychic,,60,25,35,70,80,60,3,FALSE +326,Grumpig,Psychic,,80,45,65,90,110,80,3,FALSE +327,Spinda,Normal,,60,60,60,60,60,60,3,FALSE +328,Trapinch,Ground,,45,100,45,45,45,10,3,FALSE +329,Vibrava,Ground,Dragon,50,70,50,50,50,70,3,FALSE +330,Flygon,Ground,Dragon,80,100,80,80,80,100,3,FALSE +331,Cacnea,Grass,,50,85,40,85,40,35,3,FALSE +332,Cacturne,Grass,Dark,70,115,60,115,60,55,3,FALSE +333,Swablu,Normal,Flying,45,40,60,40,75,50,3,FALSE +334,Altaria,Dragon,Flying,75,70,90,70,105,80,3,FALSE +334,AltariaMega Altaria,Dragon,Fairy,75,110,110,110,105,80,3,FALSE +335,Zangoose,Normal,,73,115,60,60,60,90,3,FALSE +336,Seviper,Poison,,73,100,60,100,60,65,3,FALSE +337,Lunatone,Rock,Psychic,70,55,65,95,85,70,3,FALSE +338,Solrock,Rock,Psychic,70,95,85,55,65,70,3,FALSE +339,Barboach,Water,Ground,50,48,43,46,41,60,3,FALSE +340,Whiscash,Water,Ground,110,78,73,76,71,60,3,FALSE +341,Corphish,Water,,43,80,65,50,35,35,3,FALSE +342,Crawdaunt,Water,Dark,63,120,85,90,55,55,3,FALSE +343,Baltoy,Ground,Psychic,40,40,55,40,70,55,3,FALSE +344,Claydol,Ground,Psychic,60,70,105,70,120,75,3,FALSE +345,Lileep,Rock,Grass,66,41,77,61,87,23,3,FALSE +346,Cradily,Rock,Grass,86,81,97,81,107,43,3,FALSE +347,Anorith,Rock,Bug,45,95,50,40,50,75,3,FALSE +348,Armaldo,Rock,Bug,75,125,100,70,80,45,3,FALSE +349,Feebas,Water,,20,15,20,10,55,80,3,FALSE +350,Milotic,Water,,95,60,79,100,125,81,3,FALSE +351,Castform,Normal,,70,70,70,70,70,70,3,FALSE +352,Kecleon,Normal,,60,90,70,60,120,40,3,FALSE +353,Shuppet,Ghost,,44,75,35,63,33,45,3,FALSE +354,Banette,Ghost,,64,115,65,83,63,65,3,FALSE +354,BanetteMega Banette,Ghost,,64,165,75,93,83,75,3,FALSE +355,Duskull,Ghost,,20,40,90,30,90,25,3,FALSE +356,Dusclops,Ghost,,40,70,130,60,130,25,3,FALSE +357,Tropius,Grass,Flying,99,68,83,72,87,51,3,FALSE +358,Chimecho,Psychic,,65,50,70,95,80,65,3,FALSE +359,Absol,Dark,,65,130,60,75,60,75,3,FALSE +359,AbsolMega Absol,Dark,,65,150,60,115,60,115,3,FALSE +360,Wynaut,Psychic,,95,23,48,23,48,23,3,FALSE +361,Snorunt,Ice,,50,50,50,50,50,50,3,FALSE +362,Glalie,Ice,,80,80,80,80,80,80,3,FALSE +362,GlalieMega Glalie,Ice,,80,120,80,120,80,100,3,FALSE +363,Spheal,Ice,Water,70,40,50,55,50,25,3,FALSE +364,Sealeo,Ice,Water,90,60,70,75,70,45,3,FALSE +365,Walrein,Ice,Water,110,80,90,95,90,65,3,FALSE +366,Clamperl,Water,,35,64,85,74,55,32,3,FALSE +367,Huntail,Water,,55,104,105,94,75,52,3,FALSE +368,Gorebyss,Water,,55,84,105,114,75,52,3,FALSE +369,Relicanth,Water,Rock,100,90,130,45,65,55,3,FALSE +370,Luvdisc,Water,,43,30,55,40,65,97,3,FALSE +371,Bagon,Dragon,,45,75,60,40,30,50,3,FALSE +372,Shelgon,Dragon,,65,95,100,60,50,50,3,FALSE +373,Salamence,Dragon,Flying,95,135,80,110,80,100,3,FALSE +373,SalamenceMega Salamence,Dragon,Flying,95,145,130,120,90,120,3,FALSE +374,Beldum,Steel,Psychic,40,55,80,35,60,30,3,FALSE +375,Metang,Steel,Psychic,60,75,100,55,80,50,3,FALSE +376,Metagross,Steel,Psychic,80,135,130,95,90,70,3,FALSE +376,MetagrossMega Metagross,Steel,Psychic,80,145,150,105,110,110,3,FALSE +377,Regirock,Rock,,80,100,200,50,100,50,3,TRUE +378,Regice,Ice,,80,50,100,100,200,50,3,TRUE +379,Registeel,Steel,,80,75,150,75,150,50,3,TRUE +380,Latias,Dragon,Psychic,80,80,90,110,130,110,3,TRUE +380,LatiasMega Latias,Dragon,Psychic,80,100,120,140,150,110,3,TRUE +381,Latios,Dragon,Psychic,80,90,80,130,110,110,3,TRUE +381,LatiosMega Latios,Dragon,Psychic,80,130,100,160,120,110,3,TRUE +382,Kyogre,Water,,100,100,90,150,140,90,3,TRUE +382,KyogrePrimal Kyogre,Water,,100,150,90,180,160,90,3,TRUE +383,Groudon,Ground,,100,150,140,100,90,90,3,TRUE +383,GroudonPrimal Groudon,Ground,Fire,100,180,160,150,90,90,3,TRUE +384,Rayquaza,Dragon,Flying,105,150,90,150,90,95,3,TRUE +384,RayquazaMega Rayquaza,Dragon,Flying,105,180,100,180,100,115,3,TRUE +385,Jirachi,Steel,Psychic,100,100,100,100,100,100,3,TRUE +386,DeoxysNormal Forme,Psychic,,50,150,50,150,50,150,3,TRUE +386,DeoxysAttack Forme,Psychic,,50,180,20,180,20,150,3,TRUE +386,DeoxysDefense Forme,Psychic,,50,70,160,70,160,90,3,TRUE +386,DeoxysSpeed Forme,Psychic,,50,95,90,95,90,180,3,TRUE +387,Turtwig,Grass,,55,68,64,45,55,31,4,FALSE +388,Grotle,Grass,,75,89,85,55,65,36,4,FALSE +389,Torterra,Grass,Ground,95,109,105,75,85,56,4,FALSE +390,Chimchar,Fire,,44,58,44,58,44,61,4,FALSE +391,Monferno,Fire,Fighting,64,78,52,78,52,81,4,FALSE +392,Infernape,Fire,Fighting,76,104,71,104,71,108,4,FALSE +393,Piplup,Water,,53,51,53,61,56,40,4,FALSE +394,Prinplup,Water,,64,66,68,81,76,50,4,FALSE +395,Empoleon,Water,Steel,84,86,88,111,101,60,4,FALSE +396,Starly,Normal,Flying,40,55,30,30,30,60,4,FALSE +397,Staravia,Normal,Flying,55,75,50,40,40,80,4,FALSE +398,Staraptor,Normal,Flying,85,120,70,50,60,100,4,FALSE +399,Bidoof,Normal,,59,45,40,35,40,31,4,FALSE +400,Bibarel,Normal,Water,79,85,60,55,60,71,4,FALSE +401,Kricketot,Bug,,37,25,41,25,41,25,4,FALSE +402,Kricketune,Bug,,77,85,51,55,51,65,4,FALSE +403,Shinx,Electric,,45,65,34,40,34,45,4,FALSE +404,Luxio,Electric,,60,85,49,60,49,60,4,FALSE +405,Luxray,Electric,,80,120,79,95,79,70,4,FALSE +406,Budew,Grass,Poison,40,30,35,50,70,55,4,FALSE +407,Roserade,Grass,Poison,60,70,65,125,105,90,4,FALSE +408,Cranidos,Rock,,67,125,40,30,30,58,4,FALSE +409,Rampardos,Rock,,97,165,60,65,50,58,4,FALSE +410,Shieldon,Rock,Steel,30,42,118,42,88,30,4,FALSE +411,Bastiodon,Rock,Steel,60,52,168,47,138,30,4,FALSE +412,Burmy,Bug,,40,29,45,29,45,36,4,FALSE +413,WormadamPlant Cloak,Bug,Grass,60,59,85,79,105,36,4,FALSE +413,WormadamSandy Cloak,Bug,Ground,60,79,105,59,85,36,4,FALSE +413,WormadamTrash Cloak,Bug,Steel,60,69,95,69,95,36,4,FALSE +414,Mothim,Bug,Flying,70,94,50,94,50,66,4,FALSE +415,Combee,Bug,Flying,30,30,42,30,42,70,4,FALSE +416,Vespiquen,Bug,Flying,70,80,102,80,102,40,4,FALSE +417,Pachirisu,Electric,,60,45,70,45,90,95,4,FALSE +418,Buizel,Water,,55,65,35,60,30,85,4,FALSE +419,Floatzel,Water,,85,105,55,85,50,115,4,FALSE +420,Cherubi,Grass,,45,35,45,62,53,35,4,FALSE +421,Cherrim,Grass,,70,60,70,87,78,85,4,FALSE +422,Shellos,Water,,76,48,48,57,62,34,4,FALSE +423,Gastrodon,Water,Ground,111,83,68,92,82,39,4,FALSE +424,Ambipom,Normal,,75,100,66,60,66,115,4,FALSE +425,Drifloon,Ghost,Flying,90,50,34,60,44,70,4,FALSE +426,Drifblim,Ghost,Flying,150,80,44,90,54,80,4,FALSE +427,Buneary,Normal,,55,66,44,44,56,85,4,FALSE +428,Lopunny,Normal,,65,76,84,54,96,105,4,FALSE +428,LopunnyMega Lopunny,Normal,Fighting,65,136,94,54,96,135,4,FALSE +429,Mismagius,Ghost,,60,60,60,105,105,105,4,FALSE +430,Honchkrow,Dark,Flying,100,125,52,105,52,71,4,FALSE +431,Glameow,Normal,,49,55,42,42,37,85,4,FALSE +432,Purugly,Normal,,71,82,64,64,59,112,4,FALSE +433,Chingling,Psychic,,45,30,50,65,50,45,4,FALSE +434,Stunky,Poison,Dark,63,63,47,41,41,74,4,FALSE +435,Skuntank,Poison,Dark,103,93,67,71,61,84,4,FALSE +436,Bronzor,Steel,Psychic,57,24,86,24,86,23,4,FALSE +437,Bronzong,Steel,Psychic,67,89,116,79,116,33,4,FALSE +438,Bonsly,Rock,,50,80,95,10,45,10,4,FALSE +439,Mime Jr.,Psychic,Fairy,20,25,45,70,90,60,4,FALSE +440,Happiny,Normal,,100,5,5,15,65,30,4,FALSE +441,Chatot,Normal,Flying,76,65,45,92,42,91,4,FALSE +442,Spiritomb,Ghost,Dark,50,92,108,92,108,35,4,FALSE +443,Gible,Dragon,Ground,58,70,45,40,45,42,4,FALSE +444,Gabite,Dragon,Ground,68,90,65,50,55,82,4,FALSE +445,Garchomp,Dragon,Ground,108,130,95,80,85,102,4,FALSE +445,GarchompMega Garchomp,Dragon,Ground,108,170,115,120,95,92,4,FALSE +446,Munchlax,Normal,,135,85,40,40,85,5,4,FALSE +447,Riolu,Fighting,,40,70,40,35,40,60,4,FALSE +448,Lucario,Fighting,Steel,70,110,70,115,70,90,4,FALSE +448,LucarioMega Lucario,Fighting,Steel,70,145,88,140,70,112,4,FALSE +449,Hippopotas,Ground,,68,72,78,38,42,32,4,FALSE +450,Hippowdon,Ground,,108,112,118,68,72,47,4,FALSE +451,Skorupi,Poison,Bug,40,50,90,30,55,65,4,FALSE +452,Drapion,Poison,Dark,70,90,110,60,75,95,4,FALSE +453,Croagunk,Poison,Fighting,48,61,40,61,40,50,4,FALSE +454,Toxicroak,Poison,Fighting,83,106,65,86,65,85,4,FALSE +455,Carnivine,Grass,,74,100,72,90,72,46,4,FALSE +456,Finneon,Water,,49,49,56,49,61,66,4,FALSE +457,Lumineon,Water,,69,69,76,69,86,91,4,FALSE +458,Mantyke,Water,Flying,45,20,50,60,120,50,4,FALSE +459,Snover,Grass,Ice,60,62,50,62,60,40,4,FALSE +460,Abomasnow,Grass,Ice,90,92,75,92,85,60,4,FALSE +460,AbomasnowMega Abomasnow,Grass,Ice,90,132,105,132,105,30,4,FALSE +461,Weavile,Dark,Ice,70,120,65,45,85,125,4,FALSE +462,Magnezone,Electric,Steel,70,70,115,130,90,60,4,FALSE +463,Lickilicky,Normal,,110,85,95,80,95,50,4,FALSE +464,Rhyperior,Ground,Rock,115,140,130,55,55,40,4,FALSE +465,Tangrowth,Grass,,100,100,125,110,50,50,4,FALSE +466,Electivire,Electric,,75,123,67,95,85,95,4,FALSE +467,Magmortar,Fire,,75,95,67,125,95,83,4,FALSE +468,Togekiss,Fairy,Flying,85,50,95,120,115,80,4,FALSE +469,Yanmega,Bug,Flying,86,76,86,116,56,95,4,FALSE +470,Leafeon,Grass,,65,110,130,60,65,95,4,FALSE +471,Glaceon,Ice,,65,60,110,130,95,65,4,FALSE +472,Gliscor,Ground,Flying,75,95,125,45,75,95,4,FALSE +473,Mamoswine,Ice,Ground,110,130,80,70,60,80,4,FALSE +474,Porygon-Z,Normal,,85,80,70,135,75,90,4,FALSE +475,Gallade,Psychic,Fighting,68,125,65,65,115,80,4,FALSE +475,GalladeMega Gallade,Psychic,Fighting,68,165,95,65,115,110,4,FALSE +476,Probopass,Rock,Steel,60,55,145,75,150,40,4,FALSE +477,Dusknoir,Ghost,,45,100,135,65,135,45,4,FALSE +478,Froslass,Ice,Ghost,70,80,70,80,70,110,4,FALSE +479,Rotom,Electric,Ghost,50,50,77,95,77,91,4,FALSE +479,RotomHeat Rotom,Electric,Fire,50,65,107,105,107,86,4,FALSE +479,RotomWash Rotom,Electric,Water,50,65,107,105,107,86,4,FALSE +479,RotomFrost Rotom,Electric,Ice,50,65,107,105,107,86,4,FALSE +479,RotomFan Rotom,Electric,Flying,50,65,107,105,107,86,4,FALSE +479,RotomMow Rotom,Electric,Grass,50,65,107,105,107,86,4,FALSE +480,Uxie,Psychic,,75,75,130,75,130,95,4,TRUE +481,Mesprit,Psychic,,80,105,105,105,105,80,4,TRUE +482,Azelf,Psychic,,75,125,70,125,70,115,4,TRUE +483,Dialga,Steel,Dragon,100,120,120,150,100,90,4,TRUE +484,Palkia,Water,Dragon,90,120,100,150,120,100,4,TRUE +485,Heatran,Fire,Steel,91,90,106,130,106,77,4,TRUE +486,Regigigas,Normal,,110,160,110,80,110,100,4,TRUE +487,GiratinaAltered Forme,Ghost,Dragon,150,100,120,100,120,90,4,TRUE +487,GiratinaOrigin Forme,Ghost,Dragon,150,120,100,120,100,90,4,TRUE +488,Cresselia,Psychic,,120,70,120,75,130,85,4,FALSE +489,Phione,Water,,80,80,80,80,80,80,4,FALSE +490,Manaphy,Water,,100,100,100,100,100,100,4,FALSE +491,Darkrai,Dark,,70,90,90,135,90,125,4,TRUE +492,ShayminLand Forme,Grass,,100,100,100,100,100,100,4,TRUE +492,ShayminSky Forme,Grass,Flying,100,103,75,120,75,127,4,TRUE +493,Arceus,Normal,,120,120,120,120,120,120,4,TRUE +494,Victini,Psychic,Fire,100,100,100,100,100,100,5,TRUE +495,Snivy,Grass,,45,45,55,45,55,63,5,FALSE +496,Servine,Grass,,60,60,75,60,75,83,5,FALSE +497,Serperior,Grass,,75,75,95,75,95,113,5,FALSE +498,Tepig,Fire,,65,63,45,45,45,45,5,FALSE +499,Pignite,Fire,Fighting,90,93,55,70,55,55,5,FALSE +500,Emboar,Fire,Fighting,110,123,65,100,65,65,5,FALSE +501,Oshawott,Water,,55,55,45,63,45,45,5,FALSE +502,Dewott,Water,,75,75,60,83,60,60,5,FALSE +503,Samurott,Water,,95,100,85,108,70,70,5,FALSE +504,Patrat,Normal,,45,55,39,35,39,42,5,FALSE +505,Watchog,Normal,,60,85,69,60,69,77,5,FALSE +506,Lillipup,Normal,,45,60,45,25,45,55,5,FALSE +507,Herdier,Normal,,65,80,65,35,65,60,5,FALSE +508,Stoutland,Normal,,85,110,90,45,90,80,5,FALSE +509,Purrloin,Dark,,41,50,37,50,37,66,5,FALSE +510,Liepard,Dark,,64,88,50,88,50,106,5,FALSE +511,Pansage,Grass,,50,53,48,53,48,64,5,FALSE +512,Simisage,Grass,,75,98,63,98,63,101,5,FALSE +513,Pansear,Fire,,50,53,48,53,48,64,5,FALSE +514,Simisear,Fire,,75,98,63,98,63,101,5,FALSE +515,Panpour,Water,,50,53,48,53,48,64,5,FALSE +516,Simipour,Water,,75,98,63,98,63,101,5,FALSE +517,Munna,Psychic,,76,25,45,67,55,24,5,FALSE +518,Musharna,Psychic,,116,55,85,107,95,29,5,FALSE +519,Pidove,Normal,Flying,50,55,50,36,30,43,5,FALSE +520,Tranquill,Normal,Flying,62,77,62,50,42,65,5,FALSE +521,Unfezant,Normal,Flying,80,115,80,65,55,93,5,FALSE +522,Blitzle,Electric,,45,60,32,50,32,76,5,FALSE +523,Zebstrika,Electric,,75,100,63,80,63,116,5,FALSE +524,Roggenrola,Rock,,55,75,85,25,25,15,5,FALSE +525,Boldore,Rock,,70,105,105,50,40,20,5,FALSE +526,Gigalith,Rock,,85,135,130,60,80,25,5,FALSE +527,Woobat,Psychic,Flying,55,45,43,55,43,72,5,FALSE +528,Swoobat,Psychic,Flying,67,57,55,77,55,114,5,FALSE +529,Drilbur,Ground,,60,85,40,30,45,68,5,FALSE +530,Excadrill,Ground,Steel,110,135,60,50,65,88,5,FALSE +531,Audino,Normal,,103,60,86,60,86,50,5,FALSE +531,AudinoMega Audino,Normal,Fairy,103,60,126,80,126,50,5,FALSE +532,Timburr,Fighting,,75,80,55,25,35,35,5,FALSE +533,Gurdurr,Fighting,,85,105,85,40,50,40,5,FALSE +534,Conkeldurr,Fighting,,105,140,95,55,65,45,5,FALSE +535,Tympole,Water,,50,50,40,50,40,64,5,FALSE +536,Palpitoad,Water,Ground,75,65,55,65,55,69,5,FALSE +537,Seismitoad,Water,Ground,105,95,75,85,75,74,5,FALSE +538,Throh,Fighting,,120,100,85,30,85,45,5,FALSE +539,Sawk,Fighting,,75,125,75,30,75,85,5,FALSE +540,Sewaddle,Bug,Grass,45,53,70,40,60,42,5,FALSE +541,Swadloon,Bug,Grass,55,63,90,50,80,42,5,FALSE +542,Leavanny,Bug,Grass,75,103,80,70,80,92,5,FALSE +543,Venipede,Bug,Poison,30,45,59,30,39,57,5,FALSE +544,Whirlipede,Bug,Poison,40,55,99,40,79,47,5,FALSE +545,Scolipede,Bug,Poison,60,100,89,55,69,112,5,FALSE +546,Cottonee,Grass,Fairy,40,27,60,37,50,66,5,FALSE +547,Whimsicott,Grass,Fairy,60,67,85,77,75,116,5,FALSE +548,Petilil,Grass,,45,35,50,70,50,30,5,FALSE +549,Lilligant,Grass,,70,60,75,110,75,90,5,FALSE +550,Basculin,Water,,70,92,65,80,55,98,5,FALSE +551,Sandile,Ground,Dark,50,72,35,35,35,65,5,FALSE +552,Krokorok,Ground,Dark,60,82,45,45,45,74,5,FALSE +553,Krookodile,Ground,Dark,95,117,80,65,70,92,5,FALSE +554,Darumaka,Fire,,70,90,45,15,45,50,5,FALSE +555,DarmanitanStandard Mode,Fire,,105,140,55,30,55,95,5,FALSE +555,DarmanitanZen Mode,Fire,Psychic,105,30,105,140,105,55,5,FALSE +556,Maractus,Grass,,75,86,67,106,67,60,5,FALSE +557,Dwebble,Bug,Rock,50,65,85,35,35,55,5,FALSE +558,Crustle,Bug,Rock,70,95,125,65,75,45,5,FALSE +559,Scraggy,Dark,Fighting,50,75,70,35,70,48,5,FALSE +560,Scrafty,Dark,Fighting,65,90,115,45,115,58,5,FALSE +561,Sigilyph,Psychic,Flying,72,58,80,103,80,97,5,FALSE +562,Yamask,Ghost,,38,30,85,55,65,30,5,FALSE +563,Cofagrigus,Ghost,,58,50,145,95,105,30,5,FALSE +564,Tirtouga,Water,Rock,54,78,103,53,45,22,5,FALSE +565,Carracosta,Water,Rock,74,108,133,83,65,32,5,FALSE +566,Archen,Rock,Flying,55,112,45,74,45,70,5,FALSE +567,Archeops,Rock,Flying,75,140,65,112,65,110,5,FALSE +568,Trubbish,Poison,,50,50,62,40,62,65,5,FALSE +569,Garbodor,Poison,,80,95,82,60,82,75,5,FALSE +570,Zorua,Dark,,40,65,40,80,40,65,5,FALSE +571,Zoroark,Dark,,60,105,60,120,60,105,5,FALSE +572,Minccino,Normal,,55,50,40,40,40,75,5,FALSE +573,Cinccino,Normal,,75,95,60,65,60,115,5,FALSE +574,Gothita,Psychic,,45,30,50,55,65,45,5,FALSE +575,Gothorita,Psychic,,60,45,70,75,85,55,5,FALSE +576,Gothitelle,Psychic,,70,55,95,95,110,65,5,FALSE +577,Solosis,Psychic,,45,30,40,105,50,20,5,FALSE +578,Duosion,Psychic,,65,40,50,125,60,30,5,FALSE +579,Reuniclus,Psychic,,110,65,75,125,85,30,5,FALSE +580,Ducklett,Water,Flying,62,44,50,44,50,55,5,FALSE +581,Swanna,Water,Flying,75,87,63,87,63,98,5,FALSE +582,Vanillite,Ice,,36,50,50,65,60,44,5,FALSE +583,Vanillish,Ice,,51,65,65,80,75,59,5,FALSE +584,Vanilluxe,Ice,,71,95,85,110,95,79,5,FALSE +585,Deerling,Normal,Grass,60,60,50,40,50,75,5,FALSE +586,Sawsbuck,Normal,Grass,80,100,70,60,70,95,5,FALSE +587,Emolga,Electric,Flying,55,75,60,75,60,103,5,FALSE +588,Karrablast,Bug,,50,75,45,40,45,60,5,FALSE +589,Escavalier,Bug,Steel,70,135,105,60,105,20,5,FALSE +590,Foongus,Grass,Poison,69,55,45,55,55,15,5,FALSE +591,Amoonguss,Grass,Poison,114,85,70,85,80,30,5,FALSE +592,Frillish,Water,Ghost,55,40,50,65,85,40,5,FALSE +593,Jellicent,Water,Ghost,100,60,70,85,105,60,5,FALSE +594,Alomomola,Water,,165,75,80,40,45,65,5,FALSE +595,Joltik,Bug,Electric,50,47,50,57,50,65,5,FALSE +596,Galvantula,Bug,Electric,70,77,60,97,60,108,5,FALSE +597,Ferroseed,Grass,Steel,44,50,91,24,86,10,5,FALSE +598,Ferrothorn,Grass,Steel,74,94,131,54,116,20,5,FALSE +599,Klink,Steel,,40,55,70,45,60,30,5,FALSE +600,Klang,Steel,,60,80,95,70,85,50,5,FALSE +601,Klinklang,Steel,,60,100,115,70,85,90,5,FALSE +602,Tynamo,Electric,,35,55,40,45,40,60,5,FALSE +603,Eelektrik,Electric,,65,85,70,75,70,40,5,FALSE +604,Eelektross,Electric,,85,115,80,105,80,50,5,FALSE +605,Elgyem,Psychic,,55,55,55,85,55,30,5,FALSE +606,Beheeyem,Psychic,,75,75,75,125,95,40,5,FALSE +607,Litwick,Ghost,Fire,50,30,55,65,55,20,5,FALSE +608,Lampent,Ghost,Fire,60,40,60,95,60,55,5,FALSE +609,Chandelure,Ghost,Fire,60,55,90,145,90,80,5,FALSE +610,Axew,Dragon,,46,87,60,30,40,57,5,FALSE +611,Fraxure,Dragon,,66,117,70,40,50,67,5,FALSE +612,Haxorus,Dragon,,76,147,90,60,70,97,5,FALSE +613,Cubchoo,Ice,,55,70,40,60,40,40,5,FALSE +614,Beartic,Ice,,95,110,80,70,80,50,5,FALSE +615,Cryogonal,Ice,,70,50,30,95,135,105,5,FALSE +616,Shelmet,Bug,,50,40,85,40,65,25,5,FALSE +617,Accelgor,Bug,,80,70,40,100,60,145,5,FALSE +618,Stunfisk,Ground,Electric,109,66,84,81,99,32,5,FALSE +619,Mienfoo,Fighting,,45,85,50,55,50,65,5,FALSE +620,Mienshao,Fighting,,65,125,60,95,60,105,5,FALSE +621,Druddigon,Dragon,,77,120,90,60,90,48,5,FALSE +622,Golett,Ground,Ghost,59,74,50,35,50,35,5,FALSE +623,Golurk,Ground,Ghost,89,124,80,55,80,55,5,FALSE +624,Pawniard,Dark,Steel,45,85,70,40,40,60,5,FALSE +625,Bisharp,Dark,Steel,65,125,100,60,70,70,5,FALSE +626,Bouffalant,Normal,,95,110,95,40,95,55,5,FALSE +627,Rufflet,Normal,Flying,70,83,50,37,50,60,5,FALSE +628,Braviary,Normal,Flying,100,123,75,57,75,80,5,FALSE +629,Vullaby,Dark,Flying,70,55,75,45,65,60,5,FALSE +630,Mandibuzz,Dark,Flying,110,65,105,55,95,80,5,FALSE +631,Heatmor,Fire,,85,97,66,105,66,65,5,FALSE +632,Durant,Bug,Steel,58,109,112,48,48,109,5,FALSE +633,Deino,Dark,Dragon,52,65,50,45,50,38,5,FALSE +634,Zweilous,Dark,Dragon,72,85,70,65,70,58,5,FALSE +635,Hydreigon,Dark,Dragon,92,105,90,125,90,98,5,FALSE +636,Larvesta,Bug,Fire,55,85,55,50,55,60,5,FALSE +637,Volcarona,Bug,Fire,85,60,65,135,105,100,5,FALSE +638,Cobalion,Steel,Fighting,91,90,129,90,72,108,5,TRUE +639,Terrakion,Rock,Fighting,91,129,90,72,90,108,5,TRUE +640,Virizion,Grass,Fighting,91,90,72,90,129,108,5,TRUE +641,TornadusIncarnate Forme,Flying,,79,115,70,125,80,111,5,TRUE +641,TornadusTherian Forme,Flying,,79,100,80,110,90,121,5,TRUE +642,ThundurusIncarnate Forme,Electric,Flying,79,115,70,125,80,111,5,TRUE +642,ThundurusTherian Forme,Electric,Flying,79,105,70,145,80,101,5,TRUE +643,Reshiram,Dragon,Fire,100,120,100,150,120,90,5,TRUE +644,Zekrom,Dragon,Electric,100,150,120,120,100,90,5,TRUE +645,LandorusIncarnate Forme,Ground,Flying,89,125,90,115,80,101,5,TRUE +645,LandorusTherian Forme,Ground,Flying,89,145,90,105,80,91,5,TRUE +646,Kyurem,Dragon,Ice,125,130,90,130,90,95,5,TRUE +646,KyuremBlack Kyurem,Dragon,Ice,125,170,100,120,90,95,5,TRUE +646,KyuremWhite Kyurem,Dragon,Ice,125,120,90,170,100,95,5,TRUE +647,KeldeoOrdinary Forme,Water,Fighting,91,72,90,129,90,108,5,FALSE +647,KeldeoResolute Forme,Water,Fighting,91,72,90,129,90,108,5,FALSE +648,MeloettaAria Forme,Normal,Psychic,100,77,77,128,128,90,5,FALSE +648,MeloettaPirouette Forme,Normal,Fighting,100,128,90,77,77,128,5,FALSE +649,Genesect,Bug,Steel,71,120,95,120,95,99,5,FALSE +650,Chespin,Grass,,56,61,65,48,45,38,6,FALSE +651,Quilladin,Grass,,61,78,95,56,58,57,6,FALSE +652,Chesnaught,Grass,Fighting,88,107,122,74,75,64,6,FALSE +653,Fennekin,Fire,,40,45,40,62,60,60,6,FALSE +654,Braixen,Fire,,59,59,58,90,70,73,6,FALSE +655,Delphox,Fire,Psychic,75,69,72,114,100,104,6,FALSE +656,Froakie,Water,,41,56,40,62,44,71,6,FALSE +657,Frogadier,Water,,54,63,52,83,56,97,6,FALSE +658,Greninja,Water,Dark,72,95,67,103,71,122,6,FALSE +659,Bunnelby,Normal,,38,36,38,32,36,57,6,FALSE +660,Diggersby,Normal,Ground,85,56,77,50,77,78,6,FALSE +661,Fletchling,Normal,Flying,45,50,43,40,38,62,6,FALSE +662,Fletchinder,Fire,Flying,62,73,55,56,52,84,6,FALSE +663,Talonflame,Fire,Flying,78,81,71,74,69,126,6,FALSE +664,Scatterbug,Bug,,38,35,40,27,25,35,6,FALSE +665,Spewpa,Bug,,45,22,60,27,30,29,6,FALSE +666,Vivillon,Bug,Flying,80,52,50,90,50,89,6,FALSE +667,Litleo,Fire,Normal,62,50,58,73,54,72,6,FALSE +668,Pyroar,Fire,Normal,86,68,72,109,66,106,6,FALSE +669,Flabébé,Fairy,,44,38,39,61,79,42,6,FALSE +670,Floette,Fairy,,54,45,47,75,98,52,6,FALSE +671,Florges,Fairy,,78,65,68,112,154,75,6,FALSE +672,Skiddo,Grass,,66,65,48,62,57,52,6,FALSE +673,Gogoat,Grass,,123,100,62,97,81,68,6,FALSE +674,Pancham,Fighting,,67,82,62,46,48,43,6,FALSE +675,Pangoro,Fighting,Dark,95,124,78,69,71,58,6,FALSE +676,Furfrou,Normal,,75,80,60,65,90,102,6,FALSE +677,Espurr,Psychic,,62,48,54,63,60,68,6,FALSE +678,MeowsticMale,Psychic,,74,48,76,83,81,104,6,FALSE +678,MeowsticFemale,Psychic,,74,48,76,83,81,104,6,FALSE +679,Honedge,Steel,Ghost,45,80,100,35,37,28,6,FALSE +680,Doublade,Steel,Ghost,59,110,150,45,49,35,6,FALSE +681,AegislashBlade Forme,Steel,Ghost,60,150,50,150,50,60,6,FALSE +681,AegislashShield Forme,Steel,Ghost,60,50,150,50,150,60,6,FALSE +682,Spritzee,Fairy,,78,52,60,63,65,23,6,FALSE +683,Aromatisse,Fairy,,101,72,72,99,89,29,6,FALSE +684,Swirlix,Fairy,,62,48,66,59,57,49,6,FALSE +685,Slurpuff,Fairy,,82,80,86,85,75,72,6,FALSE +686,Inkay,Dark,Psychic,53,54,53,37,46,45,6,FALSE +687,Malamar,Dark,Psychic,86,92,88,68,75,73,6,FALSE +688,Binacle,Rock,Water,42,52,67,39,56,50,6,FALSE +689,Barbaracle,Rock,Water,72,105,115,54,86,68,6,FALSE +690,Skrelp,Poison,Water,50,60,60,60,60,30,6,FALSE +691,Dragalge,Poison,Dragon,65,75,90,97,123,44,6,FALSE +692,Clauncher,Water,,50,53,62,58,63,44,6,FALSE +693,Clawitzer,Water,,71,73,88,120,89,59,6,FALSE +694,Helioptile,Electric,Normal,44,38,33,61,43,70,6,FALSE +695,Heliolisk,Electric,Normal,62,55,52,109,94,109,6,FALSE +696,Tyrunt,Rock,Dragon,58,89,77,45,45,48,6,FALSE +697,Tyrantrum,Rock,Dragon,82,121,119,69,59,71,6,FALSE +698,Amaura,Rock,Ice,77,59,50,67,63,46,6,FALSE +699,Aurorus,Rock,Ice,123,77,72,99,92,58,6,FALSE +700,Sylveon,Fairy,,95,65,65,110,130,60,6,FALSE +701,Hawlucha,Fighting,Flying,78,92,75,74,63,118,6,FALSE +702,Dedenne,Electric,Fairy,67,58,57,81,67,101,6,FALSE +703,Carbink,Rock,Fairy,50,50,150,50,150,50,6,FALSE +704,Goomy,Dragon,,45,50,35,55,75,40,6,FALSE +705,Sliggoo,Dragon,,68,75,53,83,113,60,6,FALSE +706,Goodra,Dragon,,90,100,70,110,150,80,6,FALSE +707,Klefki,Steel,Fairy,57,80,91,80,87,75,6,FALSE +708,Phantump,Ghost,Grass,43,70,48,50,60,38,6,FALSE +709,Trevenant,Ghost,Grass,85,110,76,65,82,56,6,FALSE +710,PumpkabooAverage Size,Ghost,Grass,49,66,70,44,55,51,6,FALSE +710,PumpkabooSmall Size,Ghost,Grass,44,66,70,44,55,56,6,FALSE +710,PumpkabooLarge Size,Ghost,Grass,54,66,70,44,55,46,6,FALSE +710,PumpkabooSuper Size,Ghost,Grass,59,66,70,44,55,41,6,FALSE +711,GourgeistAverage Size,Ghost,Grass,65,90,122,58,75,84,6,FALSE +711,GourgeistSmall Size,Ghost,Grass,55,85,122,58,75,99,6,FALSE +711,GourgeistLarge Size,Ghost,Grass,75,95,122,58,75,69,6,FALSE +711,GourgeistSuper Size,Ghost,Grass,85,100,122,58,75,54,6,FALSE +712,Bergmite,Ice,,55,69,85,32,35,28,6,FALSE +713,Avalugg,Ice,,95,117,184,44,46,28,6,FALSE +714,Noibat,Flying,Dragon,40,30,35,45,40,55,6,FALSE +715,Noivern,Flying,Dragon,85,70,80,97,80,123,6,FALSE +716,Xerneas,Fairy,,126,131,95,131,98,99,6,TRUE +717,Yveltal,Dark,Flying,126,131,95,131,98,99,6,TRUE +718,Zygarde50% Forme,Dragon,Ground,108,100,121,81,95,95,6,TRUE +719,Diancie,Rock,Fairy,50,100,150,100,150,50,6,TRUE +719,DiancieMega Diancie,Rock,Fairy,50,160,110,160,110,110,6,TRUE +720,HoopaHoopa Confined,Psychic,Ghost,80,110,60,150,130,70,6,TRUE +720,HoopaHoopa Unbound,Psychic,Dark,80,160,60,170,130,80,6,TRUE +721,Volcanion,Fire,Water,80,110,120,130,90,70,6,TRUE diff --git a/PROFESORES/PYTHON/EJERCICIOS_EVALUABLES.md b/PROFESORES/PYTHON/EJERCICIOS_EVALUABLES.md new file mode 100644 index 000000000..cbb3ab832 --- /dev/null +++ b/PROFESORES/PYTHON/EJERCICIOS_EVALUABLES.md @@ -0,0 +1,189 @@ +# Ejercicios Evaluables - Curso Python - EDEM_MDA2324 + +En este archivo Markdown encontraréis los distintos ejercicios que tendréis que realizar tras cada una de las sesiones del curso de Python. + +Estos ejercicios serán evaluables y contarán para las notas finales que recibáis por mi parte. + +Los ejercicios estarán basados en aquello que hayamos visto durante las clases y podré ayudaros en caso de necesitarlo ;) + +## Modo de entrega + +Aprovechando que conocéis GIT, la idea es que entreguéis los distintos ejercicios a través de GIT. + +Es decir, en la carpeta del repositorio donde tengáis vuestro usuario, deberéis subir la solución del ejercicioo en cuestión y después realizar una Pull Request cuando esté presentable para que pueda revisarla. + +El nombre de las carpetas y ejercicios deben dejar claro la sesión y el número de ejercicio. Por ejemplo: + +``` +├── Curso de Python + ├── Sesión 1 + ├── Ejercicio 1 + | ├── Archivo(s).py + ├── Ejercicio 2 + | ├── Archivo(s).py + └── Ejercicio 3 + | ├── Archivo(s).py + ├── Etc. +``` + +En caso de que tengas problemas con los pasos a seguir, no dudes en ponerte en decírmelo para echarte un cable. + +# Sesión 1 + +## Ejercicio 1 +1. Crea un archivo Python que muestre por consola "¡Hola, Mundo!" + +## Ejercicio 2 +2. Ahora modifícalo para que muestre "¡Hola, [Nombre]" donde el nombre es el valor (str) de una variable + + + +# Sesión 2 + +## Ejercicio +1. Crea una aplicación de consola que calcule los resultados de una inversión. Debe + 1. Pedir por consola una cantidad (numérica) de Inversión + 2. Pedir el % de interés anual + 3. Pedir el número de años que se va a mantener la inversión + 4. Finalmente, calcular la cantidad generada en los años especificados por el usuario + +Debería resultar en algo así vía consola: + +## Paso 1 + +```bash +> Hola. Bienvenido al sistema de cálculo de inversiones. +> ¿Cuánto quieres invertir? +> (EL usuario escribe aquí la cantidad) +``` + +## Paso 2 + +```bash +> ¿Cuál es el interés anual? +> (EL usuario escribe aquí el interés anual) +``` + +## Paso 3 + +```bash +> ¿Cuántos años vas a mantener la inversión? +> (EL usuario escribe aquí el nº de años) +``` + +## Paso 4 - Final + +```bash +> En [N] años habrás recibido [X]€ de interés +> (Donde [N] debes sustituirlo por el número de años y [X] por la cantidad generada) +``` + +# Sesión 3 + +## Ejercicio 1 +1. A la aplicación de la calculadora de inversión, deberás añadirle una opción para salir de la consola. + +Debería quedar algo parecido a lo siguiente + +```bash +> Hola. Bienvenido al sistema de cálculo de inversiones. ¿Qué quieres hacer? +> [1] Calcular una inversión +> [X] Salir +> (Aquí el usuario deberá escribir 1 o X. Ningón otro valor será considerado como válido. Saliendo el mismo mensaje si introduce algo distinto a 1 o X) +``` + +En caso de escribir 1 --> **Se** deberá proceder al sistema de Cálculo de inversión. En todas las pantallas posteriores, se debe mostrar la opción de [X] Salir + +```bash +> En [N] años habrás recibido [X]€ de interés. ¿Qué quieres hacer ahora? +> [1] Calcular una nueva inversión +> [X] Salir +``` + +En caso de escribir **X** --> La aplicación debe mostrar un mensaje de despedida y cerrarse: + +```bash +> ¡Nos vemos! +> (aplicación cerrada con un exit()) +``` + +## Ejercicio 2 +2. Crea un programa en Python que sea capaz de calcular y mostrar por consola todos los números primos de [1 - 100](https://es.wikipedia.org/wiki/N%C3%BAmero_primo) + +## Ejercicio 3 +3. Crea un programa en Python que sea capaz de identificar a partir de una lista de años si un año es bisiesto o no. + + +# Sesión 4 + +## Ejercicio 1 +1. A partir de las respuestas a los dos últimos ejercicios de la Sesión 3: + 1. Crea una función que reciba un rango de números como parámetro y muestre por consola únicamente los valores primos + 2. Crea una función que pueda evaluar si un número (pasado por parámetro) es primo o no + 3. Crea una función que reciba un año y pueda indicarte con True o False si es un año bisiesto o no. + +## Ejercicio 2 +2. Crea un proyecto Plantilla de Python que disponga de un archivo requirements.txt y un .venv que pueda ser ejecutado desde Visual Studio Code + +## Ejercicio 3 +3. Realiza una petición HTTPs a la ruta https://randomuser.me/api y muestra por consola el nombre y los apellidos retornados por la API + + +# Sesión 5 + +## Ejercicio 1 +1. Lee el archivo CSV con Pandas de 'pokemon_data.csv' alojado en la carpeta de datos de este repositorio y realizar las siguientes operaciones +- IMPRIMIR TODOS LOS VALORES +- IMPRIMIR LOS PRIMEROS 5 +- IMPRIMIR LOS ÚLTIMOS 5 +- OBTENER NOMBRES DE LAS COLUMNAS +- OBTENER TODOS LOS NOMBRES +- OBTENER TODOS LOS NOMBRES Y VELOCIDADES +- LOS PRIMEROS 5 NOMBRES USANDO [::] +- OBTENER TODAS LAS FILAS +- OBTENER UN RANGO DE FILAS +- OBTENER EL NOMBRE DE LA FILA 10 +- ITERAR POR TODOS Y MOSTRAR EL ÍNDICE Y NOMBRE DE CADA FILA +- POKEMONS DE TIPO 1 == WATER +- ESTADÍSTICAS (usando Describe del DafaFrame) +- ORDENACIÓN POR NOMBRE ASCENDENTE +- CREAR UNA COLUMNA EXTRA CALCULADA + - La columna debe ser la suma de HP + ATAQUE + DEFENSA + VELOCIDAD + - La columna debe llamarse TOTAL +- ELIMINAR LA COLUMNA TOTAL +- FILTRAR POKEMONS DE TIPOS "GRASS" Y "POISON" +- FILTRAR POKEMONS DE TIPO "FIRE" Ó "POISON +- FILTRAR POKEMONS DE TIPO "GRASS" Y "POISON" Y UN HP >= 70 +- FILTRAR POKEMONS CON NOMBRE "MEGA" +- FILTRAR POKEMONS SIN NOMBRE "MEGA" +- FILTRAR POKEMONS CUYOS NOMBRES COMIENCEN CON "PI" +- RENOMBRADO DE COLUMNA "FIRE" a "FLAME" +- RENOMBRAR DE NUEVO A "FIRE" LA COLUMNA "FLAME" RECIÉN CAMBIADA +- CAMBIAR A TODOS LOS POKEMON LEGENDARIOS A TIPO "FIRE" +- (Agrupación - groupBy) ESTADÍSTICAS DE MEDIA POR TIPO DE POKEMON y ORDENADOS POR DEFENSA +- (Agrupación - groupBy) ESTADÍSTICAS DE MEDIA POR TIPO DE POKEMON y ORDENADOS POR ATAQUE +- (Agrupación - groupBy) ESTADÍSTICAS DE MEDIA POR TIPO DE POKEMON y ORDENADOS POR HP +- (Agrupación - groupBy) ESTADÍSTICAS DE SUMA POR TIPO DE POKEMON +- (Agrupación - groupBy) ESTADÍSTICAS DE NÚMERO DE POKEMONS POR TIPO 1DE POKEMON +- (Agrupación - groupBy) ESTADÍSTICAS DE NÚMERO DE POKEMONS POR TIPO 1 y 2 DE POKEMON + + +Nota: +SI TENEMOS ARCHIVOS ENORMES (1TB) PODEMOS LEERLOS POR PARTES +Cada fila podría estar acumulando cerca de 20 bytes, por lo que podríamos estar trabajando con cantidades enormes + +- LEE EL ARCHIVO CVS SEPARÁNDOLO POR CHUNKS Y CON UN TAMAÑO DE (chunksize=5) +- ITERA POR LOS CHUNKS Y MUÉSTRALOS POR CONSOLA + +## Ejercicio 2 +2. Crea una clase Automóvil que disponga de los atributos necesarios y métodos para: + 1. Arrancar + 2. Acelerar + 3. Frenar + 4. Parar + +## Ejercicio 3 +3. Crea clases de automóvil distintas como y que dispongan de distintos atributos, pero hereden los métodos de Automóvil a la hora de Arrancar, Acelerar, Frenar o Parar, salvo que algunos deben tener más potencia que otros: + 1. Coche + 2. Moto + 3. Camión diff --git a/PROFESORES/PYTHON/Ejemplo IA Chat GPT/ejemplo_chatgpt.py b/PROFESORES/PYTHON/Ejemplo IA Chat GPT/ejemplo_chatgpt.py new file mode 100644 index 000000000..7e397961b --- /dev/null +++ b/PROFESORES/PYTHON/Ejemplo IA Chat GPT/ejemplo_chatgpt.py @@ -0,0 +1,23 @@ +import os +import openai + +# Configuramos la API KEY de OPEN AI +openai.api_key = '' # 'TODO: AQUÍ TIENES QUE PONER TU API KEY DE CHATGPT' + +prompt = input('Redacta con detalle el resultado que quieres: ') +file_name = input('Indica el nombre del archivo donde se va a guardar: ') +file_extension = input('Indica la extensión del archivo: .') + +# realizamos petición a Open AI +response = openai.Completion.create( + model="text-davinci-003", + prompt= prompt, + temperature=0.7, + max_tokens=900, + top_p=1, + frequency_penalty=0, + presence_penalty=0 +) + +with open(f'{file_name}.{file_extension}', 'w') as file: + file.write(response["choices"][0]["text"]) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Ejemplo IA Chat GPT/requirements.txt b/PROFESORES/PYTHON/Ejemplo IA Chat GPT/requirements.txt new file mode 100644 index 000000000..f0dd0aec5 --- /dev/null +++ b/PROFESORES/PYTHON/Ejemplo IA Chat GPT/requirements.txt @@ -0,0 +1 @@ +openai \ No newline at end of file diff --git a/PROFESORES/PYTHON/Ejemplos POO/models/empleado.py b/PROFESORES/PYTHON/Ejemplos POO/models/empleado.py new file mode 100644 index 000000000..7d9c163e8 --- /dev/null +++ b/PROFESORES/PYTHON/Ejemplos POO/models/empleado.py @@ -0,0 +1,22 @@ +from persona import Persona + +class Empleado(Persona): + puesto:str + pHora:float + hTrabajadas: int = 0 + cobro: float = 0.0 + + def __init__(self, nombre, apellidos, edad, puesto, pHora): + # Llamamos al constructor del padre (Persona) + super(Empleado, self).__init__(nombre, apellidos, edad) + # Inicializar los atributos propios de Empleado + self.puesto = puesto + self.pHora = pHora + + def trabajar(self, horas:int): + self.hTrabajadas += horas + print(f'{self.nombre} ha trabajado {horas} horas. En total lleva trabajadas: {self.hTrabajadas} horas') + + def cobrar(self): + self.cobro = self.pHora*self.hTrabajadas + print(f'{self.nombre} cobra: {self.cobro}€') \ No newline at end of file diff --git a/PROFESORES/PYTHON/Ejemplos POO/models/persona.py b/PROFESORES/PYTHON/Ejemplos POO/models/persona.py new file mode 100644 index 000000000..b2ecee0f3 --- /dev/null +++ b/PROFESORES/PYTHON/Ejemplos POO/models/persona.py @@ -0,0 +1,12 @@ +class Persona: + nombre:str + apellidos:str + edad: int + + def __init__(self,nombre, apellidos, edad): + self.nombre = nombre + self.apellidos = apellidos + self.edad = edad + + def saludar(self): + print(f'Hola, me llamo {self.nombre} {self.apellidos} y tengo {self.edad} años') \ No newline at end of file diff --git a/PROFESORES/PYTHON/PROYECTO_FINAL.md b/PROFESORES/PYTHON/PROYECTO_FINAL.md new file mode 100644 index 000000000..b7cdfe023 --- /dev/null +++ b/PROFESORES/PYTHON/PROYECTO_FINAL.md @@ -0,0 +1,38 @@ +# Proyecto Final - Curso Python - EDEM_MDA2324 + + +Una empresa de formación quiere gestionar su cartera de alumnos. + +Escribe un programa que guarde una lista de alumnos. Cada Alumno dispone de los siguientes campos: + +- NIF (string) +- Nombre (string) +- Apellidos (string) +- Teléfono (string) +- Email (string) +- Aprobado (boolean) + +El programa debe mostrar las siguientes opciones por consola para que escoja el usuario: + +(1) Añadir un alumno --> Esto activará una serie de preguntas para completar el nuevo alumno + +(2) Eliminar alumno por NIF + +(3) Actualizar datos de un alumno por NIF + +(4) Mostrar datos de un alumno por NIF + +(5) Mostrar datos de un alumno por Email + +(6) Listar TODOS os alumnos + +(7) Aprobar Alumno por NIF + +(8) Suspender Alumno por NIF + +(9) Mostrar alumnos aprobados + +(10) Mostrar alumnos suspensos + +(X) Finalizar Programa --> Únicamente se cierra el programa si el usuario pulsa la X + diff --git a/PROFESORES/PYTHON/README.md b/PROFESORES/PYTHON/README.md new file mode 100644 index 000000000..6f8d64d6e --- /dev/null +++ b/PROFESORES/PYTHON/README.md @@ -0,0 +1,28 @@ +# EDEM_MDA2324 - Curso de Python + +## Algo sobre mi - Martín San José de Vicente + +Mi nombre es **Martín San José de Vicente** y actualmente soy CTO en **[Imagina Formación](https://imaginaformacion.com)**, empresa dedicada a la formación de equipos de profesionales de empresas en todo tipo de lenguajes de programación, frameworks, herramientas y metodologías de trabajo en proyectos tecnológicos. + +### Mis credenciales +Hace más años de lo que me gustaría admitir, estudié el *Doble Grado en Ingeniería Informática y Administración y Dirección de Empresas* en la Universidad Carlos III de Madrid. Desde antes de terminar la carrera ya me fascinaba compartir mis conocimientos a través de una asociación de formación gartuita que monté con uno de mis compañeros de clase, donde enseñábamos a programar (lenguajes como Javascript o Python, además de crear aplicaciones web y móviles - Android e iOS) a perfiles de otras carreras universitarias para aproximarles un poco a este mundo que es tan interesante e importante a día de hoy. + +Al mismo tiempo que estudiaba estuve trabajando en distintos proyectos de SW para proyectos de Vodafone. Una vez acabada la carrera, entré como desarrollador fullstack en proyectos de las empresas: Banco Santander, Telefónica y Bricor, entre otras. + +Desde hace ya más de 5 años, vengo impartiendo formaciones a empresas desde IMAGINA y en los últimos años aquí en EDEM y, sinceramente, me encanta este trabajo. Poder transmitir mis conocimientos y experiencias profesionales a otros compañeros de profesión que podrán ponerlos en práctica en sus puestos de trabajo es una motivación para mi. + + +### Mi objetivo durante este curso con vosotros +Durante este curso de Python, mi objetivo principal va a ser que conozcáis los fundamentos de uno de los lenguajes de programación modernos más demandados en la actualidad y acercaros un poco más a esta herramienta que os va a servir de tanto, tanto durante el Master Data de Edem como en vuestro futuro profesional una vez lo hayáis acabado, estoy seguro, con éxito. + +Si quieres agregarme en **[LinkedIn](https://www.linkedin.com/in/mart%C3%ADn-san-jos%C3%A9-de-vicente-316b9a88/)**, estaré encantado de aceptarte como contacto. + +## Enlaces de Interés y Web del Curso + +Para facilitar el seguimiento del curso he preparado una pequeña "web" en la que iré documentando los avances que vayamos realizando, además de ubicar ejercicios, enlaces de interés y manuales que pueda ayudaros durante el curso y tras el mismo. + +Mi intención es que uséis este enlace como una herramienta más durante el curso para aprender y como un blog resumen del curso para que una vez finalizada la última sesión, no se olvide nada de lo que hemos realizado. + +Sentíos libres de entrar y comentarme cualquier aspecto del mismo o mejora que se os ocurra que pueda realizar y os ayude durante este Máster. + +¡Aquí la tenéis! **[Web para el curso](https://master-data-2023.vercel.app/)** \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto10.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto10.py" new file mode 100644 index 000000000..d5ca81a6c --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto10.py" @@ -0,0 +1,28 @@ +''' + ---------- RETO 10 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que guarde en una variable el siguiente contenido: +{'titulo':'El Más Allá','aka':'E tu vivrai nel terrore - Laldilà','director':'Lucio Fulci', 'año':1981, 'país':'Italia'} +''' + +def reto10Basico(): + pelicula: dict = dict({ + 'título': 'El Más Allá', + 'aka': 'E tu vivrai nel terrore - L\'aldilà', + 'director': 'Lucio Fulci', + 'año': 1981, + 'país': 'Italia' + }) + + print(f"- Nombre de la película {pelicula.get('título')}") + print(f"- AKA de la película {pelicula.get('aka')}") + print(f"- Director de la película {pelicula.get('director')}") + print(f"- Año de la película {pelicula.get('año')}") + print(f"- País de la película {pelicula.get('país')}") diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto11.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto11.py" new file mode 100644 index 000000000..dfbf1b046 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto11.py" @@ -0,0 +1,26 @@ +''' + ---------- RETO 11 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que pida al usuario los siguientes datos por consola: +Título de la películaDirectorAñoPaísE introduzca esos valores en una variable GLOBAL llamada "pelicula". +''' + +global pelicula + +def reto11Basico(): + pelicula: dict = dict() + + pelicula['Titulo'] = input('Introduzca el TÍTULO de la película: ') + pelicula['Director'] = input('Introduzca el DIRECTOR de la película: ') + pelicula['Año'] = int(input('Introduzca el AÑO de la película: ')) + pelicula['País'] = input('Introduzca el PAÍS de la película: ') + + + print(pelicula) \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto12.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto12.py" new file mode 100644 index 000000000..86efd4849 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto12.py" @@ -0,0 +1,24 @@ +''' + ---------- RETO 12 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que almacene en una lista (Array) todos los nombres de los alumnos del curso y los muestre en por pantalla. +''' + +def reto12Basico(): + lista_alumnos: [str] = ['Julia', 'Hugo', 'Miguel', + 'Victor', 'Juan', 'Aberto', + 'María', 'Andreu', 'Borja', + 'Claudio', 'Elisa', 'Diego', + 'Enrique', 'Estela', 'Francisco', + 'Irene', 'Isabel', 'Javier', 'Luis', 'Marta'] + + print('*** ALUMNOS ***') + for alumno in lista_alumnos: + print(f"- {alumno}") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto13.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto13.py" new file mode 100644 index 000000000..586888567 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto13.py" @@ -0,0 +1,25 @@ +''' + ---------- RETO 13 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe una función que calcule el área de un triángulo, recibiendo la altura y la base como parámetros y otra función que calcule el área de un círculo recibiendo el radio del mismo +''' + +import math + +def area_triangulo(altura: float, base: float) -> float: + return ((altura*base)/2) + +def area_circulo(radio: float) -> float: + return (math.pi*(radio**2)) + + +def reto13Basico(): + print(f"Área de triangulo: {area_triangulo(2,2)}") + print(f"Área del círculo: {area_circulo(4)}") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto14.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto14.py" new file mode 100644 index 000000000..3eb670aae --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto14.py" @@ -0,0 +1,24 @@ +''' + ---------- RETO 14 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe una función que use la función del área del círculo para devolver el volumen de un cilindro, +obteniendo por parámetro la longitud del mismo. +''' + +import math + +def area_circulo(radio: float) -> float: + return (math.pi*(radio**2)) + +def volumen_cilindro(radio:float, longitud: float) -> float: + return area_circulo(radio)*longitud + +def reto14Basico(): + print(f"El volumen del cilindro es: {volumen_cilindro(2, 3)}") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto15.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto15.py" new file mode 100644 index 000000000..17121db61 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto15.py" @@ -0,0 +1,22 @@ +''' + ---------- RETO 15 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe una función que reciba una muestra de números en una lista y devuelva otra lista con sus cuadrados. +''' + +def doble (lista:[int]) -> [int]: + for i, numero in enumerate(lista): + lista[i] *=2 + +def reto15Basico(): + miLista = [10,50,100] + # Lo pasaremos por referencia + doble(miLista) + print(f"Lista Original Modificada: {miLista}") #[20, 100, 200] \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto16.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto16.py" new file mode 100644 index 000000000..f8e6c0d65 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto16.py" @@ -0,0 +1,20 @@ +''' + ---------- RETO 16 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Crea un script que sea capaz de restar dos fechas y muestra el resultado por consola +''' + +from datetime import date + +def reto16Basico(): + fechaA = date(2000,2,28) + fechaB = date(2001,2,28) + + print(fechaB-fechaA) \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto17.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto17.py" new file mode 100644 index 000000000..926c927b6 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto17.py" @@ -0,0 +1,63 @@ +''' + ---------- RETO 17 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Partiendo de la siguiente tupla: +tupla = (2, 4, 3, 5, 4, 6, 7, 8, 6, 1) +Realiza las siguientes operaciones: + +- Encontrar los elementos de 3 a 5 +- Encontrar los 6 primeros elementos +- Muestra la tupla desde el 5 elemento hasta el final +- Muestra toda la tupla haciendo uso de [:] +- Muestra todos los elementos desde la posición 2 a la 9 de dos en dos +- Devuelve la tupla con un salto cada 4 elementos +- Usa un step negativo para mostrar la tupla desde la posición 9 a la 2 +''' + +def reto17Basico(): + tupla = (2, 4, 3, 5, 4, 6, 7, 8, 6, 1) + + # Elementos 3 a 5 + # tupla [start:stop] + _slice = tupla[3:5] + print('1-', _slice) + + # Primeros 6 elementos + # Si no se define un start, se toma desde el principio + _slice = tupla[:6] + print('2-',_slice) + + # Desde 5 Hasta el final + _slice = tupla[5:] + print('3-',_slice) + + # Toda la Tupla + _slice = tupla[:] + print('4-',_slice) + + # Los índices se pueden definir con valores negativos + _slice = tupla[-8:-4] + print('5-',_slice) + + # Reformulando la tupla + tupla = tuple("HELLO WORLD") + print('6-',tupla) + + # tupla[start:stop:step] + _slice = tupla[2:9:2] + print('7-',_slice) + + # Devuelve una tupla con un salto cada 4 elementos + _slice = tupla[::4] + print('8-',_slice) + + # Cuando tenemos el Step negativo, va desde atrás + _slice = tupla[9:2:-4] + print('9-',_slice) \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto18.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto18.py" new file mode 100644 index 000000000..eedd91686 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto18.py" @@ -0,0 +1,22 @@ +''' + ---------- RETO 18 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Crea una función que sea capaz de eliminar un caracter concreto de una cadena de texto. +''' + +def eliminar(str, n): + inicio = str[:n] + final = str[n+1:] + return inicio + final + +def reto18Basico(): + print(eliminar('Madrid', 0)) #adrid + print(eliminar('Madrid', 3)) #Madid + print(eliminar('Madrid', 5)) #Madri diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto2.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto2.py" new file mode 100644 index 000000000..b51b7b219 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto2.py" @@ -0,0 +1,28 @@ +''' + ---------- RETO 2 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa capaz de mostrar todos los números impares desde un número de inicio y otro final. +Por ejemplo: teniendo numero_inicial = 2 y numero_final = 8, el programa debe imprimir por consola: [3, 5, 7] +''' + +def reto2Basico(): + numero_inicial: int = int(input('Introduce un número: ')) + numero_final: int = int(input('Inroduce otro número: ')) + numeros_impares: [int] = [] + + while numero_final <= numero_inicial: + numero_final: int = int(input('El segundo número debe ser mayor que el primero. Inroduce otro número: ')) + + for i in range(numero_inicial, numero_final+1): + if(i % 2 != 0): + numeros_impares.append(i) + + print(f"Lista de Números impares entre {numero_inicial} y {numero_final}:") + print(numeros_impares) \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto3.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto3.py" new file mode 100644 index 000000000..62bf31708 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto3.py" @@ -0,0 +1,18 @@ +''' + ---------- RETO 3 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que sea capaz de mostrar los números del 1 al 100 en orden inverso. +''' + +def reto3Basico(): + rango_1_100 = range(1,101) + + for i in reversed(rango_1_100): + print(f"- {i}") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto4.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto4.py" new file mode 100644 index 000000000..029dd0fc9 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto4.py" @@ -0,0 +1,22 @@ +''' + ---------- RETO 4 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que sea capaz de mostrar los elementos de una lista en orden inverso al original. +Por ejemplo: teniendo [1,2,3,4,5] el programa debe mostrar por pantalla [5,4,3,2,1] +''' + +def reto4Basico(): + lista: [int] = [1,2,3,4,5] + lista_inversa: [int] = reversed(lista) + + print(lista_inversa) + + for i in lista_inversa: + print(f"- {i}") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto5.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto5.py" new file mode 100644 index 000000000..6e8d1d8ef --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto5.py" @@ -0,0 +1,29 @@ +''' + ---------- RETO 5 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que sea capaz de pedirle a un usuario por consola** que introduzca una contraseña y mientras que ésta no sea "admin", el programa seguirá pidiéndola. + +Si la contraseña es errónea, deberá sacarle un mensaje de error y volver a pedirle la contraseña hasta que la introduzca bien. Si el usuario introduce "admin" correctamente, el programa le deberá mostrar un mensaje "Bienvenido al programa señor ADMIN" y luego terminar. + +NOTA: Para pedir por pantalla y guardarlo en una variable llamada password debes hacer uso de password:str = input('Introduce una contraseña') +''' + +def reto5Basico(): + acierto: bool = False + valor_introducido: str = '' + + while not acierto: + valor_introducido = input('Introduzca una contraseña: ') + if(valor_introducido == 'admin'): + acierto = True + print(f"Bienvenido al programa señor ADMIN") + break + else: + print("¡Contraseña incorrecta!") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto6.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto6.py" new file mode 100644 index 000000000..44a1871eb --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto6.py" @@ -0,0 +1,22 @@ +''' + ---------- RETO 6 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que pregunte al usuario su edad y muestre por pantalla si es mayor de edad o no. +''' + +def reto6Basico(): + # Usaremos la función input("mensaje") para solicitar algo al usuario por consola + edad: int = input('Hola Usuario, introduce tu edad: ') + + # lo que se obtiene desde input() es siempre un str, así que lo tenemos que convertir a un número entero -> int(edad) + if(int(edad) < 18): + print('Eres menor de edad') + else: + print('Eres Mayor de edad') \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto7.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto7.py" new file mode 100644 index 000000000..41eabf53a --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto7.py" @@ -0,0 +1,22 @@ +''' + ---------- RETO 7 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que contenga dos variables. Una de ellas representa la contraseña de un usuario y la otra un texto introducido. +El programa debe poder mostrar por pantalla si las dos cadenas de texto son iguales sin tener en cuenta mayúsculas y minúsculas. +''' + +def reto7Basico(): + pass_word: str = "admin" + texto_introducido: str = "AdMiN" + + if(pass_word.lower() == texto_introducido.lower()): + print ("¡HURRA! ¡Las contraseñas son iguales!") + else: + print ("¡ERROR! ¡Las contraseñas no coinciden!") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto8.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto8.py" new file mode 100644 index 000000000..fa0cae1e3 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto8.py" @@ -0,0 +1,33 @@ +''' + ---------- RETO 8 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que pueda decirte si un número (número entero) es primo o no +''' + +''' +Número Primo: Aquellos números que son +únicamente divisibles entre sí mismos y 1 +Son mayores que 1: +[2, 3, 5, 7, 11, 13, 17, 19, 23, etc. ] +''' + +def reto8Basico(): + numero: int = int(input('Introduce un número entero: ')) + + if numero > 1: + # Buscamos los factores de número + for i in range(2,int(numero)): + if (int(numero) % i) == 0: + print(f"Lo siento, el número {numero} NO ES PRIMO. Es divisible entre {i}") + break + else: + print(f"¡ENHORABUENA!, el número {numero} ES PRIMO") + else: + print(f"Lo siento, el número {numero} NO ES PRIMO. Los números primos son mayores que 1") \ No newline at end of file diff --git "a/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto9.py" "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto9.py" new file mode 100644 index 000000000..ae46c56d5 --- /dev/null +++ "b/PROFESORES/PYTHON/Retos Solucionados/B\303\241sicos/reto9.py" @@ -0,0 +1,20 @@ +''' + ---------- RETO 9 Básico ----------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que pueda decirte si un año (número entero) es bisiesto o no +''' + +def reto9Basico(): + anio: int = int(input("Introduce un año y vamos a ver si es bisiesto... ")) + + if(anio % 4 == 0 and (anio % 100 != 0 or anio % 400 == 0)): + print(f"¡El año {anio} es bisiesto!") + else: + print(f"Lo sentimos. El año {anio} NO es bisiesto!") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto10.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto10.py new file mode 100644 index 000000000..756c7131a --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto10.py @@ -0,0 +1,29 @@ +''' + ---------- RETO 10 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribir una función que calcule el máximo común divisor de dos números y otra que calcule el mínimo común múltiplo. +''' + +import math + +def max_comun_div(numero_a, numero_b) -> int: + while numero_b: + numero_a,numero_b = numero_b, numero_a % numero_b + return numero_a + +def min_comun_mult(numero_a, numero_b) -> int: + return abs(numero_a*numero_b) + +def reto10Avanzado(): + print(f"El Máximo Común Divisor de 15 es: {max_comun_div(15, 30)}") + print(f"El Mínimo Común Múltiplo de 15 es: {max_comun_div(15, 30)}") + + # otra forma aún más sencilla + print(f"El Máximo Común Divisor de 15 es: {math.gcd(15, 30)}") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto11.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto11.py new file mode 100644 index 000000000..15811534e --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto11.py @@ -0,0 +1,118 @@ +''' + ---------- RETO 11 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Una empresa quiere gestionar su cartera de clientes. Escribe un programa que guarde los clientes en un diccionario u objeto literal en el que disponga de: + +- NIF (string) +- nombre (string) +- apellidos (string) +- teléfono (string) +- email (string) +- preferente (boolean) + +El programa debe mostrar las siguientes opciones para que escoja el usuario: + +(1) Añadir un cliente +(2) Eliminar cliente por NIF +(3) Mostrar Cliente por NIF +(4) Listar TODOS los clientes +(5) Mostrar ÚNICAMENTE los clientes preferentes +(6) Finalizar Programa + +''' + + +#Cartera de Clientes +lista_clientes: [] = [] +cliente = {} + +def crearCliente(): + global lista_clientes + cliente['NIF'] = input('NIF:') + cliente['Nombre'] = input("Nombre: ") + cliente['Apellidos'] = input("Apellidos: ") + cliente['Telefono'] = input("Telefono: ") + cliente['Email'] = input("Email: ") + cliente['Preferente'] = input('¿Es un cliente preferente? (Si / No)') + + lista_clientes.append(cliente) + +def mostrarClientes(): + global lista_clientes + for i, cliente in enumerate(lista_clientes): + print(f"{i} - {cliente['Nombre']} {cliente['Apellidos']}") + +def mostrarPreferentes(): + global lista_clientes + lista_preferentes: [] = [] + for i, cliente in enumerate(lista_clientes): + if(cliente['Preferente'] == 'Si'): + lista_preferentes.append(cliente) + print('Lista de clientes preferentes:') + for i, preferentes in enumerate(lista_preferentes): + print(f"{i} - {cliente['Nombre']} {cliente['Apellidos']}") + print('No existe ningún cliente con este NIF') + + +def mostrarClienteNIF(): + global lista_clientes + nif = input('¿NIF?') + for i, cliente in enumerate(lista_clientes): + if(nif == cliente['NIF']): + print(f"{nif} - {cliente['Nombre']} {cliente['Apellidos']}") + return + print('No existe ningún cliente con este NIF') + +def eliminarClienteNIF(): + global lista_clientes + nif = input('¿NIF?') + for i, cliente in enumerate(lista_clientes): + if(nif == cliente['NIF']): + lista_clientes.remove(cliente) + return + print('No existe ningún cliente con este NIF') + +def mensajeCierre(): + print("Cerrando...") + +def switch(opcion): + switcher = { + #invocar métodos + 1 : crearCliente, + 2 : mostrarClientes, + 3 : mostrarPreferentes, + 4 : mostrarClienteNIF, + 5 : eliminarClienteNIF, + 0 : mensajeCierre, + } + + func = switcher.get(opcion, "¡Opción no valida!") + func() # Ejecutamos la función asignada a la propiedad escogida + +opciones = """ +Opcion 1 : Añadir un cliente +Opcion 2 : Mostrar Todos los Clientes +Opcion 3 : Mostrar Clientes Preferentes +Opcion 4 : Mostrar Cliente por NIF +Opcion 5 : Eliminar Cliente por NIF +Opcion 0 : Salir +""" + +def reto11Avanzado(): + opcion = "" + + while(opcion!=0): + print(opciones) + try: + opcion = int(input("Bienvenido a tu cartera de clientes. Escoge una opción: ")) + print(f'Escogida {opcion}') + switch(opcion) + except: + print("Opcion no valida") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto12.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto12.py new file mode 100644 index 000000000..72516f94f --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto12.py @@ -0,0 +1,22 @@ +''' + ---------- RETO 12 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un script de código que haga al usuario introducir 8 alturas de edificios (deben ser float) +y que saque por consola las 3 más altas (haz uso de sorted). +''' + +def reto12Avanzado(): + print("Introduce la altura de 8 edificios:") + alturas = [int(input()) for i in range(8)] + + print("Las tres alturas más altas:") + alturas = sorted(alturas) + + print(*alturas[:4:-1], sep='\n') \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto13.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto13.py new file mode 100644 index 000000000..3ca95f76a --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto13.py @@ -0,0 +1,32 @@ +''' + ---------- RETO 13 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que sea capaz de encontrar la diferencia completa entre dos fechas, mostrando días, horas, minutos y segundos. +''' + +from datetime import datetime + +def date_diff_in_seconds(dt2, dt1): + timedelta = dt2 - dt1 + return timedelta.days * 24 * 3600 + timedelta.seconds + +def dhms_from_seconds(seconds): + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + days, hours = divmod(hours, 24) + return (days, hours, minutes, seconds) + + +def reto13Avanzado(): + date1 = datetime.strptime('2015-01-01 01:00:00', '%Y-%m-%d %H:%M:%S') + + date2 = datetime.now() + + print("\n%d días, %d horas, %d minutos, %d segundos" % dhms_from_seconds(date_diff_in_seconds(date2, date1))) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto14.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto14.py new file mode 100644 index 000000000..68ed9a7df --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto14.py @@ -0,0 +1,36 @@ +''' + ---------- RETO 14 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Partiendo de las siguientes cadenas de texto: + +miCodigo = 'print("Hola Mundo")' +otroCodigo = +""" + +def multiplicar(x,y): + return x*y + +print('Multiplica: 2 * 4: ',multiplicar(2,4)) +""" + +Haz uso de exec() para ejecutar ambas operaciones + +''' + +def reto14Avanzado(): + miCodigo = 'print("Hola Mundo")' + otroCodigo = """ + def multiplicar(x,y): + return x*y + + print('Multiplica: 2 * 4: ',multiplicar(2,4)) + """ + exec(miCodigo) + exec(otroCodigo) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto15.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto15.py new file mode 100644 index 000000000..05f39d7ae --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto15.py @@ -0,0 +1,23 @@ +''' + ---------- RETO 15 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Realiza un script que permita encontrar dentro de la lista aquellos elementos que sean tuplas. +Cada vez que encuentre una tupla, deberá incrementarse la variable cantidad. +''' + +def reto15Avanzado(): + numeros = [10,20,(1,3),30,50,69,(10,20),40] + cantidad = 0 + + for numero in numeros: + if isinstance(numero, tuple): + cantidad += 1 + + print(f"Cantidad de tuplas: {cantidad}") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto16.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto16.py new file mode 100644 index 000000000..73ea7a35a --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto16.py @@ -0,0 +1,23 @@ +''' + ---------- RETO 16 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Partiendo de la siguiente lista de tuplas: + +miLista = [(), (), ('',), ('a', 'b'), ('a', 'b', 'c'), ('d')] + +Actualiza la lista sin aquellas tuplas que estén vacías. +''' + +def reto16Avanzado(): + miLista = [(), (), ('',), ('a', 'b'), ('a', 'b', 'c'), ('d')] + + miLista = [tupla for tupla in miLista if tupla] + + print(miLista) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto17.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto17.py new file mode 100644 index 000000000..73f4a9e60 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto17.py @@ -0,0 +1,18 @@ +''' + ---------- RETO 17 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Crea un script que pueda mostrar la hora actual en milisegundos +''' +import time + +def reto17Avanzado(): + + mili_seg = int(round(time.time() * 1000)) + print(mili_seg) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto18.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto18.py new file mode 100644 index 000000000..fd1987c80 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto18.py @@ -0,0 +1,25 @@ +''' + ---------- RETO 18 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +A partir de la siguiente lista: + +colores = [("Black", "#000000", "rgb(0, 0, 0)"), ("Red", "#FF0000", "rgb(255, 0, 0)"),("Yellow", "#FFFF00", "rgb(255, 255, 0)")] + +Crea un script que pueda almacenar cada uno de los elementos (tuplas) de la lista en variable1, variable2 y variable3 para después imprimirlas por consola. +''' + +def reto18Avanzado(): + colores = [("Black", "#000000", "rgb(0, 0, 0)"), ("Red", "#FF0000", "rgb(255, 0, 0)"),("Yellow", "#FFFF00", "rgb(255, 255, 0)")] + + variable1, variable2, variable3 = colores + + print(variable1) + print(variable2) + print(variable3) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto19.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto19.py new file mode 100644 index 000000000..cbf9314fa --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto19.py @@ -0,0 +1,23 @@ +''' + ---------- RETO 19 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa capaz de imprimir los próximos tres días a partir de la fecha actual +(haz uso de datetime.datetime.today() para obtener la fecha actual). + +Pista: investiga acerca de datetime.timedelta() +''' +import datetime + +def reto19Avanzado(): + + base = datetime.datetime.today() + + for x in range(0, 3): + print(base + datetime.timedelta(days=x)) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto20.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto20.py new file mode 100644 index 000000000..469dc65e9 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto20.py @@ -0,0 +1,18 @@ +''' + ---------- RETO 20 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Haciendo uso de enumerate() muestra por consola cada uno de los caracteres de la palabra Valencia junto al índice de su posición. +''' + +def reto20Avanzado(): + palabra = "Madrid" + + for index, caracter in enumerate(palabra): + print("Caracter: ", caracter, "\nÍndice", index ) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto21.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto21.py new file mode 100644 index 000000000..7c2f00e37 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto21.py @@ -0,0 +1,22 @@ +''' + ---------- RETO 21 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' + +Haciendo uso de: +colores = ["Negro", "Rojo", "Marrón", "Amarillo"]representacion = ["#000000", "#FF0000", "#800000", "#FFFF00"] + +e investigando acerca de zip() deberás entrelazar ambas listas para obtener un diccionario que tenga la clave color cuyos valores son los de la lista colores y otra clave code que tendrá como valor los datos de la lista representación +''' + +def reto21Avanzado(): + colores = ["Negro", "Rojo", "Marrón", "Amarillo"] + representacion = ["#000000", "#FF0000", "#800000", "#FFFF00"] + + print([{'color': f, 'codigo': c} for f, c in zip(colores, representacion)]) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto22.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto22.py new file mode 100644 index 000000000..4508fcc53 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto22.py @@ -0,0 +1,27 @@ +''' + ---------- RETO 22 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +A partir de: + +listaTuplas = [("x", 1), ("x", 2), ("x", 3), ("y", 1), ("y", 2), ("z", 1)] +miDiccionario = {} + +Realiza una iteración para poder pasar de una lista de tuplas a un diccionario llamado miDiccionario + +''' + +def reto22Avanzado(): + listaTuplas = [("x", 1), ("x", 2), ("x", 3), ("y", 1), ("y", 2), ("z", 1)] + miDiccionario = {} + + for a, b in listaTuplas: + miDiccionario.setdefault(a, []).append(b) + + print (miDiccionario) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto23.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto23.py new file mode 100644 index 000000000..cc81f6af9 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto23.py @@ -0,0 +1,20 @@ +''' + ---------- RETO 23 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Investiga acerca de Counter del módulo collections y haciendo uso del siguiente diccionario, encuentra la moda en las puntuaciones de películas: + +misPeliculas = {'PeliculaA':81, 'PeliculaB':83, 'PeliculaC':87} +''' +from collections import Counter + +def reto23Avanzado(): + + puntuacion = Counter({'PeliculaA':81, 'PeliculaB':83, 'PeliculaC':87}) + print(puntuacion.most_common()) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto24.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto24.py new file mode 100644 index 000000000..85adc6399 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto24.py @@ -0,0 +1,26 @@ +''' + ---------- RETO 24 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Haciendo uso de {:.2%} + +Muestra por consola los valores 0.2564 y -0.253 como porcentajes de dos cifras. +''' + +def reto24Avanzado(): + x = 0.2564 + y = -0.253 + + print("\nNúmero Original: ", x) + print("Porcentaje: " + "{:.2%}".format(x)); + + print("Número Original: ", y) + print("Porcentaje: " + "{:.2%}".format(y)); + + print() \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto25.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto25.py new file mode 100644 index 000000000..ce044fb56 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto25.py @@ -0,0 +1,37 @@ +''' + ---------- RETO 25 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Partiendo de la lista: + +comunidades = ["Madrid", "Aragón", + "Valencia", "Cataluña", + "Extremadura", "Castilla y León", + "Castilla La Mancha", "Asturias", + "Murcia", "Cantabria", "País Vasco", + "Andalucia"] + +Crea una función que sea capaz de devolver una lista ordenada según la longitud de su nombre. +''' + +def comunidadMasLarga(lista): + longitud = [] + for comunidad in lista: + longitud.append((len(comunidad), comunidad)) + longitud.sort() + return longitud[-1][1] + + +def reto25Avanzado(): + print(comunidadMasLarga(["Madrid", "Aragón", + "Valencia", "Cataluña", + "Extremadura", "Castilla y León", + "Castilla La Mancha", "Asturias", + "Murcia", "Cantabria", "País Vasco", + "Andalucia"])) diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto26.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto26.py new file mode 100644 index 000000000..846abefa9 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto26.py @@ -0,0 +1,27 @@ +''' + ---------- RETO 26 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Crea una función que reciba una palabra y sea capaz de devolver una palabra del revés. +''' + +def reto26Avanzado(): + + ''' + def reverso(palabra): + if len(palabra) % 4 == 0: + return ''.join(reversed(palabra)) + return palabra + ''' + + def reverso2(palabra): + return ''.join(reversed(palabra)) + + print(reverso2('Madrid')) + print(reverso2('Valencia')) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto27.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto27.py new file mode 100644 index 000000000..54de66ef1 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto27.py @@ -0,0 +1,22 @@ +''' + ---------- RETO 27 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Crea un script que le pida al usuario una lista de países (separados por comas). +Éstos se deben almacenar en una lista. +No debería haber países repetidos (haz uso de set). +Finalmente, muestra por consola la lista de países ordenados alfabéticamente y separados por comas. +''' + +def reto27Avanzado(): + items = input("Introduce países separados por comas:\n") + + paises = [pais for pais in items.split(",")] + + print(",".join(sorted(list(set(paises))))) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto28.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto28.py new file mode 100644 index 000000000..7aeaee4d4 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto28.py @@ -0,0 +1,30 @@ +''' + ---------- RETO 28 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' +Crea un script que solicite al usuario que escriba una frase que contenga las palabras Madrid y Valencia. + +Una vez lo haya introducido, se debe mostrar la frase, habiendo sustituido Madrid por Valencia y Valencia por Madrid. + +Por ejemplo: Si el usuario introduce Vivo en Madrid y viajo a Valencia la salida del programa debe ser Vivo en Valencia y viajo a Madrid. +''' + +def reto28Avanzado(): + print("Introduce una frase con 'Madrid' y 'Valencia'") + text = input().split() + + for i in range(len(text)): + if "Madrid" in text[i]: + n = text[i].index("Madrid") + text[i] = text[i][:n] + "Valencia" + text[i][n + 6:] + elif "Valencia" in text[i]: + n = text[i].index("Valencia") + text[i] = text[i][:n] + "Madrid" + text[i][n + 8:] + + print(*text) \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto29.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto29.py new file mode 100644 index 000000000..d5e093e91 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto29.py @@ -0,0 +1,38 @@ +''' + ---------- RETO 29 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' + +Crea una función capaz de devolver el segundo valor numérico más pequeño de una lista de números. + +''' + +def segundoPeque(numeros): + + if (len(numeros)<2): + return + if ((len(numeros)==2) and (numeros[0] == numeros[1]) ): + return + + elementosDuplicados = set() + elementosUnicos = [] + + for x in numeros: + if x not in elementosDuplicados: + elementosUnicos.append(x) + elementosDuplicados.add(x) + elementosUnicos.sort() + return elementosUnicos[1] + +def reto29Avanzado(): + print(segundoPeque([1, 2, -8, -2, 0, -3])) #3 + print(segundoPeque([1, 1, 0, 0, 2, -2, -2])) #0 + print(segundoPeque([1, 1, 1, 0, 0, 0, 2, -2, -4])) #-2 + print(segundoPeque([2,2])) #None + print(segundoPeque([2])) #None \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto3.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto3.py new file mode 100644 index 000000000..6c0731b4d --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto3.py @@ -0,0 +1,54 @@ +''' + ---------- RETO 3 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +El nuevo gobierno ha decidido replantear el sistema de + pago de impuestos. Ha pensado que a partir de ahora: + + - si una persona es mayor de 16 años y menor de 70 ésta debe pagar impuestos. + + - En caso de no tener ingresos iguales o superiores a 800€ se + acumulará una deuda mensual del 10%. + + - Si supera los 800€, pero no llega a los 2000€, + deberá pagar un impuesto del 30% mensual + + - Si supera los 2000€ esta persona deberá pagar el 50% + en concepto de impuestos + + - si la persona es menor de 16 años, + no tiene que pagar impuestos + +Escribe un programa capaz de calcular la cantidad de impuestos, o endeudamiento, +de una lista de personas durante un año entero (12 meses). + +''' + + + + +def reto3Avanzado(): + edad = int(input('Introduce tu edad: ')) + sueldo_mensual: int = 0 + deuda_mensual: int = 0 + impuestos_mensual: int = 0 + + if(edad < 16 or edad > 70): + print('No tienes que pagar impuestos.') + else: + sueldo_mensual = int(input('Inroduce tu salario: ')) + if(sueldo_mensual <= 800): + deuda_mensual = sueldo_mensual * 0.10 + print(f"Con una edad de {edad} y unos ingresos de {sueldo_mensual},acumularás una deuda con el Estado de{deuda_mensual*12}€ este año") + elif(sueldo_mensual > 800 and sueldo_mensual < 2000): + impuestos_mensual = sueldo_mensual * 0.30 + print(f"Con una edad de {edad} y unos ingresos de {sueldo_mensual}, debes pagar {impuestos_mensual*12}€ en impuestos este año") + else: + impuestos_mensual = sueldo_mensual * 0.50 + print(f"Con una edad de {edad} y unos ingresos de {sueldo_mensual}, debes pagar {impuestos_mensual*12}€ en impuestos este año") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto30.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto30.py new file mode 100644 index 000000000..49ab8b914 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto30.py @@ -0,0 +1,27 @@ +''' + ---------- RETO 30 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------------- +''' + +''' + +Investiga acerca de ast y convierte un String en una lista. Es decir, un string que representa una lista literalmente. + +Puedes usar este ejemplo: +colores ="['Rojo', 'Verde', 'Blanco']" + + +Se trata de una cadena de texto que dentro contiene una lista. La idea es que a través de ast lo conviertas en una lista como tal. +''' + +import ast + + +def reto30Avanzado(): + colores ="['Rojo', 'Verde', 'Blanco']" + + print(ast.literal_eval(colores)) diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto4.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto4.py new file mode 100644 index 000000000..0e8229807 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto4.py @@ -0,0 +1,39 @@ +''' + ---------- RETO 4 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que almacene lenguajes de programación en una lista. +El programa deberá preguntar por consola si el usuario conoce o no el lenguaje. El usuario deberá responder "sí" o "no" y cualquier otra respuesta no será tenida en cuenta, preguntando de nuevo la misma pregunta: + +¿Conoces el lenguaje de programación "lenguaje"? (si / no) donde "lenguaje" es cada uno de los lenguajes de la lista. +Finalmente, el programa debe mostrar por pantalla la lista de los lenguajes y si el usuario los conoce o no. +Algo así: +- JavaScript: no +- TypeScript: sí +- Python: sí +- Dart: no + +''' + + +def reto4Avanzado(): + + lista_lenguajes: [str] = ['Python', 'TypeScript', 'JavaScript', 'Java', 'C#'] + lista_capacidades: [(str,str)] = [] + + + for lenguaje in lista_lenguajes: + conoce: str = input(f"¿Conoces el lenguaje {lenguaje}? (si / no) ") + while(conoce not in ['si', 'no']): + conoce = input(f"Debes responder \"si\" o \"no\". ¿Conoces el lenguaje {lenguaje}? (si / no) ") + lista_capacidades.append((lenguaje, conoce)) + + + for conocimiento in lista_capacidades: + print(f"- {conocimiento[0]}: {conocimiento[1]}") diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto5.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto5.py new file mode 100644 index 000000000..e0aed50a0 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto5.py @@ -0,0 +1,29 @@ +''' + ---------- RETO 5 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que realice lo mismo que el programa del reto 4, +pero que elimine de la lista aquellos lenguajes que el usuario conoce y únicamente muestre aquellos que no conoce. +''' + +def reto5Avanzado(): + lista_lenguajes: [str] = ['Python', 'TypeScript', 'JavaScript', 'Java', 'C#'] + lista_capacidades: [(str,str)] = [] + + for lenguaje in lista_lenguajes: + conoce: str = input(f"¿Conoces el lenguaje {lenguaje}? (si / no) ") + while(conoce not in ['si', 'no']): + conoce = input(f"Debes responder \"si\" o \"no\". ¿Conoces el lenguaje {lenguaje}? (si / no) ") + + if(conoce == 'si'): + lista_capacidades.append((lenguaje, conoce)) + + + for conocimiento in lista_capacidades: + print(f"- {conocimiento[0]}: {conocimiento[1]}") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto6.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto6.py new file mode 100644 index 000000000..53521cf13 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto6.py @@ -0,0 +1,24 @@ +''' + ---------- RETO 6 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Reto 6 +Escribe un programa que pida al usuario una palabra por consola y devuelva si se trata de un palíndormo* + +* Palíndromo: Palabra o expresión que es igual si se lee de izquierda a derecha que de derecha a izquierda +''' + +def reto6Avanzado(): + palabra = input("Ingrese una palabra: ") + inversa = palabra[::-1] + + if palabra == inversa: + print("Palindromo") + else: + print("No palindromo") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto7.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto7.py new file mode 100644 index 000000000..87772afd5 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto7.py @@ -0,0 +1,30 @@ +''' + ---------- RETO 7 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe un programa que pida 5 precios al usuario y los almacene en una lista de precios. + +Al finalizar, deberá mostrar por consola la media de los precios introducidos. +''' + +def reto7Avanzado(): + lista_precios: [float] = [] + total: float = 0.0 + media_precios: float = 0.0 + + while (lista_precios.__len__() < 5): + precio: float = float(input("Introduce un precio: ")) + lista_precios.append(precio) + + for precio in lista_precios: + total += precio + + media_precios = total / lista_precios.__len__() + + print(f"Precio Medio de la lista: {media_precios} €") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto8.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto8.py new file mode 100644 index 000000000..638daeafd --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto8.py @@ -0,0 +1,29 @@ +''' + ---------- RETO 8 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe una función que reciba un número entero positivo y devuelva su factorial. + +Ejemplo Factorial 5 es 5*4*3*2*1 +''' + +def factorial(numero: int) -> int: + factorial: int = 1 + + if(numero <= 0): + print('Queremos un número positivo distinto de 0') + return None + + for n in range(1,numero+1): + factorial *= n + + return factorial + +def reto8Avanzado(): + print(f"El Factorial de 6 es {factorial(6)}") \ No newline at end of file diff --git a/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto9.py b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto9.py new file mode 100644 index 000000000..72cf9ca08 --- /dev/null +++ b/PROFESORES/PYTHON/Retos Solucionados/Intermedios_Avanzados/reto9.py @@ -0,0 +1,26 @@ +''' + ---------- RETO 9 Avanzado -------------- + AUTOR: MARTÍN SAN JOSÉ DE VICENTE + EMAIL: martin@imaginagroup.com + AÑO: 2021 + LICENCIA CÓDIGO: OSS + ------------------------------------------ +''' + +''' +Escribe una función que convierta un número decimal en binario y otra que convierta un número binario en decimal. +''' + +numero_decimal = 10 +numero_binario = 0b1010 + +def dec_a_bin(decimal: float): + return bin(decimal) + +def bin_a_dec(binario): + return float(binario) + +def reto9Avanzado(): + print(f'El número {numero_decimal} en binario sería: {dec_a_bin(numero_decimal)}') + + print(f'El número binario {str(numero_binario)} es el número: {bin_a_dec(numero_binario)}') \ No newline at end of file diff --git a/PROFESORES/SQL/docker_database_with_pgadmin.md b/PROFESORES/SQL/docker_database_with_pgadmin.md new file mode 100644 index 000000000..2001039dd --- /dev/null +++ b/PROFESORES/SQL/docker_database_with_pgadmin.md @@ -0,0 +1,11 @@ + +Hola! + +Os dejo aquí cómo levantar una base de datos con docker y cargar la base de datos con Postgres and PGAdmin. + +Todas las instrucciones las tenéis aquí --> https://github.com/a10pepo/postgressetup. Aseguraros de tener Docker Desktop en vuestos PC instalado para que os funcione correctamente. + +Por favor, echadle un ojo y aseguraros que podáis seguir todos los pasos. Si no podéis, podéis mandarme vuestro error/duda al correo gavaldanatalia@gmail.com y trataremos de darle solución. + +Gracias! + diff --git a/PROFESORES/SQL/template-connection-sql-with-python.ipynb b/PROFESORES/SQL/template-connection-sql-with-python.ipynb new file mode 100644 index 000000000..b22416387 --- /dev/null +++ b/PROFESORES/SQL/template-connection-sql-with-python.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# My first notebook creating a templete to connect to any database\n", + "# Library\n", + "import pyodbc as pdb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Variables\n", + "host = \"myhost\"\n", + "database = \"mydb\"\n", + "user = \"myuser\"\n", + "password = \"mypass\"\n", + "port = \"myport\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Connection\n", + "connection_target = psycopg2.connect(\n", + " host=host,\n", + " database=database,\n", + " user=user,\n", + " password=password,\n", + " port=port\n", + " )\n", + "\n", + "# Cursor\n", + "cur_target = conn_target.cursor()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# My first query\n", + "query = \"\"\"SELECT * FROM PUBLIC.FILM\"\"\"\n", + "cur_target.execute(query)\n", + "\n", + "# My query transformed into a dataframe\n", + "rows = cur_target.fetchall()\n", + "\n", + "list_of_rows = []\n", + "\n", + "for row in rows:\n", + " #print(row)\n", + " list_of_rows.append(row)\n", + "\n", + "df = pd.DataFrame(list_of_rows)\n", + "display(df)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From de265ccd64ff85ed0a50a26d0bdd494c5de312ca Mon Sep 17 00:00:00 2001 From: eabadz Date: Thu, 6 Feb 2025 11:11:24 +0100 Subject: [PATCH 2/3] Entregables ML1 --- .../Eduardo_Abad/ML1_REG/01_Regression.ipynb | 1001 +++++++++++++++++ .../02_LinearRegressionImplementation.ipynb | 948 ++++++++++++++++ 2 files changed, 1949 insertions(+) create mode 100644 ALUMNOS/MIA/Eduardo_Abad/ML1_REG/01_Regression.ipynb create mode 100644 ALUMNOS/MIA/Eduardo_Abad/ML1_REG/02_LinearRegressionImplementation.ipynb diff --git a/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/01_Regression.ipynb b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/01_Regression.ipynb new file mode 100644 index 000000000..9f418a4d5 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/01_Regression.ipynb @@ -0,0 +1,1001 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Problema\n", + "\n", + "Predecir el coste del seguro" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XpPr25I0aINE" + }, + "source": [ + "## Instrucciones\n", + "\n", + " Utilizar el dataset (insurance.csv) para entrenar un modelo de regresión capaz de predecir el valor del seguro en función de las características del cliente. Realizar limpieza, preprocesado modelado y testeo del modelo aportando conclusiones de todos estos pasos." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kNF4GgV_n34K" + }, + "source": [ + "# El set de datos\n", + "\n", + "* age: age of primary beneficiary\n", + "\n", + "* sex: insurance contractor gender, female, male\n", + "\n", + "* bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height,\n", + "objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9\n", + "\n", + "* children: Number of children covered by health insurance / Number of dependents\n", + "\n", + "* smoker: Smoking\n", + "\n", + "* region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.\n", + "\n", + "* charges: Individual medical costs billed by health insurance\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "LDnXEh9vn2GX" + }, + "outputs": [], + "source": [ + "# imports\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "uZkDH4HMoERG" + }, + "outputs": [], + "source": [ + "ruta = \"insurance.csv\"\n", + "data = pd.read_csv(ruta)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "2C6TxrrjoLca" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1338, 7)\n" + ] + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " age bmi children charges\n", + "count 1338.000000 1338.000000 1338.000000 1338.000000\n", + "mean 39.207025 30.663397 1.094918 13270.422265\n", + "std 14.049960 6.098187 1.205493 12110.011237\n", + "min 18.000000 15.960000 0.000000 1121.873900\n", + "25% 27.000000 26.296250 0.000000 4740.287150\n", + "50% 39.000000 30.400000 1.000000 9382.033000\n", + "75% 51.000000 34.693750 2.000000 16639.912515\n", + "max 64.000000 53.130000 5.000000 63770.428010" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No se ven datos que no tengan sentido" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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vlySVl5crLi5OwcHB2rFjh06fPq2HHnpI7u7umj179lXXUbkUwd/fn3ALAABwA/ulJaQ31NfvTpgwQWvXrtXRo0dVWFioJk2aaOXKlbr33nslSV999ZXatm2rzMxM9ejRQ+vXr9fAgQN16tQp29PcJUuWaNKkSTpz5ow8PDyu6r6FhYUKCAhQQUEB4RYAAOAGdLV57YbZLaGkpETvv/++Hn74YVksFu3du1elpaWKiYmxjWnTpo2aNWumzMxMSVJmZqY6dOhgt0whNjZWhYWFOnjwYLX3Ki4uVmFhod0LAAAA9d8NE27XrFmj/Px8jRw5UpKUk5MjDw8PBQYG2o2zWq3KycmxjflpsK3sr+yrTkpKigICAmwvPkwGAABgDjdMuH3rrbc0YMAAhYaGXvd7JScnq6CgwPY6efLkdb8nAAAArr8bYiuwEydOaOPGjfr73/9uawsODlZJSYny8/Ptnt7m5uYqODjYNmbXrl1218rNzbX1VcfT01Oenp61OAMAAADcCG6IJ7fLli1TUFCQ4uLibG1du3aVu7u7MjIybG1HjhxRdna2oqKiJElRUVE6cOCA8vLybGPS09Pl7++vyMjIupsAAAAAbghOf3JbUVGhZcuWKSEhwW5v2oCAAI0ePVpJSUlq1KiR/P39NX78eEVFRalHjx6SpP79+ysyMlIjRozQ3LlzlZOToylTpigxMZEnswAAAL9CTg+3GzduVHZ2th5++OEqffPmzZOLi4vi4+PtvsShkqurq9auXauxY8cqKipKPj4+SkhI0IwZM+pyCgAAALhB3FD73DoL+9wCAADc2OrdPrcAAADAtSLcAgAAwDQItwAAADANwi0AAABMg3ALAAAA03D6VmCov7Kzs3X27Flnl4FfgZtuuknNmjVzdhkAgHqAcAuHZGdnq3Wbtrr034vOLgW/Al7eDXTkq8MEXADALyLcwiFnz57Vpf9eVOOBT8i9cZizy4GJlX5/Ut+vfVlnz54l3AIAfhHhFtfEvXGYPINbOrsMAAAASXygDAAAACZCuAUAAIBpEG4BAABgGoRbAAAAmAYfKAMA4P/H/t2oK+zfff0QbgEAEPt3o26xf/f1Q7gFAEDs3426w/7d1xfhFgCAn2D/bqB+4wNlAAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEyDcAsAAADTINwCAADANAi3AAAAMA3CLQAAAEzD6eH2P//5jx588EE1btxY3t7e6tChg/bs2WPrNwxDU6dOVUhIiLy9vRUTE6OjR4/aXePcuXMaPny4/P39FRgYqNGjR+vChQt1PRUAAAA4mVPD7Q8//KCePXvK3d1d69ev16FDh/Tyyy+rYcOGtjFz587VggULtGTJEmVlZcnHx0exsbG6dOmSbczw4cN18OBBpaena+3atdq2bZvGjBnjjCkBAADAidycefM5c+YoLCxMy5Yts7VFRETYfjYMQ/Pnz9eUKVM0aNAgSdK7774rq9WqNWvWaOjQoTp8+LBSU1O1e/dudevWTZL02muv6e6779ZLL72k0NDQup0UAAAAnMapT27/8Y9/qFu3brrvvvsUFBSkzp07a+nSpbb+b7/9Vjk5OYqJibG1BQQEqHv37srMzJQkZWZmKjAw0BZsJSkmJkYuLi7Kysq67H2Li4tVWFho9wIAAED959Rw+80332jx4sVq1aqVNmzYoLFjx+rxxx/XO++8I0nKycmRJFmtVrvzrFarrS8nJ0dBQUF2/W5ubmrUqJFtzM+lpKQoICDA9goLC6vtqQEAAMAJnBpuKyoq1KVLF82ePVudO3fWmDFj9Mgjj2jJkiXX9b7JyckqKCiwvU6ePHld7wcAAIC64dRwGxISosjISLu2tm3bKjs7W5IUHBwsScrNzbUbk5uba+sLDg5WXl6eXX9ZWZnOnTtnG/Nznp6e8vf3t3sBAACg/nNquO3Zs6eOHDli1/b1118rPDxc0o8fLgsODlZGRoatv7CwUFlZWYqKipIkRUVFKT8/X3v37rWN2bRpkyoqKtS9e/c6mAUAAABuFE7dLWHixIm6/fbbNXv2bN1///3atWuX3nzzTb355puSJIvFogkTJuj5559Xq1atFBERoWeffVahoaEaPHiwpB+f9N5111225QylpaUaN26chg4dyk4JAAAAvzJODbe/+c1v9PHHHys5OVkzZsxQRESE5s+fr+HDh9vGPPXUUyoqKtKYMWOUn5+v6OhopaamysvLyzZmxYoVGjdunPr16ycXFxfFx8drwYIFzpgSAAAAnMip4VaSBg4cqIEDB1bbb7FYNGPGDM2YMaPaMY0aNdLKlSuvR3kAAACoR5z+9bsAAABAbSHcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMw6nhdvr06bJYLHavNm3a2PovXbqkxMRENW7cWL6+voqPj1dubq7dNbKzsxUXF6cGDRooKChITz75pMrKyup6KgAAALgBuDm7gHbt2mnjxo22Yze3/ytp4sSJWrdunVavXq2AgACNGzdOQ4YM0fbt2yVJ5eXliouLU3BwsHbs2KHTp0/roYcekru7u2bPnl3ncwEAAIBzOT3curm5KTg4uEp7QUGB3nrrLa1cuVJ9+/aVJC1btkxt27bVzp071aNHD6WlpenQoUPauHGjrFarOnXqpJkzZ2rSpEmaPn26PDw86no6AAAAcCKnr7k9evSoQkNDdcstt2j48OHKzs6WJO3du1elpaWKiYmxjW3Tpo2aNWumzMxMSVJmZqY6dOggq9VqGxMbG6vCwkIdPHiw2nsWFxersLDQ7gUAAID6z6nhtnv37lq+fLlSU1O1ePFiffvtt+rVq5fOnz+vnJwceXh4KDAw0O4cq9WqnJwcSVJOTo5dsK3sr+yrTkpKigICAmyvsLCw2p0YAAAAnMKpyxIGDBhg+7ljx47q3r27wsPD9eGHH8rb2/u63Tc5OVlJSUm248LCQgIuAACACTh9WcJPBQYG6tZbb9WxY8cUHByskpIS5efn243Jzc21rdENDg6usntC5fHl1vFW8vT0lL+/v90LAAAA9d8NFW4vXLig48ePKyQkRF27dpW7u7syMjJs/UeOHFF2draioqIkSVFRUTpw4IDy8vJsY9LT0+Xv76/IyMg6rx8AAADO5dRlCX/5y190zz33KDw8XKdOndK0adPk6uqqYcOGKSAgQKNHj1ZSUpIaNWokf39/jR8/XlFRUerRo4ckqX///oqMjNSIESM0d+5c5eTkaMqUKUpMTJSnp6czpwYAAAAncGq4/e677zRs2DB9//33atKkiaKjo7Vz5041adJEkjRv3jy5uLgoPj5excXFio2N1euvv24739XVVWvXrtXYsWMVFRUlHx8fJSQkaMaMGc6aEgAAAJzIqeF21apVV+z38vLSokWLtGjRomrHhIeH67PPPqvt0gAAAFAP3VBrbgEAAIBrQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAabg5emJRUZG2bt2q7OxslZSU2PU9/vjj11wYAAAAUFMOhdt9+/bp7rvv1sWLF1VUVKRGjRrp7NmzatCggYKCggi3AAAAcAqHliVMnDhR99xzj3744Qd5e3tr586dOnHihLp27aqXXnqptmsEAAAAropD4Xb//v164okn5OLiIldXVxUXFyssLExz587V5MmTa7tGAAAA4Ko4FG7d3d3l4vLjqUFBQcrOzpYkBQQE6OTJk7VXHQAAAFADDoXbzp07a/fu3ZKk3r17a+rUqVqxYoUmTJig9u3bO1TICy+8IIvFogkTJtjaLl26pMTERDVu3Fi+vr6Kj49Xbm6u3XnZ2dmKi4uzrfd98sknVVZW5lANAAAAqN8cCrezZ89WSEiIJGnWrFlq2LChxo4dqzNnzujNN9+s8fV2796tN954Qx07drRrnzhxoj799FOtXr1aW7du1alTpzRkyBBbf3l5ueLi4lRSUqIdO3bonXfe0fLlyzV16lRHpgUAAIB6zqHdErp162b7OSgoSKmpqQ4XcOHCBQ0fPlxLly7V888/b2svKCjQW2+9pZUrV6pv376SpGXLlqlt27bauXOnevToobS0NB06dEgbN26U1WpVp06dNHPmTE2aNEnTp0+Xh4eHw3UBAACg/nH6lzgkJiYqLi5OMTExdu179+5VaWmpXXubNm3UrFkzZWZmSpIyMzPVoUMHWa1W25jY2FgVFhbq4MGD1d6zuLhYhYWFdi8AAADUf1f95LZLly7KyMhQw4YN1blzZ1kslmrHfv7551d1zVWrVunzzz+3rd/9qZycHHl4eCgwMNCu3Wq1Kicnxzbmp8G2sr+yrzopKSl67rnnrqpGAAAA1B9XHW4HDRokT09PSdLgwYOv+cYnT57Un//8Z6Wnp8vLy+uar1cTycnJSkpKsh0XFhYqLCysTmsAAABA7bvqcDtt2rTL/uyovXv3Ki8vT126dLG1lZeXa9u2bVq4cKE2bNigkpIS5efn2z29zc3NVXBwsCQpODhYu3btsrtu5W4KlWMux9PT0xbUAQAAYB4OrbndvXu3srKyqrRnZWVpz549V3WNfv366cCBA9q/f7/t1a1bNw0fPtz2s7u7uzIyMmznHDlyRNnZ2YqKipIkRUVF6cCBA8rLy7ONSU9Pl7+/vyIjIx2ZGgAAAOoxh8JtYmLiZb+s4T//+Y8SExOv6hp+fn5q37693cvHx0eNGzdW+/btFRAQoNGjRyspKUmbN2/W3r17NWrUKEVFRalHjx6SpP79+ysyMlIjRozQF198oQ0bNmjKlClKTEzkySwAAMCvkENbgR06dMhuOUGlzp0769ChQ9dcVKV58+bJxcVF8fHxKi4uVmxsrF5//XVbv6urq9auXauxY8cqKipKPj4+SkhI0IwZM2qtBgAAANQfDoVbT09P5ebm6pZbbrFrP336tNzcHLqkJGnLli12x15eXlq0aJEWLVpU7Tnh4eH67LPPHL4nAAAAzMOhZQn9+/dXcnKyCgoKbG35+fmaPHmy7rzzzlorDgAAAKgJhx6zvvTSS7rjjjsUHh6uzp07S5L2798vq9Wq9957r1YLBAAAAK6WQ+H25ptv1r/+9S+tWLFCX3zxhby9vTVq1CgNGzZM7u7utV0jAAAAcFUcXiDr4+OjMWPG1GYtAAAAwDVxONwePXpUmzdvVl5enioqKuz6pk6des2FAQAAADXlULhdunSpxo4dq5tuuknBwcGyWCy2PovFQrgFAACAUzgUbp9//nnNmjVLkyZNqu16AAAAAIc5tBXYDz/8oPvuu6+2awEAAACuiUPh9r777lNaWlpt1wIAAABcE4eWJbRs2VLPPvusdu7cqQ4dOlTZ/uvxxx+vleIAAACAmnAo3L755pvy9fXV1q1btXXrVrs+i8VCuAUAAIBTOBRuv/3229quAwAAALhmDq25rVRSUqIjR46orKystuoBAAAAHOZQuL148aJGjx6tBg0aqF27dsrOzpYkjR8/Xi+88EKtFggAAABcLYfCbXJysr744gtt2bJFXl5etvaYmBh98MEHtVYcAAAAUBMOrblds2aNPvjgA/Xo0cPu28natWun48eP11pxAAAAQE049OT2zJkzCgoKqtJeVFRkF3YBAACAuuRQuO3WrZvWrVtnO64MtH/9618VFRVVO5UBAAAANeTQsoTZs2drwIABOnTokMrKyvTqq6/q0KFD2rFjR5V9bwEAAIC64tCT2+joaO3fv19lZWXq0KGD0tLSFBQUpMzMTHXt2rW2awQAAACuikNPbiWpRYsWWrp0aW3WAgAAAFwTh8Jt5b621WnWrJlDxQAAAADXwqFw27x58yvuilBeXu5wQQAAAICjHAq3+/btszsuLS3Vvn379Morr2jWrFm1UhgAAABQUw6F29tuu61KW7du3RQaGqoXX3xRQ4YMuebCAAAAgJpyaLeE6rRu3Vq7d++uzUsCAAAAV82hJ7eFhYV2x4Zh6PTp05o+fbpatWpVK4UBAAAANeVQuA0MDKzygTLDMBQWFqZVq1bVSmEAAABATTkUbjdt2mQXbl1cXNSkSRO1bNlSbm4Ob50LAAAAXBOHkmifPn1quQwAAADg2jn0gbKUlBS9/fbbVdrffvttzZkz55qLAgAAABzhULh944031KZNmyrt7dq105IlS665KAAAAMARDoXbnJwchYSEVGlv0qSJTp8+fc1FAQAAAI5wKNyGhYVp+/btVdq3b9+u0NDQay4KAAAAcIRDHyh75JFHNGHCBJWWlqpv376SpIyMDD311FN64oknarVAAAAA4Go5FG6ffPJJff/993rsscdUUlIiSfLy8tKkSZOUnJxcqwUCAAAAV8uhcGuxWDRnzhw9++yzOnz4sLy9vdWqVSt5enrWdn0AAADAVXNozW2lnJwcnTt3Ti1atJCnp6cMw6itugAAAIAacyjcfv/99+rXr59uvfVW3X333bYdEkaPHs2aWwAAADiNQ+F24sSJcnd3V3Z2tho0aGBrf+CBB5SamlprxQEAAAA14dCa27S0NG3YsEFNmza1a2/VqpVOnDhRK4UBAAAANeXQk9uioiK7J7aVzp07x4fKAAAA4DQOhdtevXrp3XfftR1bLBZVVFRo7ty5+u1vf1trxQEAAAA14VC4nTt3rt58800NGDBAJSUleuqpp9S+fXtt27ZNc+bMuerrLF68WB07dpS/v7/8/f0VFRWl9evX2/ovXbqkxMRENW7cWL6+voqPj1dubq7dNbKzsxUXF6cGDRooKChITz75pMrKyhyZFgAAAOo5h8Jt+/bt9fXXXys6OlqDBg1SUVGRhgwZon379qlFixZXfZ2mTZvqhRde0N69e7Vnzx717dtXgwYN0sGDByX9+MG1Tz/9VKtXr9bWrVt16tQpDRkyxHZ+eXm54uLiVFJSoh07duidd97R8uXLNXXqVEemBQAAgHquxh8oKy0t1V133aUlS5bomWeeuaab33PPPXbHs2bN0uLFi7Vz5041bdpUb731llauXGn7it9ly5apbdu22rlzp3r06KG0tDQdOnRIGzdulNVqVadOnTRz5kxNmjRJ06dPl4eHxzXVBwAAgPqlxk9u3d3d9a9//avWCykvL9eqVatUVFSkqKgo7d27V6WlpYqJibGNadOmjZo1a6bMzExJUmZmpjp06CCr1WobExsbq8LCQtvT38spLi5WYWGh3QsAAAD1n0PLEh588EG99dZbtVLAgQMH5OvrK09PT/3pT3/Sxx9/rMjISOXk5MjDw0OBgYF2461Wq3JyciT9+A1pPw22lf2VfdVJSUlRQECA7RUWFlYrcwEAAIBzObTPbVlZmd5++21t3LhRXbt2lY+Pj13/K6+8ctXXat26tfbv36+CggJ99NFHSkhI0NatWx0p66olJycrKSnJdlxYWEjABQAAMIEahdtvvvlGzZs315dffqkuXbpIkr7++mu7MRaLpUYFeHh4qGXLlpKkrl27avfu3Xr11Vf1wAMPqKSkRPn5+XZPb3NzcxUcHCxJCg4O1q5du+yuV7mbQuWYy/H09GQ/XgAAABOqUbht1aqVTp8+rc2bN0v68et2FyxYUGVpwLWoqKhQcXGxunbtKnd3d2VkZCg+Pl6SdOTIEWVnZysqKkqSFBUVpVmzZikvL09BQUGSpPT0dPn7+ysyMrLWagIAAED9UKNwaxiG3fH69etVVFTk8M2Tk5M1YMAANWvWTOfPn9fKlSu1ZcsWbdiwQQEBARo9erSSkpLUqFEj+fv7a/z48YqKilKPHj0kSf3791dkZKRGjBihuXPnKicnR1OmTFFiYiJPZgEAAH6FHFpzW+nnYbem8vLy9NBDD+n06dMKCAhQx44dtWHDBt15552SpHnz5snFxUXx8fEqLi5WbGysXn/9ddv5rq6uWrt2rcaOHauoqCj5+PgoISFBM2bMuKa6AAAAUD/VKNxaLJYqa2prusb2p35pxwUvLy8tWrRIixYtqnZMeHi4PvvsM4drAAAAgHnUeFnCyJEjbf/J/9KlS/rTn/5UZbeEv//977VXIQAAAHCVahRuExIS7I4ffPDBWi0GAAAAuBY1CrfLli27XnUAAAAA18yhbygDAAAAbkSEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaRBuAQAAYBqEWwAAAJgG4RYAAACmQbgFAACAaTg13KakpOg3v/mN/Pz8FBQUpMGDB+vIkSN2Yy5duqTExEQ1btxYvr6+io+PV25urt2Y7OxsxcXFqUGDBgoKCtKTTz6psrKyupwKAAAAbgBODbdbt25VYmKidu7cqfT0dJWWlqp///4qKiqyjZk4caI+/fRTrV69Wlu3btWpU6c0ZMgQW395ebni4uJUUlKiHTt26J133tHy5cs1depUZ0wJAAAATuTmzJunpqbaHS9fvlxBQUHau3ev7rjjDhUUFOitt97SypUr1bdvX0nSsmXL1LZtW+3cuVM9evRQWlqaDh06pI0bN8pqtapTp06aOXOmJk2apOnTp8vDw8MZUwMAAIAT3FBrbgsKCiRJjRo1kiTt3btXpaWliomJsY1p06aNmjVrpszMTElSZmamOnToIKvVahsTGxurwsJCHTx48LL3KS4uVmFhod0LAAAA9d8NE24rKio0YcIE9ezZU+3bt5ck5eTkyMPDQ4GBgXZjrVarcnJybGN+Gmwr+yv7LiclJUUBAQG2V1hYWC3PBgAAAM5ww4TbxMREffnll1q1atV1v1dycrIKCgpsr5MnT173ewIAAOD6c+qa20rjxo3T2rVrtW3bNjVt2tTWHhwcrJKSEuXn59s9vc3NzVVwcLBtzK5du+yuV7mbQuWYn/P09JSnp2ctzwIAAADO5tQnt4ZhaNy4cfr444+1adMmRURE2PV37dpV7u7uysjIsLUdOXJE2dnZioqKkiRFRUXpwIEDysvLs41JT0+Xv7+/IiMj62YiAAAAuCE49cltYmKiVq5cqU8++UR+fn62NbIBAQHy9vZWQECARo8eraSkJDVq1Ej+/v4aP368oqKi1KNHD0lS//79FRkZqREjRmju3LnKycnRlClTlJiYyNNZAACAXxmnhtvFixdLkvr06WPXvmzZMo0cOVKSNG/ePLm4uCg+Pl7FxcWKjY3V66+/bhvr6uqqtWvXauzYsYqKipKPj48SEhI0Y8aMupoGAAAAbhBODbeGYfziGC8vLy1atEiLFi2qdkx4eLg+++yz2iwNAAAA9dANs1sCAAAAcK0ItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA0yDcAgAAwDQItwAAADANwi0AAABMg3ALAAAA03BquN22bZvuuecehYaGymKxaM2aNXb9hmFo6tSpCgkJkbe3t2JiYnT06FG7MefOndPw4cPl7++vwMBAjR49WhcuXKjDWQAAAOBG4dRwW1RUpNtuu02LFi26bP/cuXO1YMECLVmyRFlZWfLx8VFsbKwuXbpkGzN8+HAdPHhQ6enpWrt2rbZt26YxY8bU1RQAAABwA3Fz5s0HDBigAQMGXLbPMAzNnz9fU6ZM0aBBgyRJ7777rqxWq9asWaOhQ4fq8OHDSk1N1e7du9WtWzdJ0muvvaa7775bL730kkJDQy977eLiYhUXF9uOCwsLa3lmAAAAcIYbds3tt99+q5ycHMXExNjaAgIC1L17d2VmZkqSMjMzFRgYaAu2khQTEyMXFxdlZWVVe+2UlBQFBATYXmFhYddvIgAAAKgzN2y4zcnJkSRZrVa7dqvVauvLyclRUFCQXb+bm5saNWpkG3M5ycnJKigosL1OnjxZy9UDAADAGZy6LMFZPD095enp6ewyAAAAUMtu2Ce3wcHBkqTc3Fy79tzcXFtfcHCw8vLy7PrLysp07tw52xgAAAD8etyw4TYiIkLBwcHKyMiwtRUWFiorK0tRUVGSpKioKOXn52vv3r22MZs2bVJFRYW6d+9e5zUDAADAuZy6LOHChQs6duyY7fjbb7/V/v371ahRIzVr1kwTJkzQ888/r1atWikiIkLPPvusQkNDNXjwYElS27Ztddddd+mRRx7RkiVLVFpaqnHjxmno0KHV7pQAAAAA83JquN2zZ49++9vf2o6TkpIkSQkJCVq+fLmeeuopFRUVacyYMcrPz1d0dLRSU1Pl5eVlO2fFihUaN26c+vXrJxcXF8XHx2vBggV1PhcAAAA4n1PDbZ8+fWQYRrX9FotFM2bM0IwZM6od06hRI61cufJ6lAcAAIB65oZdcwsAAADUFOEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGmYJtwuWrRIzZs3l5eXl7p3765du3Y5uyQAAADUMVOE2w8++EBJSUmaNm2aPv/8c912222KjY1VXl6es0sDAABAHTJFuH3llVf0yCOPaNSoUYqMjNSSJUvUoEEDvf32284uDQAAAHXIzdkFXKuSkhLt3btXycnJtjYXFxfFxMQoMzPzsucUFxeruLjYdlxQUCBJKiwsvL7FmsiFCxckScU5x1RRcsnJ1cDMSs99J+nHf+f43yiuJ97XUFd4X3NM5e/KMIwrjqv34fbs2bMqLy+X1Wq1a7darfrqq68ue05KSoqee+65Ku1hYWHXpUYz+2HDQmeXgF+J3r17O7sE/Erwvoa6wvuaY86fP6+AgIBq++t9uHVEcnKykpKSbMcVFRU6d+6cGjduLIvF4sTKYHaFhYUKCwvTyZMn5e/v7+xyAOCa8b6GumIYhs6fP6/Q0NArjqv34famm26Sq6urcnNz7dpzc3MVHBx82XM8PT3l6elp1xYYGHi9SgSq8Pf35/8EAJgK72uoC1d6Ylup3n+gzMPDQ127dlVGRoatraKiQhkZGYqKinJiZQAAAKhr9f7JrSQlJSUpISFB3bp10//7f/9P8+fPV1FRkUaNGuXs0gAAAFCHTBFuH3jgAZ05c0ZTp05VTk6OOnXqpNTU1CofMgOczdPTU9OmTauyLAYA6ive13CjsRi/tJ8CAAAAUE/U+zW3AAAAQCXCLQAAAEyDcAsAAADTINwCN4CRI0dq8ODBzi4DgIkZhqExY8aoUaNGslgs2r9/v1Pq+Pe//+3U+8P8TLFbAgAAuLLU1FQtX75cW7Zs0S233KKbbrrJ2SUB1wXhFgCAX4Hjx48rJCREt99+u7NLAa4rliUANdSnTx+NHz9eEyZMUMOGDWW1WrV06VLbF4f4+fmpZcuWWr9+vSSpvLxco0ePVkREhLy9vdW6dWu9+uqrV7xHRUWFUlJSbOfcdttt+uijj+piegBMaOTIkRo/fryys7NlsVjUvHnzX3yf2bJliywWizZs2KDOnTvL29tbffv2VV5entavX6+2bdvK399ff/jDH3Tx4kXbeampqYqOjlZgYKAaN26sgQMH6vjx41es78svv9SAAQPk6+srq9WqESNG6OzZs9ft9wFzI9wCDnjnnXd00003adeuXRo/frzGjh2r++67T7fffrs+//xz9e/fXyNGjNDFixdVUVGhpk2bavXq1Tp06JCmTp2qyZMn68MPP6z2+ikpKXr33Xe1ZMkSHTx4UBMnTtSDDz6orVu31uEsAZjFq6++qhkzZqhp06Y6ffq0du/efdXvM9OnT9fChQu1Y8cOnTx5Uvfff7/mz5+vlStXat26dUpLS9Nrr71mG19UVKSkpCTt2bNHGRkZcnFx0e9//3tVVFRctrb8/Hz17dtXnTt31p49e5Samqrc3Fzdf//91/V3AhMzANRI7969jejoaNtxWVmZ4ePjY4wYMcLWdvr0aUOSkZmZedlrJCYmGvHx8bbjhIQEY9CgQYZhGMalS5eMBg0aGDt27LA7Z/To0cawYcNqcSYAfk3mzZtnhIeHG4Zxde8zmzdvNiQZGzdutPWnpKQYkozjx4/b2h599FEjNja22vueOXPGkGQcOHDAMAzD+Pbbbw1Jxr59+wzDMIyZM2ca/fv3tzvn5MmThiTjyJEjDs8Xv16suQUc0LFjR9vPrq6uaty4sTp06GBrq/zq57y8PEnSokWL9Pbbbys7O1v//e9/VVJSok6dOl322seOHdPFixd155132rWXlJSoc+fOtTwTAL9GNXmf+en7ndVqVYMGDXTLLbfYte3atct2fPToUU2dOlVZWVk6e/as7Yltdna22rdvX6WWL774Qps3b5avr2+VvuPHj+vWW291bJL41SLcAg5wd3e3O7ZYLHZtFotF0o9rZ1etWqW//OUvevnllxUVFSU/Pz+9+OKLysrKuuy1L1y4IElat26dbr75Zrs+vrsdQG2oyfvMz9/bLvf+99MlB/fcc4/Cw8O1dOlShYaGqqKiQu3bt1dJSUm1tdxzzz2aM2dOlb6QkJCaTQwQ4Ra47rZv367bb79djz32mK3tSh+uiIyMlKenp7Kzs9W7d++6KBHAr8z1ep/5/vvvdeTIES1dulS9evWSJP3zn/+84jldunTR3/72NzVv3lxubsQSXDv+LQKus1atWundd9/Vhg0bFBERoffee0+7d+9WRETEZcf7+fnpL3/5iyZOnKiKigpFR0eroKBA27dvl7+/vxISEup4BgDM5nq9zzRs2FCNGzfWm2++qZCQEGVnZ+vpp5++4jmJiYlaunSphg0bpqeeekqNGjXSsWPHtGrVKv31r3+Vq6urQ7Xg14twC1xnjz76qPbt26cHHnhAFotFw4YN02OPPWbbKuxyZs6cqSZNmiglJUXffPONAgMD1aVLF02ePLkOKwdgZtfjfcbFxUWrVq3S448/rvbt26t169ZasGCB+vTpU+05oaGh2r59uyZNmqT+/furuLhY4eHhuuuuu+TiwqZOqDmLYRiGs4sAAAAAagN/EgEAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwAAANMg3AIAAMA0CLcAAAAwDcItAAAATINwCwD1zEcffaQOHTrI29tbjRs3VkxMjIqKiiRJf/3rX9W2bVt5eXmpTZs2ev31123nPfzww+rYsaOKi4slSSUlJercubMeeughp8wDAK4Hwi0A1COnT5/WsGHD9PDDD+vw4cPasmWLhgwZIsMwtGLFCk2dOlWzZs3S4cOHNXv2bD377LN65513JEkLFixQUVGRnn76aUnSM888o/z8fC1cuNCZUwKAWuXm7AIAAFfv9OnTKisr05AhQxQeHi5J6tChgyRp2rRpevnllzVkyBBJUkREhA4dOqQ33nhDCQkJ8vX11fvvv6/evXvLz89P8+fP1+bNm+Xv7++0+QBAbbMYhmE4uwgAwNUpLy9XbGysdu3apdjYWPXv31/33nuvPDw85OvrK29vb7m4/N9/lCsrK1NAQIByc3NtbZMnT1ZKSoomTZqkF154wRnTAIDrhie3AFCPuLq6Kj09XTt27FBaWppee+01PfPMM/r0008lSUuXLlX37t2rnFOpoqJC27dvl6urq44dO1antQNAXWDNLQDUMxaLRT179tRzzz2nffv2ycPDQ9u3b1doaKi++eYbtWzZ0u4VERFhO/fFF1/UV199pa1btyo1NVXLli1z4kwAoPbx5BYA6pGsrCxlZGSof//+CgoKUlZWls6cOaO2bdvqueee0+OPP66AgADdddddKi4u1p49e/TDDz8oKSlJ+/bt09SpU/XRRx+pZ8+eeuWVV/TnP/9ZvXv31i233OLsqQFArWDNLQDUI4cPH9bEiRP1+eefq7CwUOHh4Ro/frzGjRsnSVq5cqVefPFFHTp0SD4+PurQoYMmTJigAQMGqGvXroqOjtYbb7xhu96gQYN09uxZbdu2zW75AgDUV4RbAAAAmAZrbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApkG4BQAAgGkQbgEAAGAahFsAAACYBuEWAAAApvH/AbGGE2KJbvyTAAAAAElFTkSuQmCC", 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", 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AsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsI2q7i4Al5aZmamjR4+6u4xKp2bNmqpTp467ywAAAOWMcFuBZWZmqlHjJjrz62l3l1Lp+Pj6ae93ewi4AAD8yRBuK7CjR4/qzK+nVeP2x+VZI8rd5VQa544d1LElr+jo0aOEWwAA/mQIt5WAZ40oeYc3cHcZAAAAFR4PlAEAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbMOt4Xb27Nlq0aKFAgMDFRgYqLi4OC1dutTa37lzZzkcDqfloYcecuojMzNTffr0kZ+fn0JDQ/Xkk0+qsLCwvC8FAAAAFYBbv8Shdu3aevHFF9WwYUMZY/Tuu++qX79+2r59u5o2bSpJGjFihCZPnmwd4+fnZ/1cVFSkPn36KDw8XOvXr1dWVpaGDBkiT09PTZ06tdyvBwAAAO7l1nDbt29fp/UpU6Zo9uzZ2rhxoxVu/fz8FB4eXuLxK1asUFpamlatWqWwsDC1atVKzz//vMaPH6/ExER5eXld82sAAABAxVFh5twWFRXpgw8+UH5+vuLi4qztCxYsUM2aNdWsWTMlJCTo9OnT1r4NGzaoefPmCgsLs7bFx8crLy9Pu3fvvui5CgoKlJeX57QAAACg8nPrnVtJ2rVrl+Li4nTmzBkFBATo448/VkxMjCTpvvvuU3R0tCIjI7Vz506NHz9ee/fu1UcffSRJys7Odgq2kqz17Ozsi54zKSlJkyZNukZXBAAAAHdxe7ht1KiRUlNTlZubqw8//FBDhw5VSkqKYmJi9OCDD1rtmjdvroiICHXt2lXp6emqX7++y+dMSEjQuHHjrPW8vDxFRUVd1XUAAADA/dw+LcHLy0sNGjRQbGyskpKS1LJlS82cObPEtm3btpUk7d+/X5IUHh6unJwcpzbn1y82T1eSvL29rTc0nF8AAABQ+bk93P5RcXGxCgoKStyXmpoqSYqIiJAkxcXFadeuXTp8+LDVZuXKlQoMDLSmNgAAAODPw63TEhISEtSrVy/VqVNHJ0+e1MKFC5WcnKzly5crPT1dCxcuVO/evVWjRg3t3LlTY8eOVceOHdWiRQtJUo8ePRQTE6PBgwdr2rRpys7O1rPPPqtRo0bJ29vbnZcGAAAAN3BruD18+LCGDBmirKwsBQUFqUWLFlq+fLm6d++ugwcPatWqVZoxY4by8/MVFRWlgQMH6tlnn7WOr1KlipYsWaKRI0cqLi5O/v7+Gjp0qNN7cQEAAPDn4dZw+/bbb190X1RUlFJSUi7bR3R0tL744ouyLAsAAACVVIWbcwsAAAC4inALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABso6q7CwBQMWRmZuro0aPuLqPSqVmzpurUqePuMioVxpprGGvAlSHcAlBmZqYaNW6iM7+edncplY6Pr5/2freH0HGFGGuuY6yVHn9Iuaay/yFFuAWgo0eP6syvp1Xj9sflWSPK3eVUGueOHdSxJa/o6NGjlfo/BOWJseYaxlrp8YeU6yr7H1KEWwAWzxpR8g5v4O4y8CfAWMO1xh9SrrHDH1KEWwAAYFv8IfXnw9sSAAAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBtuDbezZ89WixYtFBgYqMDAQMXFxWnp0qXW/jNnzmjUqFGqUaOGAgICNHDgQOXk5Dj1kZmZqT59+sjPz0+hoaF68sknVVhYWN6XAgAAgArAreG2du3aevHFF7V161Zt2bJFXbp0Ub9+/bR7925J0tixY/XZZ59p8eLFSklJ0aFDhzRgwADr+KKiIvXp00dnz57V+vXr9e6772revHmaMGGCuy4JAAAAblTVnSfv27ev0/qUKVM0e/Zsbdy4UbVr19bbb7+thQsXqkuXLpKkuXPnqkmTJtq4caPatWunFStWKC0tTatWrVJYWJhatWql559/XuPHj1diYqK8vLzccVkAAABwkwoz57aoqEgffPCB8vPzFRcXp61bt+rcuXPq1q2b1aZx48aqU6eONmzYIEnasGGDmjdvrrCwMKtNfHy88vLyrLu/AAAA+PNw651bSdq1a5fi4uJ05swZBQQE6OOPP1ZMTIxSU1Pl5eWl4OBgp/ZhYWHKzs6WJGVnZzsF2/P7z++7mIKCAhUUFFjreXl5ZXQ1AAAAcCe337lt1KiRUlNTtWnTJo0cOVJDhw5VWlraNT1nUlKSgoKCrCUqKuqang8AAADlw+3h1svLSw0aNFBsbKySkpLUsmVLzZw5U+Hh4Tp79qxOnDjh1D4nJ0fh4eGSpPDw8AvennB+/XybkiQkJCg3N9daDh48WLYXBQAAALdwe7j9o+LiYhUUFCg2Nlaenp5avXq1tW/v3r3KzMxUXFycJCkuLk67du3S4cOHrTYrV65UYGCgYmJiLnoOb29v6/Vj5xcAAABUfm6dc5uQkKBevXqpTp06OnnypBYuXKjk5GQtX75cQUFBGj58uMaNG6eQkBAFBgbqkUceUVxcnNq1aydJ6tGjh2JiYjR48GBNmzZN2dnZevbZZzVq1Ch5e3u789IAAADgBm4Nt4cPH9aQIUOUlZWloKAgtWjRQsuXL1f37t0lSa+++qo8PDw0cOBAFRQUKD4+Xm+88YZ1fJUqVbRkyRKNHDlScXFx8vf319ChQzV58mR3XRIAAADcyK3h9u23377kfh8fH82aNUuzZs26aJvo6Gh98cUXZV0aAAAAKqEKN+cWAAAAcBXhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG24Nt0lJSbrppptUrVo1hYaGqn///tq7d69Tm86dO8vhcDgtDz30kFObzMxM9enTR35+fgoNDdWTTz6pwsLC8rwUAAAAVABV3XnylJQUjRo1SjfddJMKCwv1zDPPqEePHkpLS5O/v7/VbsSIEZo8ebK17ufnZ/1cVFSkPn36KDw8XOvXr1dWVpaGDBkiT09PTZ06tVyvBwAAAO7l1nC7bNkyp/V58+YpNDRUW7duVceOHa3tfn5+Cg8PL7GPFStWKC0tTatWrVJYWJhatWql559/XuPHj1diYqK8vLyu6TUAAACg4qhQc25zc3MlSSEhIU7bFyxYoJo1a6pZs2ZKSEjQ6dOnrX0bNmxQ8+bNFRYWZm2Lj49XXl6edu/eXeJ5CgoKlJeX57QAAACg8nPrndvfKy4u1pgxY9S+fXs1a9bM2n7fffcpOjpakZGR2rlzp8aPH6+9e/fqo48+kiRlZ2c7BVtJ1np2dnaJ50pKStKkSZOu0ZUAAADAXSpMuB01apS+/fZbff31107bH3zwQevn5s2bKyIiQl27dlV6errq16/v0rkSEhI0btw4az0vL09RUVGuFQ4AAIAKo0JMSxg9erSWLFmiNWvWqHbt2pds27ZtW0nS/v37JUnh4eHKyclxanN+/WLzdL29vRUYGOi0AAAAoPJza7g1xmj06NH6+OOP9eWXX6pevXqXPSY1NVWSFBERIUmKi4vTrl27dPjwYavNypUrFRgYqJiYmGtSNwAAAComt05LGDVqlBYuXKhPP/1U1apVs+bIBgUFydfXV+np6Vq4cKF69+6tGjVqaOfOnRo7dqw6duyoFi1aSJJ69OihmJgYDR48WNOmTVN2draeffZZjRo1St7e3u68PAAAAJQzt965nT17tnJzc9W5c2dFRERYy6JFiyRJXl5eWrVqlXr06KHGjRvr8ccf18CBA/XZZ59ZfVSpUkVLlixRlSpVFBcXp/vvv19Dhgxxei8uAAAA/hzceufWGHPJ/VFRUUpJSblsP9HR0friiy/KqiwAAABUUhXigTIAAACgLBBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANgG4RYAAAC2QbgFAACAbRBuAQAAYBuEWwAAANhGVVcPzM/PV0pKijIzM3X27FmnfY8++uhVFwYAAACUlkvhdvv27erdu7dOnz6t/Px8hYSE6OjRo/Lz81NoaCjhFgAAAG7h0rSEsWPHqm/fvvrll1/k6+urjRs36sCBA4qNjdXLL79c1jUCAAAAV8SlcJuamqrHH39cHh4eqlKligoKChQVFaVp06bpmWeeKesaAQAAgCviUrj19PSUh8dvh4aGhiozM1OSFBQUpIMHD5ZddQAAAEApuDTntnXr1tq8ebMaNmyoTp06acKECTp69Kjmz5+vZs2alXWNAAAAwBVx6c7t1KlTFRERIUmaMmWKqlevrpEjR+rIkSN68803y7RAAAAA4Eq5dOe2TZs21s+hoaFatmxZmRUEAAAAuIovcQAAAIBtXPGd2xtvvFGrV69W9erV1bp1azkcjou23bZtW5kUBwAAAJTGFYfbfv36ydvbW5LUv3//a1UPAAAA4LIrDrcTJ04s8WcAAACgonBpzu3mzZu1adOmC7Zv2rRJW7ZsueqiAAAAAFe4FG5HjRpV4pc1/Pzzzxo1atRVFwUAAAC4wqVwm5aWphtvvPGC7a1bt1ZaWtpVFwUAAAC4wqVw6+3trZycnAu2Z2VlqWpVl16dCwAAAFw1l8Jtjx49lJCQoNzcXGvbiRMn9Mwzz6h79+5lVhwAAABQGi7dZn355ZfVsWNHRUdHq3Xr1pKk1NRUhYWFaf78+WVaIAAAAHClXAq31113nXbu3KkFCxZox44d8vX11bBhw3TvvffK09OzrGsEAAAArojLE2T9/f314IMPlmUtAAAAwFVxOdzu27dPa9as0eHDh1VcXOy0b8KECVfUR1JSkj766CN999138vX11S233KKXXnpJjRo1stqcOXNGjz/+uD744AMVFBQoPj5eb7zxhsLCwqw2mZmZGjlypNasWaOAgAANHTpUSUlJPNwGAADwJ+NS+nvrrbc0cuRI1axZU+Hh4XI4HNY+h8NxxeE2JSVFo0aN0k033aTCwkI988wz6tGjh9LS0uTv7y9JGjt2rD7//HMtXrxYQUFBGj16tAYMGKB169ZJkoqKitSnTx+Fh4dr/fr1ysrK0pAhQ+Tp6ampU6e6cnkAAACopFwKty+88IKmTJmi8ePHX9XJly1b5rQ+b948hYaGauvWrerYsaNyc3P19ttva+HCherSpYskae7cuWrSpIk2btyodu3aacWKFUpLS9OqVasUFhamVq1a6fnnn9f48eOVmJgoLy+vq6oRAAAAlYdLrwL75ZdfdOedd5Z1LdarxUJCQiRJW7du1blz59StWzerTePGjVWnTh1t2LBBkrRhwwY1b97caZpCfHy88vLytHv37hLPU1BQoLy8PKcFAAAAlZ9L4fbOO+/UihUryrSQ4uJijRkzRu3bt1ezZs0kSdnZ2fLy8lJwcLBT27CwMGVnZ1ttfh9sz+8/v68kSUlJCgoKspaoqKgyvRYAAAC4h0vTEho0aKDnnntOGzduVPPmzS94/dejjz5a6j5HjRqlb7/9Vl9//bUrJZVKQkKCxo0bZ63n5eURcAEAAGzApXD75ptvKiAgQCkpKUpJSXHa53A4Sh1uR48erSVLlmjt2rWqXbu2tT08PFxnz57ViRMnnO7e5uTkKDw83GrzzTffOPV3/quBz7f5I29vb3l7e5eqRgAAAFR8LoXbjIyMMjm5MUaPPPKIPv74YyUnJ6tevXpO+2NjY+Xp6anVq1dr4MCBkqS9e/cqMzNTcXFxkqS4uDhNmTJFhw8fVmhoqCRp5cqVCgwMVExMTJnUCQAAgMrhql4Ee/bsWWVkZKh+/fouvVN21KhRWrhwoT799FNVq1bNmiMbFBQkX19fBQUFafjw4Ro3bpxCQkIUGBioRx55RHFxcWrXrp0kqUePHoqJidHgwYM1bdo0ZWdn69lnn9WoUaO4OwsAAPAn49IDZadPn9bw4cPl5+enpk2bKjMzU5L0yCOP6MUXX7zifmbPnq3c3Fx17txZERER1rJo0SKrzauvvqrbb79dAwcOVMeOHRUeHq6PPvrI2l+lShUtWbJEVapUUVxcnO6//34NGTJEkydPduXSAAAAUIm5dOc2ISFBO3bsUHJysnr27Glt79atmxITE/X0009fUT/GmMu28fHx0axZszRr1qyLtomOjtYXX3xxRecEAACAfbkUbj/55BMtWrRI7dq1c/p2sqZNmyo9Pb3MigMAAABKw6VpCUeOHLEe3vq9/Px8p7ALAAAAlCeXwm2bNm30+eefW+vnA+3//d//WW8xAAAAAMqbS9MSpk6dql69eiktLU2FhYWaOXOm0tLStH79+gveewsAAACUF5fu3Hbo0EGpqakqLCxU8+bNtWLFCoWGhmrDhg2KjY0t6xoBAACAK+Lye27r16+vt956qyxrAQAAAK6KS+H2/HttL6ZOnTouFQMAAABcDZfCbd26dS/5VoSioiKXCwIAAABc5VK43b59u9P6uXPntH37dk2fPl1Tpkwpk8IAAACA0nIp3LZs2fKCbW3atFFkZKT++c9/asCAAVddGAAAAFBaLr0t4WIaNWqkzZs3l2WXAAAAwBVz6c5tXl6e07oxRllZWUpMTFTDhg3LpDAAAACgtFwKt8HBwRc8UGaMUVRUlD744IMyKQwAAAAoLZfC7ZdffukUbj08PFSrVi01aNBAVau6/OpcAAAA4Kq4lEQ7d+5cxmUAAAAAV8+lB8qSkpL0zjvvXLD9nXfe0UsvvXTVRQEAAACucCnc/u///q8aN258wfamTZtqzpw5V10UAAAA4AqXwm12drYiIiIu2F6rVi1lZWVddVEAAACAK1wKt1FRUVq3bt0F29etW6fIyMirLgoAAABwhUsPlI0YMUJjxozRuXPn1KVLF0nS6tWr9dRTT+nxxx8v0wIBAACAK+VSuH3yySd17NgxPfzwwzp79qwkycfHR+PHj1dCQkKZFggAAABcKZfCrcPh0EsvvaTnnntOe/bska+vrxo2bChvb++yrg8AAAC4Yi7NuT0vOztbx48fV/369eXt7S1jTFnVBQAAAJSaS+H22LFj6tq1q2644Qb17t3bekPC8OHDmXMLAAAAt3Ep3I4dO1aenp7KzMyUn5+ftf3uu+/WsmXLyqw4AAAAoDRcmnO7YsUKLV++XLVr13ba3rBhQx04cKBMCgMAAABKy6U7t/n5+U53bM87fvw4D5UBAADAbVwKt7feeqvee+89a93hcKi4uFjTpk3TbbfdVmbFAQAAAKXh0rSEadOmqWvXrtqyZYvOnj2rp556Srt379bx48dL/OYyAAAAoDy4dOe2WbNm+v7779WhQwf169dP+fn5GjBggLZv36769euXdY0AAADAFSn1ndtz586pZ8+emjNnjv7xj39ci5oAAAAAl5T6zq2np6d27tx5LWoBAAAAropL0xLuv/9+vf3222VdCwAAAHBVXHqgrLCwUO+8845WrVql2NhY+fv7O+2fPn16mRQHAAAAlEapwu0PP/ygunXr6ttvv9WNN94oSfr++++d2jgcjrKrDgAAACiFUoXbhg0bKisrS2vWrJH029ftvvbaawoLC7smxQEAAAClUao5t8YYp/WlS5cqPz+/TAsCAAAAXOXSA2Xn/THsAgAAAO5UqnDrcDgumFPLHFsAAABUFKWelvDAAw9owIABGjBggM6cOaOHHnrIWj+/XKm1a9eqb9++ioyMlMPh0CeffOK0/4EHHrAC9fmlZ8+eTm2OHz+uQYMGKTAwUMHBwRo+fLhOnTpVmssCAACATZTqgbKhQ4c6rd9///1XdfL8/Hy1bNlSf/3rXy8ainv27Km5c+da697e3k77Bw0apKysLK1cuVLnzp3TsGHD9OCDD2rhwoVXVRsAAAAqn1KF29+HzLLQq1cv9erV65JtvL29FR4eXuK+PXv2aNmyZdq8ebPatGkjSXr99dfVu3dvvfzyy4qMjCzTegEAAFCxXdUDZeUhOTlZoaGhatSokUaOHKljx45Z+zZs2KDg4GAr2EpSt27d5OHhoU2bNrmjXAAAALiRS99QVl569uypAQMGqF69ekpPT9czzzyjXr16acOGDapSpYqys7MVGhrqdEzVqlUVEhKi7Ozsi/ZbUFCggoICaz0vL++aXQMAAADKT4UOt/fcc4/1c/PmzdWiRQvVr19fycnJ6tq1q8v9JiUladKkSWVRIgAAACqQCj8t4feuv/561axZU/v375ckhYeH6/Dhw05tCgsLdfz48YvO05WkhIQE5ebmWsvBgwevad0AAAAoH5Uq3P700086duyYIiIiJElxcXE6ceKEtm7darX58ssvVVxcrLZt2160H29vbwUGBjotAAAAqPzcOi3h1KlT1l1YScrIyFBqaqpCQkIUEhKiSZMmaeDAgQoPD1d6erqeeuopNWjQQPHx8ZKkJk2aqGfPnhoxYoTmzJmjc+fOafTo0brnnnt4UwIAAMCfkFvv3G7ZskWtW7dW69atJUnjxo1T69atNWHCBFWpUkU7d+7UHXfcoRtuuEHDhw9XbGysvvrqK6d33S5YsECNGzdW165d1bt3b3Xo0EFvvvmmuy4JAAAAbuTWO7edO3eWMeai+5cvX37ZPkJCQvjCBgAAAEiqZHNuAQAAgEsh3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbMOt4Xbt2rXq27evIiMj5XA49MknnzjtN8ZowoQJioiIkK+vr7p166Z9+/Y5tTl+/LgGDRqkwMBABQcHa/jw4Tp16lQ5XgUAAAAqCreG2/z8fLVs2VKzZs0qcf+0adP02muvac6cOdq0aZP8/f0VHx+vM2fOWG0GDRqk3bt3a+XKlVqyZInWrl2rBx98sLwuAQAAABVIVXeevFevXurVq1eJ+4wxmjFjhp599ln169dPkvTee+8pLCxMn3zyie655x7t2bNHy5Yt0+bNm9WmTRtJ0uuvv67evXvr5ZdfVmRkZLldCwAAANyvws65zcjIUHZ2trp162ZtCwoKUtu2bbVhwwZJ0oYNGxQcHGwFW0nq1q2bPDw8tGnTpov2XVBQoLy8PKcFAAAAlV+FDbfZ2dmSpLCwMKftYWFh1r7s7GyFhoY67a9atapCQkKsNiVJSkpSUFCQtURFRZVx9QAAAHCHChtur6WEhATl5uZay8GDB91dEgAAAMpAhQ234eHhkqScnByn7Tk5Oda+8PBwHT582Gl/YWGhjh8/brUpibe3twIDA50WAAAAVH4VNtzWq1dP4eHhWr16tbUtLy9PmzZtUlxcnCQpLi5OJ06c0NatW602X375pYqLi9W2bdtyrxkAAADu5da3JZw6dUr79++31jMyMpSamqqQkBDVqVNHY8aM0QsvvKCGDRuqXr16eu655xQZGan+/ftLkpo0aaKePXtqxIgRmjNnjs6dO6fRo0frnnvu4U0JAAAAf0JuDbdbtmzRbbfdZq2PGzdOkjR06FDNmzdPTz31lPLz8/Xggw/qxIkT6tChg5YtWyYfHx/rmAULFmj06NHq2rWrPDw8NHDgQL322mvlfi0AAABwP7eG286dO8sYc9H9DodDkydP1uTJky/aJiQkRAsXLrwW5QEAAKCSqbBzbgEAAIDSItwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsI0KHW4TExPlcDiclsaNG1v7z5w5o1GjRqlGjRoKCAjQwIEDlZOT48aKAQAA4E4VOtxKUtOmTZWVlWUtX3/9tbVv7Nix+uyzz7R48WKlpKTo0KFDGjBggBurBQAAgDtVdXcBl1O1alWFh4dfsD03N1dvv/22Fi5cqC5dukiS5s6dqyZNmmjjxo1q165deZcKAAAAN6vwd2737dunyMhIXX/99Ro0aJAyMzMlSVu3btW5c+fUrVs3q23jxo1Vp04dbdiw4ZJ9FhQUKC8vz2kBAABA5Vehw23btm01b948LVu2TLNnz1ZGRoZuvfVWnTx5UtnZ2fLy8lJwcLDTMWFhYcrOzr5kv0lJSQoKCrKWqKioa3gVAAAAKC8VelpCr169rJ9btGihtm3bKjo6Wv/+97/l6+vrcr8JCQkaN26ctZ6Xl0fABQAAsIEKfef2j4KDg3XDDTdo//79Cg8P19mzZ3XixAmnNjk5OSXO0f09b29vBQYGOi0AAACo/CpVuD116pTS09MVERGh2NhYeXp6avXq1db+vXv3KjMzU3FxcW6sEgAAAO5SoaclPPHEE+rbt6+io6N16NAhTZw4UVWqVNG9996roKAgDR8+XOPGjVNISIgCAwP1yCOPKC4ujjclAAAA/ElV6HD7008/6d5779WxY8dUq1YtdejQQRs3blStWrUkSa+++qo8PDw0cOBAFRQUKD4+Xm+88YabqwYAAIC7VOhw+8EHH1xyv4+Pj2bNmqVZs2aVU0UAAACoyCrVnFsAAADgUgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGzDNuF21qxZqlu3rnx8fNS2bVt988037i4JAAAA5cwW4XbRokUaN26cJk6cqG3btqlly5aKj4/X4cOH3V0aAAAAypEtwu306dM1YsQIDRs2TDExMZozZ478/Pz0zjvvuLs0AAAAlKNKH27Pnj2rrVu3qlu3btY2Dw8PdevWTRs2bHBjZQAAAChvVd1dwNU6evSoioqKFBYW5rQ9LCxM3333XYnHFBQUqKCgwFrPzc2VJOXl5V27Ql1w6tQpSVJB9n4Vnz3j5moqj3PHf5L02+dX0f5NKyrGmmsYa6XHWHMNY630GGuuqchj7Xw9xphLNzSV3M8//2wkmfXr1zttf/LJJ83NN99c4jETJ040klhYWFhYWFhYWCrZcvDgwUtmw0p/57ZmzZqqUqWKcnJynLbn5OQoPDy8xGMSEhI0btw4a724uFjHjx9XjRo15HA4rmm9dpGXl6eoqCgdPHhQgYGB7i4HNsZYQ3lhrKG8MNZcY4zRyZMnFRkZecl2lT7cenl5KTY2VqtXr1b//v0l/RZWV69erdGjR5d4jLe3t7y9vZ22BQcHX+NK7SkwMJD/YaJcMNZQXhhrKC+MtdILCgq6bJtKH24lady4cRo6dKjatGmjm2++WTNmzFB+fr6GDRvm7tIAAABQjmwRbu+++24dOXJEEyZMUHZ2tlq1aqVly5Zd8JAZAAAA7M0W4VaSRo8efdFpCCh73t7emjhx4gXTO4CyxlhDeWGsobww1q4thzGXe58CAAAAUDlU+i9xAAAAAM4j3AIAAMA2CLcAAACwDcItSqVu3bqaMWOGu8uAjTHGUB6Sk5PlcDh04sQJd5eCPwHGW/ki3KJE8+bNq7BfbEH4sYeKPMbO+/HHH+VwOJSamuruUnAVOnfurDFjxri7jEtKTExUq1at3F0GykBFHW9/poBNuAUA2NLZs2fdXQL+RBhvFQfhthL78MMP1bx5c/n6+qpGjRrq1q2b8vPzVVxcrMmTJ6t27dry9va2vtTivJL+ektNTZXD4dCPP/6o5ORkDRs2TLm5uXI4HHI4HEpMTLTanj59Wn/9619VrVo11alTR2+++aZTXQcPHtRdd92l4OBghYSEqF+/fvrxxx+t/Zs3b1b37t1Vs2ZNBQUFqVOnTtq2bZu13xijxMRE1alTR97e3oqMjNSjjz4q6be/iA8cOKCxY8dateHaqYhj7C9/+YvTO63HjBkjh8Oh7777TtJv/4Hx9/fXqlWrJP32ddxJSUmqV6+efH191bJlS3344YfW8b/88osGDRqkWrVqydfXVw0bNtTcuXMlSfXq1ZMktW7dWg6HQ507dy6zzxYX6ty5sx599FE99dRTCgkJUXh4uNO4yMzMVL9+/RQQEKDAwEDdddddysnJsfafv/v5f//3f6pXr558fHz0wAMPKCUlRTNnzrTG2u9/H23dulVt2rSRn5+fbrnlFu3du1eSlJubqypVqmjLli2SfhtHISEhateunXXs+++/r6ioKGv9cr/7kpOTdfPNN8vf31/BwcFq3769Dhw4oHnz5mnSpEnasWOHVeO8efPK9sPFBSrSeDvv008/1Y033igfHx9df/31mjRpkgoLC63906dPV/PmzeXv76+oqCg9/PDDOnXqlLX/wIED6tu3r6pXry5/f381bdpUX3zxhX788UfddtttkqTq1avL4XDogQceKNsPtCIxqJQOHTpkqlataqZPn24yMjLMzp07zaxZs8zJkyfN9OnTTWBgoPnXv/5lvvvuO/PUU08ZT09P8/333xtjjFmzZo2RZH755Rerv+3btxtJJiMjwxQUFJgZM2aYwMBAk5WVZbKysszJkyeNMcZER0ebkJAQM2vWLLNv3z6TlJRkPDw8zHfffWeMMebs2bOmSZMm5q9//avZuXOnSUtLM/fdd59p1KiRKSgoMMYYs3r1ajN//nyzZ88ek5aWZoYPH27CwsJMXl6eMcaYxYsXm8DAQPPFF1+YAwcOmE2bNpk333zTGGPMsWPHTO3atc3kyZOt2nBtVNQx9tprr5mmTZta/bZq1crUrFnTzJ492xhjzNdff208PT1Nfn6+McaYF154wTRu3NgsW7bMpKenm7lz5xpvb2+TnJxsjDFm1KhRplWrVmbz5s0mIyPDrFy50vz3v/81xhjzzTffGElm1apVJisryxw7duzafuh/cp06dTKBgYEmMTHRfP/99+bdd981DofDrFixwhQVFZlWrVqZDh06mC1btpiNGzea2NhY06lTJ+v4iRMnGn9/f9OzZ0+zbds2s2PHDnPixAkTFxdnRowYYY21wsJCa4y2bdvWJCcnm927d5tbb73V3HLLLVZ/N954o/nnP/9pjDEmNTXVhISEGC8vL2us/u1vfzODBg0yxlz+d9+5c+dMUFCQeeKJJ8z+/ftNWlqamTdvnjlw4IA5ffq0efzxx03Tpk2tGk+fPl1+H/yfVEUbb2vXrjWBgYFm3rx5Jj093axYscLUrVvXJCYmWm1effVV8+WXX5qMjAyzevVq06hRIzNy5Ehrf58+fUz37t3Nzp07TXp6uvnss89MSkqKKSwsNP/5z3+MJLN3716TlZVlTpw4US6fszsQbiuprVu3Gknmxx9/vGBfZGSkmTJlitO2m266yTz88MPGmMsHD2OMmTt3rgkKCrqg7+joaHP//fdb68XFxSY0NNQKFvPnzzeNGjUyxcXFVpuCggLj6+trli9fXuK1FBUVmWrVqpnPPvvMGGPMK6+8Ym644QZz9uzZEttHR0ebV199tcR9KDsVdYzt3LnTOBwOc/jwYXP8+HHj5eVlnn/+eXP33XcbY34Ls+f/g3HmzBnj5+dn1q9f73SO4cOHm3vvvdcYY0zfvn3NsGHDSvwMMjIyjCSzffv2i3xKKEudOnUyHTp0cNp20003mfHjx5sVK1aYKlWqmMzMTGvf7t27jSTzzTffGGN+Cxuenp7m8OHDF/T72GOPOW07P0ZXrVplbfv888+NJPPrr78aY4wZN26c6dOnjzHGmBkzZpi7777btGzZ0ixdutQYY0yDBg2sP7wv97vv2LFjRpL1R9UfTZw40bRs2fJKPyqUgYo23rp27WqmTp3qdNz8+fNNRETERa9h8eLFpkaNGtZ68+bNncJwSTX8/veyXTEtoZJq2bKlunbtqubNm+vOO+/UW2+9pV9++UV5eXk6dOiQ2rdv79S+ffv22rNnT5mcu0WLFtbPDodD4eHhOnz4sCRpx44d2r9/v6pVq6aAgAAFBAQoJCREZ86cUXp6uiQpJydHI0aMUMOGDRUUFKTAwECdOnVKmZmZkqQ777xTv/76q66//nqNGDFCH3/8sdP/LYPyUVHHWLNmzRQSEqKUlBR99dVXat26tW6//XalpKRIklJSUqzpA/v379fp06fVvXt3azwGBATovffes8bjyJEj9cEHH6hVq1Z66qmntH79+jK5Brjm9//2khQREaHDhw9rz549ioqKcpoGEBMTo+DgYKdxFx0drVq1arl0voiICEmyxlqnTp309ddfq6ioyBpXnTt3VnJysg4dOqT9+/dbY+1yv/tCQkL0wAMPKD4+Xn379tXMmTOVlZVV6s8HZasijbcdO3Zo8uTJTr+rRowYoaysLJ0+fVqStGrVKnXt2lXXXXedqlWrpsGDB+vYsWPW/kcffVQvvPCC2rdvr4kTJ2rnzp2l/ETsgXBbSVWpUkUrV67U0qVLFRMTo9dff12NGjVSRkbGZY/18Pjtn9387puXz507d8Xn9vT0dFp3OBwqLi6WJJ06dUqxsbFKTU11Wr7//nvdd999kqShQ4cqNTVVM2fO1Pr165WamqoaNWpYk/GjoqK0d+9evfHGG/L19dXDDz+sjh07lqpGXL2KOsYcDoc6duyo5ORkK3C0aNFCBQUF+vbbb7V+/Xp16tRJkqy5aJ9//rnTeExLS7Pm3fbq1cuax33o0CF17dpVTzzxxBXXirJ1qX/7K+Hv7+/y+c7P4T9/vo4dO+rkyZPatm2b1q5d6xRuU1JSFBkZqYYNG0q6st99c+fO1YYNG3TLLbdo0aJFuuGGG7Rx48ZS1YuyVZHG26lTpzRp0iSn8bNr1y7t27dPPj4++vHHH3X77berRYsW+s9//qOtW7dq1qxZkv7/D7P97W9/0w8//KDBgwdr165datOmjV5//fVS1WgHhNtKzOFwqH379po0aZK2b98uLy8vrV69WpGRkVq3bp1T23Xr1ikmJkaSrL8yf3/X4I+vOvLy8lJRUVGpa7rxxhu1b98+hYaGqkGDBk5LUFCQVcujjz6q3r17q2nTpvL29tbRo0ed+vH19VXfvn312muvKTk5WRs2bNCuXbuuqjaUXkUcY9Jvd9SSk5OVnJyszp07y8PDQx07dtQ///lPFRQUWHeVY2Ji5O3trczMzAvG4+/vyNSqVUtDhw7V+++/rxkzZlgPsHl5eUkS460CaNKkiQ4ePKiDBw9a29LS0nTixAlr3F2Mq2MtODhYLVq00P/8z//I09NTjRs3VseOHbV9+3YtWbLE+iNKurLffdJvDycmJCRo/fr1atasmRYuXHhVNeLacMd4u/HGG7V3794Lxk+DBg3k4eGhrVu3qri4WK+88oratWunG264QYcOHbqgn6ioKD300EP66KOP9Pjjj+utt96y6pL+HL/PCLeV1KZNmzR16lRt2bJFmZmZ+uijj3TkyBE1adJETz75pF566SUtWrRIe/fu1dNPP63U1FQ99thjkmT9hz0xMVH79u3T559/rldeecWp/7p16+rUqVNavXq1jh49av1fHpczaNAg1axZU/369dNXX32ljIwMJScn69FHH9VPP/0kSWrYsKHmz5+vPXv2aNOmTRo0aJB8fX2tPubNm6e3335b3377rX744Qe9//778vX1VXR0tFXb2rVr9fPPP18QilF2KuoYk357yjktLU27d+9Whw4drG0LFixQmzZtrLsp1apV0xNPPKGxY8fq3XffVXp6urZt26bXX39d7777riRpwoQJ+vTTT7V//37t3r1bS5YsUZMmTSRJoaGh8vX11bJly5STk6Pc3Nyr/lzhmm7duql58+YaNGiQtm3bpm+++UZDhgxRp06d1KZNm0seW7duXW3atEk//vijjh49Wqo7c+fH1fkgGxISoiZNmmjRokVO4fZyv/syMjKUkJCgDRs26MCBA1qxYoX27dtnjbW6desqIyNDqampOnr0qAoKClz4lFBW3DHeJkyYoPfee0+TJk3S7t27tWfPHn3wwQd69tlnJf32e/XcuXN6/fXX9cMPP2j+/PmaM2eOUx9jxozR8uXLlZGRoW3btmnNmjXWGIuOjpbD4dCSJUt05MgRp7cs2I67J/3CNWlpaSY+Pt7UqlXLeHt7mxtuuMG8/vrrxpjfHtBKTEw01113nfH09HR6AOK8r7/+2jRv3tz4+PiYW2+91SxevNjpYR9jjHnooYdMjRo1jCQzceJEY0zJD3O1bNnS2m+MMVlZWWbIkCGmZs2axtvb21x//fVmxIgRJjc31xhjzLZt20ybNm2Mj4+PadiwoVm8eLFTvx9//LFp27atCQwMNP7+/qZdu3ZOk/A3bNhgWrRoYby9vQ1D+NqpyGOsqKjIVK9e3bRt29badv6Btaefftrp2OLiYjNjxgzTqFEj4+npaWrVqmXi4+NNSkqKMcaY559/3jRp0sT4+vqakJAQ069fP/PDDz9Yx7/11lsmKirKeHh4OD0pjbJX0oM4/fr1M0OHDjXGGHPgwAFzxx13GH9/f1OtWjVz5513muzsbKvtxR7K2rt3r2nXrp3x9fW1xuCVPPRozG+/jyRZDzQaY8xjjz1mJFlv8DjvUr/7srOzTf/+/U1ERITx8vIy0dHRZsKECaaoqMgY89vDjwMHDjTBwcFGkpk7d65LnyGuXEUcb8uWLTO33HKL8fX1NYGBgebmm2+2Hlo0xpjp06ebiIgI4+vra+Lj4817773n1O/o0aNN/fr1jbe3t6lVq5YZPHiwOXr0qHX85MmTTXh4uHE4HNZ12pHDmN9NigMAAAAqMaYlAAAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALADZWt25dzZgxw91lAEC54UscAMDGjhw5In9/f/n5+bm7FAAoF4RbAKiAzp49Ky8vL3eXAQCVDtMSAKAC6Ny5s0aPHq0xY8aoZs2aio+P17fffqtevXopICBAYWFhGjx4sI4ePWodc/LkSQ0aNEj+/v6KiIjQq6++qs6dO2vMmDFWmz9OS8jMzFS/fv0UEBCgwMBA3XXXXcrJybH2JyYmqlWrVpo/f77q1q2roKAg3XPPPTp58mR5fAwAcNUItwBQQbz77rvy8vLSunXr9OKLL6pLly5q3bq1tmzZomXLliknJ0d33XWX1X7cuHFat26d/vvf/2rlypX66quvtG3btov2X1xcrH79+un48eNKSUnRypUr9cMPP+juu+92apeenq5PPvlES5Ys0ZIlS5SSkqIXX3zxml03AJSlqu4uAADwm4YNG2ratGmSpBdeeEGtW7fW1KlTrf3vvPOOoqKi9P333ysiIkLvvvuuFi5cqK5du0qS5s6dq8jIyIv2v3r1au3atUsZGRmKioqSJL333ntq2rSpNm/erJtuuknSbyF43rx5qlatmiRp8ODBWr16taZMmXJNrhsAyhLhFgAqiNjYWOvnHTt2aM2aNQoICLigXXp6un799VedO3dON998s7U9KChIjRo1umj/e/bsUVRUlBVsJSkmJkbBwcHas2ePFW7r1q1rBVtJioiI0OHDh6/q2gCgvBBuAaCC8Pf3t34+deqU+vbtq5deeumCdhEREdq/f/81q8PT09Np3eFwqLi4+JqdDwDKEnNuAaACuvHGG7V7927VrVtXDRo0cFr8/f11/fXXy9PTU5s3b7aOyc3N1ffff3/RPps0aaKDBw/q4MGD1ra0tDSdOHFCMTEx1/R6AKC8EG4BoAIaNWqUjh8/rnvvvVebN29Wenq6li9frmHDhqmoqEjVqlXT0KFD9eSTT2rNmjXavXu3hg8fLg8PDzkcjhL77Natm5o3b65BgwZp27Zt+uabbzRkyBB16tRJbdq0KecrBIBrg3ALABVQZGSk1q1bp6KiIvXo0UPNmzfXmDFjFBwcLA+P3351T58+XXFxcbr99tvVrVs3tW/fXk2aNJGPj0+JfTocDn366aeqXr26OnbsqG7duun666/XokWLyvPSAOCa4kscAMAm8vPzdd111+mVV17R8OHD3V0OALgFD5QBQCW1fft2fffdd7r55puVm5uryZMnS5L69evn5soAwH0ItwBQib388svau3evvLy8FBsbq6+++ko1a9Z0d1kA4DZMSwAAAIBt8EAZAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbOP/A1JV4IX7ok5AAAAAAElFTkSuQmCC", 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", 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Seleccionar variables categóricas (tipo 'object' o 'category')\n", + "categorical_vars = data.select_dtypes(include=['object', 'category']).columns\n", + "\n", + "# Seleccionar variables numéricas (tipo numérico)\n", + "numerical_vars = data.select_dtypes(include=['number']).columns\n", + "\n", + "# Graficar variables categóricas: gráfico de barras de las frecuencias\n", + "for var in categorical_vars:\n", + " plt.figure(figsize=(8, 5))\n", + " data[var].value_counts().plot(kind='bar', edgecolor='black')\n", + " plt.title(f\"Distribución de {var}\")\n", + " plt.xlabel(var)\n", + " plt.ylabel(\"Frecuencia\")\n", + " plt.xticks(rotation=0)\n", + " plt.show()\n", + "\n", + "# Graficar variables numéricas: boxplot\n", + "for var in numerical_vars:\n", + " plt.figure(figsize=(8, 5))\n", + " data.boxplot(column=var)\n", + " plt.title(f\"Boxplot de {var}\")\n", + " plt.ylabel(var)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Matriz de correlación\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Seleccionar solo las variables numéricas\n", + "numerical_vars = data.select_dtypes(include=['number'])\n", + "\n", + "# Calcular la matriz de correlación solo con variables numéricas\n", + "correlation_matrix = numerical_vars.corr()\n", + "\n", + "# Crear un heatmap de correlación\n", + "plt.figure(figsize=(5, 3))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=0.5)\n", + "plt.title(\"Matriz de Correlación entre Variables Numéricas\")\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parece que la edad y el bmi son de las numericas las que mas correlacion tienen con el label" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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ON728/fXJJ58gPj6+3PtF5ebmoqSkBACUumILhUKu6FX8ny1PZV5X2Rx8fHxQXFyMn3/+mZsvl8sRHh6u9LqWLVvi5s2bvK6+ly9f5nru1TYfHx9IJBIsWbIExcXFSvMVeVlZWaF9+/bYunUr7/9QbGzsW8/m1dUxRd4dnXEhauHo0aO4efMmSkpKkJWVhZMnTyIqKgq2trY4cOBAhQNFhYaGIi4uDr6+vrC1tcWjR4+wdu1aNGvWjBuHo2XLljAyMsL69ethaGgIAwMDuLm5KV1LrywTExP06NEDY8eORVZWFsLCwtCqVSteQ8cJEyZgz549+OCDD/DJJ5/g7t272L59u1L35s8++wxbt25FQEAAzp8/j549e+L58+f4888/MWXKFAwePLjcHBYtWsSNXzNlyhRoaWnhp59+QmFhIZYtW1at7SrP0qVL4evrix49emDcuHHIzs7G6tWr4ejoyPty8PLywpdffomlS5ciKSkJffv2hba2Nm7fvo3IyEisWrUKH3/88RvX0717d4SHh2PKlClo27Ytb+TcmJgYHDhwAIsWLapS7hKJBJ6enli2bBmKi4vRtGlTnDhxAqmpqZVehrm5OaZPn47ly5dj0KBB+OCDD3D58mUcPXoUpqamvLN5s2fPxoEDBzBgwAB8/vnncHV1xfPnz3H16lXs2bMH9+/fh6mpKSZMmIDs7Gz07t0bzZo1Q1paGlavXg0XFxe0a9fujblU5nWVzWHIkCHo2rUrZs2ahTt37qBt27Y4cOAA90Og7HaNGzcOK1asgI+PD8aPH49Hjx5h/fr1cHR0VGqwWhskEgnWrVuH0aNHo1OnThg+fDiaNGmC9PR0HD58GB4eHlwhuWTJEgwePBgeHh4YO3YscnJysGbNGrRv377cgrisujqmyDtSWX8mQtj/uhUqHmKxmFlYWLD333+frVq1itflWOH17tDR0dFs8ODBzMrKionFYmZlZcVGjBih1B10//79zMHBgWlpafG6e3p5eTFHR8dy83tTd+jffvuNBQYGMjMzM6anp8d8fX1ZWlqa0uuXL1/OmjZtynR0dJiHhwe7ePFiuV1LX7x4webOncvs7OyYtrY2s7CwYB9//DGvWyZe67rJGGOJiYnMx8eHNWrUiOnr67P33nuPnTlzptz3+PUu54ptOXXqVLnbXtbvv//O2rVrx3R0dJiDgwP7448/lLrCKmzYsIG5uroyPT09ZmhoyJycnNicOXNYRkbGW9fDGGMJCQls5MiRzMrKimlrazNjY2PWp08ftmXLFiaTyRhj/+t2+/333yu9/vX36Z9//mEffvghMzIyYlKplA0bNoxlZGQoxSn+X5XXrb6kpITNnz+fWVhYMD09Pda7d29248YN1rhxYzZp0iRe7LNnz1hgYCBr1aoVE4vFzNTUlHXv3p398MMPrKioiDHG2J49e1jfvn2ZmZkZE4vFzMbGhn355Zfs4cOHFb43lX1dZXJgjLHHjx+zkSNHMkNDQyaVStnnn3/OTp8+zQCwnTt38pa5fft21qJFCyYWi5mLiws7fvz4G7tDV2a/lOdtwyOcOnWK+fj4MKlUynR1dVnLli3Z559/zi5evMiL27lzJ2vbti3T0dFh7du3ZwcOHGBDhw5lbdu2fWtOdXVMkeoTMEatiAghpKpyc3NhbGyMRYsWYe7cuapOp8bs27cPH374If7++294eHioOp0a4+LigiZNmlS5CzpRP9TGhRBC3uLly5dK0xTtZMobCl9TvL5dMpkMq1evhkQiQadOnVSU1bspLi7m2vEoxMTE4PLlyxq9r8j/UBsXQgh5i127dmHz5s3o378/GjVqhL///hu//fYb+vbtq9FnJaZNm4aXL1/C3d0dhYWF+OOPP3DmzBksWbKkVoYIqAv//vsvvL298emnn8LKygo3b97E+vXrYWFhoTS4INFMVLgQQshbODs7Q0tLC8uWLUN+fj7XYLeqjYXVTe/evbF8+XIcOnQIr169QqtWrbB69WpMnTpV1alVm7GxMVxdXbFx40Y8fvwYBgYG8PX1xbfffovGjRurOj1SA6iNCyGEEEI0BrVxIYQQQojGoMKFEEIIIRqD2rjUELlcjoyMDBgaGtb48PKEEEJIfcYYw7Nnz2BlZQWhsOJzKlS41JCMjAy6jwUhhBDyDh48eIBmzZpVGEOFSw0xNDQEUPqm0/0sCCGEkMrLz8+HtbU1911aESpcaoji8pBEIqHChRBCCKmGyjS1oMa5hBBCCNEYVLgQQgghRGPQpSJCGjCZTIYrV64gOzsbJiYmcHZ2hkgkUnVahBDyRlS4ENJAxcXFYe3atcjMzOSmWVhYYMqUKfD09FRhZoQQ8mZ0qYiQBiguLg7BwcFo0aIFwsPDceTIEYSHh6NFixYIDg5GXFycqlMkhJBy0b2Kakh+fj6kUiny8vKoVxFRazKZDKNGjUKLFi2waNEi3mBPcrkc8+bNQ2pqKrZv306XjQghdaIq36F0xoWQBubKlSvIzMzEqFGjwBjDpUuXEB0djUuXLoExhlGjRuHhw4e4cuWKqlMlhBAl1MaFkAYmOzsbQOlozwsXLlRq4zJ+/HheHCGEqBMqXAhpYExMTAAAS5Ysgbu7O+bPnw87OzukpqYiIiICS5Ys4cURQog6oUtFhDQwjo6OEIlEMDIyQmhoKBwdHaGvrw9HR0eEhobCyMgIIpEIjo6Oqk6VEEKUUOFCSAOTnJwMmUyGnJwcBAUFITk5GS9evEBycjKCgoKQk5MDmUyG5ORkVadKCCFK6FIRIQ2Mou3K3Llz8csvv8Df35+bZ2lpiblz52Lx4sXUxoUQopaocCGkgVG0XbGyskJERITSyLk3b97kxRFCiDqhS0WENDDOzs6wsLBAREQEBAIBOnbsiD59+qBjx44QCASIiIiApaUlnJ2dVZ0qIYQoocKFkAZGJBJhypQpiI+Px7x583htXObNm4f4+HhMnjyZBp8jhKgllRcu//77Lz799FM0btwYenp6cHJywsWLF7n5jDEEBQXB0tISenp68Pb2xu3bt3nLyM7OxqhRoyCRSGBkZITx48ejoKCAF3PlyhX07NkTurq6sLa2xrJly5RyiYyMRNu2baGrqwsnJyccOXKkdjaaEBXz9PRESEgI7t27B39/f/Tv3x/+/v5ITU1FSEgI3atIQ8lkMt6AgjKZTNUpEVLjVNrGJScnBx4eHnjvvfdw9OhRNGnSBLdv34axsTEXs2zZMvz444/YsmUL7OzsMH/+fPj4+OD69evQ1dUFAG6kz6ioKBQXF2Ps2LH44osvsGPHDgClQwn37dsX3t7eWL9+Pa5evYpx48bByMgIX3zxBQDgzJkzGDFiBJYuXYoBAwZgx44dGDJkCBITE9G+ffu6f3MIqWWenp7w8PCgu0PXE3TTTNJgMBX6z3/+w3r06PHG+XK5nFlYWLDvv/+em5abm8t0dHTYb7/9xhhj7Pr16wwAu3DhAhdz9OhRJhAI2L///ssYY2zt2rXM2NiYFRYW8tZtb2/PPf/kk0+Yr68vb/1ubm7syy+/rNS25OXlMQAsLy+vUvGEEFJTYmNjWa9evVhgYCC7du0ae/78Obt27RoLDAxkvXr1YrGxsapOkZAKVeU7VKWXig4cOIDOnTtj2LBhMDMzQ8eOHfHzzz9z81NTU5GZmQlvb29umlQqhZubG+Lj4wEA8fHxMDIyQufOnbkYb29vCIVCnDt3jovx9PSEWCzmYnx8fJCSkoKcnBwupux6FDGK9RBCiDqSyWRYu3Yt3N3dsWjRIt6AgosWLYK7uzvWrVtHl41IvaHSwuXevXtYt24dWrdujePHj2Py5Mn46quvsGXLFgDgTnmam5vzXmdubs7Ny8zMhJmZGW++lpYWTExMeDHlLaPsOt4UU/a0a1mFhYXIz8/nPQghpK6VvWlm2Tt9A4BQKKSbZpJ6R6VtXORyOTp37szdG6Vjx464du0a1q9fjzFjxqgytbdaunQpQkJCVJ0GIaSBUwwUaGdnV+58xXQaUFDzyGQyaoNWDpUWLpaWlnBwcOBNa9euHX7//XcApQ3LACArKwuWlpZcTFZWFlxcXLiYR48e8ZZRUlKC7Oxs7vUWFhbIysrixSievy1GMf91gYGBCAgI4J7n5+fD2tr67RtNCCE1SDFQYGpqarn3l0pNTeXFEc1Aja3fTKWXijw8PJCSksKbduvWLdja2gIo/aVgYWGB6Ohobn5+fj7OnTsHd3d3AIC7uztyc3ORkJDAxZw8eRJyuRxubm5cTFxcHIqLi7mYqKgo2Nvbcz2Y3N3deetRxCjW8zodHR1IJBLegxBC6lrZAQXlcjlvnlwupwEFNVBcXByCg4PRokULhIeH48iRIwgPD0eLFi0QHByMuLg4VaeoWnXQWPiNzp8/z7S0tNjixYvZ7du3WUREBNPX12fbt2/nYr799ltmZGTE9u/fz65cucIGDx7M7Ozs2MuXL7mYDz74gHXs2JGdO3eO/f3336x169ZsxIgR3Pzc3Fxmbm7ORo8eza5du8Z27tzJ9PX12U8//cTFnD59mmlpabEffviB3bhxgwUHBzNtbW129erVSm0L9SoihKgK9SqqP0pKSpifnx8LDAxkMpmMN08mk7HAwEA2fPhwVlJSoqIMa0dVvkNVWrgwxtjBgwdZ+/btmY6ODmvbti3bsGEDb75cLmfz589n5ubmTEdHh/Xp04elpKTwYp4+fcpGjBjBGjVqxCQSCRs7dix79uwZL+by5cusR48eTEdHhzVt2pR9++23Srns3r2btWnThonFYubo6MgOHz5c6e2gwoUQokqxsbHMz8+PeXl5cY/hw4dT0aJhEhMTmZeXF7t27Vq5869du8a8vLxYYmJiHWdWu6ryHSpgjDHVnvOpH/Lz8yGVSpGXl0eXjQghKkGNOTVfdHQ0Fi5ciCNHjkBfX19p/osXL9C/f3/Mnz8fffr0UUGGtaMq36F0d2hCCKknRCIROnbsqOo0yDugxtZvp/J7FRFCCCGkFDW2fjsqXAghhBA1QXdvfztq41JDqI0LIYSQmlLeOC6WlpaYPHlyvRzHpSrfoVS41BAqXAghhNSkhtTYmhrnEkJIA9SQvugaAmpsXT4qXAghpB6gIeJJQ0GNcwkhRMPREPGkIaE2LjWE2rgQQlRBJpNh1KhRaNGiBRYtWgSh8H+/R+VyOebNm4fU1FRs376dLhsRtVWV71A640IIIRrsypUryMzMxKhRo3hFCwAIhUKMGjUKDx8+xJUrV1SUISE1iwoXQgjRYNnZ2QAAOzu7cucrpiviCNF0VLgQQogGKztEfHloiHhS31DhQgghGoyGiCcNDRUuhBCiwWiIeNLQUK+iGtIQehXR4FaEqK+GNkQ8qV9oyH8VqO+FCw1uRYj6ox8XRFNR4aIC9blwUQxu5e7ujlGjRsHOzg6pqamIiIhAfHw8QkJCqHghhBBSbVS4qEB9LVxocCtCCCG1jQagIzWm7OBWjDFcunQJ0dHRuHTpEhhjNLgVIYSQOkU3WSQVUgxalZGRgYULFyq1cRk/fjwvjhBCCKlNVLiQCikGrVq8eDG6d++O+fPn89q4LF68mBdHCCGE1Ca6VEQq5OjoCJFIBGNjY4SGhsLR0RH6+vpwdHREaGgojI2NIRKJ4OjoqOpUCSGENABUuJAKJScnQyaTITc3F0FBQbzBrYKCgpCbmwuZTIbk5GRVp0oIIaQBUGnhsmDBAggEAt6jbdu23PxXr17B398fjRs3RqNGjTB06FBkZWXxlpGeng5fX1/o6+vDzMwMs2fPRklJCS8mJiYGnTp1go6ODlq1aoXNmzcr5RIeHo7mzZtDV1cXbm5uOH/+fK1ss6ZRtF355ptvcO/ePfj7+6N///7w9/dHamoqvvnmG14cIYQQUptU3sbF0dERf/75J/dcS+t/Kc2cOROHDx9GZGQkpFIppk6dio8++ginT58GUNpV19fXFxYWFjhz5gwePnyIzz77DNra2liyZAmA0huM+fr6YtKkSYiIiEB0dDQmTJgAS0tL+Pj4AAB27dqFgIAArF+/Hm5ubggLC4OPjw9SUlJgZmZWh++G+lG0XbGyskJERITS4FY3b97kxRHNQgOWEUI0DlOh4OBg1qFDh3Ln5ebmMm1tbRYZGclNu3HjBgPA4uPjGWOMHTlyhAmFQpaZmcnFrFu3jkkkElZYWMgYY2zOnDnM0dGRt2w/Pz/m4+PDPe/atSvz9/fnnstkMmZlZcWWLl1a6W3Jy8tjAFheXl6lX6MJSkpKmJ+fHwsMDGQymYw3TyaTscDAQDZ8+HBWUlKiogxJdcXGxjI/Pz/m5eXFPfz8/FhsbKyqUyOENDBV+Q5VeRuX27dvw8rKCi1atMCoUaOQnp4OAEhISEBxcTG8vb252LZt28LGxgbx8fEAgPj4eDg5OcHc3JyL8fHxQX5+PtfmIj4+nrcMRYxiGUVFRUhISODFCIVCeHt7czHlKSwsRH5+Pu9RH5W9gdvcuXOxd+9eHDlyBHv37sXcuXPpBm4aSjEacvPmzTF06FAMHDgQQ4cORfPmzREcHIy4uDhVp0gIIeVS6aUiNzc3bN68Gfb29nj48CFCQkLQs2dPXLt2DZmZmRCLxTAyMuK9xtzcnBtLJDMzk1e0KOYr5lUUk5+fj5cvXyInJwcymazcGMVlkPIsXboUISEh1dpuTePp6Qk/Pz9ERkbyijmRSAQ/Pz8a7l/DyGQyrF27FpaWljh37hxYmcGzBQIBLC0tsW7dOnh4eFBBSogKFRUVYf/+/cjIyICVlRUGDx4MsVis6rRUTqWFS79+/bi/nZ2d4ebmBltbW+zevRt6enoqzOztAgMDERAQwD3Pz8+HtbW1CjOqPXFxcdi1axe6deuGrl27QldXF69evcL58+exa9cuODg4UPGiQRSjIZeHMYaMjAwurmPHjnWZGiHk/61fvx6RkZGQyWS8acOGDcOkSZNUmJnqqfxSUVlGRkZo06YN7ty5AwsLCxQVFSE3N5cXk5WVBQsLCwClI7e+3stI8fxtMRKJBHp6ejA1NYVIJCo3RrGM8ujo6EAikfAe9ZHi17m7uzuCg4NRUlKCW7duoaSkhLvx4rp163gHF1Fvjx494v42MjLC119/jd9//x1ff/017wxn2ThCSN1Zv349du7cCYlEgk8++QQzZszAJ598AolEgp07d2L9+vWqTlGl1KpwKSgowN27d2FpaQlXV1doa2sjOjqam5+SkoL09HS4u7sDANzd3XH16lXeB2xUVBQkEgkcHBy4mLLLUMQoliEWi+Hq6sqLkcvliI6O5mIaMsWvc319ffj6+iI8PBx79+5FeHg4fH19oaenR/cq0jDXrl0DUFp879mzBwMGDEDjxo0xYMAA7NmzBzo6Orw4QkjdKSoqQmRkJAwMDCAWi7F7926EhYVh9+7dEIvFMDAwQGRkJIqKilSdqsqo9FLR119/jYEDB8LW1hYZGRkIDg6GSCTCiBEjIJVKMX78eAQEBMDExAQSiQTTpk2Du7s7unXrBgDo27cvHBwcMHr0aCxbtgyZmZmYN28e/P39uQ/fSZMmYc2aNZgzZw7GjRuHkydPYvfu3Th8+DCXR0BAAMaMGYPOnTuja9euCAsLw/PnzzF27FiVvC/qRDE+y59//gkjIyPY2tqCMQaBQIC0tDSu4KNxXDTHvXv3AJSejWT/f+NMRXfo9u3bw8LCAmlpaVwc0RzUvV3z7d+/HzKZDM+fP0eHDh0QFBTEu83KmTNnuLhhw4apOFvVUGnh8s8//2DEiBF4+vQpmjRpgh49euDs2bNo0qQJAGDlypUQCoUYOnQoCgsL4ePjg7Vr13KvF4lEOHToECZPngx3d3cYGBhgzJgxCA0N5WLs7Oxw+PBhzJw5E6tWrUKzZs2wceNGbgwXAPDz88Pjx48RFBSEzMxMuLi44NixY0oNdhsixSUwLS0t5ObmKl2609LSQklJSb29VFYfCQQCAEBaWhp8fX15v9zEYjH3XBFHNENcXBzWrl2rdCPUKVOmUBs0DfLvv/8CADp37oxFixZBKCy9MOLo6IhFixZhzpw5uHjxIhfXEKm0cNm5c2eF83V1dREeHo7w8PA3xtja2uLIkSMVLqdXr164dOlShTFTp07F1KlTK4xpiBS/uktKSqCtrY1hw4ahf//+OHLkCCIjI1FcXMzFdenSRZWpkkry8PDgLgO9frq57HMPD486zYtUn6J7e7du3eDn5wcdHR0UFhbi/PnzCA4ORkhICBUvGqZNmzZc0aIgFArRunVrXLx4UUVZqQe1auNC1M8///zD/d2pUyd4eHjAxMQEHh4e6NSpU7lxRL0NGTKkRuOIaika0Ldp0wapqalYtWoVli1bhlWrViE1NRVt2rShBvQapF27dgCAI0eOKN2+pqSkBEePHuXFNUQqH/KfqLfU1FQAQMuWLXH//n34+/tz8ywsLNCyZUvcvXuXiyPqr7KNbq9du0Zn0TSAogF9ZmYm17ZPIScnh7t0RN3bNYPiNjO5ubn4+OOP0bdvX1hZWSEjIwMnTpzgLtc35NvRUOFCKqT4IMzIyFBqx1J2zI/XPzCJ+jp+/Hil46hwUX9Pnjzh/i47mODrz8vGEfXl7OwMCwsLFBYWIicnB7t37+bNNzY2hq6uLpydnVWUoepR4UIqZG1tjYSEBLx8+RIlJSUYMWIE18Zlz549XBuX+jr4Xn1UtvGmQCBQGjlX8fxNg9QR9VK2R1+nTp3g5ubGDRJ57tw5nD17VimOqC+RSIRevXph586dMDIyQvPmzSGXyyEUCnH//n3k5ORg+PDhDbq3GBUupEITJkzAvn37AADFxcX47bff8NtvvwHg9zqZMGGCKtIj1aCtrc393bVrV3z22Wdcd8utW7fi3LlzSnFEfeXl5QEoPeuZmprKFSpA6a1LFA11FXFEvclkMsTExMDKygqZmZlISkri5gmFQlhZWSE2NhYTJ05ssMULFS6kQrdv3+b+1tLSgpOTExo3boynT5/i6tWrXOOx27dv0/VzDXXr1i2kpaWhsLBQ1amQanj8+DGA0hu/FhUV4euvv4a7uzvi4+Pxyy+/cPtVEUfUW9lbcri7uyvdZkVxv7iG3GaJChdSIcXp5W7duuHs2bNK3coV0+k0tOZQXN4DgHPnznFnWCqKI+pLMe6Vrq4utLW18cMPP3DzzM3NuS89RRxRb4q2SG5ubggNDcW1a9eQnZ0NCwsLDBgwAPPmzcO5c+cadJslKlxIhUxMTAAAo0ePxrx587B06VLuTqWBgYFIS0vD2bNnuTii/iwsLCrVs6iie3UR9SGVSgEAr169gouLC0aMGMFdHirbxkURR9SboteQubk5Ro8erTSgYNeuXXlxDREVLqRCihbuP/74I3Jzc7mbUaampmL8+PEwMjKCpaVlg27hrmnef/99/Pnnn5WKI+qv7I+GS5cu8dq4lO3tRz8uNIPiRqcHDhyAu7s75s+fz7VB2759Ow4cOMCLa4hoADpSIUUL95SUFBQVFWHWrFnYs2cPZs2ahaKiIqSkpMDLy6vBNhLTRFpalfu9Utk4olqmpqbc3xV1hy4bR9RX2QKTMYZbt24hJiYGt27d4u3PhlyI0icTqZCihbu9vT3y8vKwfPlybp6lpSXs7e0bfAt3TVP2buo1EUdU6/VxP8oqKiqicT80lKmpKc6fP887gyYSiWBqatqg27cAVLiQt1C0cJ8/fz7atm2rdOfZmzdvwt/fv0G3cNc0cXFx3N8VjeMSFxeHfv361Xl+pGpEIhFatmyJ06dPlzs/JycHHh4e9MNCQyjarjx58gRSqRQdO3bkGlhfunSJK1qojQshb6DoLWRnZ1fufMV06lWkOdLS0gCUjglx4MABHD16lGtw3a9fPwwaNAhyuZyLI+qtqKiI6yKrra3N6w2meB4fH4+ioiKIxWJVpUkqSdF2xdTUFDk5OYiJieHmlT3j0pDbuFDhQiqkuI66d+9eHDx4UKmF+4ABA3hxRP29fPkSACCXy/HRRx/x7gj9888/Qy6X8+KIetu7dy/kcjnMzc0BgGtAD/zvuMzKysLevXvh5+enkhxJ1T158gTdunV740jIDRk1ziUVcnZ2hpGREX7++WfY2dkhPDwcR44cQXh4OOzs7LBx40YYGxvT9XMNYmlpyf1dtmh5/XnZOKK+rl69CqC0OHn98kHZnoCKOKLeyjt7/Xqj6zfFNRR0xoW8s/IOKqK+evTogevXr1cqjqg/XV1d7u9OnTrh008/5XWfVVxGKhtH1Jei+OzcuXO5jXM7d+6MixcvNug2LnTGhVToypUryM3NxcSJE3Hv3j34+/ujf//+8Pf3R2pqKiZOnIjc3FxcuXJF1amSSmrZsmWNxhHVatGiBYDSNkshISFwdHSEvr4+HB0dERISAqFQyIsj6k3RduXixYvcZVsFmUyGixcv8uIaIipcSIUUpyPNzMx4N1VUMDMz48UR9VfZIpOKUc3w/PlzAKVtlvz8/HDw4EE8efIEBw8ehJ+fH/flp4gj6q1se0EtLS2MHDkS27dvx8iRI3ljKzXkdoV0qYhUSHFwLFmyRGkUx4iICCxZsoQXR9SfYnyWTp06ISkpiferTiQSwdnZGZcuXaJxXDSE4owKUNr1uexYS2+KI+pLceNaoVAIqVSKHTt2YMeOHQBKexplZ2dDLpdzcQ0RFS6kQo6OjhCJRJBIJAgNDeUqfkdHR4SGhmLYsGHIz8+Ho6OjijOtv169eoX09PQaW55iH+bk5ODHH39EXFwcHj9+jCZNmsDT05P74tPS0sKtW7dqZJ02NjbUxqKWuLi4YNu2bbCxscGLFy94g5M1adIEenp6SE9Ph4uLi+qSJJWmuB2HXC7H06dPefPK7ts///wTbm5udZqbuqDChVQoOTkZMpkMOTk5CAoKwqhRo3hnXBQjdSYnJ9MAdLUkPT0dX3zxRY0vNzU1FVOnTuVN2717N/f30aNHcfTo0RpZ14YNG9CmTZsaWRbhc3FxgZGREdLT05XGacnLy8Pjx49hZGREhYuGqOwwBA15uAIqXEiFFG1X5s6di40bN8Lf35+bZ2Fhgblz52Lx4sXUxqUW2djYYMOGDTW2PLlcjtmzZ+PZs2fQ0tLinXJWDFhmaGiI77//vsYuL9jY2NTIcogykUiEDz74ADt37lS6fKB4/sEHH9DIuRrCwcEBf//9NwDAzc0N3bp148ZxOXv2LM6dO8fFNVRUuJAKKdquPHr0SKnbs1wu58aIoDYutUdXV7fGz1bMnj0bwcHBEIlEvC87oVAIgUCA2bNno23btjW6TlI7yt5PLDs7G48fP+bmmZqawtjYmO4npsHatGnDneWmwedKqU1rrW+//RYCgQAzZszgpr169Qr+/v5o3LgxGjVqhKFDh/JGhQRKT6P7+vpCX18fZmZmmD17ttKvjpiYGHTq1Ak6Ojpo1aoVNm/erLT+8PBwNG/eHLq6unBzc8P58+drYzM1TtkB6F5vrPno0SMagE5DeXp6IiQkBMbGxrzpJiYmCAkJgaenp4oyI1WluJ+YtbV1uW0irK2t8fDhQ+olpiHKjrF0/vx53hAUZb+XKjMWU32lFoXLhQsX8NNPPyl9+c2cORMHDx5EZGQkYmNjkZGRgY8++oibL5PJ4Ovri6KiIpw5cwZbtmzB5s2bERQUxMWkpqbC19cX7733HpKSkjBjxgxMmDABx48f52J27dqFgIAABAcHIzExER06dICPjw/1qvh/L168eKf5RD15enoiIiICs2bNAgDMmjUL27dvp6JFwygu0yoadb5OMZ0u52oGPT09AIBUKlU6y80Yg1Qq5cU1RCovXAoKCjBq1Cj8/PPPvF9/eXl5+OWXX7BixQr07t0brq6u2LRpE86cOcOdLjtx4gSuX7+O7du3w8XFBf369cPChQsRHh7ODV2+fv162NnZYfny5WjXrh2mTp2Kjz/+GCtXruTWtWLFCkycOBFjx46Fg4MD1q9fD319ffz66691+2aoocTERKVh4V9XWFiIxMTEOsqI1CSRSAR7e3sAgL29PV1K0EASiYT7283NjXdbjrK9TsrGEfXVt29fAKXfgQKBAFZWVmjWrBmsrKwgEAiQl5fHi2uIqlW4vHz5kvcrOy0tDWFhYThx4kSVl+Xv7w9fX194e3vzpickJKC4uJg3vW3btrCxseGGsI6Pj4eTkxN3czEA8PHxQX5+PpKTk7mY15ft4+PDLaOoqAgJCQm8GKFQCG9vby6mPIWFhcjPz+c96qOyZ6a0tbV588o+LxtHCKk7d+7cAQDo6+tjwYIF3N2ii4qKsGDBAujr6/PiiHpr164d9zdjDBkZGfjnn3+QkZHBOwNTNq6hqVbj3MGDB+Ojjz7CpEmTkJubCzc3N2hra+PJkydYsWIFJk+eXKnl7Ny5E4mJibhw4YLSvMzMTIjFYqVhjc3Nzbk7FGdmZvKKFsV8xbyKYvLz8/Hy5Uvk5ORAJpOVG3Pz5s035r506VKEhIRUajs1Wdm7QZd32rK8OEJI3VH8SHvx4gUGDRqEwsJCbp6Ojg73XBFH1NvGjRsrHVe2TWhDUq0zLomJiejZsycAYM+ePTA3N0daWhq2bt2KH3/8sVLLePDgAaZPn46IiAiNHJgqMDAQeXl53OPBgweqTqlWlB0X4k1dLV+PI4TUncq2dWjIbSI0ieK7RCwWKw1HIBQKuc/a+vqdUxnVOuPy4sULGBoaAihtZ/LRRx9BKBSiW7duSEtLq9QyEhIS8OjRI3Tq1ImbJpPJEBcXhzVr1uD48eMoKipCbm4u76xLVlYWLCwsAJSOI/J67x9Fr6OyMa/3RMrKyoJEIoGenh5EIhFEIlG5MYpllEdHRwc6OjqV2lZNVva919LSgpeXF+zt7ZGSkoLY2FiueGnIN/wiRJW8vb0RFRUFkUiEffv24ebNm8jOzoaJiQnatm2LAQMGQCaTKV0yJ+pJcYasqKgI7u7u6Nq1K3fm7Pz581wThrJn1hqaap1xadWqFfbt24cHDx7g+PHjXCOhR48eVboBWJ8+fXD16lUkJSVxj86dO2PUqFHc39ra2oiOjuZek5KSgvT0dLi7uwMA3N3dcfXqVV7vn6ioKEgkEm5wHnd3d94yFDGKZYjFYri6uvJi5HI5oqOjuRhSqqSkBNHR0Vi7di2io6Mb9L0yCFEXils4yGQyDB8+HPHx8Xj27Bni4+MxfPhwyGQyXhxRb82bN+f+DgwMRElJCW7fvo2SkhIEBgaWG9fQVOt/clBQEEaOHImZM2eid+/e3Bf8iRMnKj3su6GhIdq3b8+bZmBggMaNG3PTx48fj4CAAJiYmEAikWDatGlwd3dHt27dAJS2qnZwcMDo0aOxbNkyZGZmYt68efD39+fOhkyaNAlr1qzBnDlzMG7cOJw8eRK7d+/G4cOHufUGBARgzJgx6Ny5M7p27YqwsDA8f/4cY8eOrc7bU6/k5ubWaBwhpGaVPfby8vJ4t214UxxRX2ULzEGDBvHmhYeHlxvX0FRryz/++GP06NEDDx8+RIcOHbjpffr0wYcfflhjya1cuRJCoRBDhw5FYWEhfHx8sHbtWm6+SCTCoUOHMHnyZLi7u8PAwABjxoxBaGgoF2NnZ4fDhw9j5syZWLVqFZo1a4aNGzfCx8eHi/Hz88Pjx48RFBSEzMxMuLi44NixY0oNdhuisu2P3jQ8/OtxhJC6U3bUarFYzBu+oOxzGt1aM7Rr1w779u2rVFxDVe2SzcLCAgUFBYiKioKnpyf09PTQpUsXCASCaicTExPDe66rq4vw8HBelfk6W1tbHDlypMLl9urVC5cuXaowZurUqUo3nCOlQ4YrvH5pSFG0vB5HCKk7iju46+rqolGjRrz2esbGxigoKMCrV6/oDu4aomyBKRKJ0KJFC64AvXfvHnfpryEXotVq4/L06VP06dMHbdq0Qf/+/fHw4UMApZd2FKNwkvqhsh929KFIiGoo7uD+/PlzFBUV4euvv8bvv/+Or7/+GkVFRXj+/DlkMhl1h9YQd+/eBVB6hpsxhtu3byM5ORm3b9/mppeNa4iqdcZl5syZ0NbWRnp6Ou90lZ+fHwICArB8+fIaS5CoVmWr+oZc/ROiSk+ePAEAtG7dGvn5+fjhhx+4eRYWFmjdujVu377NxRH1du3aNQClZ7jd3Nygo6ODZ8+ewdDQEIWFhdzdoRVxDVG1CpcTJ07g+PHjaNasGW9669atK90dmmiGyo62eefOHXTp0qWWsyGEvE7R6LZdu3bcl5oCYwxt27bF7du3qXGuhlCMt9OxY0dcvHiRuzQElF466tixIy5dutSgx+WpVuHy/PlzbhjpsrKzsxvE2CYNSdmqXiQSwcLCAgKBAIwxZGZmcgdVQ67+CVElxRhKBw4cgLu7O4KCgmBnZ4fU1FRs374dBw8e5MUR9da3b19ERUXh0qVLcHNzQ7du3aCrq4tXr17h7NmzXHHakO9VVK3CpWfPnti6dSsWLlwIABAIBJDL5Vi2bBnee++9Gk2QqNbLly8BlO5jmUyGf//9lzdfUcQo4gghdYsu59YvHTp04D5XU1JSoKenxxUuKSkpAEo/d8v26G1oqlW4LFu2DH369MHFixdRVFSEOXPmIDk5GdnZ2Th9+nRN50jUwOv3KXrbdEJI3bKxsUFqair8/f25aZaWlrCxsUF6eroKMyNVkZyczH2u5ubmKvW2BUo/d5OTkys9blp9U63CpX379rh16xbWrFkDQ0NDFBQU4KOPPoK/vz8sLS1rOkeiQpW99EeXCAlRDUXblQcPHqBbt27w8/PjDRF/9uxZXhxRb9nZ2dzfijMv5T0vG9fQVHscF6lUirlz59ZkLoQQQqpIcQlowoQJOHjwIHcvG6D0jMuECRPw888/06UiDaG4bY6enh4aNWqEx48fc/NMTU1RUFCAly9fVvr2OvVRtQqXK1eulDtdIBBAV1cXNjY29Au8npDL5TUaRwipWc7OzrCwsEBycjI2b96MgwcPIiMjA1ZWVhg4cCBCQ0NhaWkJZ2dnVadKKuHevXsAStsXvv65mp+fz91c8d69ew22J2e1ChcXFxduhFzFaauyI+Zqa2vDz88PP/30Ew0Fr+H++eefGo0jhNQskUiEKVOmICgoCIMHD+bdNXjjxo0oLCxEaGgoRCKRCrMklaUY0BUA9PX1uXv0xcfH45dffuH2b9m4hqZaI+fu3bsXrVu3xoYNG3D58mVcvnwZGzZsgL29PXbs2IFffvkFJ0+exLx582o6X0IIIW9Qtmgp7zlRf4qTASYmJhCLxfjhhx8wdOhQ/PDDD9DR0eEu+TXkjhHVOuOyePFirFq1inejQicnJzRr1gzz58/H+fPnYWBggFmzZvFGcSSap+wgRxKJBJ06deK65iUmJiI/P18pjhBSd2QyGVasWFFhzMqVK+Hh4UFnXTSAgYEBAKCoqAi//fYbrl+/juzsbJiYmMDBwQFDhw7lxTVE1Spcrl69CltbW6Xptra2uHr1KoDSy0kN+VRWfWFtbc2Nnpufn19u1zxFHCGk7iUlJXE9hoyMjODi4gI9PT28fPmSm5eTk4OkpCS4urqqNlnyVorisqCgAMOHD8e4ceO4S0WhoaEoKCjgxTVE1Spc2rZti2+//RYbNmyAWCwGUHqn4G+//RZt27YFAPz7778wNzevuUyJSlhZWdVoHCGkZl28eBFAadvCvLw83o8LgUAAbW1tFBcX4+LFi1S41JJXr17V2Fg5pqamAEqL0Ly8PN69/4RCIYyMjJCbmwtTU1PcunWrRtZpY2OjUe1Rq1W4hIeHY9CgQWjWrBnXUv3q1auQyWQ4dOgQgNIWz1OmTKm5TIlKdOrUCREREZWKI4TUPcWXV3FxMYyNjTF+/HheY86cnBxeHKl56enp+OKLL2p0meWNuyOXy7npK1eurLF1bdiwAW3atKmx5dW2ahUu3bt3R2pqKiIiIriDYdiwYRg5ciQMDQ0BAKNHj665LInKuLi4cBW+lpYWSkpKuHmKX3LGxsZwcXFRXZKENGDa2toASn+N79q1izsLPmDAAPTt2xcffPAB5HI5F0dqno2NDTZs2FBjy0tMTMT69eu5z1gFsViMoqIiTJo0qUZ/LNrY2NTYsupClQuX4uJitG3bFocOHcKkSZNqIyeiRkQiEQICAhAcHAyRSMQrXIRCIQQCAWbOnNmgr7cSokpCYWnnULlcjuDgYHz66ae8mywqxgJRxJGap6urW6NnLNq0aQMrKyusXbsWmZmZ3PTGjRtj8uTJ8PT0rLF1aaIqFy7a2tp49epVbeRC1JSnpydCQkKUDiITExM6iAhRsSZNmnB/JyQk8EbOVZx9eT2OqD9PT094eHjgyJEjWL58OWbNmoX+/fvTj0RU81KRv78/vvvuO2zcuBFaWtW+awDRIIqD6MqVK1zXPGdnZzqICFGxZs2acX+Xvazw+vOycUQziEQi2NvbAwDs7e3p8/b/VavquHDhAqKjo3HixAk4OTkp9Sf/448/aiQ5ol5EIlGDvRspIepq8ODBWL9+PbS0tFBcXMwbmEwoFHJt0wYPHqzCLAmpOdUqXIyMjLhBcAghhKiOWCzGsGHDsHPnThgZGcHW1haMMQgEAqSlpSE3NxfDhw/nXTYiRJNVq3DZtGlTTedBCCGkmiZNmoQHDx7g9OnTSt1oPTw8qCMFqVdU2sx83bp1cHZ2hkQigUQigbu7O44ePcrNf/XqFfz9/dG4cWM0atQIQ4cORVZWFm8Z6enp8PX1hb6+PszMzDB79mxezxcAiImJQadOnaCjo4NWrVph8+bNSrmEh4ejefPm0NXVhZubG86fP18r20wIITUtLi4Op0+fLnfe6dOnERcXV8cZEVJ7ql247NmzB5988gm6deuGTp068R6V1axZM3z77bdISEjAxYsX0bt3bwwePBjJyckAgJkzZ+LgwYOIjIxEbGwsMjIy8NFHH3Gvl8lk8PX1RVFREc6cOYMtW7Zg8+bNCAoK4mJSU1Ph6+uL9957D0lJSZgxYwYmTJiA48ePczG7du3iuvwmJiaiQ4cO8PHxwaNHj6r79hBCSJ2QyWT49ttvAQDGxsb4+uuv8fvvv+Prr7+GsbExAOC7776DTCZTZZqE1BxWDatWrWKNGjViU6dOZWKxmH355ZfM29ubSaVS9s0331RnkRxjY2O2ceNGlpuby7S1tVlkZCQ378aNGwwAi4+PZ4wxduTIESYUCllmZiYXs27dOiaRSFhhYSFjjLE5c+YwR0dH3jr8/PyYj48P97xr167M39+fey6TyZiVlRVbunRppfPOy8tjAFheXl7VNpgQFUtJSWFeXl4sJSVF1amQajh//jzz8vJiAwYMYMXFxbx5xcXFbMCAAczLy4udP39eRRmSd9FQjs+qfIdW64zL2rVrsWHDBqxevRpisRhz5sxBVFQUvvrqK+Tl5VWrgJLJZNi5cyeeP38Od3d3JCQkoLi4GN7e3lxM27ZtYWNjw41TEB8fDycnJ949kXx8fJCfn8+dtYmPj+ctQxGjWEZRURESEhJ4MUKhEN7e3rzxEF5XWFiI/Px83oMQQuraiRMnAABjx45VGp5CS0sLn3/+OS+OEE1XrcIlPT0d3bt3BwDo6enh2bNnAEqH+f/tt9+qtKyrV6+iUaNG0NHRwaRJk7B37144ODggMzMTYrEYRkZGvHhzc3NuELTMzEylGzkqnr8tJj8/Hy9fvsSTJ08gk8nKjSk72Nrrli5dCqlUyj3o7siEEFV4+fIlAMDS0rLc+RYWFrw4QjRdtQoXCwsLZGdnAyi9x8HZs2cBlLYnYWXGEKgMe3t7JCUl4dy5c5g8eTLGjBmD69evVyetOhUYGIi8vDzu8eDBA1WnRAhpgJycnAAAGzdu5Ib3V5DL5fjll194cYRoump1h+7duzcOHDiAjh07YuzYsZg5cyb27NmDixcv8hrPVoZYLEarVq0AAK6urrhw4QJWrVoFPz8/FBUVITc3l3fWJSsri/sFYWFhodT7R9HrqGzM6z2RsrKyIJFIoKenB5FIBJFIVG6MYhnl0dHRgY6OTpW2VdPJZDIaOZeQGvDq1Sukp6fXyLKcnJwgEAhw9+5dTJ8+Hf3790fTpk3x77//4siRI7h37x4EAgGcnJxq7A7RNjY20NXVrZFlEVJV1SpcNmzYwFX2iu7KZ86cwaBBg/Dll1++U0JyuRyFhYVwdXWFtrY2oqOjucHuUlJSkJ6eDnd3dwCAu7s7Fi9ejEePHsHMzAwAEBUVBYlEAgcHBy7myJEjvHVERUVxyxCLxXB1dUV0dDSGDBnC5RAdHY2pU6e+07bUJ3FxcUr3KrKwsMCUKVPoXkWEVFF6ejq++OKLGl/u1atXcfXqVaXpjDFMmTKlxtazYcOGGr2pICFVUa3CRSgU8u40Onz4cAwfPrzKywkMDES/fv1gY2ODZ8+eYceOHYiJicHx48chlUoxfvx4BAQEwMTEBBKJBNOmTYO7uzu6desGAOjbty8cHBwwevRoLFu2DJmZmZg3bx78/f25syGTJk3CmjVrMGfOHIwbNw4nT57E7t27cfjwYS6PgIAAjBkzBp07d0bXrl0RFhaG58+fY+zYsdV5e+qduLg4BAcHw93dHfPnz+fuPBsREYHg4GCEhIRQ8UJIFdjY2GDDhg01usw9e/bgzz//5F0uUnQ0+Pjjj2t0XTY2NjW6PEKqpLpdl3Jyctjx48fZtm3b2JYtW3iPyho3bhyztbVlYrGYNWnShPXp04edOHGCm//y5Us2ZcoUZmxszPT19dmHH37IHj58yFvG/fv3Wb9+/Zienh4zNTVls2bNUuoSeOrUKebi4sLEYjFr0aIF27Rpk1Iuq1evZjY2NkwsFrOuXbuys2fPVun9qK/doUtKSpifnx8LDAxkMpmMN08mk7HAwEA2fPhwVlJSoqIMybtqKN0tG4LCwkIWHh7OvLy8WHh4ODcsBNFcDeX4rMp3aLUKlwMHDjBDQ0MmEAiYVCplRkZG3MPY2Lg6i9R49bVwSUxMZF5eXuzatWvlzr927Rrz8vJiiYmJdZwZqSkN5YOxoaD9Wb80lP1Z6+O4zJo1C+PGjUNBQQFyc3ORk5PDPRS9jUj9oNifdnZ25c5XTKf9TgghpC5Uq3D5999/8dVXX0FfX7+m8yFqxsTEBEBpV/fyKKYr4gghhJDaVK3CxcfHBxcvXqzpXIgacnZ2hoWFBSIiIsodIyIiIgKWlpZwdnZWUYaEEEIakkr3Kjpw4AD3t6+vL2bPno3r16/DyckJ2travNhBgwbVXIZEpUQiEaZMmYLg4GDMmzcPo0aN4vUqio+PR0hICI3nQgghpE5UunBRjHFSVmhoqNI0gUBAdyGtZzw9PRESEoK1a9fC39+fm25paUldoQkhhNSpShcur18mIOqrJkflVLCwsMCCBQtw+/Zt5OXlQSqVonXr1hAKhTU2GmdZNDInIYSQ8lRpALqTJ09i6tSpOHv2LCQSCW9eXl4eunfvjvXr16Nnz541miSpmtoalbMu0cichBBCylOlwiUsLAwTJ05UKloAQCqV4ssvv8SKFSuocFGx2hiVUyEtLQ2LFy/G3LlzYWtrWyvrAGhkTkIIIeWrUuFy+fJlfPfdd2+c37dvX/zwww/vnBR5N7q6urV+tsLW1pbOiBBCCKlzVeoOnZWVpdSDqCwtLS08fvz4nZMihBBCCClPlQqXpk2b4tq1a2+cf+XKFVhaWr5zUoQQQggh5alS4dK/f3/Mnz8fr169Upr38uVLBAcHY8CAATWWHCGEEEJIWVVq4zJv3jz88ccfaNOmDaZOnQp7e3sAwM2bNxEeHg6ZTIa5c+fWSqKEEEIIIVUqXMzNzXHmzBlMnjwZgYGBYIwBKB10zsfHB+Hh4TA3N6+VRAkhhBBCqlS4AKW9SY4cOYKcnBzcuXMHjDG0bt0axsbGtZEfIYQQQginyoWLgrGxMbp06VKTuRBCCCGEVKhad4cmhBBCCFEFKlwIIYQQojGocCGEEEKIxqDChRBCCCEagwoXQgghhGgMKlwIIYQQojGocCGEEEKIxlBp4bJ06VJ06dIFhoaGMDMzw5AhQ5CSksKLefXqFfz9/dG4cWM0atQIQ4cORVZWFi8mPT0dvr6+0NfXh5mZGWbPno2SkhJeTExMDDp16gQdHR20atUKmzdvVsonPDwczZs3h66uLtzc3HD+/Pka32ZCCCGEVJ9KC5fY2Fj4+/vj7NmziIqKQnFxMfr27Yvnz59zMTNnzsTBgwcRGRmJ2NhYZGRk4KOPPuLmy2Qy+Pr6oqioCGfOnMGWLVuwefNmBAUFcTGpqanw9fXFe++9h6SkJMyYMQMTJkzA8ePHuZhdu3YhICAAwcHBSExMRIcOHeDj44NHjx7VzZtBCCGEkLdjauTRo0cMAIuNjWWMMZabm8u0tbVZZGQkF3Pjxg0GgMXHxzPGGDty5AgTCoUsMzOTi1m3bh2TSCSssLCQMcbYnDlzmKOjI29dfn5+zMfHh3vetWtX5u/vzz2XyWTMysqKLV26tFK55+XlMQAsLy+vilutWVJSUpiXlxdLSUlRdSqkhtA+rV9of9YvDWV/VuU7VK3auOTl5QEATExMAAAJCQkoLi6Gt7c3F9O2bVvY2NggPj4eABAfHw8nJyfezR19fHyQn5+P5ORkLqbsMhQximUUFRUhISGBFyMUCuHt7c3FvK6wsBD5+fm8ByGEEEJql9oULnK5HDNmzICHhwfat28PAMjMzIRYLIaRkREv1tzcHJmZmVzM63ekVjx/W0x+fj5evnyJJ0+eQCaTlRujWMbrli5dCqlUyj2sra2rt+GEEEIIqbRq32Sxpvn7++PatWv4+++/VZ1KpQQGBiIgIIB7np+fT8VLA5eVlcWdNdQkaWlpvH81iVQqVfrBQQip39SicJk6dSoOHTqEuLg4NGvWjJtuYWGBoqIi5Obm8s66ZGVlwcLCgot5vfePotdR2ZjXeyJlZWVBIpFAT08PIpEIIpGo3BjFMl6no6MDHR2d6m0wqXeysrLw6ejPUFxUqOpUqm3x4sWqTqHKtMU62L5tKxUvhDQgKi1cGGOYNm0a9u7di5iYGNjZ2fHmu7q6QltbG9HR0Rg6dCgAICUlBenp6XB3dwcAuLu7Y/HixXj06BHMzMwAAFFRUZBIJHBwcOBijhw5wlt2VFQUtwyxWAxXV1dER0djyJAhAEovXUVHR2Pq1Km1tv2k/sjLy0NxUSFetvCCXFeq6nQaBOGrPOBeLPLy8qhwIaQBUWnh4u/vjx07dmD//v0wNDTk2pNIpVLo6elBKpVi/PjxCAgIgImJCSQSCaZNmwZ3d3d069YNANC3b184ODhg9OjRWLZsGTIzMzFv3jz4+/tzZ0QmTZqENWvWYM6cORg3bhxOnjyJ3bt34/Dhw1wuAQEBGDNmDDp37oyuXbsiLCwMz58/x9ixY+v+jSEaS64rhdzAVNVpEEJIvaXSwmXdunUAgF69evGmb9q0CZ9//jkAYOXKlRAKhRg6dCgKCwvh4+ODtWvXcrEikQiHDh3C5MmT4e7uDgMDA4wZMwahoaFcjJ2dHQ4fPoyZM2di1apVaNasGTZu3AgfHx8uxs/PD48fP0ZQUBAyMzPh4uKCY8eO0S85QgghRI2o/FLR2+jq6iI8PBzh4eFvjLG1tVW6FPS6Xr164dKlSxXGTJ06lS4NEUIIIWpMbbpDE0IIIYS8DRUuhBBCCNEYVLgQQgghRGOoxTguhBBCSG3TxEEiNXmASKB2BomkwoUQQki9p+mDRGriAJFA7QwSSYULIYSQeo8Giax7tTVIJBUuhBBCGgwaJFLzUeNcQgghhGgMKlwIIYQQojGocCGEEEKIxqA2LoQQUg5N7DoLaHb32droOkvqHypcCCHkNZredRbQzO6ztdF1ltQ/VLgQQshrqOts3autrrOk/qHChRBC3oC6zhKifqhwUSFNvIauydfPAbqGTgghmo4KFxXR9Gvomnj9HKBr6IQQoumocFERuoZe9+gaOiGEaD4qXFSMrqETQgghlUcD0BFCCCFEY1DhQgghhBCNQYULIYQQQjQGFS6EEEII0RgqLVzi4uIwcOBAWFlZQSAQYN++fbz5jDEEBQXB0tISenp68Pb2xu3bt3kx2dnZGDVqFCQSCYyMjDB+/HgUFBTwYq5cuYKePXtCV1cX1tbWWLZsmVIukZGRaNu2LXR1deHk5IQjR47U+PYSQggh5N2otHB5/vw5OnTogPDw8HLnL1u2DD/++CPWr1+Pc+fOwcDAAD4+Pnj16hUXM2rUKCQnJyMqKgqHDh1CXFwcvvjiC25+fn4++vbtC1tbWyQkJOD777/HggULsGHDBi7mzJkzGDFiBMaPH49Lly5hyJAhGDJkCK5du1Z7G08IIYSQKlNpd+h+/fqhX79+5c5jjCEsLAzz5s3D4MGDAQBbt5YOHLZv3z4MHz4cN27cwLFjx3DhwgV07twZALB69Wr0798fP/zwA6ysrBAREYGioiL8+uuvEIvFcHR0RFJSElasWMEVOKtWrcIHH3yA2bNnAwAWLlyIqKgorFmzBuvXr6+Dd4IQQgghlaG247ikpqYiMzMT3t7e3DSpVAo3NzfEx8dj+PDhiI+Ph5GREVe0AIC3tzeEQiHOnTuHDz/8EPHx8fD09IRYLOZifHx88N133yEnJwfGxsaIj49HQEAAb/0+Pj5Kl67KKiwsRGHh/0a9zc/Pr4GtJoQQUpuEL3NVnUKDUVvvtdoWLpmZmQCgNMKpubk5Ny8zMxNmZma8+VpaWjAxMeHF2NnZKS1DMc/Y2BiZmZkVrqc8S5cuRUhISDW2jBBCiKropcapOgXyjtS2cFF3gYGBvLM0+fn5sLa2VmFGhBBC3ualnSfkekaqTqNBEL7MrZVCUW0LFwsLCwClNyO0tLTkpmdlZcHFxYWLefToEe91JSUlyM7O5l5vYWGBrKwsXozi+dtiFPPLo6OjAx0dnWpsGanP6DR03aH3mlSHXM+IbrOi4dS2cLGzs4OFhQWio6O5QiU/Px/nzp3D5MmTAQDu7u7Izc1FQkICXF1dAQAnT56EXC6Hm5sbFzN37lwUFxdDW1sbABAVFQV7e3sYGxtzMdHR0ZgxYwa3/qioKLi7u9f6dtKHb92pi/eaTkPXL3R81h16r0llqbRwKSgowJ07d7jnqampSEpKgomJCWxsbDBjxgwsWrQIrVu3hp2dHebPnw8rKysMGTIEANCuXTt88MEHmDhxItavX4/i4mJMnToVw4cPh5WVFQBg5MiRCAkJwfjx4/Gf//wH165dw6pVq7By5UpuvdOnT4eXlxeWL18OX19f7Ny5ExcvXuR1ma4t9EVXv9Bp6LpTW6ehy6LjkxD1o9LC5eLFi3jvvfe454o2I2PGjMHmzZsxZ84cPH/+HF988QVyc3PRo0cPHDt2DLq6utxrIiIiMHXqVPTp0wdCoRBDhw7Fjz/+yM2XSqU4ceIE/P394erqClNTUwQFBfHGeunevTt27NiBefPm4ZtvvkHr1q2xb98+tG/fvtbfA/qiqzt18UVHp6HrFzo+605dHJ+kflBp4dKrVy8wxt44XyAQIDQ0FKGhoW+MMTExwY4dOypcj7OzM/76668KY4YNG4Zhw4ZVnHAtoC86QtQXHZ+EqB+6VxEhhBBCNAYVLoQQQgjRGFS4EEIIIURjUOFCCCGEEI1BhQshhBBCNAYVLoQQQgjRGFS4EEIIIURjUOFCCCGEEI1BhQshhBBCNIba3mSREEIIqWnCV3mqTqHBqK33mgoXFaODqO7Qe02qiv7P1J3afq+lUim0xTrAvdhaXQ/h0xbrQCqV1ugyqXBRETqIVKM2DqKy6Iuu7tTme03Hp2rU5vFpbm6O7du2Ii9Ps47RtLQ0LF68GHPnzoWtra2q06kyqVQKc3PzGl0mFS4qQgeRatTGQaRYLn3R1b3a+qLT1OMT0OxjtLaOTwVzc/NaXX5tsrW1RZs2bVSdhlqgwkWF6CCqP+iLTjVq84tOk49PgI5RUn9R4UJIDaEvOkIIqX3UHZoQQgghGoMKF0IIIYRoDCpcCCGEEKIxqHAhhBBCiMagwoUQQgghGoN6FRFCCCHv4NWrV0hPT6+VZaelpfH+rQ02NjbQ1dWtteXXNCpcCCGkjtEXXf2Snp6OL774olbXsXjx4lpb9oYNGzRqKAQqXOohTf9QBOiDsaza3J8AfdGpAn3R1S82NjbYsGGDqtOoNhsbG1WnUCUCxhhTdRLqJDw8HN9//z0yMzPRoUMHrF69Gl27dn3r6/Lz8yGVSpGXlweJRFIHmb7ZrVu3av1DsbbRB+P/0P6sf2q7GK1tVIiSmlaV71AqXMrYtWsXPvvsM6xfvx5ubm4ICwtDZGQkUlJSYGZmVuFr1alw0fQPRYA+GMui/UkIqe+ocKkmNzc3dOnSBWvWrAEAyOVyWFtbY9q0afjvf/9b4WvVqXAhhBBCNElVvkOpO/T/KyoqQkJCAry9vblpQqEQ3t7eiI+PV4ovLCxEfn4+70EIIYSQ2kWFy/978uQJZDKZ0k3yzM3NkZmZqRS/dOlSSKVS7mFtbV1XqRJCCCENFhUu1RQYGIi8vDzu8eDBA1WnRAghhNR71B36/5mamkIkEiErK4s3PSsrCxYWFkrxOjo60NHRqav0CCGEEAI648IRi8VwdXVFdHQ0N00ulyM6Ohru7u4qzIwQQgghCnTGpYyAgACMGTMGnTt3RteuXREWFobnz59j7Nixqk6NEEIIIaDChcfPzw+PHz9GUFAQMjMz4eLigmPHjik12CWEEEKIatA4LjUkLy8PRkZGePDgAY3jQgghhFRBfn4+rK2tkZubC6lUWmEsnXGpIc+ePQMA6hZNCCGEVNOzZ8/eWrjQGZcaIpfLkZGRAUNDQwgEAlWnU2sUVTGdWao/aJ/WL7Q/65eGsj8ZY3j27BmsrKwgFFbcb4jOuNQQoVCIZs2aqTqNOiORSOr1QdQQ0T6tX2h/1i8NYX++7UyLAnWHJoQQQojGoMKFEEIIIRqDChdSJTo6OggODqZRg+sR2qf1C+3P+oX2pzJqnEsIIYQQjUFnXAghhBCiMahwIYQQQojGoMKFEEIIIRqDChdSoebNmyMsLEzVaZAaRvtV88XExEAgECA3N1fVqZAaRPv17ahwIQCAzZs3w8jISNVpVOj+/fsQCARISkpSdSoaQ533KxVPlderVy/MmDFD1WlUaMGCBXBxcVF1GhpFXferuhdPVLgQQoiaKioqUnUKpBbQfn1HjGiMyMhI1r59e6arq8tMTExYnz59WEFBAZPJZCwkJIQ1bdqUicVi1qFDB3b06FHudadOnWIAWE5ODjft0qVLDABLTU3l5pd9BAcHM8YYs7W1ZYsXL2Zjx45ljRo1YtbW1uynn37iljN06FDm7+/PPZ8+fToDwG7cuMEYY6ywsJDp6+uzqKgoxhhjMpmMLVmyhDVv3pzp6uoyZ2dnFhkZyb0+OzubjRw5kpmamjJdXV3WqlUr9uuvvzLGmFKOXl5eNf0Wq4Q67lfGGEtPT2fDhg1jUqmUGRsbs0GDBrHU1FRu/vnz55m3tzdr3Lgxk0gkzNPTkyUkJHDz5XI5Cw4OZtbW1kwsFjNLS0s2bdo0xhhjXl5eSrnVB15eXmzatGls9uzZzNjYmJmbm3PvOWOMpaWlsUGDBjEDAwNmaGjIhg0bxjIzM7n5wcHBrEOHDuznn39mzZs3ZwKBgI0ZM0bpvSq7f//880/m6urK9PT0mLu7O7t58yZjjLHc3FwmFArZhQsXGGOlx56xsTFzc3Pj1rdt2zbWrFkz7vnb9vmpU6dYly5dmL6+PpNKpax79+7s/v37bNOmTUo5btq0qXbeZBVQp/2qsG/fPtaxY0emo6PD7Ozs2IIFC1hxcTE3f/ny5ax9+/ZMX1+fNWvWjE2ePJk9e/aMm3///n02YMAAZmRkxPT19ZmDgwM7fPgwS01NVcprzJgxtfbeVkf9+LRoADIyMpiWlhZbsWIFS01NZVeuXGHh4eHs2bNnbMWKFUwikbDffvuN3bx5k82ZM4dpa2uzW7duMcbe/gVXWFjIwsLCmEQiYQ8fPmQPHz7k/oPb2toyExMTFh4ezm7fvs2WLl3KhEIhdxD9+OOPzNHRkVuui4sLMzU1ZevWrWOMMfb3338zbW1t9vz5c8YYY4sWLWJt27Zlx44dY3fv3mWbNm1iOjo6LCYmhjHGmL+/P3NxcWEXLlxgqampLCoqih04cIAxVvpFqTigHz58yJ4+fVq7b3odUNf9WlRUxNq1a8fGjRvHrly5wq5fv85GjhzJ7O3tWWFhIWOMsejoaLZt2zZ248YNdv36dTZ+/Hhmbm7O8vPzGWOlBZlEImFHjhxhaWlp7Ny5c2zDhg2MMcaePn3KmjVrxkJDQ7nc6gMvLy8mkUjYggUL2K1bt9iWLVuYQCBgJ06cYDKZjLm4uLAePXqwixcvsrNnzzJXV1deAR4cHMwMDAzYBx98wBITE9nly5dZbm4uc3d3ZxMnTuTeq5KSEm7/u7m5sZiYGJacnMx69uzJunfvzi2vU6dO7Pvvv2eMMZaUlMRMTEyYWCzm/h9MmDCBjRo1ijH29n1eXFzMpFIp+/rrr9mdO3fY9evX2ebNm1laWhp78eIFmzVrFnN0dORyfPHiRd298bVM3fZrXFwck0gkbPPmzezu3bvsxIkTrHnz5mzBggVczMqVK9nJkydZamoqi46OZvb29mzy5MncfF9fX/b++++zK1eusLt377KDBw+y2NhYVlJSwn7//XcGgKWkpLCHDx+y3NzcOnmfK4sKFw2RkJDAALD79+8rzbOysmKLFy/mTevSpQubMmUKY+ztX3CMMbZp0yYmlUqVlm1ra8s+/fRT7rlcLmdmZmZcYXLlyhUmEAjYo0ePWHZ2NhOLxWzhwoXMz8+PMVZaqCgOuFevXjF9fX125swZ3jrGjx/PRowYwRhjbODAgWzs2LHlvgeKXwKXLl16w7ukedR1v27bto3Z29szuVzOxRQWFjI9PT12/PjxcrdFJpMxQ0NDdvDgQcZY6S++Nm3asKKionLjbW1t2cqVK8udp6m8vLxYjx49eNO6dOnC/vOf/7ATJ04wkUjE0tPTuXnJyckMADt//jxjrPQLTltbmz169EhpudOnT+dNK/vLXOHw4cMMAHv58iVjjLGAgADm6+vLGGMsLCyM+fn58c7ctWrViism37bPnz59ygBwPzJepzirUB+p237t06cPW7JkCe9127ZtY5aWlm/chsjISNa4cWPuuZOTE6/QKS+Hsp8t6oTauGiIDh06oE+fPnBycsKwYcPw888/IycnB/n5+cjIyICHhwcv3sPDAzdu3KiRdTs7O3N/CwQCWFhY4NGjRwCA9u3bw8TEBLGxsfjrr7/QsWNHDBgwALGxsQCA2NhY9OrVCwBw584dvHjxAu+//z4aNWrEPbZu3Yq7d+8CACZPnoydO3fCxcUFc+bMwZkzZ2pkG9SVuu7Xy5cv486dOzA0NOT2k4mJCV69esXtq6ysLEycOBGtW7eGVCqFRCJBQUEB0tPTAQDDhg3Dy5cv0aJFC0ycOBF79+5FSUlJjeSuzsq+rwBgaWmJR48e4caNG7C2toa1tTU3z8HBAUZGRrx9amtriyZNmlRrfZaWlgDA7UcvLy/8/fffkMlk3LHYq1cvxMTEICMjA3fu3OGOz7ftcxMTE3z++efw8fHBwIEDsWrVKjx8+LDK74+mUqf9evnyZYSGhvI+RydOnIiHDx/ixYsXAIA///wTffr0QdOmTWFoaIjRo0fj6dOn3PyvvvoKixYtgoeHB4KDg3HlypUqviOqQ4WLhhCJRIiKisLRo0fh4OCA1atXw97eHqmpqW99rVBYuptZmbs7FBcXV3rd2travOcCgQByuZz729PTEzExMdwHo7OzMwoLC3Ht2jWcOXMGXl5eAICCggIAwOHDh5GUlMQ9rl+/jj179gAA+vXrh7S0NMycORMZGRno06cPvv7660rnqmnUdb8WFBTA1dWVt5+SkpJw69YtjBw5EgAwZswYJCUlYdWqVThz5gySkpLQuHFjruGhtbU1UlJSsHbtWujp6WHKlCnw9PSsUo6aqKL3tTIMDAyqvT6BQAAA3Po8PT3x7NkzJCYmIi4ujle4xMbGwsrKCq1btwZQuX2+adMmxMfHo3v37ti1axfatGmDs2fPVilfTaVO+7WgoAAhISG8/XT16lXcvn0burq6uH//PgYMGABnZ2f8/vvvSEhIQHh4OID/NQyeMGEC7t27h9GjR+Pq1avo3LkzVq9eXaUcVYUKFw0iEAjg4eGBkJAQXLp0CWKxGNHR0bCyssLp06d5sadPn4aDgwMAcFV+2V9Hr3cpFovFkMlk1crLy8sLMTExiImJQa9evSAUCuHp6Ynvv/8ehYWF3FkDBwcH6OjoID09Ha1ateI9yv5aadKkCcaMGYPt27cjLCwMGzZs4HIEUO081ZU67tdOnTrh9u3bMDMzU9pXUqmUy+Wrr75C//794ejoCB0dHTx58oS3HD09PQwcOBA//vgjYmJiEB8fj6tXr75TbpqqXbt2ePDgAR48eMBNu379OnJzc7l9+ibVfa+MjIzg7OyMNWvWQFtbG23btoWnpycuXbqEQ4cOcT8qgMrtcwDo2LEjAgMDcebMGbRv3x47dux4pxw1nSr2a6dOnZCSkqK0n1q1agWhUIiEhATI5XIsX74c3bp1Q5s2bZCRkaG0HGtra0yaNAl//PEHZs2ahZ9//pnLC1Dfz1oqXDTEuXPnsGTJEly8eBHp6en4448/8PjxY7Rr1w6zZ8/Gd999h127diElJQX//e9/kZSUhOnTpwMAVxgsWLAAt2/fxuHDh7F8+XLe8ps3b46CggJER0fjyZMn3OnEyujVqxeuX7+O5ORk9OjRg5sWERGBzp07c780DA0N8fXXX2PmzJnYsmUL7t69i8TERKxevRpbtmwBAAQFBWH//v24c+cOkpOTcejQIbRr1w4AYGZmBj09PRw7dgxZWVnIy8t75/dV1dR1v44aNQqmpqYYPHgw/vrrL6SmpiImJgZfffUV/vnnHwBA69atsW3bNty4cQPnzp3DqFGjoKenxy1j8+bN+OWXX3Dt2jXcu3cP27dvh56eHmxtbbnc4uLi8O+//yoVPPWRt7c3nJycMGrUKCQmJuL8+fP47LPP4OXlhc6dO1f42ubNm+PcuXO4f/8+njx5UqVf+opjUVGkmJiYoF27dti1axevcHnbPk9NTUVgYCDi4+ORlpaGEydO4Pbt29zx2bx5c6SmpiIpKQlPnjxBYWFhNd4lzaOK/RoUFIStW7ciJCQEycnJuHHjBnbu3Il58+YBKP1sKC4uxurVq3Hv3j1s27YN69ev5y1jxowZOH78OFJTU5GYmIhTp05x+9LW1hYCgQCHDh3C48ePubPlakPVjWxI5Vy/fp35+PiwJk2aMB0dHdamTRu2evVqxlhpo8gFCxawpk2bMm1tbaVus4yV9u5xcnJiurq6rGfPniwyMpLXiJMxxiZNmsQaN26s1G329QaUHTp04HUFLK+bpaKR6H//+1/ea+VyOQsLC2P29vZMW1ubNWnShPn4+LDY2FjGGGMLFy5k7dq1Y3p6eszExIQNHjyY3bt3j3v9zz//zKytrZlQKKwX3aHVeb8+fPiQffbZZ8zU1JTp6OiwFi1asIkTJ7K8vDzGGGOJiYmsc+fOTFdXl7Vu3ZpFRkbylrt3717m5ubGJBIJMzAwYN26deM1OIyPj2fOzs5MR0enXnWHfr2x5eDBg7nupJXtNvu6lJQU1q1bN6anp6fUbbaixtmMle4HAFzDa8b+N2zB611sK9rnmZmZbMiQIczS0pKJxWJma2vLgoKCmEwmY4yVNr4fOnQoMzIyqpfdodVtvx47dox1796d6enpMYlEwrp27co1tGaMsRUrVjBLS0ump6fHfHx82NatW3nLnTp1KmvZsiXT0dFhTZo0YaNHj2ZPnjzhXh8aGsosLCy4rtvqRMBYmQvkhBBCCCFqjC4VEUIIIURjUOFCCCGEEI1BhQshhBBCNAYVLoQQQgjRGFS4EEIIIURjUOFCCCGEEI1BhQshhBBCNAYVLoSQBqV58+YICwtTdRqEkGqiAegIIQ3K48ePYWBgAH19fVWnQgipBipcCCEaoaioiLv5GyGk4aJLRYQQtdSrVy9MnToVM2bMgKmpKXx8fHDt2jX069cPjRo1grm5OUaPHs27QeOzZ88watQoGBgYwNLSEitXrkSvXr0wY8YMLub1S0Xp6ekYPHgwGjVqBIlEgk8++QRZWVnc/AULFsDFxQXbtm1D8+bNIZVKMXz4cDx79qwu3gZCyGuocCGEqK0tW7ZALBbj9OnT+Pbbb9G7d2907NgRFy9e5O4S/sknn3DxAQEBOH36NA4cOICoqCj89ddfSExMfOPy5XI5Bg8ejOzsbMTGxiIqKgr37t2Dn58fL+7u3bvYt28fDh06hEOHDiE2NhbffvttrW03IeTNtFSdACGEvEnr1q2xbNkyAMCiRYvQsWNHLFmyhJv/66+/wtraGrdu3YKlpSW2bNmCHTt2oE+fPgCATZs2wcrK6o3Lj46OxtWrV5Gamgpra2sAwNatW+Ho6IgLFy6gS5cuAEoLnM2bN8PQ0BAAMHr0aERHR2Px4sW1st2EkDejwoUQorZcXV25vy9fvoxTp06hUaNGSnF3797Fy5cvUVxcjK5du3LTpVIp7O3t37j8GzduwNramitaAMDBwQFGRka4ceMGV7g0b96cK1oAwNLSEo8ePXqnbSOEVA8VLoQQtWVgYMD9XVBQgIEDB+K7775TirO0tMSdO3dqLQ9tbW3ec4FAALlcXmvrI4S8GbVxIYRohE6dOiE5ORnNmzdHq1ateA8DAwO0aNEC2trauHDhAveavLw83Lp1643LbNeuHR48eIAHDx5w065fv47c3Fw4ODjU6vYQQqqHChdCiEbw9/dHdnY2RowYgQsXLuDu3bs4fvw4xo4dC5lMBkNDQ4wZMwazZ8/GqVOnkJycjPHjx0MoFEIgEJS7TG9vbzg5OWHUqFFITEzE+fPn8dlnn8HLywudO3eu4y0khFQGFS6EEI1gZWWF06dPQyaToW/fvnBycsKMGTNgZGQEobD0o2zFihVwd3fHgAED4O3tDQ8PD7Rr1w66urrlLlMgEGD//v0wNjaGp6cnvL290aJFC+zatasuN40QUgU0AB0hpN56/vw5mjZtiuXLl2P8+PGqTocQUgOocS4hpN64dOkSbt68ia5duyIvLw+hoaEAgMGDB6s4M0JITaHChRBSr/zwww9ISUmBWCyGq6sr/vrrL5iamqo6LUJIDaFLRYQQQgjRGNQ4lxBCCCEagwoXQgghhGgMKlwIIYQQojGocCGEEEKIxqDChRBCCCEagwoXQgghhGgMKlwIIYQQojGocCGEEEKIxqDChRBCCCEa4/8ABaE1UJuwmaEAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Definir las variables categóricas\n", + "categorical_vars = [\"sex\", \"smoker\", \"region\"]\n", + "\n", + "# Crear boxplots para comparar cada variable categórica con 'charges'\n", + "for var in categorical_vars:\n", + " plt.figure(figsize=(6, 3))\n", + " sns.boxplot(x=data[var], y=data[\"charges\"])\n", + " plt.title(f\"Distribución de Charges según {var}\")\n", + " plt.xlabel(var)\n", + " plt.ylabel(\"Charges\")\n", + " plt.xticks(rotation=0)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Se ve claramente que las que más repercusion sobre los cargos es la variable smoker" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Aplicamos la transformación de las variables categóricas\n", + "data = pd.get_dummies(data, columns=[\"sex\", \"smoker\", \"region\"], drop_first=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1338 entries, 0 to 1337\n", + "Data columns (total 9 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 age 1338 non-null int64 \n", + " 1 bmi 1338 non-null float64\n", + " 2 children 1338 non-null int64 \n", + " 3 charges 1338 non-null float64\n", + " 4 sex_male 1338 non-null bool \n", + " 5 smoker_yes 1338 non-null bool \n", + " 6 region_northwest 1338 non-null bool \n", + " 7 region_southeast 1338 non-null bool \n", + " 8 region_southwest 1338 non-null bool \n", + "dtypes: bool(5), float64(2), int64(2)\n", + "memory usage: 48.5 KB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ya tenemos todas las features en formato numerico. No hemos normalizado, luego podemos probar a normalizar a ver si mejoran los resultados" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ee-3csHRJxqW" + }, + "source": [ + "## Implementación" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# Importamos las librerias de sklearn\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, mean_absolute_percentage_error, root_mean_squared_error, mean_squared_log_error" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "oGEq8EGTpp7g" + }, + "outputs": [], + "source": [ + "## A completar\n", + "# Dividir el DataFrame en características (X) y variable objetivo (y)\n", + "X = data.drop('charges', axis=1)\n", + "y = data['charges']" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "# Dividir los datos en entrenamiento y prueba\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((1070, 8), (268, 8))" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape, X_test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con regresion lineal\n", + "linear_model = LinearRegression()\n", + "linear_model.fit(X_train, y_train)\n", + "linear_predictions = linear_model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con regresion polinomica\n", + "\n", + "# Transformar las características a polinómicas\n", + "polynomial_transformer = PolynomialFeatures(degree=2, include_bias=False)\n", + "X_train_poly = polynomial_transformer.fit_transform(X_train)\n", + "X_test_poly = polynomial_transformer.transform(X_test)\n", + "\n", + "# Entrenar el modelo de regresión polinómica\n", + "polynomial_model = LinearRegression()\n", + "polynomial_model.fit(X_train_poly, y_train)\n", + "\n", + "# Realizar predicciones con el modelo polinómico\n", + "\n", + "polynomial_predictions = polynomial_model.predict(X_test_poly)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con regularización L1 (Lasso)\n", + "lasso_reg = Lasso(alpha=0.1)\n", + "lasso_reg.fit(X_train, y_train)\n", + "lasso_predictions = lasso_reg.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con regularización L2 (Ridge)\n", + "ridge_reg = Ridge(alpha=0.1)\n", + "ridge_reg.fit(X_train, y_train)\n", + "ridge_predictions = ridge_reg.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con Elastic Net\n", + "elastic_net = ElasticNet(alpha=0.001, l1_ratio=0.3)\n", + "elastic_net.fit(X_train, y_train)\n", + "elastic_net_predictions = elastic_net.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar modelos con Decision Tree\n", + "decision_tree = DecisionTreeRegressor(max_depth=5)\n", + "decision_tree.fit(X_train, y_train)\n", + "decision_tree_predictions = decision_tree.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# Entrenar los modelos con Random Forest\n", + "random_forest = RandomForestRegressor(n_estimators=100, max_depth=5)\n", + "random_forest.fit(X_train, y_train)\n", + "random_forest_predictions = random_forest.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE Linear: 4181.194473753649\n", + "MAE Lasso: 4181.31763185589\n", + "MAE Ridge: 4182.428134240747\n", + "MAE Elastic Net: 4190.470970322809\n", + "MAE Decision Tree: 2930.7675712410432\n", + "MAE Random Forest: 2558.905610463312\n", + "--------------------------------------------------\n", + "--------------------------------------------------\n", + "MSE Linear: 33596915.85136145\n", + "MSE Lasso: 33597770.054583974\n", + "MSE Ridge: 33601489.86482401\n", + "MSE Elastic Net: 33632467.79117218\n", + "MSE Decision Tree: 25831862.599857908\n", + "MSE Random Forest: 19754563.83787683\n", + "--------------------------------------------------\n", + "RMSE Linear: 5796.284659276273\n", + "RMSE Lasso: 5796.358344217857\n", + "RMSE Ridge: 5796.679210101592\n", + "RMSE Elastic Net: 5799.350635301523\n", + "RMSE Decision Tree: 5082.505543514725\n", + "RMSE Random Forest: 4444.6106508756\n", + "--------------------------------------------------\n", + "RMSLE Linear: 1.0683606396039087\n", + "RMSLE Lasso: 1.0683429325202027\n", + "RMSLE Ridge: 1.0682292298507527\n", + "RMSLE Elastic Net: 1.0674578307789122\n", + "RMSLE Decision Tree: 0.4311496306651408\n", + "RMSLE Random Forest: 0.41006921525471884\n", + "--------------------------------------------------\n", + "R2 Linear: 0.7835929767120724\n", + "R2 Lasso: 0.7835874745529608\n", + "R2 Ridge: 0.7835635142268166\n", + "R2 Elastic Net: 0.7833639768389713\n", + "R2 Decision Tree: 0.8336098314514943\n", + "R2 Random Forest: 0.8727553929229779\n", + "--------------------------------------------------\n", + "R2 ajustado Linear: 0.7769085898923681\n", + "R2 ajustado Lasso: 0.776902917782396\n", + "R2 ajustado Ridge: 0.7768782173689577\n", + "R2 ajustado Elastic Net: 0.7766725166641133\n", + "R2 ajustado Decision Tree: 0.8284703667859034\n", + "R2 ajustado Random Forest: 0.8688250575692474\n", + "--------------------------------------------------\n", + "MAPE Linear: 0.4688825597914681\n", + "MAPE Lasso: 0.46892505362488435\n", + "MAPE Ridge: 0.4691341987819791\n", + "MAPE Elastic Net: 0.4707825095150712\n", + "MAPE Decision Tree: 0.35035325031038117\n", + "MAPE Random Forest: 0.3133584206956886\n" + ] + } + ], + "source": [ + "# Imprimir los errores Mean Absolute Error (MAE)\n", + "print(f'MAE Linear: {mean_absolute_error(y_test, linear_predictions)}')\n", + "# print(f'MAE Polynomial: {mean_absolute_error(y_test, polynomial_predictions)}')\n", + "print(f'MAE Lasso: {mean_absolute_error(y_test, lasso_predictions)}')\n", + "print(f'MAE Ridge: {mean_absolute_error(y_test, ridge_predictions)}')\n", + "print(f'MAE Elastic Net: {mean_absolute_error(y_test, elastic_net_predictions)}')\n", + "print(f'MAE Decision Tree: {mean_absolute_error(y_test, decision_tree_predictions)}')\n", + "print(f'MAE Random Forest: {mean_absolute_error(y_test, random_forest_predictions)}')\n", + "print('-'*50)\n", + "\n", + "# Evaluar los modelos con Mean Squared Error (MSE)\n", + "mse_linear = mean_squared_error(y_test, linear_predictions)\n", + "# mse_polynomial = mean_squared_error(y_test, polynomial_predictions)\n", + "mse_lasso = mean_squared_error(y_test, lasso_predictions)\n", + "mse_ridge = mean_squared_error(y_test, ridge_predictions)\n", + "mse_elastic_net = mean_squared_error(y_test, elastic_net_predictions)\n", + "mse_decision_tree = mean_squared_error(y_test, decision_tree_predictions)\n", + "mse_random_forest = mean_squared_error(y_test, random_forest_predictions)\n", + "print('-'*50)\n", + "\n", + "\n", + "# Imprimir los errores cuadráticos medios (MSE)\n", + "print(f'MSE Linear: {mse_linear}')\n", + "# print(f'MSE Polynomial: {mse_polynomial}')\n", + "print(f'MSE Lasso: {mse_lasso}')\n", + "print(f'MSE Ridge: {mse_ridge}')\n", + "print(f'MSE Elastic Net: {mse_elastic_net}')\n", + "print(f'MSE Decision Tree: {mse_decision_tree}')\n", + "print(f'MSE Random Forest: {mse_random_forest}')\n", + "print('-'*50)\n", + "# Imprimir los errores Root Mean Squared Error (RMSE)\n", + "print(f'RMSE Linear: {mse_linear**0.5}')\n", + "# print(f'RMSE Polynomial: {mse_polynomial**0.5}')\n", + "print(f'RMSE Lasso: {mse_lasso**0.5}')\n", + "print(f'RMSE Ridge: {mse_ridge**0.5}')\n", + "print(f'RMSE Elastic Net: {mse_elastic_net**0.5}')\n", + "print(f'RMSE Decision Tree: {mse_decision_tree**0.5}')\n", + "print(f'RMSE Random Forest: {mse_random_forest**0.5}')\n", + "print('-'*50)\n", + "\n", + "# Imprimir los errores Root Mean Squared Log Error (RMSLE)\n", + "# Función para calcular RMSLE correctamente\n", + "def rmsle(y_true, y_pred):\n", + " return np.sqrt(mean_squared_log_error(y_true, np.maximum(y_pred, 0))) # Evitar valores negativos en predicciones\n", + "\n", + "# Imprimir RMSLE para cada modelo\n", + "print(f'RMSLE Linear: {rmsle(y_test, linear_predictions)}')\n", + "# print(f'RMSLE Polynomial: {rmsle(y_test, polynomial_predictions)}')\n", + "print(f'RMSLE Lasso: {rmsle(y_test, lasso_predictions)}')\n", + "print(f'RMSLE Ridge: {rmsle(y_test, ridge_predictions)}')\n", + "print(f'RMSLE Elastic Net: {rmsle(y_test, elastic_net_predictions)}')\n", + "print(f'RMSLE Decision Tree: {rmsle(y_test, decision_tree_predictions)}')\n", + "print(f'RMSLE Random Forest: {rmsle(y_test, random_forest_predictions)}')\n", + "print('-'*50)\n", + "\n", + "# Imprimir los errores R2 \n", + "\n", + "print(f'R2 Linear: {linear_model.score(X_test, y_test)}')\n", + "# print(f'R2 Polynomial: {polynomial_model.score(X_test, y_test)}')\n", + "print(f'R2 Lasso: {lasso_reg.score(X_test, y_test)}')\n", + "print(f'R2 Ridge: {ridge_reg.score(X_test, y_test)}')\n", + "print(f'R2 Elastic Net: {elastic_net.score(X_test, y_test)}')\n", + "print(f'R2 Decision Tree: {decision_tree.score(X_test, y_test)}')\n", + "print(f'R2 Random Forest: {random_forest.score(X_test, y_test)}')\n", + "print('-'*50)\n", + "\n", + "# Imprimir los errores R2 ajustado\n", + "print(f'R2 ajustado Linear: {1 - (1-linear_model.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "# print(f'R2 ajustado Polynomial: {1 - (1-polynomial_model.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print(f'R2 ajustado Lasso: {1 - (1-lasso_reg.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print(f'R2 ajustado Ridge: {1 - (1-ridge_reg.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print(f'R2 ajustado Elastic Net: {1 - (1-elastic_net.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print(f'R2 ajustado Decision Tree: {1 - (1-decision_tree.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print(f'R2 ajustado Random Forest: {1 - (1-random_forest.score(X_test, y_test))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1)}')\n", + "print('-'*50)\n", + "\n", + "# Imprimir los errores MAPE\n", + "print(f'MAPE Linear: {mean_absolute_percentage_error(y_test, linear_predictions)}')\n", + "# print(f'MAPE Polynomial: {mean_absolute_percentage_error(y_test, polynomial_predictions)}')\n", + "print(f'MAPE Lasso: {mean_absolute_percentage_error(y_test, lasso_predictions)}')\n", + "print(f'MAPE Ridge: {mean_absolute_percentage_error(y_test, ridge_predictions)}')\n", + "print(f'MAPE Elastic Net: {mean_absolute_percentage_error(y_test, elastic_net_predictions)}')\n", + "print(f'MAPE Decision Tree: {mean_absolute_percentage_error(y_test, decision_tree_predictions)}')\n", + "print(f'MAPE Random Forest: {mean_absolute_percentage_error(y_test, random_forest_predictions)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j2PGzN9SpfcP" + }, + "source": [ + "# Conclusiones" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* MAE (Error Absoluto Medio) → Mide cuánto se equivoca el modelo en promedio, sin importar si es mucho o poco.\n", + "* MSE (Error Cuadrático Medio) → Similar a MAE, pero da más importancia a los errores grandes.\n", + "* RMSE (Raíz del Error Cuadrático Medio) → Es como el MSE, pero en la misma unidad que el label, más fácil de interpretar.\n", + "* RMSLE (Raíz del Error Cuadrático Logarítmico Medio) → Similar a RMSE, pero reduce el impacto de valores muy grandes o muy pequeños.\n", + "* R² (Coeficiente de Determinación) → Proporciona una medida de cuanto la variabilidad de y es explicada por el modelo en comparación con la variabilidad total de los datos. Si es cercano a 1, el modelo es bueno.\n", + "* R² Ajustado → Igual que R², pero penaliza si agregamos variables innecesarias.\n", + "* MAPE (Error Absoluto Porcentual Medio) → Mide el error en porcentaje, útil si quieres comparar errores en distintas escalas." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos ver que los modelos que mejor funcionan son el RandomForest y el DecisionTree, ya que en todos los errores se obtiene un mejor resultado. La diferencia entre R2 y R2 ajustado es muy poca, por lo que nos indica que la cantidad de features que tenemos es correcta. Los modelos Linear, Lasso, Ridge y Elastic tiene un desempeño muy similiar. Probando a normalizar las variables podríamos llegar a mejorar los resultados. El RMSE en RandomForest nos indica que solo oscilaria unos 4400 sobre los charges" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/02_LinearRegressionImplementation.ipynb b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/02_LinearRegressionImplementation.ipynb new file mode 100644 index 000000000..3ee88736f --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/02_LinearRegressionImplementation.ipynb @@ -0,0 +1,948 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "JrhuQqfgDrqi" + }, + "source": [ + "# Problema\n", + "\n", + "Implementación de la regresión lineal" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "an5O5onxbVm6" + }, + "source": [ + "## Instrucciones\n", + "\n", + "Generar una implementación correcta de la regresión lineal usando el descenso del gradiente como método de optimización de los pesos y compararlo con la implementación directa de la librería sklearn (los valores de w y b no serán exactamente iguales pero muy similares)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "AHtMXAFslx52" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_absolute_percentage_error\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Pj2c9YxsmUPF" + }, + "outputs": [], + "source": [ + "# Generación del dataset\n", + "np.random.seed(0) # Para reproducibilidad\n", + "X = np.random.uniform(1, 10, 5000) # Generar 5000 valores aleatorios para las horas de estudio\n", + "error = np.random.normal(0, 5, 5000) # Generar errores aleatorios\n", + "y = 50 + 5 * X + error # Generar la puntuación del examen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.813047017599905" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[5]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((5000,), (5000,))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "_gTEPYQEmZbX" + }, + "outputs": [ + { + 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualización de los datos\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(X, y, color='blue', label='Datos reales')\n", + "plt.xlabel('Horas de estudio')\n", + "plt.ylabel('Puntuación del examen')\n", + "plt.title('Horas de estudio vs. Puntuación del examen')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "zMyPHBqPmza2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Datos de Entrenamiento: 4000\n", + "Datos de Validación: 500\n", + "Datos de Test: 500\n" + ] + } + ], + "source": [ + "# Dividir los datos en conjuntos de entrenamiento, validación y prueba\n", + "# 80 % TRAIN Y VAL 20 % TEST\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=42) \n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) \n", + "\n", + "# A COMPLETAR\n", + "\n", + "print('Datos de Entrenamiento:', X_train.shape[0])\n", + "print('Datos de Validación:', X_val.shape[0])\n", + "print('Datos de Test:', X_test.shape[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (4000,)\n", + "y_train shape: (4000,)\n", + "X_val shape: (500,)\n", + "y_val shape: (500,)\n" + ] + } + ], + "source": [ + "print(f\"X_train shape: {X_train.shape}\")\n", + "print(f\"y_train shape: {y_train.shape}\")\n", + "print(f\"X_val shape: {X_val.shape}\")\n", + "print(f\"y_val shape: {y_val.shape}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HXMEo6cf6xXb" + }, + "source": [ + "## Implementación manual de la regresión con gradiente descendente" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "2qjOLdbY3sGy" + }, + "outputs": [], + "source": [ + "\n", + "# Función de costo (Mean Squared Error)\n", + "def cost_function(y_true, y_pred):\n", + " n = len(y_true)\n", + " cost = (1/n) * np.sum((y_true - y_pred) ** 2)\n", + " return cost\n", + "\n", + "\n", + "def train_regression(X_train, y_train, X_val, y_val, lr, epochs):\n", + " w = 0 # Como solo tienes una característica, w es un escalar\n", + " b = 0\n", + "\n", + " train_loss_per_epoch = []\n", + " val_loss_per_epoch = []\n", + "\n", + " for epoch in range(epochs):\n", + " # Predicción para entrenamiento\n", + " y_pred_train = w * X_train + b\n", + "\n", + " # Calcular pérdida\n", + " train_loss = cost_function(y_train, y_pred_train)\n", + " train_loss_per_epoch.append(train_loss)\n", + "\n", + " # Calcular gradientes\n", + " dw = (-2 / len(X_train)) * np.sum((y_train - y_pred_train) * X_train)\n", + " db = (-2 / len(X_train)) * np.sum(y_train - y_pred_train)\n", + "\n", + " # Actualizar pesos\n", + " w -= lr * dw\n", + " b -= lr * db\n", + "\n", + " # Predicción para validación\n", + " y_pred_val = w * X_val + b\n", + " val_loss = cost_function(y_val, y_pred_val)\n", + " val_loss_per_epoch.append(val_loss)\n", + "\n", + " return w, b, train_loss_per_epoch, val_loss_per_epoch\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "1KlQjWwLqkdg" + }, + "outputs": [], + "source": [ + "# Hiperparámetros\n", + "lr = 0.01 # A COMPLETAR\n", + "epochs = 1000 # A COMPLETAR\n", + "\n", + "# Entrenar el modelo\n", + "w, b, train_loss_per_epoch, val_loss_per_epoch = train_regression(X_train, y_train, X_val, y_val, lr=lr, epochs=epochs)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "xV7aq7znqnLS" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualización del error de entrenamiento por epoch\n", + "plt.figure()\n", + "plt.plot(range(epochs), train_loss_per_epoch, '-b')\n", + "plt.plot(range(epochs), val_loss_per_epoch, '-r')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Error cuadrático promedio')\n", + "plt.title('Error por Epoch')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "Jbl8OsBslP4r" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Costo del modelo: 23.97435038843393\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Realizar predicciones en test\n", + "y_pred = w * X_test + b # A COMPLETAR\n", + "\n", + "# Calcular el costo\n", + "cost = cost_function(y_test, y_pred) # A COMPLETAR\n", + "\n", + "print(\"Costo del modelo:\", cost)\n", + "\n", + "# Visualización del modelo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(X_test, y_test, color='blue', label='Datos reales')\n", + "plt.plot(X_test, y_pred, color='red', label='Predicciones')\n", + "plt.xlabel('Horas de estudio')\n", + "plt.ylabel('Puntuación del examen')\n", + "plt.title('Regresión Lineal Simple - Horas de estudio vs. Puntuación del examen')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "Tv2sYALGlRts" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE en el conjunto de test: 23.97435038843393\n", + "MAE en el conjunto de test: 3.797033978534661\n", + "R^2 en el conjunto de test: 0.8802214959097939\n", + "MAPE en el conjunto de test: 0.050673489316305935\n" + ] + } + ], + "source": [ + "# Calcular las métricas de evaluación mse, mae, r2 y mape\n", + "# Calcular las métricas de evaluación\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "mape = mean_absolute_percentage_error(y_test, y_pred)\n", + "\n", + "\n", + "print(\"MSE en el conjunto de test:\", mse)\n", + "print(\"MAE en el conjunto de test:\", mae)\n", + "print(\"R^2 en el conjunto de test:\", r2)\n", + "print(\"MAPE en el conjunto de test:\", mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PjS2hFmf81Z-" + }, + "source": [ + "## Implementación de la regresión lineal usando sklearn" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "Z6je867ntgnp" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    LinearRegression()
    In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
    On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
    " + ], + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train = X_train.reshape(-1, 1)\n", + "X_test = X_test.reshape(-1, 1)\n", + "# Crear el modelo de regresión lineal con sklearn\n", + "# A COMPLETAR\n", + "# Entrenar modelos con regresion lineal\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "# linear_predictions = linear_model.predict(X_test)\n", + "# Entrenar el modelo con los datos de entrenamiento\n", + "# A COMPLETAR" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "S1qfQCDOwC2u" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Costo del modelo: 23.446722805288243\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Realizar predicciones en test\n", + "y_pred =model.predict(X_test) # A COMPLETAR\n", + "\n", + "# Calcular el costo\n", + "cost = cost_function(y_test, y_pred)\n", + "\n", + "print(\"Costo del modelo:\", cost)\n", + "\n", + "# Visualización del modelo\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(X_test, y_test, color='blue', label='Datos reales')\n", + "plt.plot(X_test, y_pred, color='red', label='Predicciones')\n", + "plt.xlabel('Horas de estudio')\n", + "plt.ylabel('Puntuación del examen')\n", + "plt.title('Regresión Lineal Simple - Horas de estudio vs. Puntuación del examen')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "Bkd-rulrwUMS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE Linear: 23.446722805288243\n", + "MAE Linear: 3.7514313225138145\n", + "R2 Linear: 0.882857581626486\n", + "MAPE Linear: 0.05025211495560561\n" + ] + } + ], + "source": [ + "# Calcular las métricas de evaluación mse, mae, r2 y mape\n", + "mse_linear = mean_squared_error(y_test, y_pred)\n", + "print(f'MSE Linear: {mse_linear}')\n", + "print(f'MAE Linear: {mean_absolute_error(y_test, y_pred)}')\n", + "print(f'R2 Linear: {model.score(X_test, y_test)}')\n", + "print(f'MAPE Linear: {mean_absolute_percentage_error(y_test, y_pred)}')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vTfr3xxA89rT" + }, + "source": [ + "## Comparación de parámetros obtenidos" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "GugveGrc8bQm" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coeficiente de intercepción (b) - Manual: 48.563533994272284\n", + "Coeficiente de pendiente (w) - Manual: 5.201209038210091\n", + "Coeficiente de intercepción (b) - Scikit-learn: 49.883537234215794\n", + "Coeficiente de pendiente (w) - Scikit-learn: 5.002940257756677\n" + ] + } + ], + "source": [ + "b_sklearn = model.intercept_\n", + "w_sklearn = model.coef_[0]\n", + "\n", + "print(\"Coeficiente de intercepción (b) - Manual:\", b)\n", + "print(\"Coeficiente de pendiente (w) - Manual:\", w)\n", + "print(\"Coeficiente de intercepción (b) - Scikit-learn:\", b_sklearn)\n", + "print(\"Coeficiente de pendiente (w) - Scikit-learn:\", w_sklearn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Los valores de b y w son muy similares para ambos métodos, esto quiere decir que el desciente de gradiente se ha hecho bien. Mirando las métricas de error vemos que también son prácticamente iguales aunque haciendolo con el modelo linear de scikit-learn se pueden obtener resultados ligeramente mejores" + ] + } + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyMIy+IL0ZuT+NUisbNmxTaw", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From cb708c5a2e03f1cb6aa2db173451c302d3bba44e Mon Sep 17 00:00:00 2001 From: eabadz Date: Wed, 5 Mar 2025 12:15:13 +0100 Subject: [PATCH 3/3] =?UTF-8?q?Ejercicios=20regresi=C3=B3n?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../MIA/Eduardo_Abad/ML1_REG/insurance.csv | 1339 +++++++++++++++++ 1 file changed, 1339 insertions(+) create mode 100644 ALUMNOS/MIA/Eduardo_Abad/ML1_REG/insurance.csv diff --git a/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/insurance.csv b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/insurance.csv new file mode 100644 index 000000000..04bc36e41 --- /dev/null +++ b/ALUMNOS/MIA/Eduardo_Abad/ML1_REG/insurance.csv @@ -0,0 +1,1339 @@ +age,sex,bmi,children,smoker,region,charges +19,female,27.9,0,yes,southwest,16884.924 +18,male,33.77,1,no,southeast,1725.5523 +28,male,33,3,no,southeast,4449.462 +33,male,22.705,0,no,northwest,21984.47061 +32,male,28.88,0,no,northwest,3866.8552 +31,female,25.74,0,no,southeast,3756.6216 +46,female,33.44,1,no,southeast,8240.5896 +37,female,27.74,3,no,northwest,7281.5056 +37,male,29.83,2,no,northeast,6406.4107 +60,female,25.84,0,no,northwest,28923.13692 +25,male,26.22,0,no,northeast,2721.3208 +62,female,26.29,0,yes,southeast,27808.7251 +23,male,34.4,0,no,southwest,1826.843 +56,female,39.82,0,no,southeast,11090.7178 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