diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..feaca95 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.ipynb_checkpoints +.vscode diff --git a/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM-MK.ipynb b/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM-MK.ipynb new file mode 100644 index 0000000..2171bd1 --- /dev/null +++ b/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM-MK.ipynb @@ -0,0 +1,2361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customer Segmentation using RFM analysis\n", + "\n", + "This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\n", + "\n", + "We will create cutomer segments as per the Recency,Frequency and Monetary analysis by analyzing the data to know our customer base. This knowlwdge can then be used to target customers to retain customers, pitch offers etc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source \n", + "UCI ML Repo - [Online Retail Data Set](https://archive.ics.uci.edu/ml/datasets/online+retail)\n", + "\n", + "\n", + "## Attribute Information:\n", + "|Column|Description|Type|\n", + "|---|---|---|\n", + "|InvoiceNo| Invoice number.| Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.|\n", + "|StockCode| Product (item) code. |Nominal, a 5-digit integral number uniquely assigned to each distinct product.|\n", + "|Description| Product (item) name.| Nominal.|\n", + "|Quantity| The quantities of each product (item) per transaction.| Numeric.|\n", + "|InvoiceDate| Invice Date and time. |Numeric, the day and time when each transaction was generated.|\n", + "|UnitPrice| Unit price.| Numeric, Product price per unit in sterling.|\n", + "|CustomerID| Customer number.| Nominal, a 5-digit integral number uniquely assigned to each customer.|\n", + "|Country| Country name.| Nominal, the name of the country where each customer resides.|\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "import time, warnings\n", + "import datetime as dt\n", + "\n", + "#visualizations\n", + "import matplotlib.pyplot as plt\n", + "from pandas.plotting import scatter_matrix\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('../data/commercial_data.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking for cancelled orders" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "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", + "
InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
173363A563185BAdjust bad debt18/12/2011 14:5011062.06NaNUnited Kingdom
173364A563186BAdjust bad debt18/12/2011 14:51-11062.06NaNUnited Kingdom
173365A563187BAdjust bad debt18/12/2011 14:52-11062.06NaNUnited Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity InvoiceDate \\\n", + "173363 A563185 B Adjust bad debt 1 8/12/2011 14:50 \n", + "173364 A563186 B Adjust bad debt 1 8/12/2011 14:51 \n", + "173365 A563187 B Adjust bad debt 1 8/12/2011 14:52 \n", + "\n", + " UnitPrice CustomerID Country \n", + "173363 11062.06 NaN United Kingdom \n", + "173364 -11062.06 NaN United Kingdom \n", + "173365 -11062.06 NaN United Kingdom " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.InvoiceNo.str.contains('\\D').replace(pd.NA,False)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "no cancel orders, but some bad debt corrections, however there seem to be missing customer ID.." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
7354523020960WATERMELON BATH SPONGE13/1/2011 9:552.46NaNUnited Kingdom
7454523021082SET/20 FRUIT SALAD PAPER NAPKINS13/1/2011 9:551.63NaNUnited Kingdom
7554523021488RED WHITE SCARF HOT WATER BOTTLE13/1/2011 9:558.29NaNUnited Kingdom
7654523035970ZINC FOLKART SLEIGH BELLS13/1/2011 9:554.13NaNUnited Kingdom
7754523082583HOT BATHS METAL SIGN13/1/2011 9:554.13NaNUnited Kingdom
7854523082583HOT BATHS METAL SIGN73/1/2011 9:554.96NaNUnited Kingdom
33854529921730GLASS STAR FROSTED T-LIGHT HOLDER13/1/2011 12:194.95NaNUnited Kingdom
54054531582482WOODEN PICTURE FRAME WHITE FINISH23/1/2011 14:144.96NaNUnited Kingdom
54154531582600NO SINGING METAL SIGN13/1/2011 14:144.13NaNUnited Kingdom
54254531584969BOX OF 6 ASSORTED COLOUR TEASPOONS13/1/2011 14:148.29NaNUnited Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "73 545230 20960 WATERMELON BATH SPONGE 1 \n", + "74 545230 21082 SET/20 FRUIT SALAD PAPER NAPKINS 1 \n", + "75 545230 21488 RED WHITE SCARF HOT WATER BOTTLE 1 \n", + "76 545230 35970 ZINC FOLKART SLEIGH BELLS 1 \n", + "77 545230 82583 HOT BATHS METAL SIGN 1 \n", + "78 545230 82583 HOT BATHS METAL SIGN 7 \n", + "338 545299 21730 GLASS STAR FROSTED T-LIGHT HOLDER 1 \n", + "540 545315 82482 WOODEN PICTURE FRAME WHITE FINISH 2 \n", + "541 545315 82600 NO SINGING METAL SIGN 1 \n", + "542 545315 84969 BOX OF 6 ASSORTED COLOUR TEASPOONS 1 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "73 3/1/2011 9:55 2.46 NaN United Kingdom \n", + "74 3/1/2011 9:55 1.63 NaN United Kingdom \n", + "75 3/1/2011 9:55 8.29 NaN United Kingdom \n", + "76 3/1/2011 9:55 4.13 NaN United Kingdom \n", + "77 3/1/2011 9:55 4.13 NaN United Kingdom \n", + "78 3/1/2011 9:55 4.96 NaN United Kingdom \n", + "338 3/1/2011 12:19 4.95 NaN United Kingdom \n", + "540 3/1/2011 14:14 4.96 NaN United Kingdom \n", + "541 3/1/2011 14:14 4.13 NaN United Kingdom \n", + "542 3/1/2011 14:14 8.29 NaN United Kingdom " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.CustomerID.isna()].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "59942" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.CustomerID.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why missing CustID?\n", + "- possibly guest checkout feature on the website\n", + "\n", + "\n", + "What could be possible features that you would collect if you want to segment \"guest\" customers?\n", + " - browser, IP, location, cookie" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove rows where customerID are NA" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(176137, 8)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dropna(subset=['CustomerID'], inplace=True)\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RFM Analysis\n", + "RFM (Recency, Frequency, Monetary) analysis is a customer segmentation technique that uses past purchase behavior to divide customers into groups. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.\n", + "\n", + "**RECENCY (R)**: Days since last purchase\n", + "\n", + "**FREQUENCY (F):** Total number of purchases\n", + "\n", + "**MONETARY VALUE (M):** Total money this customer spent.\n", + "\n", + "We will create those 3 customer attributes for each customer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recency\n", + "To calculate recency, we need to choose a date point from which we evaluate how many days ago was the customer's last purchase." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find out the latest date in the data to use it as for reference" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'9/9/2011 9:52'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.InvoiceDate.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2011-12-09\n" + ] + } + ], + "source": [ + "now = dt.date(2011, 12, 9)\n", + "print(now)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a new column called date which contains the date of invoice only" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydate
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-01
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom2011-03-01
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom2011-03-01
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom2011-03-01
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom2011-03-01
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom 2011-03-01 \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom 2011-03-01 \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom 2011-03-01 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['date'] = pd.DatetimeIndex(data.InvoiceDate).date\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check the last date of purchase with respect to CustomerID and calculate the RECENCY" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDLastPurchaseDate
012747.02011-08-22
112748.02011-09-30
212749.02011-08-01
312820.02011-09-26
412821.02011-05-09
\n", + "
" + ], + "text/plain": [ + " CustomerID LastPurchaseDate\n", + "0 12747.0 2011-08-22\n", + "1 12748.0 2011-09-30\n", + "2 12749.0 2011-08-01\n", + "3 12820.0 2011-09-26\n", + "4 12821.0 2011-05-09" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recency_df = data.groupby('CustomerID', as_index=False).date.max()\n", + "recency_df.columns = ['CustomerID', 'LastPurchaseDate']\n", + "recency_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDLastPurchaseDateRecency
012747.02011-08-22109
112748.02011-09-3070
212749.02011-08-01130
312820.02011-09-2674
412821.02011-05-09214
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" + ], + "text/plain": [ + " CustomerID LastPurchaseDate Recency\n", + "0 12747.0 2011-08-22 109\n", + "1 12748.0 2011-09-30 70\n", + "2 12749.0 2011-08-01 130\n", + "3 12820.0 2011-09-26 74\n", + "4 12821.0 2011-05-09 214" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recency_df['Recency'] = recency_df.LastPurchaseDate.apply(lambda x: (now-x).days)\n", + "recency_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Frequency\n", + "Frequency helps us to know how many times a customer purchased from us. To do that we need to check how many invoices are registered by the same customer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drop duplicate data from the data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydate
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-01
1554522122021BLUE FELT EASTER EGG BASKET63/1/2011 8:351.6514740.0United Kingdom2011-03-01
4554522222957SET 3 PAPER VINTAGE CHICK PAPER EGG63/1/2011 8:492.9513880.0United Kingdom2011-03-01
5454522322487WHITE WOOD GARDEN PLANT LADDER43/1/2011 8:588.5016462.0United Kingdom2011-03-01
5554522422664TOY TIDY DOLLY GIRL DESIGN53/1/2011 9:032.1017068.0United Kingdom2011-03-01
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "15 545221 22021 BLUE FELT EASTER EGG BASKET 6 \n", + "45 545222 22957 SET 3 PAPER VINTAGE CHICK PAPER EGG 6 \n", + "54 545223 22487 WHITE WOOD GARDEN PLANT LADDER 4 \n", + "55 545224 22664 TOY TIDY DOLLY GIRL DESIGN 5 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "15 3/1/2011 8:35 1.65 14740.0 United Kingdom 2011-03-01 \n", + "45 3/1/2011 8:49 2.95 13880.0 United Kingdom 2011-03-01 \n", + "54 3/1/2011 8:58 8.50 16462.0 United Kingdom 2011-03-01 \n", + "55 3/1/2011 9:03 2.10 17068.0 United Kingdom 2011-03-01 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "invoice_level_data = data.copy()\n", + "invoice_level_data.drop_duplicates(subset=['InvoiceNo', 'CustomerID'], keep='first', inplace=True)\n", + "invoice_level_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate the frequency of purchases" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDFrequency
012747.05
112748.096
212749.03
312820.01
412821.01
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" + ], + "text/plain": [ + " CustomerID Frequency\n", + "0 12747.0 5\n", + "1 12748.0 96\n", + "2 12749.0 3\n", + "3 12820.0 1\n", + "4 12821.0 1" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_df = invoice_level_data.groupby('CustomerID', as_index=False).InvoiceNo.count()\n", + "frequency_df.columns = ['CustomerID', 'Frequency']\n", + "frequency_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monetary\n", + "\n", + "**Monetary attribute answers the question: How much money did the customer spent over time?**\n", + "\n", + "### To do that, first, we will create a new column total cost to have the total price per invoice." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "data['TotalCost'] = data.Quantity * data.UnitPrice" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydateTotalCost
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-0115.90
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom2011-03-0115.90
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom2011-03-0119.80
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom2011-03-0114.85
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom2011-03-0115.00
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date TotalCost \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 15.90 \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 15.90 \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom 2011-03-01 19.80 \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom 2011-03-01 14.85 \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom 2011-03-01 15.00 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDMonetary
012747.01760.09
112748.014680.85
212749.02755.23
312820.0217.77
412821.092.72
\n", + "
" + ], + "text/plain": [ + " CustomerID Monetary\n", + "0 12747.0 1760.09\n", + "1 12748.0 14680.85\n", + "2 12749.0 2755.23\n", + "3 12820.0 217.77\n", + "4 12821.0 92.72" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monetary_df = data.groupby('CustomerID', as_index=False).TotalCost.sum()\n", + "monetary_df.columns = ['CustomerID', 'Monetary']\n", + "monetary_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create RFM Table" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetary
CustomerID
12747.02011-08-2210951760.09
12748.02011-09-30709614680.85
12749.02011-08-0113032755.23
12820.02011-09-26741217.77
12821.02011-05-09214192.72
\n", + "
" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09\n", + "12748.0 2011-09-30 70 96 14680.85\n", + "12749.0 2011-08-01 130 3 2755.23\n", + "12820.0 2011-09-26 74 1 217.77\n", + "12821.0 2011-05-09 214 1 92.72" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df = (recency_df\n", + " .merge(frequency_df, on='CustomerID')\n", + " .merge(monetary_df, on='CustomerID')\n", + ")\n", + "\n", + "rfm_df.set_index('CustomerID', inplace=True)\n", + "rfm_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customer segments with RFM Model\n", + "\n", + "**The simplest way to create customers segments from RFM Model is to use Quartiles. We assign a score from 1 to 4 to Recency, Frequency and Monetary. Four is the best/highest value, and one is the lowest/worst value. A final RFM score is calculated simply by combining individual RFM score numbers.**\n", + "\n", + "Note: Quintiles (score from 1-5) offer better granularity, in case the business needs that but it will be more challenging to create segments since we will have 555 possible combinations. So, we will use quartiles." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find RFM quartiles" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecencyFrequencyMonetary
0.0070.01.02.9000
0.2585.01.0258.0775
0.50119.02.0518.3500
0.75183.03.01182.9725
1.00283.096.0141789.3200
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" + ], + "text/plain": [ + " Recency Frequency Monetary\n", + "0.00 70.0 1.0 2.9000\n", + "0.25 85.0 1.0 258.0775\n", + "0.50 119.0 2.0 518.3500\n", + "0.75 183.0 3.0 1182.9725\n", + "1.00 283.0 96.0 141789.3200" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quantiles = rfm_df.quantile(q=[0,0.25,0.5,0.75,1])\n", + "quantiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we have duplicate bin edges for `Frequency` column, we can custom define the range for it" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 1.0\n", + "0.5 2.0\n", + "0.7 3.0\n", + "0.8 4.0\n", + "1.0 96.0\n", + "Name: Frequency, dtype: float64" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.Frequency.quantile(q=[0,0.5,0.7,0.8,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creation of RFM Segments\n", + "\n", + "We will create two segmentation classes since, high recency is bad, while high frequency and monetary value is good.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create functions as per the appropriate quaritle values and apply them to create segments" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 3\n", + "12748.0 4\n", + "12749.0 2\n", + "12820.0 4\n", + "12821.0 1\n", + "Name: Recency, dtype: category\n", + "Categories (4, int64): [4 < 3 < 2 < 1]" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_labels = range(4,0,-1)\n", + "r_groups = pd.qcut(rfm_df.Recency, q=4, labels=r_labels)\n", + "r_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 4\n", + "12748.0 4\n", + "12749.0 4\n", + "12820.0 1\n", + "12821.0 1\n", + "Name: Monetary, dtype: category\n", + "Categories (4, int64): [1 < 2 < 3 < 4]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_labels = range(1,5)\n", + "m_groups = pd.qcut(rfm_df.Monetary, q=4, labels=m_labels)\n", + "m_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 4\n", + "12748.0 4\n", + "12749.0 2\n", + "12820.0 1\n", + "12821.0 1\n", + "Name: Frequency, dtype: category\n", + "Categories (4, int64): [1 < 2 < 3 < 4]" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_labels = range(1,5)\n", + "f_groups = pd.qcut(rfm_df.Frequency, q=[0,0.5,0.7,0.8,1], labels=f_labels)\n", + "\n", + "f_groups.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now that we have the score of each customer, we can represent our customer segmentation, combine the scores (R_Quartile, F_Quartile,M_Quartile) together." + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "rfm_df = rfm_df.assign(R=r_groups,F=f_groups,M=m_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetaryRFM
CustomerID
12747.02011-08-2210951760.09344
12748.02011-09-30709614680.85444
12749.02011-08-0113032755.23224
12820.02011-09-26741217.77411
12821.02011-05-09214192.72111
........................
18280.02011-03-072771180.60111
18281.02011-06-12180180.82211
18282.02011-08-051261100.21211
18283.02011-09-05958802.77343
18287.02011-05-222011765.28113
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2864 rows × 7 columns

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" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary R F M\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09 3 4 4\n", + "12748.0 2011-09-30 70 96 14680.85 4 4 4\n", + "12749.0 2011-08-01 130 3 2755.23 2 2 4\n", + "12820.0 2011-09-26 74 1 217.77 4 1 1\n", + "12821.0 2011-05-09 214 1 92.72 1 1 1\n", + "... ... ... ... ... .. .. ..\n", + "18280.0 2011-03-07 277 1 180.60 1 1 1\n", + "18281.0 2011-06-12 180 1 80.82 2 1 1\n", + "18282.0 2011-08-05 126 1 100.21 2 1 1\n", + "18283.0 2011-09-05 95 8 802.77 3 4 3\n", + "18287.0 2011-05-22 201 1 765.28 1 1 3\n", + "\n", + "[2864 rows x 7 columns]" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetaryRFMRFM_segmentRFM_score
CustomerID
12747.02011-08-2210951760.0934434411.0
12748.02011-09-30709614680.8544444412.0
12749.02011-08-0113032755.232242248.0
12820.02011-09-26741217.774114116.0
12821.02011-05-09214192.721111113.0
\n", + "
" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary R F M \\\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09 3 4 4 \n", + "12748.0 2011-09-30 70 96 14680.85 4 4 4 \n", + "12749.0 2011-08-01 130 3 2755.23 2 2 4 \n", + "12820.0 2011-09-26 74 1 217.77 4 1 1 \n", + "12821.0 2011-05-09 214 1 92.72 1 1 1 \n", + "\n", + " RFM_segment RFM_score \n", + "CustomerID \n", + "12747.0 344 11.0 \n", + "12748.0 444 12.0 \n", + "12749.0 224 8.0 \n", + "12820.0 411 6.0 \n", + "12821.0 111 3.0 " + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df['RFM_segment'] = rfm_df.apply(lambda x : '{}{}{}'.format(x.R , x.F, x.M), axis=1)\n", + "rfm_df['RFM_score'] = rfm_df.loc[:,['R','F','M']].sum(axis=1)\n", + "rfm_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find out the best customers" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([12748., 12839., 12901., 12910., 12921., 12957., 12971., 13004.,\n", + " 13014., 13018., 13021., 13078., 13089., 13094., 13097., 13102.,\n", + " 13178., 13263., 13266., 13268., 13384., 13394., 13408., 13418.,\n", + " 13468., 13488., 13576., 13599., 13630., 13694., 13695., 13704.,\n", + " 13767., 13777., 13798., 13842., 13862., 13881., 13985., 14004.,\n", + " 14031., 14056., 14060., 14062., 14096., 14159., 14191., 14194.,\n", + " 14221., 14227., 14235., 14239., 14282., 14298., 14367., 14395.,\n", + " 14401., 14456., 14462., 14524., 14527., 14543., 14562., 14606.,\n", + " 14659., 14667., 14676., 14680., 14688., 14709., 14735., 14755.,\n", + " 14769., 14800., 14808., 14854., 14868., 14944., 14952., 14961.,\n", + " 15005., 15039., 15044., 15061., 15078., 15114., 15140., 15144.,\n", + " 15150., 15152., 15187., 15194., 15218., 15241., 15290., 15301.,\n", + " 15311., 15356., 15358., 15410., 15465., 15498., 15547., 15555.,\n", + " 15640., 15674., 15796., 15804., 15827., 15838., 15867., 15955.,\n", + " 15981., 15984., 16011., 16013., 16029., 16033., 16076., 16103.,\n", + " 16133., 16145., 16156., 16161., 16168., 16187., 16326., 16407.,\n", + " 16422., 16458., 16523., 16525., 16558., 16607., 16626., 16656.,\n", + " 16672., 16681., 16700., 16705., 16709., 16710., 16713., 16729.,\n", + " 16746., 16779., 16813., 16818., 16839., 16928., 16931., 16945.,\n", + " 17017., 17049., 17061., 17068., 17069., 17220., 17238., 17243.,\n", + " 17306., 17315., 17340., 17377., 17389., 17402., 17416., 17428.,\n", + " 17450., 17491., 17511., 17576., 17581., 17611., 17613., 17644.,\n", + " 17651., 17652., 17656., 17669., 17675., 17677., 17686., 17716.,\n", + " 17719., 17725., 17730., 17750., 17757., 17758., 17811., 17841.,\n", + " 17857., 17858., 17865., 17920., 17949., 17997., 18008., 18041.,\n", + " 18094., 18102., 18109., 18118., 18144., 18172., 18198., 18225.,\n", + " 18226., 18229., 18241.])" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.RFM_segment == '444'].index.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Learner Activity\n", + "\n", + "**1. Find the following:**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Best Customer \n", + "\n", + "- See above filter RFM_seg = 444" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Loyal Customer \n", + "\n", + "- we are treating our most frequent customer as our loyal customer, hence filter RFM_seg = x4x" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "444 211\n", + "344 155\n", + "244 32\n", + "343 30\n", + "443 26\n", + "243 12\n", + "143 5\n", + "144 1\n", + "242 1\n", + "442 1\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.F == 4,'RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Big Spenders \n", + "- since Monetary value distribution is skewed, we might consider our big spender to be filtered by RFM_seg = xx3+" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "444 253\n", + "344 214\n", + "233 109\n", + "333 103\n", + "433 85\n", + "343 63\n", + "244 60\n", + "443 44\n", + "243 41\n", + "133 38\n", + "234 38\n", + "323 37\n", + "423 37\n", + "434 33\n", + "334 32\n", + "113 31\n", + "223 30\n", + "123 29\n", + "413 25\n", + "213 23\n", + "424 15\n", + "224 14\n", + "313 14\n", + "324 13\n", + "124 11\n", + "114 8\n", + "143 7\n", + "134 7\n", + "414 6\n", + "214 5\n", + "144 4\n", + "314 3\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.M >= 3,'RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Almost lost customers \n", + "- RFM_seg = <=2 <=2 <=2 \n", + "- or if you have periodic data, like for every financial quarter FY Q1, FY Q2, FY Q3 check the customer trend" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111 333\n", + "112 234\n", + "211 192\n", + "212 163\n", + "222 22\n", + "122 8\n", + "121 5\n", + "221 3\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.query('R<=2 and F<=2 and M<=2').RFM_segment.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Lost customers \n", + "- customers with the lowest score RFM_seg = 111 " + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111 333\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.RFM_segment=='111','RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Now that we know our customers segments, how will you target them?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- best -> make them feel valued send vouchers on bday, anniversary\n", + "- loyal-> upsell, crosssell\n", + "- almost/ lost -> discounts" + ] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM.ipynb b/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM.ipynb index 90e8e35..2171bd1 100644 --- a/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM.ipynb +++ b/Customer_Segmentation_with_RFM analysis/notebook/Customer_segentation_with_RFM.ipynb @@ -11,6 +11,27 @@ "We will create cutomer segments as per the Recency,Frequency and Monetary analysis by analyzing the data to know our customer base. This knowlwdge can then be used to target customers to retain customers, pitch offers etc" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Source \n", + "UCI ML Repo - [Online Retail Data Set](https://archive.ics.uci.edu/ml/datasets/online+retail)\n", + "\n", + "\n", + "## Attribute Information:\n", + "|Column|Description|Type|\n", + "|---|---|---|\n", + "|InvoiceNo| Invoice number.| Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.|\n", + "|StockCode| Product (item) code. |Nominal, a 5-digit integral number uniquely assigned to each distinct product.|\n", + "|Description| Product (item) name.| Nominal.|\n", + "|Quantity| The quantities of each product (item) per transaction.| Numeric.|\n", + "|InvoiceDate| Invice Date and time. |Numeric, the day and time when each transaction was generated.|\n", + "|UnitPrice| Unit price.| Numeric, Product price per unit in sterling.|\n", + "|CustomerID| Customer number.| Nominal, a 5-digit integral number uniquely assigned to each customer.|\n", + "|Country| Country name.| Nominal, the name of the country where each customer resides.|\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -20,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,26 +61,461 @@ "warnings.filterwarnings(\"ignore\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('../data/commercial_data.csv')\n", + "data.head()" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Read the data" + "### Checking for cancelled orders" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
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173364A563186BAdjust bad debt18/12/2011 14:51-11062.06NaNUnited Kingdom
173365A563187BAdjust bad debt18/12/2011 14:52-11062.06NaNUnited Kingdom
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity InvoiceDate \\\n", + "173363 A563185 B Adjust bad debt 1 8/12/2011 14:50 \n", + "173364 A563186 B Adjust bad debt 1 8/12/2011 14:51 \n", + "173365 A563187 B Adjust bad debt 1 8/12/2011 14:52 \n", + "\n", + " UnitPrice CustomerID Country \n", + "173363 11062.06 NaN United Kingdom \n", + "173364 -11062.06 NaN United Kingdom \n", + "173365 -11062.06 NaN United Kingdom " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.InvoiceNo.str.contains('\\D').replace(pd.NA,False)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "no cancel orders, but some bad debt corrections, however there seem to be missing customer ID.." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
7354523020960WATERMELON BATH SPONGE13/1/2011 9:552.46NaNUnited Kingdom
7454523021082SET/20 FRUIT SALAD PAPER NAPKINS13/1/2011 9:551.63NaNUnited Kingdom
7554523021488RED WHITE SCARF HOT WATER BOTTLE13/1/2011 9:558.29NaNUnited Kingdom
7654523035970ZINC FOLKART SLEIGH BELLS13/1/2011 9:554.13NaNUnited Kingdom
7754523082583HOT BATHS METAL SIGN13/1/2011 9:554.13NaNUnited Kingdom
7854523082583HOT BATHS METAL SIGN73/1/2011 9:554.96NaNUnited Kingdom
33854529921730GLASS STAR FROSTED T-LIGHT HOLDER13/1/2011 12:194.95NaNUnited Kingdom
54054531582482WOODEN PICTURE FRAME WHITE FINISH23/1/2011 14:144.96NaNUnited Kingdom
54154531582600NO SINGING METAL SIGN13/1/2011 14:144.13NaNUnited Kingdom
54254531584969BOX OF 6 ASSORTED COLOUR TEASPOONS13/1/2011 14:148.29NaNUnited Kingdom
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "73 545230 20960 WATERMELON BATH SPONGE 1 \n", + "74 545230 21082 SET/20 FRUIT SALAD PAPER NAPKINS 1 \n", + "75 545230 21488 RED WHITE SCARF HOT WATER BOTTLE 1 \n", + "76 545230 35970 ZINC FOLKART SLEIGH BELLS 1 \n", + "77 545230 82583 HOT BATHS METAL SIGN 1 \n", + "78 545230 82583 HOT BATHS METAL SIGN 7 \n", + "338 545299 21730 GLASS STAR FROSTED T-LIGHT HOLDER 1 \n", + "540 545315 82482 WOODEN PICTURE FRAME WHITE FINISH 2 \n", + "541 545315 82600 NO SINGING METAL SIGN 1 \n", + "542 545315 84969 BOX OF 6 ASSORTED COLOUR TEASPOONS 1 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "73 3/1/2011 9:55 2.46 NaN United Kingdom \n", + "74 3/1/2011 9:55 1.63 NaN United Kingdom \n", + "75 3/1/2011 9:55 8.29 NaN United Kingdom \n", + "76 3/1/2011 9:55 4.13 NaN United Kingdom \n", + "77 3/1/2011 9:55 4.13 NaN United Kingdom \n", + "78 3/1/2011 9:55 4.96 NaN United Kingdom \n", + "338 3/1/2011 12:19 4.95 NaN United Kingdom \n", + "540 3/1/2011 14:14 4.96 NaN United Kingdom \n", + "541 3/1/2011 14:14 4.13 NaN United Kingdom \n", + "542 3/1/2011 14:14 8.29 NaN United Kingdom " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.CustomerID.isna()].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "59942" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.CustomerID.isna().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why missing CustID?\n", + "- possibly guest checkout feature on the website\n", + "\n", + "\n", + "What could be possible features that you would collect if you want to segment \"guest\" customers?\n", + " - browser, IP, location, cookie" + ] }, { "cell_type": "markdown", @@ -70,10 +526,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "(176137, 8)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dropna(subset=['CustomerID'], inplace=True)\n", + "data.shape" + ] }, { "cell_type": "markdown", @@ -108,10 +578,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "'9/9/2011 9:52'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.InvoiceDate.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2011-12-09\n" + ] + } + ], + "source": [ + "now = dt.date(2011, 12, 9)\n", + "print(now)" + ] }, { "cell_type": "markdown", @@ -122,10 +623,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydate
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-01
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom2011-03-01
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom2011-03-01
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom2011-03-01
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom2011-03-01
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom 2011-03-01 \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom 2011-03-01 \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom 2011-03-01 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['date'] = pd.DatetimeIndex(data.InvoiceDate).date\n", + "\n", + "data.head()" + ] }, { "cell_type": "markdown", @@ -136,10 +759,168 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDLastPurchaseDate
012747.02011-08-22
112748.02011-09-30
212749.02011-08-01
312820.02011-09-26
412821.02011-05-09
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" + ], + "text/plain": [ + " CustomerID LastPurchaseDate\n", + "0 12747.0 2011-08-22\n", + "1 12748.0 2011-09-30\n", + "2 12749.0 2011-08-01\n", + "3 12820.0 2011-09-26\n", + "4 12821.0 2011-05-09" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recency_df = data.groupby('CustomerID', as_index=False).date.max()\n", + "recency_df.columns = ['CustomerID', 'LastPurchaseDate']\n", + "recency_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDLastPurchaseDateRecency
012747.02011-08-22109
112748.02011-09-3070
212749.02011-08-01130
312820.02011-09-2674
412821.02011-05-09214
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" + ], + "text/plain": [ + " CustomerID LastPurchaseDate Recency\n", + "0 12747.0 2011-08-22 109\n", + "1 12748.0 2011-09-30 70\n", + "2 12749.0 2011-08-01 130\n", + "3 12820.0 2011-09-26 74\n", + "4 12821.0 2011-05-09 214" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recency_df['Recency'] = recency_df.LastPurchaseDate.apply(lambda x: (now-x).days)\n", + "recency_df.head()" + ] }, { "cell_type": "markdown", @@ -158,10 +939,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydate
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-01
1554522122021BLUE FELT EASTER EGG BASKET63/1/2011 8:351.6514740.0United Kingdom2011-03-01
4554522222957SET 3 PAPER VINTAGE CHICK PAPER EGG63/1/2011 8:492.9513880.0United Kingdom2011-03-01
5454522322487WHITE WOOD GARDEN PLANT LADDER43/1/2011 8:588.5016462.0United Kingdom2011-03-01
5554522422664TOY TIDY DOLLY GIRL DESIGN53/1/2011 9:032.1017068.0United Kingdom2011-03-01
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "15 545221 22021 BLUE FELT EASTER EGG BASKET 6 \n", + "45 545222 22957 SET 3 PAPER VINTAGE CHICK PAPER EGG 6 \n", + "54 545223 22487 WHITE WOOD GARDEN PLANT LADDER 4 \n", + "55 545224 22664 TOY TIDY DOLLY GIRL DESIGN 5 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 \n", + "15 3/1/2011 8:35 1.65 14740.0 United Kingdom 2011-03-01 \n", + "45 3/1/2011 8:49 2.95 13880.0 United Kingdom 2011-03-01 \n", + "54 3/1/2011 8:58 8.50 16462.0 United Kingdom 2011-03-01 \n", + "55 3/1/2011 9:03 2.10 17068.0 United Kingdom 2011-03-01 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "invoice_level_data = data.copy()\n", + "invoice_level_data.drop_duplicates(subset=['InvoiceNo', 'CustomerID'], keep='first', inplace=True)\n", + "invoice_level_data.head()" + ] }, { "cell_type": "markdown", @@ -172,10 +1075,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDFrequency
012747.05
112748.096
212749.03
312820.01
412821.01
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" + ], + "text/plain": [ + " CustomerID Frequency\n", + "0 12747.0 5\n", + "1 12748.0 96\n", + "2 12749.0 3\n", + "3 12820.0 1\n", + "4 12821.0 1" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "frequency_df = invoice_level_data.groupby('CustomerID', as_index=False).InvoiceNo.count()\n", + "frequency_df.columns = ['CustomerID', 'Frequency']\n", + "frequency_df.head()" + ] }, { "cell_type": "markdown", @@ -190,10 +1166,225 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "data['TotalCost'] = data.Quantity * data.UnitPrice" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountrydateTotalCost
054522021955DOORMAT UNION JACK GUNS AND ROSES23/1/2011 8:307.9514620.0United Kingdom2011-03-0115.90
154522048194DOORMAT HEARTS23/1/2011 8:307.9514620.0United Kingdom2011-03-0115.90
254522022556PLASTERS IN TIN CIRCUS PARADE123/1/2011 8:301.6514620.0United Kingdom2011-03-0119.80
354522022139RETROSPOT TEA SET CERAMIC 11 PC33/1/2011 8:304.9514620.0United Kingdom2011-03-0114.85
454522084029GKNITTED UNION FLAG HOT WATER BOTTLE43/1/2011 8:303.7514620.0United Kingdom2011-03-0115.00
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 545220 21955 DOORMAT UNION JACK GUNS AND ROSES 2 \n", + "1 545220 48194 DOORMAT HEARTS 2 \n", + "2 545220 22556 PLASTERS IN TIN CIRCUS PARADE 12 \n", + "3 545220 22139 RETROSPOT TEA SET CERAMIC 11 PC 3 \n", + "4 545220 84029G KNITTED UNION FLAG HOT WATER BOTTLE 4 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country date TotalCost \n", + "0 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 15.90 \n", + "1 3/1/2011 8:30 7.95 14620.0 United Kingdom 2011-03-01 15.90 \n", + "2 3/1/2011 8:30 1.65 14620.0 United Kingdom 2011-03-01 19.80 \n", + "3 3/1/2011 8:30 4.95 14620.0 United Kingdom 2011-03-01 14.85 \n", + "4 3/1/2011 8:30 3.75 14620.0 United Kingdom 2011-03-01 15.00 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CustomerIDMonetary
012747.01760.09
112748.014680.85
212749.02755.23
312820.0217.77
412821.092.72
\n", + "
" + ], + "text/plain": [ + " CustomerID Monetary\n", + "0 12747.0 1760.09\n", + "1 12748.0 14680.85\n", + "2 12749.0 2755.23\n", + "3 12820.0 217.77\n", + "4 12821.0 92.72" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monetary_df = data.groupby('CustomerID', as_index=False).TotalCost.sum()\n", + "monetary_df.columns = ['CustomerID', 'Monetary']\n", + "monetary_df.head()" + ] }, { "cell_type": "markdown", @@ -204,10 +1395,107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetary
CustomerID
12747.02011-08-2210951760.09
12748.02011-09-30709614680.85
12749.02011-08-0113032755.23
12820.02011-09-26741217.77
12821.02011-05-09214192.72
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" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09\n", + "12748.0 2011-09-30 70 96 14680.85\n", + "12749.0 2011-08-01 130 3 2755.23\n", + "12820.0 2011-09-26 74 1 217.77\n", + "12821.0 2011-05-09 214 1 92.72" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df = (recency_df\n", + " .merge(frequency_df, on='CustomerID')\n", + " .merge(monetary_df, on='CustomerID')\n", + ")\n", + "\n", + "rfm_df.set_index('CustomerID', inplace=True)\n", + "rfm_df.head()" + ] }, { "cell_type": "markdown", @@ -227,6 +1515,123 @@ "### Find RFM quartiles" ] }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RecencyFrequencyMonetary
0.0070.01.02.9000
0.2585.01.0258.0775
0.50119.02.0518.3500
0.75183.03.01182.9725
1.00283.096.0141789.3200
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" + ], + "text/plain": [ + " Recency Frequency Monetary\n", + "0.00 70.0 1.0 2.9000\n", + "0.25 85.0 1.0 258.0775\n", + "0.50 119.0 2.0 518.3500\n", + "0.75 183.0 3.0 1182.9725\n", + "1.00 283.0 96.0 141789.3200" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quantiles = rfm_df.quantile(q=[0,0.25,0.5,0.75,1])\n", + "quantiles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we have duplicate bin edges for `Frequency` column, we can custom define the range for it" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 1.0\n", + "0.5 2.0\n", + "0.7 3.0\n", + "0.8 4.0\n", + "1.0 96.0\n", + "Name: Frequency, dtype: float64" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.Frequency.quantile(q=[0,0.5,0.7,0.8,1])" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -246,10 +1651,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 166, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 3\n", + "12748.0 4\n", + "12749.0 2\n", + "12820.0 4\n", + "12821.0 1\n", + "Name: Recency, dtype: category\n", + "Categories (4, int64): [4 < 3 < 2 < 1]" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r_labels = range(4,0,-1)\n", + "r_groups = pd.qcut(rfm_df.Recency, q=4, labels=r_labels)\n", + "r_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 4\n", + "12748.0 4\n", + "12749.0 4\n", + "12820.0 1\n", + "12821.0 1\n", + "Name: Monetary, dtype: category\n", + "Categories (4, int64): [1 < 2 < 3 < 4]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_labels = range(1,5)\n", + "m_groups = pd.qcut(rfm_df.Monetary, q=4, labels=m_labels)\n", + "m_groups.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CustomerID\n", + "12747.0 4\n", + "12748.0 4\n", + "12749.0 2\n", + "12820.0 1\n", + "12821.0 1\n", + "Name: Frequency, dtype: category\n", + "Categories (4, int64): [1 < 2 < 3 < 4]" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_labels = range(1,5)\n", + "f_groups = pd.qcut(rfm_df.Frequency, q=[0,0.5,0.7,0.8,1], labels=f_labels)\n", + "\n", + "f_groups.head()" + ] }, { "cell_type": "markdown", @@ -260,24 +1746,396 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 170, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "rfm_df = rfm_df.assign(R=r_groups,F=f_groups,M=m_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetaryRFM
CustomerID
12747.02011-08-2210951760.09344
12748.02011-09-30709614680.85444
12749.02011-08-0113032755.23224
12820.02011-09-26741217.77411
12821.02011-05-09214192.72111
........................
18280.02011-03-072771180.60111
18281.02011-06-12180180.82211
18282.02011-08-051261100.21211
18283.02011-09-05958802.77343
18287.02011-05-222011765.28113
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2864 rows × 7 columns

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" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary R F M\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09 3 4 4\n", + "12748.0 2011-09-30 70 96 14680.85 4 4 4\n", + "12749.0 2011-08-01 130 3 2755.23 2 2 4\n", + "12820.0 2011-09-26 74 1 217.77 4 1 1\n", + "12821.0 2011-05-09 214 1 92.72 1 1 1\n", + "... ... ... ... ... .. .. ..\n", + "18280.0 2011-03-07 277 1 180.60 1 1 1\n", + "18281.0 2011-06-12 180 1 80.82 2 1 1\n", + "18282.0 2011-08-05 126 1 100.21 2 1 1\n", + "18283.0 2011-09-05 95 8 802.77 3 4 3\n", + "18287.0 2011-05-22 201 1 765.28 1 1 3\n", + "\n", + "[2864 rows x 7 columns]" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LastPurchaseDateRecencyFrequencyMonetaryRFMRFM_segmentRFM_score
CustomerID
12747.02011-08-2210951760.0934434411.0
12748.02011-09-30709614680.8544444412.0
12749.02011-08-0113032755.232242248.0
12820.02011-09-26741217.774114116.0
12821.02011-05-09214192.721111113.0
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" + ], + "text/plain": [ + " LastPurchaseDate Recency Frequency Monetary R F M \\\n", + "CustomerID \n", + "12747.0 2011-08-22 109 5 1760.09 3 4 4 \n", + "12748.0 2011-09-30 70 96 14680.85 4 4 4 \n", + "12749.0 2011-08-01 130 3 2755.23 2 2 4 \n", + "12820.0 2011-09-26 74 1 217.77 4 1 1 \n", + "12821.0 2011-05-09 214 1 92.72 1 1 1 \n", + "\n", + " RFM_segment RFM_score \n", + "CustomerID \n", + "12747.0 344 11.0 \n", + "12748.0 444 12.0 \n", + "12749.0 224 8.0 \n", + "12820.0 411 6.0 \n", + "12821.0 111 3.0 " + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df['RFM_segment'] = rfm_df.apply(lambda x : '{}{}{}'.format(x.R , x.F, x.M), axis=1)\n", + "rfm_df['RFM_score'] = rfm_df.loc[:,['R','F','M']].sum(axis=1)\n", + "rfm_df.head()" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### FInd out the best customers" + "### Find out the best customers" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 209, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "array([12748., 12839., 12901., 12910., 12921., 12957., 12971., 13004.,\n", + " 13014., 13018., 13021., 13078., 13089., 13094., 13097., 13102.,\n", + " 13178., 13263., 13266., 13268., 13384., 13394., 13408., 13418.,\n", + " 13468., 13488., 13576., 13599., 13630., 13694., 13695., 13704.,\n", + " 13767., 13777., 13798., 13842., 13862., 13881., 13985., 14004.,\n", + " 14031., 14056., 14060., 14062., 14096., 14159., 14191., 14194.,\n", + " 14221., 14227., 14235., 14239., 14282., 14298., 14367., 14395.,\n", + " 14401., 14456., 14462., 14524., 14527., 14543., 14562., 14606.,\n", + " 14659., 14667., 14676., 14680., 14688., 14709., 14735., 14755.,\n", + " 14769., 14800., 14808., 14854., 14868., 14944., 14952., 14961.,\n", + " 15005., 15039., 15044., 15061., 15078., 15114., 15140., 15144.,\n", + " 15150., 15152., 15187., 15194., 15218., 15241., 15290., 15301.,\n", + " 15311., 15356., 15358., 15410., 15465., 15498., 15547., 15555.,\n", + " 15640., 15674., 15796., 15804., 15827., 15838., 15867., 15955.,\n", + " 15981., 15984., 16011., 16013., 16029., 16033., 16076., 16103.,\n", + " 16133., 16145., 16156., 16161., 16168., 16187., 16326., 16407.,\n", + " 16422., 16458., 16523., 16525., 16558., 16607., 16626., 16656.,\n", + " 16672., 16681., 16700., 16705., 16709., 16710., 16713., 16729.,\n", + " 16746., 16779., 16813., 16818., 16839., 16928., 16931., 16945.,\n", + " 17017., 17049., 17061., 17068., 17069., 17220., 17238., 17243.,\n", + " 17306., 17315., 17340., 17377., 17389., 17402., 17416., 17428.,\n", + " 17450., 17491., 17511., 17576., 17581., 17611., 17613., 17644.,\n", + " 17651., 17652., 17656., 17669., 17675., 17677., 17686., 17716.,\n", + " 17719., 17725., 17730., 17750., 17757., 17758., 17811., 17841.,\n", + " 17857., 17858., 17865., 17920., 17949., 17997., 18008., 18041.,\n", + " 18094., 18102., 18109., 18118., 18144., 18172., 18198., 18225.,\n", + " 18226., 18229., 18241.])" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.RFM_segment == '444'].index.values" + ] }, { "cell_type": "markdown", @@ -285,19 +2143,198 @@ "source": [ "## Learner Activity\n", "\n", - "**1. Find the following:**\n", - "1. Best Customer\n", - "\n", - "2. Loyal Customer\n", - "\n", - "3. Big Spenders\n", - "\n", - "4. Almost lost customers\n", + "**1. Find the following:**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Best Customer \n", "\n", - "5. Lost customers\n", + "- See above filter RFM_seg = 444" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Loyal Customer \n", "\n", + "- we are treating our most frequent customer as our loyal customer, hence filter RFM_seg = x4x" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "444 211\n", + "344 155\n", + "244 32\n", + "343 30\n", + "443 26\n", + "243 12\n", + "143 5\n", + "144 1\n", + "242 1\n", + "442 1\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.F == 4,'RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Big Spenders \n", + "- since Monetary value distribution is skewed, we might consider our big spender to be filtered by RFM_seg = xx3+" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "444 253\n", + "344 214\n", + "233 109\n", + "333 103\n", + "433 85\n", + "343 63\n", + "244 60\n", + "443 44\n", + "243 41\n", + "133 38\n", + "234 38\n", + "323 37\n", + "423 37\n", + "434 33\n", + "334 32\n", + "113 31\n", + "223 30\n", + "123 29\n", + "413 25\n", + "213 23\n", + "424 15\n", + "224 14\n", + "313 14\n", + "324 13\n", + "124 11\n", + "114 8\n", + "143 7\n", + "134 7\n", + "414 6\n", + "214 5\n", + "144 4\n", + "314 3\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.M >= 3,'RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Almost lost customers \n", + "- RFM_seg = <=2 <=2 <=2 \n", + "- or if you have periodic data, like for every financial quarter FY Q1, FY Q2, FY Q3 check the customer trend" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111 333\n", + "112 234\n", + "211 192\n", + "212 163\n", + "222 22\n", + "122 8\n", + "121 5\n", + "221 3\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.query('R<=2 and F<=2 and M<=2').RFM_segment.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Lost customers \n", + "- customers with the lowest score RFM_seg = 111 " + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111 333\n", + "Name: RFM_segment, dtype: int64" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rfm_df.loc[rfm_df.RFM_segment=='111','RFM_segment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "**2. Now that we know our customers segments, how will you target them?**" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- best -> make them feel valued send vouchers on bday, anniversary\n", + "- loyal-> upsell, crosssell\n", + "- almost/ lost -> discounts" + ] } ], "metadata": { diff --git a/Daily_Power_Generation/notebook/daily_power_generation-MK.ipynb b/Daily_Power_Generation/notebook/daily_power_generation-MK.ipynb new file mode 100644 index 0000000..a66dca3 --- /dev/null +++ b/Daily_Power_Generation/notebook/daily_power_generation-MK.ipynb @@ -0,0 +1,3393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IKVe6h6kov4X" + }, + "source": [ + "# Daily Power Generation Data Cleaning and Analysis\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fIJD74wYozv5" + }, + "source": [ + "\n", + "India is the world's third-largest producer and third largest consumer of electricity. The national electric grid in India has an installed capacity of 370.106 GW as of 31 March 2020. Renewable power plants, which also include large hydroelectric plants, constitute 35.86% of India's total installed capacity.\n", + "India has a surplus power generation capacity but lacks adequate distribution infrastructure.\n", + "\n", + "India's electricity sector is dominated by fossil fuels, in particular coal, which during the 2018-19 fiscal year produced about three-quarters of the country's electricity. The government is making efforts to increase investment in renewable energy. The government's National Electricity Plan of 2018 states that the country does not need more non-renewable power plants in the utility sector until 2027, with the commissioning of 50,025 MW coal-based power plants under construction and addition of 275,000 MW total renewable power capacity after the retirement of nearly 48,000 MW old coal-fired plants.\n", + "\n", + "India has recorded rapid growth in electricity generation since 1985, increasing from 179 TW-hr in 1985 to 1,057 TW-hr in 2012. The majority of the increase came from coal-fired plants and non-conventional renewable energy sources (RES), with the contribution from natural gas, oil, and hydro plants decreasing in 2012-2017. The gross utility electricity generation (excluding imports from Bhutan) was 1,384 billion kWh in 2019-20, representing 1.0 % annual growth compared to 2018-2019. The contribution from renewable energy sources was nearly 20% of the total. In the year 2019-20, all the incremental electricity generation is contributed by renewable energy sources as the power generation from fossil fuels decreased.\n", + "The drivers for India's electricity sector are its rapidly growing economy, rising exports, improving infrastructure, and increasing household incomes.\n", + "\n", + "\n", + "## Data\n", + "There are 2 CSV files for the study. Each file has detailed file, and row, and column description for easier understanding of the user.\n", + "\n", + "## Acknowledgements\n", + "Data has been extracted from openly available reports of National Power Portal at \"https://npp.gov.in/\". See more details [here](https://www.kaggle.com/navinmundhra/daily-power-generation-in-india-20172020)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resources:\n", + "- [Pandas Groupby Named Aggregations](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#groupby-aggregate-named)\n", + "- [Pandas Select columns that startswith/ endswith](https://stackoverflow.com/a/27275344/8210613)\n", + "- [Pandas Visuals](https://pandas.pydata.org/docs/user_guide/visualization.html#pie-plot)\n", + "- [Pandas Visuals custom pct - pie plot](https://stackoverflow.com/a/6170354/8210613)\n", + "- [Pandas Sum rows](https://www.kite.com/python/answers/how-to-sum-rows-of-a-pandas-dataframe-in-python)\n", + "- [Pandas Convert string to float](https://datatofish.com/convert-string-to-float-dataframe/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CsOsqQuCozb3" + }, + "source": [ + "## Session flow:\n", + "\n", + "* Data Cleaning and basic analysis will be done for the first 90 - 100 minutes.\n", + "\n", + "* Brainstorming activity to form atleast 3 questions which are to be answered and try to individually code it to get the desired output(20-30 minutes)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4oIXyK75o71U" + }, + "source": [ + "## Load the libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DT0e5F87ox4n" + }, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pzhl8usGpH7m" + }, + "source": [ + "## Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fgem045TpLVr" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexDateRegionThermal Generation Actual (in MU)Thermal Generation Estimated (in MU)Nuclear Generation Actual (in MU)Nuclear Generation Estimated (in MU)Hydro Generation Actual (in MU)Hydro Generation Estimated (in MU)
002017-09-01Northern624.23484.2130.3635.57273.27320.81
112017-09-01Western1,106.891,024.3325.173.8172.0021.53
222017-09-01Southern576.66578.5562.7349.80111.5764.78
332017-09-01Eastern441.02429.39NaNNaN85.9469.36
442017-09-01NorthEastern29.1115.91NaNNaN24.6421.21
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" + ], + "text/plain": [ + " index Date Region Thermal Generation Actual (in MU) \\\n", + "0 0 2017-09-01 Northern 624.23 \n", + "1 1 2017-09-01 Western 1,106.89 \n", + "2 2 2017-09-01 Southern 576.66 \n", + "3 3 2017-09-01 Eastern 441.02 \n", + "4 4 2017-09-01 NorthEastern 29.11 \n", + "\n", + " Thermal Generation Estimated (in MU) Nuclear Generation Actual (in MU) \\\n", + "0 484.21 30.36 \n", + "1 1,024.33 25.17 \n", + "2 578.55 62.73 \n", + "3 429.39 NaN \n", + "4 15.91 NaN \n", + "\n", + " Nuclear Generation Estimated (in MU) Hydro Generation Actual (in MU) \\\n", + "0 35.57 273.27 \n", + "1 3.81 72.00 \n", + "2 49.80 111.57 \n", + "3 NaN 85.94 \n", + "4 NaN 24.64 \n", + "\n", + " Hydro Generation Estimated (in MU) \n", + "0 320.81 \n", + "1 21.53 \n", + "2 64.78 \n", + "3 69.36 \n", + "4 21.21 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df = pd.read_csv('../data/power_generation.csv') \n", + "power_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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State / Union territory (UT)Area (km2)RegionNational Share (%)
0Rajasthan342239Northern10.55
1Madhya Pradesh308350Central9.37
2Maharashtra307713Western9.36
3Uttar Pradesh240928Northern7.33
4Gujarat196024Western5.96
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" + ], + "text/plain": [ + " State / Union territory (UT) Area (km2) Region National Share (%)\n", + "0 Rajasthan 342239 Northern 10.55\n", + "1 Madhya Pradesh 308350 Central 9.37\n", + "2 Maharashtra 307713 Western 9.36\n", + "3 Uttar Pradesh 240928 Northern 7.33\n", + "4 Gujarat 196024 Western 5.96" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df = pd.read_csv('../data/State_Region_corrected.csv')\n", + "states_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tTwjxqDSpNyL" + }, + "source": [ + "## Remove the column `index` from power_df dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cXcK0DxKpkIv" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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02017-09-01Northern624.23484.2130.3635.57273.27320.81
12017-09-01Western1,106.891,024.3325.173.8172.0021.53
22017-09-01Southern576.66578.5562.7349.80111.5764.78
32017-09-01Eastern441.02429.39NaNNaN85.9469.36
42017-09-01NorthEastern29.1115.91NaNNaN24.6421.21
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" + ], + "text/plain": [ + " Date Region Thermal Generation Actual (in MU) \\\n", + "0 2017-09-01 Northern 624.23 \n", + "1 2017-09-01 Western 1,106.89 \n", + "2 2017-09-01 Southern 576.66 \n", + "3 2017-09-01 Eastern 441.02 \n", + "4 2017-09-01 NorthEastern 29.11 \n", + "\n", + " Thermal Generation Estimated (in MU) Nuclear Generation Actual (in MU) \\\n", + "0 484.21 30.36 \n", + "1 1,024.33 25.17 \n", + "2 578.55 62.73 \n", + "3 429.39 NaN \n", + "4 15.91 NaN \n", + "\n", + " Nuclear Generation Estimated (in MU) Hydro Generation Actual (in MU) \\\n", + "0 35.57 273.27 \n", + "1 3.81 72.00 \n", + "2 49.80 111.57 \n", + "3 NaN 85.94 \n", + "4 NaN 24.64 \n", + "\n", + " Hydro Generation Estimated (in MU) \n", + "0 320.81 \n", + "1 21.53 \n", + "2 64.78 \n", + "3 69.36 \n", + "4 21.21 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.drop(columns='index', axis=1, inplace=True)\n", + "power_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fl3mMuAgpl5G" + }, + "source": [ + "## Cleaning the power_df dataframe column names :\n", + "* Remove the substring `' (in MU)'` from all the columns in the power_df dataframe.\n", + "* Replace all the spaces with underscore in the power_df datafame\n", + "* All the column names to be converted to small case in the power_df dataframe\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vGBuE_oTpqMf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['date',\n", + " 'region',\n", + " 'thermal_generation_actual',\n", + " 'thermal_generation_estimated',\n", + " 'nuclear_generation_actual',\n", + " 'nuclear_generation_estimated',\n", + " 'hydro_generation_actual',\n", + " 'hydro_generation_estimated']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols= [] \n", + "for col in power_df.columns:\n", + " cols.append(col.replace(' (in MU)', '').replace(' ','_').lower())\n", + " \n", + "cols" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vGBuE_oTpqMf" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateregionthermal_generation_actualthermal_generation_estimatednuclear_generation_actualnuclear_generation_estimatedhydro_generation_actualhydro_generation_estimated
02017-09-01Northern624.23484.2130.3635.57273.27320.81
12017-09-01Western1,106.891,024.3325.173.8172.0021.53
22017-09-01Southern576.66578.5562.7349.80111.5764.78
32017-09-01Eastern441.02429.39NaNNaN85.9469.36
42017-09-01NorthEastern29.1115.91NaNNaN24.6421.21
\n", + "
" + ], + "text/plain": [ + " date region thermal_generation_actual \\\n", + "0 2017-09-01 Northern 624.23 \n", + "1 2017-09-01 Western 1,106.89 \n", + "2 2017-09-01 Southern 576.66 \n", + "3 2017-09-01 Eastern 441.02 \n", + "4 2017-09-01 NorthEastern 29.11 \n", + "\n", + " thermal_generation_estimated nuclear_generation_actual \\\n", + "0 484.21 30.36 \n", + "1 1,024.33 25.17 \n", + "2 578.55 62.73 \n", + "3 429.39 NaN \n", + "4 15.91 NaN \n", + "\n", + " nuclear_generation_estimated hydro_generation_actual \\\n", + "0 35.57 273.27 \n", + "1 3.81 72.00 \n", + "2 49.80 111.57 \n", + "3 NaN 85.94 \n", + "4 NaN 24.64 \n", + "\n", + " hydro_generation_estimated \n", + "0 320.81 \n", + "1 21.53 \n", + "2 64.78 \n", + "3 69.36 \n", + "4 21.21 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.columns = [col.replace(' (in MU)', '').replace(' ','_').lower() for col in power_df.columns]\n", + "power_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UK3mNYdGprSP" + }, + "source": [ + "## Cleaning state_df column names:\n", + "* Replace the column names 'State / Union territory (UT)', 'Area (km2)', 'Region' and 'National Share (%)' with 'state','area','region' and 'national_share' respectively" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MhAVWRhkpu9t" + }, + "outputs": [], + "source": [ + "state_cols = {\n", + " 'State / Union territory (UT)' : 'state',\n", + " 'Area (km2)' : 'area',\n", + " 'National Share (%)': 'national_share',\n", + " 'Region':'region'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MhAVWRhkpu9t" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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statearearegionnational_share
0Rajasthan342239Northern10.55
1Madhya Pradesh308350Central9.37
2Maharashtra307713Western9.36
3Uttar Pradesh240928Northern7.33
4Gujarat196024Western5.96
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" + ], + "text/plain": [ + " state area region national_share\n", + "0 Rajasthan 342239 Northern 10.55\n", + "1 Madhya Pradesh 308350 Central 9.37\n", + "2 Maharashtra 307713 Western 9.36\n", + "3 Uttar Pradesh 240928 Northern 7.33\n", + "4 Gujarat 196024 Western 5.96" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.rename(columns=state_cols, inplace=True)\n", + "states_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 10.55\n", + "1 9.37\n", + "2 9.36\n", + "3 7.33\n", + "4 5.96\n", + "Name: national_share, dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.national_share.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3zBHvMndpw6P" + }, + "source": [ + "## The region names in both the dataframes have to be the same, check for the same, if not, make the necessary changes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "k3KHNOuyp1oP" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Northern', 'Western', 'Southern', 'Eastern', 'NorthEastern'],\n", + " dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.region.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Northern', 'Central', 'Western', 'Southern', 'Eastern',\n", + " 'Northeastern'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.region.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Northern', 'Central', 'Western', 'Southern', 'Eastern',\n", + " 'NorthEastern'], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.replace('Northeastern', 'NorthEastern', inplace=True)\n", + "states_df.region.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "O8WeQOt1p2wW" + }, + "source": [ + "## Basic Data Study" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CBIZN4Lzp9MU" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4945 entries, 0 to 4944\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 date 4945 non-null object \n", + " 1 region 4945 non-null object \n", + " 2 thermal_generation_actual 4945 non-null object \n", + " 3 thermal_generation_estimated 4945 non-null object \n", + " 4 nuclear_generation_actual 2967 non-null float64\n", + " 5 nuclear_generation_estimated 2967 non-null float64\n", + " 6 hydro_generation_actual 4945 non-null float64\n", + " 7 hydro_generation_estimated 4945 non-null float64\n", + "dtypes: float64(4), object(4)\n", + "memory usage: 309.2+ KB\n" + ] + } + ], + "source": [ + "power_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 34 entries, 0 to 33\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 state 34 non-null object \n", + " 1 area 34 non-null int64 \n", + " 2 region 34 non-null object \n", + " 3 national_share 34 non-null float64\n", + "dtypes: float64(1), int64(1), object(2)\n", + "memory usage: 1.2+ KB\n" + ] + } + ], + "source": [ + "states_df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " nuclear_generation_actual nuclear_generation_estimated \\\n", + "count 2967.000000 2967.000000 \n", + "mean 37.242208 36.987877 \n", + "std 15.883968 11.491292 \n", + "min 0.000000 0.000000 \n", + "25% 26.140000 30.190000 \n", + "50% 30.720000 34.840000 \n", + "75% 46.830000 43.075000 \n", + "max 68.740000 76.640000 \n", + "\n", + " hydro_generation_actual hydro_generation_estimated \n", + "count 4945.000000 4945.000000 \n", + "mean 73.305921 76.842965 \n", + "std 74.482145 82.043952 \n", + "min 0.000000 0.000000 \n", + "25% 26.910000 23.310000 \n", + "50% 52.960000 50.270000 \n", + "75% 85.940000 95.800000 \n", + "max 348.720000 397.380000 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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areanational_share
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" + ], + "text/plain": [ + " area national_share\n", + "count 34.000000 34.000000\n", + "mean 92889.117647 2.826265\n", + "std 95464.810113 2.914077\n", + "min 114.000000 0.003000\n", + "25% 21392.500000 0.650000\n", + "50% 57409.500000 1.750000\n", + "75% 133907.750000 4.070000\n", + "max 342239.000000 10.550000" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight\n", + "- not all columns are correctly data-typed\n", + "- there are missing values in nuclear\n", + "- hydro is twice as high as nuclear\n", + "- ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zY4lJY_Rp-Uj" + }, + "source": [ + "## Find if there are any null values in both the dataframes, if there are, what is the possile strategy to deal with them?" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jBU-3zSIqEyf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "date 0\n", + "region 0\n", + "thermal_generation_actual 0\n", + "thermal_generation_estimated 0\n", + "nuclear_generation_actual 1978\n", + "nuclear_generation_estimated 1978\n", + "hydro_generation_actual 0\n", + "hydro_generation_estimated 0\n", + "dtype: int64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "state 0\n", + "area 0\n", + "region 0\n", + "national_share 0\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WJf1HQjiqFUv" + }, + "source": [ + "## Subset the dataframe with only the null values and check for pattern" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CFEBkmJCqNr6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Eastern', 'NorthEastern'], dtype=object)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.loc[power_df.nuclear_generation_actual.isnull(), 'region'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Eastern', 'NorthEastern'], dtype=object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.loc[power_df.nuclear_generation_estimated.isnull(), 'region'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "all the missing values are from Eastern and NorthEastern regions. Do these regions have no nuclear plants?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nuclear_generation_actualnuclear_generation_estimatedhydro_generation_actualhydro_generation_estimated
region
Eastern0.000.0048686.6252461.95
NorthEastern0.000.0017612.3214058.66
Northern26964.0831378.94188854.16201204.03
Southern55855.2146483.2471109.3477083.80
Western27678.3431880.8536235.3435180.02
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" + ], + "text/plain": [ + " nuclear_generation_actual nuclear_generation_estimated \\\n", + "region \n", + "Eastern 0.00 0.00 \n", + "NorthEastern 0.00 0.00 \n", + "Northern 26964.08 31378.94 \n", + "Southern 55855.21 46483.24 \n", + "Western 27678.34 31880.85 \n", + "\n", + " hydro_generation_actual hydro_generation_estimated \n", + "region \n", + "Eastern 48686.62 52461.95 \n", + "NorthEastern 17612.32 14058.66 \n", + "Northern 188854.16 201204.03 \n", + "Southern 71109.34 77083.80 \n", + "Western 36235.34 35180.02 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.groupby('region').sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ww5cCZE8qOr1" + }, + "source": [ + "### Nuclear Generation columns are empty for Eastern and NorthEastern region. Could be due to no nuclear plants in that region.So, Replacing the NaN values with 0" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "dPzEGB46qTaW" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "date 0\n", + "region 0\n", + "thermal_generation_actual 0\n", + "thermal_generation_estimated 0\n", + "nuclear_generation_actual 0\n", + "nuclear_generation_estimated 0\n", + "hydro_generation_actual 0\n", + "hydro_generation_estimated 0\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.fillna(0, inplace=True)\n", + "power_df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DYV6cxi7qUZ1" + }, + "source": [ + "## Covert the thermal generation values to float in the power_df" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9Vk5MG32qbm6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "date object\n", + "region object\n", + "thermal_generation_actual object\n", + "thermal_generation_estimated object\n", + "nuclear_generation_actual float64\n", + "nuclear_generation_estimated float64\n", + "hydro_generation_actual float64\n", + "hydro_generation_estimated float64\n", + "dtype: object" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "tags": [ + "raises-exception" + ] + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: '1,106.89'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpower_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthermal_generation_actual\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/.local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 5696\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5697\u001b[0m \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5698\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5699\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 581\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.7/site-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, filter, **kwargs)\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 443\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.7/site-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 626\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 895\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 896\u001b[0m \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 897\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 898\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '1,106.89'" + ] + } + ], + "source": [ + "power_df.thermal_generation_actual.astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 624.23\n", + "1 1,106.89\n", + "2 576.66\n", + "3 441.02\n", + "4 29.11\n", + "Name: thermal_generation_actual, dtype: object" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.thermal_generation_actual.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "988" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.thermal_generation_actual.str.contains(',').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "967" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.thermal_generation_estimated.str.contains(',').sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 624.23\n", + "1 NaN\n", + "2 576.66\n", + "3 441.02\n", + "4 29.11\n", + " ... \n", + "4940 669.47\n", + "4941 NaN\n", + "4942 494.66\n", + "4943 482.86\n", + "4944 34.42\n", + "Name: thermal_generation_actual, Length: 4945, dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.to_numeric(power_df.thermal_generation_actual, errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "date object\n", + "region object\n", + "thermal_generation_actual float64\n", + "thermal_generation_estimated float64\n", + "nuclear_generation_actual float64\n", + "nuclear_generation_estimated float64\n", + "hydro_generation_actual float64\n", + "hydro_generation_estimated float64\n", + "dtype: object" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.thermal_generation_actual = power_df.thermal_generation_actual.str.replace(',','').astype(float)\n", + "power_df.thermal_generation_estimated = power_df.thermal_generation_estimated.str.replace(',','').astype(float)\n", + "power_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " thermal_generation_actual thermal_generation_estimated \\\n", + "count 4945.000000 4945.000000 \n", + "mean 603.978358 575.395116 \n", + "std 383.534208 383.387299 \n", + "min 12.340000 12.380000 \n", + "25% 470.050000 427.460000 \n", + "50% 615.280000 535.980000 \n", + "75% 689.530000 672.740000 \n", + "max 1395.970000 1442.380000 \n", + "\n", + " nuclear_generation_actual nuclear_generation_estimated \\\n", + "count 4945.000000 4945.000000 \n", + "mean 22.345325 22.192726 \n", + "std 22.006882 20.189857 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 0.000000 \n", + "50% 25.130000 28.460000 \n", + "75% 34.020000 36.600000 \n", + "max 68.740000 76.640000 \n", + "\n", + " hydro_generation_actual hydro_generation_estimated \n", + "count 4945.000000 4945.000000 \n", + "mean 73.305921 76.842965 \n", + "std 74.482145 82.043952 \n", + "min 0.000000 0.000000 \n", + "25% 26.910000 23.310000 \n", + "50% 52.960000 50.270000 \n", + "75% 85.940000 95.800000 \n", + "max 348.720000 397.380000 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "po8qVHobqcTO" + }, + "source": [ + "## Coverting the date values to DateTime format" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ALWIMSmIqg6f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "date datetime64[ns]\n", + "region object\n", + "thermal_generation_actual float64\n", + "thermal_generation_estimated float64\n", + "nuclear_generation_actual float64\n", + "nuclear_generation_estimated float64\n", + "hydro_generation_actual float64\n", + "hydro_generation_estimated float64\n", + "dtype: object" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.date = pd.to_datetime(power_df.date)\n", + "power_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dateregionthermal_generation_actualthermal_generation_estimatednuclear_generation_actualnuclear_generation_estimatedhydro_generation_actualhydro_generation_estimated
02017-09-01Northern624.23484.2130.3635.57273.27320.81
12017-09-01Western1106.891024.3325.173.8172.0021.53
22017-09-01Southern576.66578.5562.7349.80111.5764.78
32017-09-01Eastern441.02429.390.000.0085.9469.36
42017-09-01NorthEastern29.1115.910.000.0024.6421.21
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" + ], + "text/plain": [ + " date region thermal_generation_actual \\\n", + "0 2017-09-01 Northern 624.23 \n", + "1 2017-09-01 Western 1106.89 \n", + "2 2017-09-01 Southern 576.66 \n", + "3 2017-09-01 Eastern 441.02 \n", + "4 2017-09-01 NorthEastern 29.11 \n", + "\n", + " thermal_generation_estimated nuclear_generation_actual \\\n", + "0 484.21 30.36 \n", + "1 1024.33 25.17 \n", + "2 578.55 62.73 \n", + "3 429.39 0.00 \n", + "4 15.91 0.00 \n", + "\n", + " nuclear_generation_estimated hydro_generation_actual \\\n", + "0 35.57 273.27 \n", + "1 3.81 72.00 \n", + "2 49.80 111.57 \n", + "3 0.00 85.94 \n", + "4 0.00 24.64 \n", + "\n", + " hydro_generation_estimated \n", + "0 320.81 \n", + "1 21.53 \n", + "2 64.78 \n", + "3 69.36 \n", + "4 21.21 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZSuEUGEkqiGg" + }, + "source": [ + "## Find out the region which has the highest number of states and find out which states they are.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "sBxti6YCqn4j" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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statearearegionnational_share
0Rajasthan342239Northern10.550
1Madhya Pradesh308350Central9.370
2Maharashtra307713Western9.360
3Uttar Pradesh240928Northern7.330
4Gujarat196024Western5.960
5Karnataka191791Southern5.830
6Andhra Pradesh162970Southern4.870
7Odisha155707Eastern4.730
8Chhattisgarh135191Central4.110
9Tamil Nadu130058Southern3.950
10Telangana112077Southern3.490
11Bihar94163Eastern2.860
12West Bengal88752Eastern2.700
13Arunachal Pradesh83743NorthEastern2.540
14Jharkhand79714Eastern2.420
15Assam78438NorthEastern2.380
16Ladakh59146Northern1.800
17Himachal Pradesh55673Northern1.700
18Uttarakhand53483Northern1.620
19Punjab50362Northern1.530
20Haryana44212Northern1.340
21Jammu and Kashmir42241Northern1.280
22Kerala38863Southern1.180
23Meghalaya22429NorthEastern0.680
24Manipur22327NorthEastern0.680
25Mizoram21081NorthEastern0.640
26Nagaland16579NorthEastern0.500
27Tripura10486NorthEastern0.310
28Sikkim7096NorthEastern0.210
29Goa3702Western0.110
30Delhi1483Northern0.040
31Dadra and Nagar Haveli and Daman and Diu603Western0.010
32Puducherry492Southern0.010
33Chandigarh114Northern0.003
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" + ], + "text/plain": [ + " state area region \\\n", + "0 Rajasthan 342239 Northern \n", + "1 Madhya Pradesh 308350 Central \n", + "2 Maharashtra 307713 Western \n", + "3 Uttar Pradesh 240928 Northern \n", + "4 Gujarat 196024 Western \n", + "5 Karnataka 191791 Southern \n", + "6 Andhra Pradesh 162970 Southern \n", + "7 Odisha 155707 Eastern \n", + "8 Chhattisgarh 135191 Central \n", + "9 Tamil Nadu 130058 Southern \n", + "10 Telangana 112077 Southern \n", + "11 Bihar 94163 Eastern \n", + "12 West Bengal 88752 Eastern \n", + "13 Arunachal Pradesh 83743 NorthEastern \n", + "14 Jharkhand 79714 Eastern \n", + "15 Assam 78438 NorthEastern \n", + "16 Ladakh 59146 Northern \n", + "17 Himachal Pradesh 55673 Northern \n", + "18 Uttarakhand 53483 Northern \n", + "19 Punjab 50362 Northern \n", + "20 Haryana 44212 Northern \n", + "21 Jammu and Kashmir 42241 Northern \n", + "22 Kerala 38863 Southern \n", + "23 Meghalaya 22429 NorthEastern \n", + "24 Manipur 22327 NorthEastern \n", + "25 Mizoram 21081 NorthEastern \n", + "26 Nagaland 16579 NorthEastern \n", + "27 Tripura 10486 NorthEastern \n", + "28 Sikkim 7096 NorthEastern \n", + "29 Goa 3702 Western \n", + "30 Delhi 1483 Northern \n", + "31 Dadra and Nagar Haveli and Daman and Diu 603 Western \n", + "32 Puducherry 492 Southern \n", + "33 Chandigarh 114 Northern \n", + "\n", + " national_share \n", + "0 10.550 \n", + "1 9.370 \n", + "2 9.360 \n", + "3 7.330 \n", + "4 5.960 \n", + "5 5.830 \n", + "6 4.870 \n", + "7 4.730 \n", + "8 4.110 \n", + "9 3.950 \n", + "10 3.490 \n", + "11 2.860 \n", + "12 2.700 \n", + "13 2.540 \n", + "14 2.420 \n", + "15 2.380 \n", + "16 1.800 \n", + "17 1.700 \n", + "18 1.620 \n", + "19 1.530 \n", + "20 1.340 \n", + "21 1.280 \n", + "22 1.180 \n", + "23 0.680 \n", + "24 0.680 \n", + "25 0.640 \n", + "26 0.500 \n", + "27 0.310 \n", + "28 0.210 \n", + "29 0.110 \n", + "30 0.040 \n", + "31 0.010 \n", + "32 0.010 \n", + "33 0.003 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.head(100)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Northern'" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_state_region = states_df.groupby('region')['state'].count().idxmax()\n", + "max_state_region" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "96.093" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.national_share.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Rajasthan', 'Uttar Pradesh', 'Ladakh', 'Himachal Pradesh',\n", + " 'Uttarakhand', 'Punjab', 'Haryana', 'Jammu and Kashmir', 'Delhi',\n", + " 'Chandigarh'], dtype=object)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.groupby('region')['state'].unique()[max_state_region]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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statearea
uniquecountsummean
region
Central[Madhya Pradesh, Chhattisgarh]2443541221770.500000
Eastern[Odisha, Bihar, West Bengal, Jharkhand]4418336104584.000000
NorthEastern[Arunachal Pradesh, Assam, Meghalaya, Manipur,...826217932772.375000
Northern[Rajasthan, Uttar Pradesh, Ladakh, Himachal Pr...1088988188988.100000
Southern[Karnataka, Andhra Pradesh, Tamil Nadu, Telang...6636251106041.833333
Western[Maharashtra, Gujarat, Goa, Dadra and Nagar Ha...4508042127010.500000
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" + ], + "text/plain": [ + " state area \\\n", + " unique count sum \n", + "region \n", + "Central [Madhya Pradesh, Chhattisgarh] 2 443541 \n", + "Eastern [Odisha, Bihar, West Bengal, Jharkhand] 4 418336 \n", + "NorthEastern [Arunachal Pradesh, Assam, Meghalaya, Manipur,... 8 262179 \n", + "Northern [Rajasthan, Uttar Pradesh, Ladakh, Himachal Pr... 10 889881 \n", + "Southern [Karnataka, Andhra Pradesh, Tamil Nadu, Telang... 6 636251 \n", + "Western [Maharashtra, Gujarat, Goa, Dadra and Nagar Ha... 4 508042 \n", + "\n", + " \n", + " mean \n", + "region \n", + "Central 221770.500000 \n", + "Eastern 104584.000000 \n", + "NorthEastern 32772.375000 \n", + "Northern 88988.100000 \n", + "Southern 106041.833333 \n", + "Western 127010.500000 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_df.groupby('region').agg({'state': ['unique','count'],\n", + " 'area':['sum','mean']})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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statestotal_statestotal_areaaverage_area
region
Central[Madhya Pradesh, Chhattisgarh]2443541221770.500000
Eastern[Odisha, Bihar, West Bengal, Jharkhand]4418336104584.000000
NorthEastern[Arunachal Pradesh, Assam, Meghalaya, Manipur,...826217932772.375000
Northern[Rajasthan, Uttar Pradesh, Ladakh, Himachal Pr...1088988188988.100000
Southern[Karnataka, Andhra Pradesh, Tamil Nadu, Telang...6636251106041.833333
Western[Maharashtra, Gujarat, Goa, Dadra and Nagar Ha...4508042127010.500000
\n", + "
" + ], + "text/plain": [ + " states total_states \\\n", + "region \n", + "Central [Madhya Pradesh, Chhattisgarh] 2 \n", + "Eastern [Odisha, Bihar, West Bengal, Jharkhand] 4 \n", + "NorthEastern [Arunachal Pradesh, Assam, Meghalaya, Manipur,... 8 \n", + "Northern [Rajasthan, Uttar Pradesh, Ladakh, Himachal Pr... 10 \n", + "Southern [Karnataka, Andhra Pradesh, Tamil Nadu, Telang... 6 \n", + "Western [Maharashtra, Gujarat, Goa, Dadra and Nagar Ha... 4 \n", + "\n", + " total_area average_area \n", + "region \n", + "Central 443541 221770.500000 \n", + "Eastern 418336 104584.000000 \n", + "NorthEastern 262179 32772.375000 \n", + "Northern 889881 88988.100000 \n", + "Southern 636251 106041.833333 \n", + "Western 508042 127010.500000 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "states_agg_df = states_df.groupby('region').agg(states=('state','unique'),\n", + " total_states=('state','count'),\n", + " total_area=('area','sum'),\n", + " average_area=('area','mean'))\n", + "\n", + "states_agg_df" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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dateregionthermal_generation_actualthermal_generation_estimatednuclear_generation_actualnuclear_generation_estimatedhydro_generation_actualhydro_generation_estimatedall_actual
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" + ], + "text/plain": [ + " date region thermal_generation_actual \\\n", + "0 2017-09-01 Northern 624.23 \n", + "1 2017-09-01 Western 1106.89 \n", + "2 2017-09-01 Southern 576.66 \n", + "3 2017-09-01 Eastern 441.02 \n", + "4 2017-09-01 NorthEastern 29.11 \n", + "\n", + " thermal_generation_estimated nuclear_generation_actual \\\n", + "0 484.21 30.36 \n", + "1 1024.33 25.17 \n", + "2 578.55 62.73 \n", + "3 429.39 0.00 \n", + "4 15.91 0.00 \n", + "\n", + " nuclear_generation_estimated hydro_generation_actual \\\n", + "0 35.57 273.27 \n", + "1 3.81 72.00 \n", + "2 49.80 111.57 \n", + "3 0.00 85.94 \n", + "4 0.00 24.64 \n", + "\n", + " hydro_generation_estimated all_actual \n", + "0 320.81 927.86 \n", + "1 21.53 1204.06 \n", + "2 64.78 750.96 \n", + "3 69.36 526.96 \n", + "4 21.21 53.75 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_df['all_actual'] = power_df.loc[:,['thermal_generation_actual','nuclear_generation_actual','hydro_generation_actual']].sum(axis=1)\n", + "power_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "get the average daily production across all regions " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oedxXWs1qtmi" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " thermal_generation_actual thermal_generation_estimated \\\n", + "date \n", + "2017-09-01 555.582 506.478 \n", + "2017-09-02 555.582 512.674 \n", + "2017-09-03 555.554 506.646 \n", + "2017-09-04 555.554 542.856 \n", + "2017-09-05 558.170 555.930 \n", + "\n", + " nuclear_generation_actual nuclear_generation_estimated \\\n", + "date \n", + "2017-09-01 23.652 17.836 \n", + "2017-09-02 23.652 18.456 \n", + "2017-09-03 23.652 18.514 \n", + "2017-09-04 23.652 18.524 \n", + "2017-09-05 23.652 18.542 \n", + "\n", + " hydro_generation_actual hydro_generation_estimated all_actual \n", + "date \n", + "2017-09-01 113.484 99.538 692.718 \n", + "2017-09-02 113.484 99.128 692.718 \n", + "2017-09-03 113.484 94.610 692.690 \n", + "2017-09-04 113.484 100.072 692.690 \n", + "2017-09-05 113.484 94.032 695.306 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_df = power_df.groupby('date').mean()\n", + "mean_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wu-Nh9-IqzDV" + }, + "source": [ + "## Plotting a graph of mean of all the types of power gernerations in all of India, with total power generation" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, True, False, True, False, True])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "actual_col_list = mean_df.columns.str.endswith('actual')\n", + "actual_col_list" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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thermal_generation_actualnuclear_generation_actualhydro_generation_actualall_actual
date
2017-09-01555.58223.652113.484692.718
2017-09-02555.58223.652113.484692.718
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2017-09-04555.55423.652113.484692.690
2017-09-05558.17023.652113.484695.306
...............
2020-07-28592.32626.216105.154723.696
2020-07-29592.32626.216105.154723.696
2020-07-30594.87226.216105.154726.242
2020-07-31596.80026.216105.154728.170
2020-08-01559.48226.216122.794708.492
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989 rows × 4 columns

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" + ], + "text/plain": [ + " thermal_generation_actual nuclear_generation_actual \\\n", + "date \n", + "2017-09-01 555.582 23.652 \n", + "2017-09-02 555.582 23.652 \n", + "2017-09-03 555.554 23.652 \n", + "2017-09-04 555.554 23.652 \n", + "2017-09-05 558.170 23.652 \n", + "... ... ... \n", + "2020-07-28 592.326 26.216 \n", + "2020-07-29 592.326 26.216 \n", + "2020-07-30 594.872 26.216 \n", + "2020-07-31 596.800 26.216 \n", + "2020-08-01 559.482 26.216 \n", + "\n", + " hydro_generation_actual all_actual \n", + "date \n", + "2017-09-01 113.484 692.718 \n", + "2017-09-02 113.484 692.718 \n", + "2017-09-03 113.484 692.690 \n", + "2017-09-04 113.484 692.690 \n", + "2017-09-05 113.484 695.306 \n", + "... ... ... \n", + "2020-07-28 105.154 723.696 \n", + "2020-07-29 105.154 723.696 \n", + "2020-07-30 105.154 726.242 \n", + "2020-07-31 105.154 728.170 \n", + "2020-08-01 122.794 708.492 \n", + "\n", + "[989 rows x 4 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_df.loc[:, actual_col_list]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mean_df.loc[:,actual_col_list].plot.line(title='Avg power generated across regions',figsize=(10,10))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "similarly, we can plot, daily power generated across all regions" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(power_df\n", + " .groupby('date').sum()\n", + " .loc[:,actual_col_list]\n", + " .plot.line(\n", + " title='Total power generated across all regions', \n", + " figsize=(10,10))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4g9NzXApq4kP" + }, + "source": [ + "## With data cleaning and some basic eda done, what other questions would would you like to get the answer for?\n", + "\n", + "## Come up with atleat 3 such questions and find the anwers for the same individually before the session ends.\n", + "\n", + "## Try forming as many questions and their answers later as per your availability and share your notebooks on slack." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tmEQ2aV3q5he" + }, + "source": [ + "## Some ideas\n", + "- Clustering, [example in R](https://www.kaggle.com/aishwaryasharma1992/exploratory-data-analysis-with-k-means-clustering)\n", + "- Additional plots, [example using Plotly](https://www.kaggle.com/shakka/eda-and-visualization-using-pandas-plotly)\n", + "- Regression, [example linear reg](https://www.kaggle.com/kuroganedecimo/power-generation-eda-linear-regression)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "daily_power_generation.ipynb", + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Data Exploration of Automobile Data/notebook/Data Exploration of Automobile Data_MK.ipynb b/Data Exploration of Automobile Data/notebook/Data Exploration of Automobile Data_MK.ipynb new file mode 100644 index 0000000..1ce7d4c --- /dev/null +++ b/Data Exploration of Automobile Data/notebook/Data Exploration of Automobile Data_MK.ipynb @@ -0,0 +1,2029 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + " \n", + " \n", + " View in Colab\n", + " \n", + "

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resources used in the session:\n", + "* [What is EDA?](https://www.itl.nist.gov/div898/handbook/eda/section1/eda11.htm)\n", + "* [Categorical Encoding](https://pbpython.com/categorical-encoding.html)\n", + "* [Pandas Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)\n", + "* [Data Science Lifecycle](http://sudeep.co/data-science/Understanding-the-Data-Science-Lifecycle/)\n", + "* [TDSP-documentation](https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/lifecycle)\n", + "* [TDSP-presentation](https://slideplayer.com/slide/13392497/)\n", + "* [ML Code in production system](https://images.anandtech.com/doci/14466/DataPipelineSculley.png) or [full article](https://www.anandtech.com/show/14466/intel-xeon-cascade-lake-vs-nvidia-turing)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Exploration of Automobile Data\n", + "\n", + "Cars contain 50 or more sensors which collect data on speed, emissions, fuel consumption, usage data for resources, and security. All these data can be used to find patterns and resolve quality issues either in the nick of time or prevent them from happening altogether. Analytics is being used to increase both customer satisfaction and quality management at a cost-effective level. In this session we are doing some basic analysis of automobile data which the learners can further expand on.\n", + "\n", + "\n", + "#### Data Set 1:\n", + "\n", + "Cleaned data.\n", + "\n", + "#### Data set 2:\n", + "\n", + "Raw data with missing values.\n", + "\n", + "#### Data Description:\n", + "\n", + "[Source: UCI ML Repo](https://archive.ics.uci.edu/ml/datasets/Automobile)\n", + "\n", + "This data set consists of three types of entities:\n", + "\n", + "(a) the specification of an auto in terms of various characteristics\n", + "\n", + "(b) its assigned insurance risk rating\n", + "\n", + "(c) its normalized losses in use as compared to other cars.\n", + "\n", + "The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process \"symboling\". A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.\n", + "\n", + "The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.\n", + "\n", + "Note: Several of the attributes in the database could be used as a \"class\" attribute.\n", + "\n", + "\n", + "No of instances : 205\n", + "\n", + "No of attributes : 26\n", + "\n", + "\n", + "\n", + "\n", + "#### Attribute Information:\n", + "* mpg: continuous\n", + "* cylinders: multi-valued discrete\n", + "* displacement: continuous\n", + "* horsepower: continuous\n", + "* weight: continuous\n", + "* acceleration: continuous\n", + "* model year: multi-valued discrete\n", + "* origin: multi-valued discrete\n", + "* car name: string (unique for each instance)\n", + "\n", + "Missing Attribute Values: normalized-losses and horsepower have missing values\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from sklearn.preprocessing import Imputer,LabelEncoder\n", + "from scipy.stats import norm, skew\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part A: EDA \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 1: Load the data stored in `data_1` using `.read_csv()` api.\n", + "Get an overview of your data by using `info()` and `describe()` functions of pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingmakefuel-typebody-styledrive-wheelswheel-baselengthwidthheighthorsepowerpeak-rpmhighway-mpgcity-mpgprice
03alfa-romerogasconvertiblerwd88.6168.864.148.81115000272113495
13alfa-romerogasconvertiblerwd88.6168.864.148.81115000272116500
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32audigassedanfwd99.8176.666.254.31025500302413950
42audigassedan4wd99.4176.666.454.31155500221817450
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" + ], + "text/plain": [ + " symboling make fuel-type body-style drive-wheels wheel-base \\\n", + "0 3 alfa-romero gas convertible rwd 88.6 \n", + "1 3 alfa-romero gas convertible rwd 88.6 \n", + "2 1 alfa-romero gas hatchback rwd 94.5 \n", + "3 2 audi gas sedan fwd 99.8 \n", + "4 2 audi gas sedan 4wd 99.4 \n", + "\n", + " length width height horsepower peak-rpm highway-mpg city-mpg price \n", + "0 168.8 64.1 48.8 111 5000 27 21 13495 \n", + "1 168.8 64.1 48.8 111 5000 27 21 16500 \n", + "2 171.2 65.5 52.4 154 5000 26 19 16500 \n", + "3 176.6 66.2 54.3 102 5500 30 24 13950 \n", + "4 176.6 66.4 54.3 115 5500 22 18 17450 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('../data/data_1.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 205 entries, 0 to 204\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 symboling 205 non-null int64 \n", + " 1 make 205 non-null object \n", + " 2 fuel-type 205 non-null object \n", + " 3 body-style 205 non-null object \n", + " 4 drive-wheels 205 non-null object \n", + " 5 wheel-base 205 non-null float64\n", + " 6 length 205 non-null float64\n", + " 7 width 205 non-null float64\n", + " 8 height 205 non-null float64\n", + " 9 horsepower 205 non-null int64 \n", + " 10 peak-rpm 205 non-null int64 \n", + " 11 highway-mpg 205 non-null int64 \n", + " 12 city-mpg 205 non-null int64 \n", + " 13 price 205 non-null int64 \n", + "dtypes: float64(4), int64(6), object(4)\n", + "memory usage: 22.5+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Seems no null values" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingwheel-baselengthwidthheighthorsepowerpeak-rpmhighway-mpgcity-mpgprice
count205.000000205.000000205.000000205.000000205.000000205.000000205.000000205.000000205.000000205.000000
mean0.83414698.756585174.04926865.90780553.724878104.9365855127.80487830.75122025.21951213476.258537
std1.2453076.02177612.3372892.1452042.44352240.609702478.4140076.8864436.5421428114.166248
min-2.00000086.600000141.10000060.30000047.80000048.0000004150.00000016.00000013.0000005118.000000
25%0.00000094.500000166.30000064.10000052.00000070.0000004800.00000025.00000019.0000007788.000000
50%1.00000097.000000173.20000065.50000054.10000095.0000005200.00000030.00000024.00000010595.000000
75%2.000000102.400000183.10000066.90000055.500000116.0000005500.00000034.00000030.00000016558.000000
max3.000000120.900000208.10000072.30000059.800000288.0000006600.00000054.00000049.00000045400.000000
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" + ], + "text/plain": [ + " symboling wheel-base length width height horsepower \\\n", + "count 205.000000 205.000000 205.000000 205.000000 205.000000 205.000000 \n", + "mean 0.834146 98.756585 174.049268 65.907805 53.724878 104.936585 \n", + "std 1.245307 6.021776 12.337289 2.145204 2.443522 40.609702 \n", + "min -2.000000 86.600000 141.100000 60.300000 47.800000 48.000000 \n", + "25% 0.000000 94.500000 166.300000 64.100000 52.000000 70.000000 \n", + "50% 1.000000 97.000000 173.200000 65.500000 54.100000 95.000000 \n", + "75% 2.000000 102.400000 183.100000 66.900000 55.500000 116.000000 \n", + "max 3.000000 120.900000 208.100000 72.300000 59.800000 288.000000 \n", + "\n", + " peak-rpm highway-mpg city-mpg price \n", + "count 205.000000 205.000000 205.000000 205.000000 \n", + "mean 5127.804878 30.751220 25.219512 13476.258537 \n", + "std 478.414007 6.886443 6.542142 8114.166248 \n", + "min 4150.000000 16.000000 13.000000 5118.000000 \n", + "25% 4800.000000 25.000000 19.000000 7788.000000 \n", + "50% 5200.000000 30.000000 24.000000 10595.000000 \n", + "75% 5500.000000 34.000000 30.000000 16558.000000 \n", + "max 6600.000000 54.000000 49.000000 45400.000000 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: No cars have -3 symboling rating,\n", + "\n", + "looking for skewness?\n", + "* med > mean = right \n", + "* med < mean = left\n", + "\n", + "**price distribution is left skewed**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 2: Plot a histogram showing the distribution of the car prices (target variable)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(df.price)\n", + "plt.title('Histogram of car prices with KDE')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Same plot with Pandas\n", + "df.price.plot.hist(title=\"Histogram of car prices\")\n", + "plt.xlabel('price')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's define the problem statement: \n", + "\n", + "**Predict the price of the budget cars i.e. price < \\\\$20K**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3: Plot a countplot of the 'make' column of the dataset which represents the different car makers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# By default SNS countplot will order the bars according to ascending order of the label, here makers\n", + "# Change the order to ascending/ descending order of size of bars use this pandas trick\n", + "## see Github issue for details https://github.com/mwaskom/seaborn/issues/1029#issuecomment-342365439\n", + "sns.countplot(y='make', data=df, order = df['make'].value_counts().index)\n", + "plt.title('Car makers in the dataset')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Same using Pandas, somewhat straight forward\n", + "df.make.value_counts(ascending=True).plot.barh(title='Car makers in the dataset')\n", + "plt.xlabel('count')\n", + "plt.ylabel('make')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Top 6 car makers are Japanese" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3.1: Do these Japanese car maker manufacture budget cars?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# lets make a box-plot with while keeping the sorting order as per the countplot above\n", + "plt.figure(figsize=(10,6))\n", + "sns.boxplot(y='make',x='price',data=df, order=df['make'].value_counts().index)\n", + "plt.title('Car price distribution by car makers')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Japanese makers are the ones making more of these budget cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 4: Plot a boxplot that shows the variability of each 'body-style' with respect to the 'price'.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.boxplot(by='body-style', column=['price'], grid=False, rot=0, figsize=(10,6))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: body-style is a good predictor, mostly hactchback and wagon are the budget cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 5: Plot a jointplot that shows the relationship between the 'horsepower' and 'price' of the car." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(x='horsepower', y='price', data=df, kind='reg')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: horsepower seems to have a linear relationship with price, so its a good predictor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 6: Plot the correlation heatmap of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "symboling False\n", + "wheel-base False\n", + "length True\n", + "width True\n", + "height False\n", + "horsepower True\n", + "peak-rpm False\n", + "highway-mpg True\n", + "city-mpg True\n", + "price True\n", + "Name: price, dtype: bool" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# one way to check the correlation is to use df.corr() and check for a threshold like 0.7\n", + "(df.corr().abs()>0.6)['price']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Dy1sMLR8DHNOWf8xvqr3Dx7oBeOks4ycCJ3acf2lbfOOM8c8Cn51l/is6jvNvDG4UlCRJmly2VUiSJEmTyZeASJIkqds87g8eB5NjSZIkdbOtQpIkSZpMVo4lSZLUbcLaKqwcS5IkSY2VY0mSJHWz51iSJEmaTFaOJUmS1G3CKscmx5IkSermDXmSJEnSZLJyLEmSpG4T1lZh5ViSJElqrBxLkiSp24T1HJscS5IkqZttFZIkSdJksnIsSZKkbhPWVmHlWJIkSWqsHI/ITpveqe8QRm7qyu/1HcJYbLLdDn2HMHK1+vq+QxiLW35wdd8hjMVtH7a07xBGbutzV/cdwlhccv0P+g5h5G6zZNO+QxiLTe7ygL5DWLwmrOfY5FiSJEndJiw5tq1CkiRJaqwcS5IkqVtV3xFsVFaOJUmSpMbKsSRJkrrZcyxJkiRNJivHkiRJ6jZhlWOTY0mSJHXzDXmSJEnSZLJyLEmSpG4T1lZh5ViSJElqrBxLkiSp24S9BMTkWJIkSd1sq5AkSZImk5VjSZIkdbNyLEmSJE0mK8eSJEnqNmEvATE5liRJUqeamqynVdhWIUmSJDVWjiVJktTNG/LmtyTXj+GYuyV58tD6oUkOGvV5JEmSNL8tuOR4THYDnrzWWZIkSZOmpsbzWQdJnpjkoiQXJ3ltx5xnJ7kgyflJPjbXy13QyXGSVyc5Pck5Sd7YxpYmuTDJ+9uXdEKSzdq2PdvcU5K8Lcl5SW4NHAbsl2RVkv3a4XdOcmKSS5K8sqdLlCRJmkhJlgCHA08Cdgaem2TnGXN2AF4H/EFV3R/467med8Emx0keD+wA7MWg8rt7kke0zTsAh7cv6RrgGW38g8CBVbU3cAtAVd0IHAIcVVW7VdVRbe59gSe0478hyaYb4bIkSZLml6kaz2ft9gIurqpLWr72CeCpM+a8hEHO9zOAqvrRXC93wSbHwOPb5yzgTAbJ7A5t26VVtaotnwEsTbIVsGVVfbuNr63s/oWqWl1VPwZ+BGw7c0KSZUlWJln5vesvnePlSJIkzUNTU2P5DOdR7bNsxpnvBlw+tH5FGxu2I7Bjkm8l+U6SJ871chfy0yoCvLmq3vdbg8lSYPXQ0C3AZm3++ph5jN/5rqpqObAc4M/u+SeT9RBASZKkORjOozrMlrvNzLduxaA4ui9wd+CkJLtU1TUbGtdCrhwfD/x5ki0AktwtyV26Jrdy+3VJHtqGnjO0+Tpgy7FFKkmStFCNqXK8Dq4A7jG0fnfgylnmfLaqbqqqS4GL+E0nwQZZsMlxVZ3AoDXilCTnAsew9gT3AGB5klMY/Gnk2jb+DQY34A3fkCdJkqT+nA7skORe7QEKzwGOmzHnM8CjAJJsw6DN4pK5nHTBtVVU1RZDy/8G/Nss03YZmvP2ofHzq2pXgPY4kJVtzk+BPddwzl26tkmSJC1q1U/naFXdnOTlDLoFlgBHVNX5SQ4DVlbVcW3b45NcwKAN9tVV9ZO5nHfBJcdz9JQkr2Nw3f8L7N9vOJIkSfNcj2/Iq6ovAl+cMXbI0HIBr2qfkZio5Lg9pu2otU6UJEnSRJqo5FiSJEnrad2eSbxoLNgb8iRJkqRRs3IsSZKkbtVfz3EfTI4lSZLUzbYKSZIkaTJZOZYkSVKn6vFRbn2wcixJkiQ1Vo4lSZLUzZ5jSZIkaTJZOZYkSVI3H+UmSZIkNbZVSJIkSZPJyrEkSZK6+Sg3SZIkaTJZOZYkSVK3Ces5NjmWJElSN59WoQ2xfW7bdwgjN/XT7/cdwljU6uv7DmHkltzrQX2HMBbXfOtdfYcwFrd//vZ9hzByd+BHfYcwFrdesvj+N7kYrwmAbe7SdwRaJBbpvyGSJEkaiQlrq/CGPEmSJKmxcixJkqRONWGPcjM5liRJUjfbKiRJkqTJZOVYkiRJ3awcS5IkSZPJyrEkSZK6TdhLQKwcS5IkSY2VY0mSJHWbsJ5jk2NJkiR1qglLjm2rkCRJkhorx5IkSepm5ViSJEmaTFaOJUmS1G1qsh7lZnIsSZKkbrZVSJIkSZPJyrEkSZK6WTmWJEmSJpOVY0mSJHWqsnK8qCT5YpKtZhk/NMlBbXn/JNsNbbssyTYbM05JkqR5aarG85mnFn1yXFVPrqpr1jJtf2C7tcyRJEnSIrfgk+Mkf5fklW35nUm+3pYfk+Qjw1XgJAcnuSjJV4Gd2tgzgT2AjyZZlWSzduhXJDkzyblJ7rvxr0ySJGkesHK84KwAHt6W9wC2SLIpsA9w0vSkJLsDzwEeBPwJsCdAVR0DrASeV1W7VdUNbZcfV9WDgfcAB22MC5EkSVK/FkNyfAawe5ItgdXAKQyS5IczlBy39WOr6pdV9XPguLUc99NDx18624Qky5KsTLLyrOsunsMlSJIkzU81VWP5zFcLPjmuqpuAy4AXAd9mkBA/Crg3cOHM6etx6NXt5y10PNWjqpZX1R5VtceDtrzP+oQtSZKkeWjBJ8fNCgatDysYJMcHAqvqt589sgJ4epLNWpX5j4a2XQdsubGClSRJWjDsOV6QTgLuCpxSVT8EfsVvt1RQVWcCRwGrgE/N2H4k8N4ZN+RJkiRpakyfeWpRvASkqr4GbDq0vuPQ8tKh5TcBb5pl/08xSJinDe+zEth3lPFKkiRpfloUybEkSZLGYz7fPDcOi6WtQpIkSZozK8eSJEnqNmGVY5NjSZIkdZvHN8+Ng20VkiRJUmPlWJIkSZ28IU+SJEmaUFaOJUmS1G3Ceo5NjiVJktTJtgpJkiRpQlk5liRJUrcJa6uwcixJkiQ1Vo4lSZLUqSascmxyLEmSpG4TlhzbViFJkiQ1Vo4lSZLUadLaKqwcS5IkSY3JsSRJkrpNjemzDpI8MclFSS5O8tpZth+Y5Nwkq5KcnGTnOVwpYHIsSZKkeSjJEuBw4EnAzsBzZ0l+P1ZVD6iq3YB/Bt4x1/PaczwiJ9x4Rd8hjNzBZ5/WdwhjccsPru47hJG75lvv6juEsbjTp4/oO4SxOHK3Q/oOYeSuutWNfYcwFm/cYve+Qxi52y/SVwGf97JT+g5hLPZ8at8R9NpzvBdwcVVdApDkE8BTgQumJ1TVz4fmbw7M+R9wk2NJkiR1GldynGQZsGxoaHlVLR9avxtw+dD6FcBDZjnOXwKvAm4NPHqucZkcS5IkaaNrifDyNUzJbLvNcpzDgcOT/CnweuCFc4nL5FiSJEmdemyruAK4x9D63YEr1zD/E8B75npSb8iTJEnSfHQ6sEOSeyW5NfAc4LjhCUl2GFp9CvC9uZ7UyrEkSZK61WzdDRvhtFU3J3k5cDywBDiiqs5PchiwsqqOA16e5LHATcDPmGNLBZgcS5IkaQ36fENeVX0R+OKMsUOGlv9q1Oe0rUKSJElqrBxLkiSpU03101bRFyvHkiRJUmPlWJIkSZ367Dnug8mxJEmSOlVPT6voi20VkiRJUmPlWJIkSZ0mra3CyrEkSZLUWDmWJElSJx/lJkmSJE0oK8eSJEnqVNV3BBuXybEkSZI62VYhSZIkTagFlxwnWZrkvPWYf2CSF6xlzv5J3t2x7e/XN0ZJkqTFoqYyls98teCS4/VVVe+tqv+awyFMjiVJkibEQk2OlyR5f5Lzk5yQZLMk907y5SRnJDkpyX0Bkhya5KC2vGeSc5KckuRtMyrQ27X9v5fkn9v8twCbJVmV5KMb/zIlSZL6VTWez3y1UJPjHYDDq+r+wDXAM4DlwCuqanfgIOA/Ztnvg8CBVbU3cMuMbbsB+wEPAPZLco+qei1wQ1XtVlXPG9O1SJIkzVu2VSwMl1bVqrZ8BrAUeBhwdJJVwPuAuw7vkGQrYMuq+nYb+tiMY36tqq6tql8BFwD3XFsQSZYlWZlk5dW/vGrDr0aSJEnzwkJ9lNvqoeVbgG2Ba6pqtzXss7Y/osw85lq/m6pazqBizZ7bPWIe/4JAkiRpw1TN3yrvOCzUyvFMPwcuTfIsgAw8cHhCVf0MuC7JQ9vQc9bx2Dcl2XR0oUqSJGm+WizJMcDzgAOSnA2cDzx1ljkHAMuTnMKgknztOhx3OXCON+RJkqRJVFPj+cxXC66toqouA3YZWn/70OYnzjL/0KHV86tqV4AkrwVWtjlHAkcO7fOHQ8uvAV4zitglSZIWmqkJa6tYcMnxHD0lyesYXPf/Avv3G44kSZLmk4lKjqvqKOCovuOQJElaKLwhT5IkSZpQE1U5liRJ0vqZzy/sGAcrx5IkSVJj5ViSJEmdasJec2ZyLEmSpE62VUiSJEkTysqxJEmSOk3aS0CsHEuSJEmNlWNJkiR1mrSXgJgcS5IkqdOkPa3CtgpJkiSpsXIsSZKkTt6QJ0mSJE0oK8eSJEnq5A15kiRJUuMNeZIkSdKEsnIsSZKkTpN2Q57J8Yj8y9S2fYcwcjd89cK+QxiL2z5sad8hjNztn7993yGMxZG7HdJ3CGOx/6rD+g5h5PZ48N/0HcJYPOwnp/UdwsjddMvNfYcwFtcd++q+Q9AiYXIsSZKkTpN2Q549x5IkSVJj5ViSJEmd7DmWJEmSmgl7kpttFZIkSdI0K8eSJEnqNGltFVaOJUmSpMbKsSRJkjpN2qPcTI4lSZLUaarvADYy2yokSZKkxsqxJEmSOhWT1VZh5ViSJElqrBxLkiSp09SEvQXE5FiSJEmdpmyrkCRJkiaTlWNJkiR18oY8SZIkaUJZOZYkSVInXwIiSZIkTSgrx5IkSepkz/EGSrI0yXmjOp4kSZL6NzWmz3w1L9oqkiyICvZCiVOSJEkbZtTJ8ZIk709yfpITkmyWZLck30lyTpJjk9wRIMmJSf4pyTeBv0ryrCTnJTk7yYo2Z0mStyU5ve3/0ja+b5IV7XgXJHlvkk3atucmObcd661t7NlJ3tGW/yrJJW353klObsu7J/lmkjOSHJ/krrPFOeLvS5IkaV7rs3Kc5IlJLkpycZLXzrL9NkmOattPTbJ0Q69z2qiT4x2Aw6vq/sA1wDOA/wJeU1W7AucCbxiav1VVPbKq/gU4BHhCVT0Q+OO2/QDg2qraE9gTeEmSe7VtewF/CzwAuDfwJ0m2A94KPBrYDdgzydOAFcDD234PB36S5G7APsBJSTYF3gU8s6p2B44A3tQRpyRJksYsyRLgcOBJwM7Ac5PsPGPaAcDPquo+wDsZ5IFzMurk+NKqWtWWz2CQtG5VVd9sYx8CHjE0/6ih5W8BRyZ5CbCkjT0eeEGSVcCpwJ0YJOAAp1XVJVV1C/BxBonunsCJVXV1Vd0MfBR4RFX9ANgiyZbAPYCPtTgeDpwE7ATsAnylnev1wN074vy1JMuSrEyy8rhfXrKOX5EkSdLCUWQsn3WwF3Bxy/duBD4BPHXGnKcyyC8BjgEek2ROdxCOuod29dDyLcBWa5n/i+mFqjowyUOApwCrkuwGBHhFVR0/vFOSfYGacaxq87ucArwIuIhBQvznwN4Mqs/bA+dX1d5ri/O3Tli1HFgOsOL3njUzHkmSpAVvakwPq0iyDFg2NLS85VbT7gZcPrR+BfCQGYf59ZyqujnJtQyKqT/e0LjGfUPetcDPkky3NPwZ8M3ZJia5d1WdWlWHMLigewDHA3/R2h5IsmOSzdsueyW5V+s13g84mUF1+ZFJtmml+OcOnW8FcFD7eRbwKGB1VV3LIGG+c5K923k2TXL/0X0NkiRJGlZVy6tqj6HP8hlTZkvLZxYj12XOetkYT194IfDeJLcDLmFQvZ3N25LswOAivwacDZwDLAXObCXyq4GntfmnAG9h0HO8Aji2qqaSvA74RjvOF6vqs23+SQwS7hVVdUuSy4HvAlTVjUmeCfx7kjsw+F7+FTh/RN+BJEnSgjTV33OOr2CQu027O3Blx5wr2lPF7gD8dC4nHVlyXFWXMejbnV5/+9Dmh84yf98Z638y22GBv2+fX2utJL+sqv1mOe7HGPQUzxz/H4b+dFFVj5+xfRW/3Q89a5ySJEnaKE4HdmgPY/g+8BzgT2fMOY5BIfYU4JnA16tq3leOJUmStED1dVNV6yF+OYM22yXAEVV1fpLDgJVVdRzwn8CHk1zMoGL8nLmed0Emx1V1InBiz2FIkiQten2+za6qvgh8ccbYIUPLvwKeNcpzzos35EmSJAwwBCkAACAASURBVEnzwYKsHEuSJGnjmJrbY4MXHCvHkiRJUmPlWJIkSZ0m7S1nVo4lSZKkxsqxJEmSOvX5tIo+mBxLkiSp09Rk3Y9nW4UkSZI0zcqxJEmSOk0xWaVjK8eSJElSY+VYkiRJnSbtUW4mx5IkSerkDXmSJEnShLJyLEmSpE6T9pxjK8eSJElSY+VYkiRJnbwhTxtkp/te3XcII/fX59yp7xDGYutzV/cdwsjdgR/1HcJYXHWrG/sOYSz2ePDf9B3CyO1y5jv7DmEsPrDrIX2HMHLXLek7gvE4etnKvkMYixd8v+8IvCFPkiRJmlhWjiVJktTJG/IkSZKkCWXlWJIkSZ2sHEuSJEkTysqxJEmSOtWEPa3C5FiSJEmdbKuQJEmSJpSVY0mSJHWycixJkiRNKCvHkiRJ6lR9B7CRmRxLkiSp09SEPa3CtgpJkiSpsXIsSZKkTt6QJ0mSJE0oK8eSJEnqNGmVY5NjSZIkdZq0p1XYViFJkiQ1Vo4lSZLUyUe5SZIkSRNqwSbHSY5M8sy+45AkSVrMpsb0ma8WbHK8vpIs6TsGSZIkzW8bPTlOsjTJd5N8KMk5SY5Jcrskuyf5ZpIzkhyf5K5t/kuSnJ7k7CSfSnK7WY75D62SvMmM8X2TfCPJx4Bzu87d5l6W5J+SnJJkZZIHtzj+J8mBG+XLkSRJmmdqTJ/5qq/K8U7A8qraFfg58JfAu4BnVtXuwBHAm9rcT1fVnlX1QOBC4IDhAyX5Z+AuwIuqarYq/V7AwVW1c8e5XzY09/Kq2hs4CTgSeCbwUOCwOV6vJEnSgjRFjeUzX/WVHF9eVd9qyx8BngDsAnwlySrg9cDd2/ZdkpyU5FzgecD9h47z/4CtquqlVdX1LZ9WVZeu4dz7DG07rv08Fzi1qq6rqquBXyXZauaBkyxrVeaVH77yynW6cEmSJM1ffT3KbWYiex1wfqvaznQk8LSqOjvJ/sC+Q9tOB3ZPsnVV/TTJQ4D3tW2HMKgM/2It5x5eX91+Tg0tT6//zndVVcuB5QA/3Hff+ftHIEmSpA00n2+eG4e+KsfbJ5lOhJ8LfAe48/RYkk2TTFeItwSuSrIpg8rxsC8DbwG+kGTLqjq1qnZrn+OY3cxznzyqi5IkSdLC1ldyfCHwwiTnAFvT+o2BtyY5G1gFPKzN/X/AqcBXgO/OPFBVHQ28HzguyWYbcO73zPFaJEmSFq1JuyGvr7aKqaqa+QSIVcAjZk6sqvcwSwJbVfsPLR/B4Ca+mXNOBE5ch3NTVUuHlo9k0M7xO9skSZImiW0VkiRJ0oTa6JXjqrqMwZMpNro+zy1JkrQQTaXvCDYuK8eSJElS01fPsSRJkhaA+fzCjnEwOZYkSVKnyUqNbauQJEmSfs3KsSRJkjr5KDdJkiRpQlk5liRJUidvyJMkSZKayUqNbauQJEmSfs3KsSRJkjp5Q54kSZI0oawcS5IkqdOk3ZBn5ViSJElqrBxLkiSp02TVjU2OJUmStAbekCdJkiRNKJNjSZIkdaox/TUXSbZO8pUk32s/7zjLnHsmOSPJqiTnJzlwXY5tW8WInHPhtn2HMHIXbnJl3yGMxSXX/6DvEEbu1ksW57/Kb9xi975DGIuH/eS0vkMYuQ/sekjfIYzFs885rO8QRq5uvKHvEMbiPrs8p+8QxuIFfQcwf70W+FpVvSXJa9v6a2bMuQp4WFWtTrIFcF6S46pqjQmOlWNJkiR1mhrTZ46eCnyoLX8IeNrMCVV1Y1Wtbqu3YR3zXpNjSZIkdZqixvJJsizJyqHPsvUIa9uqugqg/bzLbJOS3CPJOcDlwFvXVjUG2yokSZLUg6paDizv2p7kq8DvzbLp4PU4x+XArkm2Az6T5Jiq+uGa9jE5liRJUqe+nnNcVY/t2pbkh0nuWlVXJbkr8KO1HOvKJOcDDweOWdNc2yokSZK00BwHvLAtvxD47MwJSe6eZLO2fEfgD4CL1nZgK8eSJEnqNDU/35H3FuCTSQ4A/g94FkCSPYADq+rFwP2Af0lSQIC3V9W5azuwybEkSZI6zcc35FXVT4DHzDK+EnhxW/4KsOv6Htu2CkmSJKmxcixJkqROc32b3UJj5ViSJElqrBxLkiSp03zsOR4nK8eSJElSY+VYkiRJnSat59jkWJIkSZ1sq5AkSZImlJVjSZIkdZqqyWqrsHIsSZIkNVaOJUmS1Gmy6sYmx5IkSVqDqQlLj9faVpFkaZLzZhk/LMlj17LvoUkOmkuAkiRJ0saywZXjqjpklIFIkiRp/pm05xyv6w15S5K8P8n5SU5IslmSI5M8EyDJk5N8N8nJSf49yeeH9t05yYlJLknyyjb/74aW35nk6235MUk+0pbfk2RlO+cbh7YfO33gJI9L8umZwSbZP8lnknwuyaVJXp7kVUnOSvKdJFu3eScm+dck305yXpK92vidk3wlyZlJ3pfkf5Nss75friRJkhaWdU2OdwAOr6r7A9cAz5jekOS2wPuAJ1XVPsCdZ+x7X+AJwF7AG5JsCqwAHt627wFs0cb3AU5q4wdX1R7ArsAjk+wKfB24X5Lpc7wI+GBHzLsAf9rO+ybgl1X1IOAU4AVD8zavqocBLwOOaGNvAL5eVQ8GjgW2X8v3I0mStChNjekzX61rcnxpVa1qy2cAS4e23Re4pKoubesfn7HvF6pqdVX9GPgRsG07xu5JtgRWM0hY92CQME8nx89OciZwFnB/YOeqKuDDwPOTbAXsDXypI+ZvVNV1VXU1cC3wuTZ+7oz4Pw5QVSuA27fj7gN8oo1/GfjZbCdIsqxVt1d+4Yb/6QhDkiRp4ZqixvKZr9Y1OV49tHwLv92rnPXdt6puAi5jUPn9NoOE+FHAvYELk9wLOAh4TFXtCnwBuG07xgeB5wPPBY6uqpuTPD3JqvbZY5bzTg2tT82If+bfnVqHaxpMrFpeVXtU1R5P2eze67KLJEmS5rFRvATku8DvJ1na1vdbx/1WMEiAVzBIjg8EVrXq8O2BXwDXJtkWeNL0TlV1JXAl8HrgyDZ2bFXt1j4r1zP+/QCS7ANcW1XXAicDz27jjwfuuJ7HlCRJWhRqTH/NV3N+znFV3ZDkZcCXk/wYOG0ddz0JOBg4pap+keRXbYyqOjvJWcD5wCXAt2bs+1HgzlV1wVzjB36W5NsMEvI/b2NvBD6eZD/gm8BVwHUjOJckSZLmsbUmx1V1GYOb26bX3z7LtG9U1X2TBDgcWNnmHjrjWMPH+Rqw6dD6jjPm7r+GsPYB3r+GmI+kVZXb+tKubcCnqup1Mw5xLfCE1rKxN/CoqlqNJEnShJnPN8+Nw6jekPeSJC8Ebs3gBrr3jei4vyPJGQxaLv52XOdg8HSKTybZBLgReMkYzyVJkqR5YiTJcVW9E3jnKI61DufafYTH2rdj/HvAg0Z1HkmSpIVqcDvY5BhV5ViSJEmL0Hx+7No4jOJpFZIkSdKiYOVYkiRJnSbthjwrx5IkSVJj5ViSJEmd5vMLO8bB5FiSJEmdvCFPkiRJmlBWjiVJktRp0p5zbOVYkiRJaqwcS5IkqdOkPcrN5FiSJEmdJu1pFbZVSJIkSY2VY0mSJHXyUW6SJEnShLJyLEmSpE4+yk2SJEmaUFaOJUmS1GnSeo5Njkfkmk0W31e51a1u13cIY3GbJZv2HcLI3XrJ4vvnD+D2U4vzP8g33XJz3yGM3HVL+o5gPOrGG/oOYeRy6836DmEsbrUI/z88X/goN0mSJGlC+ccsSZIkdZryhjxJkiRpMlk5liRJUqfJqhubHEuSJGkNJu1pFbZVSJIkSY2VY0mSJHWycixJkiRNKCvHkiRJ6lQT9ig3k2NJkiR1sq1CkiRJmlBWjiVJktSprBxLkiRJk8nKsSRJkjpN2g15Vo4lSZKkxsqxJEmSOk3a0ypMjiVJktTJtgpJkiRpQlk5liRJUqdJa6uwcixJkiQ18zI5TnJgkhe05f2TbNd3TJIkSZOoxvTXfDUv2yqq6r1Dq/sD5wFX9hONJEnS5JqasBvy5kVy3KrEBwEFnAP8D3A9cBmwB/DRJDcABwMvrqqnt/0eB/xFVf3JjOPtDzwNWALsAvwLcGvgz4DVwJOr6qdJTgRWAXsBtwf+vKpOS3Jn4GPAnYDTgScCu1fVj8f0FUiSJGke6L2tIsn9GSS9j66qBwJ/Nb2tqo4BVgLPq6rdgC8C92vJK8CLgA92HHoX4E8ZJL5vAn5ZVQ8CTgFeMDRv86p6GPAy4Ig29gbg61X1YOBYYPuO2JclWZlk5Vd/efF6XrkkSdL8Nx/bKpJsneQrSb7Xft6xY972SU5IcmGSC5IsXduxe0+OgUcDx0xXZavqp10Ta/CgvQ8Dz0+yFbA38KWO6d+oquuq6mrgWuBzbfxcYOnQvI+3Y68Abt+Ouw/wiTb+ZeBnHfEsr6o9qmqPx97uPutyrZIkSZq71wJfq6odgK+19dn8F/C2qrofg4Lpj9Z24PnQVhFYrz8+fJBBovsr4OiqujnJ0xlUewFe3H6uHtpnamh9it++7pnnrhaTJEnSxJunPcdPBfZtyx8CTgReMzwhyc7ArarqKwBVdf26HHg+VI6/Bjw7yZ1gUCafsf06YMvplaq6ksHNea8Hjmxjx1bVbu2zcj3Pv1877z7AtVV1LXAy8Ow2/nhg1lK9JEnSYjeutorh9tT2WbYeYW1bVVcBtJ93mWXOjsA1ST6d5Kwkb0uyZG0H7r1yXFXnJ3kT8M0ktwBnMbgRb9qRwHvbDXl7V9UNwEeBO1fVBSMI4WdJvk27Ia+NvRH4eJL9gG8CVzFI0iVJkjQCVbUcWN61PclXgd+bZdPB63iKWwEPBx4E/B9wFIOnoP3n2nbqXVV9iEFJfLZtnwI+NWN4H+D9azjekbSqcltf2rUN+FRVvW7GIa4FntBaNvYGHlVVq5EkSZowfbVVVNVju7Yl+WGSu1bVVUnuyuy9xFcAZ1XVJW2fzwAPZS3J8Xxoq1gvSc4AdgU+MsbTbA+cnuRs4N+Bl4zxXJIkSVo/xwEvbMsvBD47y5zTgTsOPeXs0cBauw7mReV4fVTV7iM81r4d499jUIKXJEmaaPP0bXZvAT6Z5AAGLRPPAkiyB3BgVb24qm5JchDwtSQBzmANnQfTFlxyLEmSpMlWVT8BHjPL+Ep+8+Qy2pMqdl2fY5scS5IkqdM8fZTb2JgcS5IkqdM8basYmwV3Q54kSZI0LlaOJUmS1Klqqu8QNiorx5IkSVJj5ViSJEmdpias59jkWJIkSZ1qwp5WYVuFJEmS1Fg5liRJUqdJa6uwcixJkiQ1Vo4lSZLUadJ6jk2OJUmS1GnSXh9tW4UkSZLUWDmWJElSp/KGPEmSJGkyWTkekes3Sd8hjNxnX/Z7fYcwFpvc5QF9hzB629yl7wjG4ryXndJ3CGNx3bGv7juEkTt62cq+QxiL++zynL5DGLlbbbI4/9d/0Xc/1XcIi9ak3ZBn5ViSJElqFucfHyVJkjQSk/YSEJNjSZIkdbKtQpIkSZpQVo4lSZLUyZeASJIkSRPKyrEkSZI6TVrPscmxJEmSOk3a0ypsq5AkSZIaK8eSJEnqNGltFVaOJUmSpMbKsSRJkjpN2qPcTI4lSZLUqbwhT5IkSZpMVo4lSZLUadLaKqwcS5IkSY2VY0mSJHXyUW6SJEnShLJyLEmSpE6T9rQKk2NJkiR1sq1iAiQ5LMlj+45DkiRJ88vEVY6TLKmqQ/qOQ5IkaSGwcryAJVma5LtJPpTknCTHJLldksuSHJLkZOBZSY5M8sy2z55Jvp3k7CSnJdkyyZIkb0tyejvOS3u+NEmSJG0Ei7FyvBNwQFV9K8kRwMva+K+qah+AJE9sP28NHAXsV1WnJ7k9cANwAHBtVe2Z5DbAt5KcUFWXbvSrkSRJ6tFk1Y0hi6lUnmQpsKKqtm/rjwZeCewGPLKq/reNHwl8HrgIeG9V/cGM4xwD7Ar8sg3dAXhpVZ0wY94yYFlbXV5Vy0d/Vb8rybKNda6NaTFe12K8Jlic17UYrwkW53UtxmsCr2shWYzXpN9YVG0Vzcxsf3r9F7PMzSzzp8dfUVW7tc+9ZibGAFW1vKr2aJ+N+S/JsrVPWZAW43UtxmuCxXldi/GaYHFe12K8JvC6FpLFeE1qFmNyvH2Svdvyc4GT1zD3u8B2SfYEaP3GtwKOB/4iyaZtfMckm48zaEmSJPVvMSbHFwIvTHIOsDXwnq6JVXUjsB/wriRnA18Bbgt8ALgAODPJecD7WJz92ZIkSRqyGBO+qao6cMbY0uGVqtp/aPl04KGzHOfv22c+Wqx9TovxuhbjNcHivK7FeE2wOK9rMV4TeF0LyWK8JjWL8Ya8z1fVLj2HIkmSpAVoUSXHkiRJ0lwsxp5jSZIkaYOYHEuSJEmNybF6lWSfJC9qy3dOcq++YxqF9gry7ZJsP/3pOyb9riQfXpexhaT9s/c3fccxLklun2Tr6U/f8czV8LUMfTbtO665WIzXNC3JPZM8ti1vlmTLvmPS6NlzvEAk+fdZhq8FVlbVZzd2PKOQ5A3AHsBOVbVjku2Ao2e+sXChSfIK4A3AD4GpNlxVtWt/Uc1Nkh2BVwP3ZOgpN1X16N6CGoEkZ1bVg4fWlwDnVtXOPYY1Z0lOrKp9+45jlJK8FDgMuIHfvLypqur3+4tq7pJcBtwD+BmDF1BtBVwF/Ah4SVWd0V90G2YxXhNAkpcwePnH1lV17yQ7MHjL7mN6Dk0jthgf5bZY3Ra4L3B0W38GcD5wQJJHVdVf9xbZhns68CDgTICqunKR/Cn8rxgk/D/pO5AROhp4L/B+4JaeY5mzJK9j8KjGzZL8fHoYuJHF8YimbyV5N3AUQ28Hraoz+wtpzg4C7l9VP+47kBH7MnBsVR0PkOTxwBOBTwL/ATykx9g21GK8JoC/BPYCTgWoqu8luUu/IWkcTI4XjvsAj66qmwGSvAc4AXgccG6fgc3BjVVVSQpgEb2F8HIGVf3F5Oaq6nyhzkJTVW8G3pzkzVX1ur7jGYOHtZ+HDY0VsJAr/f8D/LLvIMZgj+Fn81fVCUn+qapeleQ2fQY2B4vxmgBWV9WNSQBob9T11++LkMnxwnE3YHN+k3RtDmxXVbckWd1fWHPyySTvA7Zqv676cwaVyQUpyava4iXAiUm+APz6701VvaOXwOZgqKfzc0leBhzLb1/TT3sJbESq6nVJ7sbvtous6C+quauqR/Udwxi8Dvh2klP57X8GX9lfSCPx0ySvAT7R1vcDftZafKa6d5vXFuM1AXwzyfRvnB4HvAz4XM8xaQzsOV4gkhwAvB44kcGvfx8B/BPwceDQqnp1f9FtuPYfmMczuKbjq+orPYe0wVoPdZeqqsPWsH1eSnIpg8pIZtm8GPo93wI8h8Hr4qfbRaqq/ri/qOYuybYM/vuwXVU9KcnOwN5V9Z89h7bBkpwGnMzgN2W/TrCq6kO9BTUCSbZhcI/CPgz+PTsZeCODQsj2VXVxj+FtkMV4TQBJNgEOYOj/WcAHykRq0TE5XkCS3JVBv1OA06rqyp5DmpPWRvGrVv3eCdgJ+FJV3dRzaHOS5FlVdfTaxhaSJLetql+tbWyhSXIRsGtVLdTfvswqyZeADwIHV9UD269/z6qqB/Qc2gZL8u36/+3debTdVXnG8e8TkCQQAyhUEAyzIFMMCAkSgkRQUaCVWQURJxAHKqhrCYJCsSwpQ2lqLSiDMlihQQZFREhMAgmkQICEqRYsUMSpGhIhFJGnf+x9uCeXm4Tcc2723b/7ftbKSs65uWs9v3WTc/bZv3e/r/32Ff/NOklaG3jJ9uLSWbqladfU/p6VH68GDLfdxHKfIS1audVlGPA74A/AlpImFc7TqZnA8Hxb+xbgaODSoom6o68a1trrWme/yudq8xjQiBZTvaxn+yryDms+q1D7Qcrpkj4pacOGtXLbRdJ84D5gvqT7JO1cOlcnmnhN2a3AyLbHI0nvXaFhoua4EpK+QarbeoC29mCkBWatZPu5XDIyxfZZkuaVDtVfkvYF3gts1Kv13mjgxTKpOiNpA1K9+0hJ4+gprxgNrFksWIckTSH9/3kOuFfSrTSrjvVZSa8nHxaSNIH6D4l+MP/e/kHTQNWlPcBFwHG2Z0Hq/U7a9a+29SPNvCaAEbb/1Hpg+0+Sqn0dDMsWi+N6/A2pPViTbv9K0m7Ah0h1XFD3v8lfAXcBBwDtfTwXA7UOZXg38BFgY6D9QOFiUiu0Wt2Vf78buL5kkAFyIum6tpB0O7A+cHDZSJ2x3YgBQX1Y3FpEAti+TVLtZQhNvCZIHzp3arVEzLvhSwpnCgMgao4rkWsID2n/1Fq7XBbyBeB229+QtDnwt7Xv2kl6Te11071JOsj21NI5wquX64y3Ju32P1L7v0lJI0jdASaSdoxnkQYw1F73fh7pLsz3Sdd1GGl4xlSoszd1E68JUrkIqQNH67zPhsBhtQ41CcsWi+NKSJoKjCXVPDXp9m/j5Fq73v+xniHtVp5R03CQtvZ0faqxPV27Jv2s2kmaRSq5mkX68Fn9rp2kq0h3LC7PT30AWNf2IeVSdU7S9OV82TVOoWziNbUojcFufeh8uPYPnaFvsTiuhKSj+nq+5jZGktYHvgRsR5oACDRiJPFZpMNPV+anDie9kD4DTLS9f6lsK6utPd3WwC70lCDsD8y0/fEiwbqkST+rdvkuzERgD2AC6QP1LNu1lvcg6T7bY1f0XAjdJmmy7WmSDuzr67avWdWZwsCqub5zSKl5EbwcV5DG2+4HHAscRerGUbvdbe/e9ni+pNtt7y7piGKp+sH2aQCSbgZ2au1ASvoaPaPMa9aYn1U7249JWkIah/0CsBfwlrKpOjZP0gTbdwBIGg/cXjhTxyStA3wY2JSlB9FUe1ewgde0JzCNtCnQm4FYHDdMLI4HOUlX2T50Gbd/sV3z6d/X275I0vG2Z5CmD80oHaoLRkkab/tOAEm7AqPy16rsWgGMIS2yWl4gvfHVrok/KyQ9CvyetCN+EfBZ2zVPJgMYD3xY0hP58RjgodZrY8WvhTcCd9BruEnlGnVNtr+aB4D8JLdIDA0Xi+PB7/j8+35FUwyMVq3W05LeRzrksHHBPN3yceBiSaNIt+gXAR/PDeTPLJqs/y4D5kr6IelD2vuB75WN1BVN/FkB/BOprOIDwDjSB8+Zth8tG6sj7ykdYICMsL3c2v4KNe6abL8k6TNALI6HgKg5DsVI2o90YOhNwBRS79zTbDeitVaeDiXbC0tn6QZJO5FqWCHVG1fbk7q3pv2sWvKi/2hSV5iNba9WOFK/5F27+21vXzpLt0n6PPAn4Ecsfdj6D8VCdaiJ1wQg6RRS67YfAM+2nq/9usIrxeJ4kMu9Idt/SMqPRbqVOLpIsLBMkoYDB/HKervTS2XqL0mjbS9a1iSyWt8UJB1h+/JldeNoQBeOc0g7x6NIt7dnkg7kPVY0WAckXQF82fYTK/zLFZH0aeDrwEJ6Xuttu9rhJk28JgBJv6Tv8saqryu8UpRVDHK2X1s6w0DJJ+rPB3Yj1aXNAT5f8xt4dh2p28HdtO2aVOpKUknP3fS8KbSm5NU8nWyt/HtT/3/dAZxl+zelg3TRhsADkuay9K7dAeUidcUJwJa2f186SBc18ZoAtqWPXttFE4UBETvHFZE0lqVva99fMk+nJN0BfJPUKB5SG63P2h5fLlXnJC1o2u1fSZfRs/v4cOk8YcUkHQBMyg9n2L6hZJ5OSdqzr+fzYd5qSboeONz2c6WzdEsTrwle7rW9iNRpCVJN/zq2Dy2XKgyEWBxXQtLxwCfoaRnzfuBC21PKpeqMpDt7L4Ql3WF7QqlM3SDpQmCK7fmls3SLpMn09M3dHJhHWiifXzRYhyS9GfgW8Abb20vaETjA9hmFo3VE0pnAriz9Jn6X7S+XS9U9kvaz/aPSObohH3LdDphOQwY8NfGaIHptDyWxOK6EpPuB3Ww/mx+vBcypsX1RW/3ql0g1af9Gz4jR4bb/rlS2bpD0ILAl8EvSG0OrPry6n1U7SauRBoHsRepLvcT2NmVTdSa3DvwicIHtcfm56nf+8+vFW1vt2/LPbl7t/wZbJN1je6fSObqhoQOeGndNAJIuJY0sb++1fZTt44oGC10XNcf1EGmSV8tf6Kn9rE2rfrWV/5i2rxmoenEM7Fs6QLdJupVUpzuHVGe3i+3flk3VFWvanist9V+p2v7GvawDtA5Mrl0yyACo9bXvFdoXjJJ2sn1PyTzd0MRrypraazv0EovjelwC3JlvVwn4a1Jz/+rY3qx0hoFk+3FJE4GtbF+Sx2SPWtH3DXL3AzsD25MOGy6UNMf2krKxOvZ7SVuQDxtKOhh4umykrjiTNFFuOun1YhLQiJKK7BhInWFs137otd13gEbsiLdp0jU1tdd26CXKKiqS+8xOzA9nNazP7IW2P1k6RzdI+irwNmBr22+W9Ebg6l5jiqvUq2/uBraHF47Ukdwx5ULg7cAfSaUwH7L9eNFgXSBpQ1IZDMBc278umadTki62/dG2x6OA62y/s2CsrpI0r1Xe0xRNvKbQfLFzXB+R2p415rZi9rbSAbro/aSpZPcA2P6VpKpbhuXJUHuQdo8fBy4mlVfU7inSXZnpwOtIJ9GPAqrrSd2H3ehpObUa8MOycTr2lKRv2f6UpHWBHwPfLh2qy04rHWAANPGaQsMNKx0gvDqSTgW+C6wLrAdcIukrZVN1VRPqV1tecLol07pVv9YK/n4NRgLnAtvYfqft02xPKx2qC64D9ieNMv8VaarXs8v9jgpI+hfSocn5wALgGEnfLJuqM7ZPARZJ+lfgZuAc25cUjtUxSVMlvU/SMNvXls7TDU28pjC0RFlFJSQ9BIyz/Xx+PBK4x/ZbyiYLvUn6ArAVsA+p9vOjwJU1t91rqiZ0puiLpAeA7fOHtNb45fm2tyubbOVJOrD9IXAKMBe4M0uVawAACbJJREFUCcD2NX19Xy0k7U0qVZoAXA1cWnsv8SZeUxhaoqyiHv8NjACez4+HA48WS9MBSTfQxwjOltonXtk+W9I+pFv0WwOn2v5Z4Vihb7Ml7dCkntTZI6ST9K3a6TeRDlXWaP9ej+cBr8nPm57e71WyfQtwi6S1Sf2ofybpSVLJyOW2/1w0YD808ZrC0BI7x4OcpCmkN4AxpMM1P8uP9wFus314wXj9sqxJVy21T7wKg1+r9RJpg2Ar4DGa1ZN6Bun1Ym5+ahdSG77noP4PoE0j6fXAEcCRpPKeK0j14jvYfkfBaP3WxGsKQ0csjge5ZTVTb2lAU/WRwBjbj5TO0ilJi+l7R7y14Bq9iiOFZZC0yfK+Xnu3iiZ+AJU0AvgYafLaiNbz7R0saiTpGmAb4DJS+cHTbV+7y3Z1h5WbeE1haInFcShG0v7A2cAatjeT9Fbg9NjVCqFz+QPAVrZvyR9CV7e9uHSu/pJ0NfAw8EFSN5EPAQ/ZPr5osA5JmtyQw60vk/Re2zf2eq5pPalDg8XiuBKS9iNNjtuEdCu4+t1ISXcDk4Gft43uvb/2W9ohlCbpE8AngdfZ3kLSVqSxt9X2BG71y229Rkh6DfBT25NLZ+uUpO2BbVl6R/x75RJ1pq/x3k0a+R2aLw7k1eMfgQNJJ86b8onmRdvP9BrdG0Lo3KeBXYE7AWz/QtJflY3UsdYhroV5MflrYNNycbojDw16B2lxfCNp/PxtQHWLY0kbABsBIyWNo6cf/2hgzWLBQlhJsTiux5PAggYtjAEWSPogsFre2focMLtwphCa4P9sv9D64ClpdZbTIaYSF+bhH6cA15NGsp9aNlJXHAyMBebZPlrSG0gjl2v0buAjwMakvugti4GTSgQKoT+irKISknYhlVXMIJ2qB8D2ucv8pkFO0prAycC78lM/Bc5o9XIOIfSPpLOAhcCHgc8CxwEP2j65aLDwCpLm2t41l5ntRVpILqixJ3WLpINsTy2dI4T+isVxJSTdTJreNZ80PhoA29WP5pS0lu3qp5KFMFjkoR8fI33wFOmD53dqvvOUd1T/Hnij7X0lbQvsZvuiwtE6kqcZngQcDpxIep2/1/bRRYP1g6QjbF8u6UT6uFNR82ZOGFpicVyJJra/kfR20u3DUbbHSBoLHGP7uMLRQmgMSa8DNrZd6xAQACT9BLgEONn22FwqMs/2DoWjdY2kTYHRtf6sJB1j+4JcR92bbZ++ykOF0A9Rc1yPWyS9y/bNpYN00XmkGrXrAWzfJ2lS2Ugh1E/Sz4EDSK/x9wK/kzTD9glFg3VmPdtXSfoygO0XJf2ldKhOSfoeMAuYVfuIZdsX5D9uDhxveyFArhU/p1iwEFbSsNIBwqv2aeAmSUskLZK0WNKi0qE6ZfvJXk9V/2YXwiCwtu1FpA43l9jeGdi7cKZOPZunrhlA0gTgmbKRuuJSYENgiqRHJU2VVHXvZmDH1sIYwPYfgXEF84SwUmLnuBK2X1s6wwB4MpdWWNIapG4VDxXOFEITrC5pQ+BQ0qHXJjiBdJdpc0m3A+uTOj1Uzfa0tnHfewHHkqYAnl80WGeGSVo3L4pbpT2x3gjViH+slZD078DFwE22X1rR36/EsaQ3gI2A/wFuJu2QhxA6cxrpEN5ttv9D0ubALwpn6tSDwA+B50gdHa4F/rNooi6QdCuwFjCHVF6xi+3flk3VsXOA2fl9y6QPaV8vGymEVy8O5FVC0t7A0cAE4GrSvPqq69NCCN0naTXgc7bPK52lmyRdBSwCrshPfQBY1/Yh5VJ1TtJ5wM6kFp23AzOBObaXFA3WodxNZDKpW8qtth8sHCmEVy0Wx5WRtDbpTeFk0mCQbwOX2/7zcr9xEJK0PvAJ0pSrl+9i2P5oqUwhNIGk6bb3Kp2jmyTdZ3vsip6rlaRRpA2QLwAb2B5eOFIIQ1aUVVQkH0Y5AjgSmEfaQZkIHEUaP1qb60i3EW8hDuKF0E2zJf0z8APg5R7itu8pF6lj8yRNsH0HgKTxpJ3Wqkn6DLAHaff4cVL53KyioUIY4mLnuBKSrgG2AS4jlVQ83fa1KnsgS7rX9ltL5wihaSRN7+Np2568ysN0iaSHgK2BJ/JTY0gHeF8iXduOpbJ1QtIXSaUUd9t+sXSeEEIsjqsh6VDSYbxFkr4C7EQatVztTpCkM4DZtm8snSWEMLhJ2mR5X7f9+KrK0m25TvwNLF1e9sSyvyOEMJBicVwJSffb3lHSROBM4GzgJNvjC0dbaZIW0zNadBTpIEprx8S2RxcJFkJD5LMJXwVaQ3VmAKfbbkJf4EbJZRVfA35D2gWHinfCQ2iCWBxXQtI82+MknQnMt31l67nS2fpL0mX0TIaK/sYhdImkqcAC4Lv5qSOBsbYPLJcq9EXSfwHjbf9v6SwhhCQWx5WQ9CPgKdKUq52BJcDcmk9qS5pMOlC4B2nc6DzSQrnm5vchFNdXPX/U+A9OuT58n6g3DmHwiMVxJSStCbyHtGv8izz9agfbNxeO1pFca9c+GWqJ7W3KpgqhbpLmAF+0fVt+vDtwtu3dyiYLLZJOyH/cjnTQ8MekEjMAbJ9bIlcIIVq5VcP2c8A1bY+fBp5e9ncMfg2dDBXCYPAp4Lu59hjgj6SWj2HweG3+/Yn8a438K4RQWOwch2KaOhkqhNIkDQcOBrYA1gGeIR3yOr1osBBCqEAsjkNxMRkqhO6SdBOwELiHtgE7ts8pFir0SdIN9HTvaXkGuAu4wPbzqz5VCENbLI5DMX1MhppJOpA3rWiwEConaYHt7UvnCCsm6XxgfeD7+anDgF8DI4HRto8slS2EoSpqjkNJI4FziclQIXTbbEk72J5fOkhYoXG2J7U9vkHSTNuTJD1QLFUIQ1gsjkMxtv+hdIYQmkTSfNIt+tWBoyU9RqrpFzFYYrBaX9KY1kQ8SWOA9fL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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# if you'd like to see correlation with all the numeric columns then use heatmap\n", + "plt.figure(figsize=(12,8))\n", + "sns.heatmap(df.corr())\n", + "plt.title('Correlation heatmap')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part B: Data Cleaning and Feature Engineering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 7: Load the data stored in `data_2` using `.read_csv()` api.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"../data/data_2.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 205 entries, 0 to 204\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 symboling 205 non-null int64 \n", + " 1 normalized-losses 205 non-null object \n", + " 2 make 205 non-null object \n", + " 3 fuel-type 205 non-null object \n", + " 4 body-style 205 non-null object \n", + " 5 drive-wheels 205 non-null object \n", + " 6 engine-location 205 non-null object \n", + " 7 width 205 non-null float64\n", + " 8 height 205 non-null float64\n", + " 9 engine-type 205 non-null object \n", + " 10 engine-size 205 non-null int64 \n", + " 11 horsepower 205 non-null object \n", + " 12 city-mpg 205 non-null int64 \n", + " 13 highway-mpg 205 non-null int64 \n", + " 14 price 205 non-null int64 \n", + "dtypes: float64(2), int64(5), object(8)\n", + "memory usage: 24.1+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Seems no null values or is it?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingnormalized-lossesmakefuel-typebody-styledrive-wheelsengine-locationwidthheightengine-typeengine-sizehorsepowercity-mpghighway-mpgprice
03?alfa-romerogasconvertiblerwdfront64.148.8dohc130111212713495
13?alfa-romerogasconvertiblerwdfront64.148.8dohc130111212716500
21?alfa-romerogashatchbackrwdfront65.552.4ohcv152154192616500
32164audigassedanfwdfront66.254.3ohc109102243013950
42164audigassedan4wdfront66.454.3ohc136115182217450
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" + ], + "text/plain": [ + " symboling normalized-losses make fuel-type body-style \\\n", + "0 3 ? alfa-romero gas convertible \n", + "1 3 ? alfa-romero gas convertible \n", + "2 1 ? alfa-romero gas hatchback \n", + "3 2 164 audi gas sedan \n", + "4 2 164 audi gas sedan \n", + "\n", + " drive-wheels engine-location width height engine-type engine-size \\\n", + "0 rwd front 64.1 48.8 dohc 130 \n", + "1 rwd front 64.1 48.8 dohc 130 \n", + "2 rwd front 65.5 52.4 ohcv 152 \n", + "3 fwd front 66.2 54.3 ohc 109 \n", + "4 4wd front 66.4 54.3 ohc 136 \n", + "\n", + " horsepower city-mpg highway-mpg price \n", + "0 111 21 27 13495 \n", + "1 111 21 27 16500 \n", + "2 154 19 26 16500 \n", + "3 102 24 30 13950 \n", + "4 115 18 22 17450 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a quick peek in the data shows missing values are actually marked as '?'\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 8: Impute the missing values of the numerical data with mean of the particular column (Make sure you replace \"?\" by \"NaN\" before Imputing).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# replace the \"?\" with special string \"NaN\", because thats how Imputer likes it\n", + "df.replace('?', 'NaN', inplace=True) # this is not np.NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize the Imputer to replace missing values with the mean of the column\n", + "numeric_imp = Imputer(missing_values=\"NaN\", strategy='mean', axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((205,), (205,), (205, 1))" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# remember fit_transform wants a 2D array \n", + "df.horsepower.shape , df['horsepower'].shape, df[['horsepower']].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Lets find the mean i.e. do the fit and then replace missing values with it i.e. transform\n", + "df.horsepower = numeric_imp.fit_transform(df[['horsepower']])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# and do the same for normalized-losses column\n", + "df['normalized-losses'] = numeric_imp.fit_transform(df[['normalized-losses']])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 205 entries, 0 to 204\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 symboling 205 non-null int64 \n", + " 1 normalized-losses 205 non-null float64\n", + " 2 make 205 non-null object \n", + " 3 fuel-type 205 non-null object \n", + " 4 body-style 205 non-null object \n", + " 5 drive-wheels 205 non-null object \n", + " 6 engine-location 205 non-null object \n", + " 7 width 205 non-null float64\n", + " 8 height 205 non-null float64\n", + " 9 engine-type 205 non-null object \n", + " 10 engine-size 205 non-null int64 \n", + " 11 horsepower 205 non-null float64\n", + " 12 city-mpg 205 non-null int64 \n", + " 13 highway-mpg 205 non-null int64 \n", + " 14 price 205 non-null int64 \n", + "dtypes: float64(4), int64(5), object(6)\n", + "memory usage: 24.1+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now, normalized-losses and horsepower are numeric types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 9: Check the skewness of the numeric features and apply square root transformation on features with skewness greater than 1.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingnormalized-lossesmakefuel-typebody-styledrive-wheelsengine-locationwidthheightengine-typeengine-sizehorsepowercity-mpghighway-mpgprice
03122.0alfa-romerogasconvertiblerwdfront64.148.8dohc130111.0212713495
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21122.0alfa-romerogashatchbackrwdfront65.552.4ohcv152154.0192616500
32164.0audigassedanfwdfront66.254.3ohc109102.0243013950
42164.0audigassedan4wdfront66.454.3ohc136115.0182217450
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" + ], + "text/plain": [ + " symboling normalized-losses make fuel-type body-style \\\n", + "0 3 122.0 alfa-romero gas convertible \n", + "1 3 122.0 alfa-romero gas convertible \n", + "2 1 122.0 alfa-romero gas hatchback \n", + "3 2 164.0 audi gas sedan \n", + "4 2 164.0 audi gas sedan \n", + "\n", + " drive-wheels engine-location width height engine-type engine-size \\\n", + "0 rwd front 64.1 48.8 dohc 130 \n", + "1 rwd front 64.1 48.8 dohc 130 \n", + "2 rwd front 65.5 52.4 ohcv 152 \n", + "3 fwd front 66.2 54.3 ohc 109 \n", + "4 4wd front 66.4 54.3 ohc 136 \n", + "\n", + " horsepower city-mpg highway-mpg price \n", + "0 111.0 21 27 13495 \n", + "1 111.0 21 27 16500 \n", + "2 154.0 19 26 16500 \n", + "3 102.0 24 30 13950 \n", + "4 115.0 18 22 17450 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# before sqrt transformation\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "transforming engine-size\n", + "transforming horsepower\n", + "transforming price\n" + ] + } + ], + "source": [ + "# perform sqrt transformation to numeric columns\n", + "numeric_featuers = df.select_dtypes(exclude=['object']).columns\n", + "for feature in numeric_featuers:\n", + " if skew(df[feature]) > 1:\n", + " print('transforming', feature)\n", + " df[feature] = np.sqrt(df[feature])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingnormalized-lossesmakefuel-typebody-styledrive-wheelsengine-locationwidthheightengine-typeengine-sizehorsepowercity-mpghighway-mpgprice
03122.0alfa-romerogasconvertiblerwdfront64.148.8dohc11.40175410.5356542127116.167982
13122.0alfa-romerogasconvertiblerwdfront64.148.8dohc11.40175410.5356542127128.452326
21122.0alfa-romerogashatchbackrwdfront65.552.4ohcv12.32882812.4096741926128.452326
32164.0audigassedanfwdfront66.254.3ohc10.44030710.0995052430118.110118
42164.0audigassedan4wdfront66.454.3ohc11.66190410.7238051822132.098448
\n", + "
" + ], + "text/plain": [ + " symboling normalized-losses make fuel-type body-style \\\n", + "0 3 122.0 alfa-romero gas convertible \n", + "1 3 122.0 alfa-romero gas convertible \n", + "2 1 122.0 alfa-romero gas hatchback \n", + "3 2 164.0 audi gas sedan \n", + "4 2 164.0 audi gas sedan \n", + "\n", + " drive-wheels engine-location width height engine-type engine-size \\\n", + "0 rwd front 64.1 48.8 dohc 11.401754 \n", + "1 rwd front 64.1 48.8 dohc 11.401754 \n", + "2 rwd front 65.5 52.4 ohcv 12.328828 \n", + "3 fwd front 66.2 54.3 ohc 10.440307 \n", + "4 4wd front 66.4 54.3 ohc 11.661904 \n", + "\n", + " horsepower city-mpg highway-mpg price \n", + "0 10.535654 21 27 116.167982 \n", + "1 10.535654 21 27 128.452326 \n", + "2 12.409674 19 26 128.452326 \n", + "3 10.099505 24 30 118.110118 \n", + "4 10.723805 18 22 132.098448 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# after sqrt transformation\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 10: Label Encode the categorical features.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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symbolingnormalized-lossesmakefuel-typebody-styledrive-wheelsengine-locationwidthheightengine-typeengine-sizehorsepowercity-mpghighway-mpgprice
03122.0alfa-romerogasconvertiblerwdfront64.148.8dohc11.40175410.5356542127116.167982
13122.0alfa-romerogasconvertiblerwdfront64.148.8dohc11.40175410.5356542127128.452326
21122.0alfa-romerogashatchbackrwdfront65.552.4ohcv12.32882812.4096741926128.452326
32164.0audigassedanfwdfront66.254.3ohc10.44030710.0995052430118.110118
42164.0audigassedan4wdfront66.454.3ohc11.66190410.7238051822132.098448
\n", + "
" + ], + "text/plain": [ + " symboling normalized-losses make fuel-type body-style \\\n", + "0 3 122.0 alfa-romero gas convertible \n", + "1 3 122.0 alfa-romero gas convertible \n", + "2 1 122.0 alfa-romero gas hatchback \n", + "3 2 164.0 audi gas sedan \n", + "4 2 164.0 audi gas sedan \n", + "\n", + " drive-wheels engine-location width height engine-type engine-size \\\n", + "0 rwd front 64.1 48.8 dohc 11.401754 \n", + "1 rwd front 64.1 48.8 dohc 11.401754 \n", + "2 rwd front 65.5 52.4 ohcv 12.328828 \n", + "3 fwd front 66.2 54.3 ohc 10.440307 \n", + "4 4wd front 66.4 54.3 ohc 11.661904 \n", + "\n", + " horsepower city-mpg highway-mpg price \n", + "0 10.535654 21 27 116.167982 \n", + "1 10.535654 21 27 128.452326 \n", + "2 12.409674 19 26 128.452326 \n", + "3 10.099505 24 30 118.110118 \n", + "4 10.723805 18 22 132.098448 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# before label encoding\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# perform label encoding to categorical columns\n", + "categorical_features = df.select_dtypes(include=['object']).columns\n", + "for feature in categorical_features:\n", + " le = LabelEncoder()\n", + " df[feature] = le.fit_transform(df[feature])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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42164.01130066.454.3311.66190410.7238051822132.098448
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" + ], + "text/plain": [ + " symboling normalized-losses make fuel-type body-style drive-wheels \\\n", + "0 3 122.0 0 1 0 2 \n", + "1 3 122.0 0 1 0 2 \n", + "2 1 122.0 0 1 2 2 \n", + "3 2 164.0 1 1 3 1 \n", + "4 2 164.0 1 1 3 0 \n", + "\n", + " engine-location width height engine-type engine-size horsepower \\\n", + "0 0 64.1 48.8 0 11.401754 10.535654 \n", + "1 0 64.1 48.8 0 11.401754 10.535654 \n", + "2 0 65.5 52.4 5 12.328828 12.409674 \n", + "3 0 66.2 54.3 3 10.440307 10.099505 \n", + "4 0 66.4 54.3 3 11.661904 10.723805 \n", + "\n", + " city-mpg highway-mpg price \n", + "0 21 27 116.167982 \n", + "1 21 27 128.452326 \n", + "2 19 26 128.452326 \n", + "3 24 30 118.110118 \n", + "4 18 22 132.098448 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# after label encoding\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 205 entries, 0 to 204\n", + "Data columns (total 15 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 symboling 205 non-null int64 \n", + " 1 normalized-losses 205 non-null float64\n", + " 2 make 205 non-null int64 \n", + " 3 fuel-type 205 non-null int64 \n", + " 4 body-style 205 non-null int64 \n", + " 5 drive-wheels 205 non-null int64 \n", + " 6 engine-location 205 non-null int64 \n", + " 7 width 205 non-null float64\n", + " 8 height 205 non-null float64\n", + " 9 engine-type 205 non-null int64 \n", + " 10 engine-size 205 non-null float64\n", + " 11 horsepower 205 non-null float64\n", + " 12 city-mpg 205 non-null int64 \n", + " 13 highway-mpg 205 non-null int64 \n", + " 14 price 205 non-null float64\n", + "dtypes: float64(6), int64(9)\n", + "memory usage: 24.1 KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now, all the features are numerical, we are almost ready to do model training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 11: Combine the 'height' and 'width' to make a new feature 'area' of the frame of the car." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 3128.08\n", + "1 3128.08\n", + "2 3432.20\n", + "3 3594.66\n", + "4 3605.52\n", + " ... \n", + "200 3823.95\n", + "201 3818.40\n", + "202 3823.95\n", + "203 3823.95\n", + "204 3823.95\n", + "Name: area, Length: 205, dtype: float64" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Since height and width usually related, let us engineer a new feature called area\n", + "df['area'] = df.height * df.width\n", + "df.area" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Data_cleaning_with_obesity_data/notebook/Data_cleaning_with_obeseity_data-MK.ipynb b/Data_cleaning_with_obesity_data/notebook/Data_cleaning_with_obeseity_data-MK.ipynb new file mode 100644 index 0000000..69312ea --- /dev/null +++ b/Data_cleaning_with_obesity_data/notebook/Data_cleaning_with_obeseity_data-MK.ipynb @@ -0,0 +1,6277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Cleaning with Obeseity data \n", + "\n", + "Data cleaning is such an integral part of data analysis.Unlike on Kaggle,almost all data you see in the real world would be dirty and messy. Some even say data cleaning would take 80% of data analysis time.\n", + "\n", + "The very fisrt step of any given data analysis project would be getting to know your data especially when you are dealing a messy one.\n", + "\n", + "So, lets clean this messy data to start our analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read and Know Your Data\n", + "\n", + "Take a look at our data as below, the data is quite obscure,it's hard to understand for a human,not mentioned for a computer.in this kind of situation,you have ways to get acquaintance with your data as follows:\n", + "\n", + "1. Go to the data source page [WHO OBESITY DATA](https://apps.who.int/gho/data/node.main.A900A?lang=en)\n", + "2. If solution 1 doesn not work or hard to do, you can always go to ask data curator directly." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 020162016.12016.220152015.12015.220142014.12014.2...1978.219771977.11977.219761976.11976.219751975.11975.2
0NaNPrevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great......Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...Prevalence of obesity among adults, BMI &Great...
1NaN18+ years18+ years18+ years18+ years18+ years18+ years18+ years18+ years18+ years...18+ years18+ years18+ years18+ years18+ years18+ years18+ years18+ years18+ years18+ years
2CountryBoth sexesMaleFemaleBoth sexesMaleFemaleBoth sexesMaleFemale...FemaleBoth sexesMaleFemaleBoth sexesMaleFemaleBoth sexesMaleFemale
3Afghanistan5.5 [3.4-8.1]3.2 [1.3-6.4]7.6 [4.3-12.4]5.2 [3.3-7.7]3.0 [1.3-6.0]7.3 [4.1-11.8]4.9 [3.1-7.3]2.8 [1.2-5.6]7.0 [4.0-11.3]...0.9 [0.3-2.2]0.6 [0.2-1.2]0.2 [0.0-0.7]0.9 [0.3-2.1]0.5 [0.2-1.1]0.2 [0.0-0.7]0.8 [0.2-2.0]0.5 [0.2-1.1]0.2 [0.0-0.6]0.8 [0.2-2.0]
4Albania21.7 [17.0-26.7]21.6 [14.8-29.0]21.8 [15.3-28.9]21.1 [16.6-26.0]20.9 [14.4-28.1]21.3 [15.1-28.1]20.5 [16.2-25.1]20.2 [13.9-27.3]20.8 [14.9-27.4]...9.1 [4.6-15.5]6.8 [4.0-10.7]4.8 [2.0-9.3]8.9 [4.3-15.4]6.7 [3.8-10.6]4.6 [1.8-9.2]8.8 [4.1-15.4]6.5 [3.6-10.5]4.4 [1.7-9.2]8.6 [3.9-15.4]
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" + ], + "text/plain": [ + " Unnamed: 0 2016 \\\n", + "0 NaN Prevalence of obesity among adults, BMI &Great... \n", + "1 NaN 18+ years \n", + "2 Country Both sexes \n", + "3 Afghanistan 5.5 [3.4-8.1] \n", + "4 Albania 21.7 [17.0-26.7] \n", + "\n", + " 2016.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 3.2 [1.3-6.4] \n", + "4 21.6 [14.8-29.0] \n", + "\n", + " 2016.2 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 7.6 [4.3-12.4] \n", + "4 21.8 [15.3-28.9] \n", + "\n", + " 2015 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Both sexes \n", + "3 5.2 [3.3-7.7] \n", + "4 21.1 [16.6-26.0] \n", + "\n", + " 2015.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 3.0 [1.3-6.0] \n", + "4 20.9 [14.4-28.1] \n", + "\n", + " 2015.2 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 7.3 [4.1-11.8] \n", + "4 21.3 [15.1-28.1] \n", + "\n", + " 2014 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Both sexes \n", + "3 4.9 [3.1-7.3] \n", + "4 20.5 [16.2-25.1] \n", + "\n", + " 2014.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 2.8 [1.2-5.6] \n", + "4 20.2 [13.9-27.3] \n", + "\n", + " 2014.2 ... \\\n", + "0 Prevalence of obesity among adults, BMI &Great... ... \n", + "1 18+ years ... \n", + "2 Female ... \n", + "3 7.0 [4.0-11.3] ... \n", + "4 20.8 [14.9-27.4] ... \n", + "\n", + " 1978.2 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 0.9 [0.3-2.2] \n", + "4 9.1 [4.6-15.5] \n", + "\n", + " 1977 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Both sexes \n", + "3 0.6 [0.2-1.2] \n", + "4 6.8 [4.0-10.7] \n", + "\n", + " 1977.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 0.2 [0.0-0.7] \n", + "4 4.8 [2.0-9.3] \n", + "\n", + " 1977.2 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 0.9 [0.3-2.1] \n", + "4 8.9 [4.3-15.4] \n", + "\n", + " 1976 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Both sexes \n", + "3 0.5 [0.2-1.1] \n", + "4 6.7 [3.8-10.6] \n", + "\n", + " 1976.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 0.2 [0.0-0.7] \n", + "4 4.6 [1.8-9.2] \n", + "\n", + " 1976.2 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 0.8 [0.2-2.0] \n", + "4 8.8 [4.1-15.4] \n", + "\n", + " 1975 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Both sexes \n", + "3 0.5 [0.2-1.1] \n", + "4 6.5 [3.6-10.5] \n", + "\n", + " 1975.1 \\\n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Male \n", + "3 0.2 [0.0-0.6] \n", + "4 4.4 [1.7-9.2] \n", + "\n", + " 1975.2 \n", + "0 Prevalence of obesity among adults, BMI &Great... \n", + "1 18+ years \n", + "2 Female \n", + "3 0.8 [0.2-2.0] \n", + "4 8.6 [3.9-15.4] \n", + "\n", + "[5 rows x 127 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('../data/data.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Why and how the data is messy?\n", + "\n", + "In most cases, data is collected by human or machines,a tiny glich would cause a long strip of bad data.if the data is collected by human, then it is a big chance that it would be messy. data can be dirty in many different ways,but mostly fall into those categories :\n", + "\n", + "1. Missing data : like NAN\n", + "2. Validity of data : like 2016.1 / 2016.2 in the column\n", + "3. Outliers : like if a BMI entry is greater than 100\n", + "4. Consistency of data : the unit of every entry is not the same\n", + "5. Correctness of data: we are not gonna go through this ,but it is an important part of doing analysis in bussiness world, basically you need external data source or database to cross check the data in your hand because as we always say:\n", + "\n", + "You dont know what you dont know\n", + "\n", + "6. Data is in wide form not in long form : we are gonna go deeper about this one.\n", + "\n", + "Are you ready, it's time to get our hands dirt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## long form VS wide form\n", + "\n", + "The original data we have here is in wide form which means the form is very wide literally.\n", + "\n", + "The .1 .2 in year number stand for gender, we gonna fix that later.\n", + "\n", + "Wide data is not easy to analyze or stored effectively in computer, so we want to change it as soon as we can. go to read this tidy-data if you want to know more.\n", + "\n", + "![img](https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rename the columns appropriately and unpivot the data in the desirable format using pandas melt()." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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3Afghanistan5.5 [3.4-8.1]3.2 [1.3-6.4]7.6 [4.3-12.4]5.2 [3.3-7.7]3.0 [1.3-6.0]7.3 [4.1-11.8]4.9 [3.1-7.3]2.8 [1.2-5.6]7.0 [4.0-11.3]...0.9 [0.3-2.2]0.6 [0.2-1.2]0.2 [0.0-0.7]0.9 [0.3-2.1]0.5 [0.2-1.1]0.2 [0.0-0.7]0.8 [0.2-2.0]0.5 [0.2-1.1]0.2 [0.0-0.6]0.8 [0.2-2.0]
4Albania21.7 [17.0-26.7]21.6 [14.8-29.0]21.8 [15.3-28.9]21.1 [16.6-26.0]20.9 [14.4-28.1]21.3 [15.1-28.1]20.5 [16.2-25.1]20.2 [13.9-27.3]20.8 [14.9-27.4]...9.1 [4.6-15.5]6.8 [4.0-10.7]4.8 [2.0-9.3]8.9 [4.3-15.4]6.7 [3.8-10.6]4.6 [1.8-9.2]8.8 [4.1-15.4]6.5 [3.6-10.5]4.4 [1.7-9.2]8.6 [3.9-15.4]
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6Andorra25.6 [20.1-31.3]25.9 [18.0-34.3]25.3 [17.7-33.7]25.4 [20.1-31.0]25.5 [17.8-33.8]25.2 [17.7-33.4]25.2 [20.0-30.7]25.2 [17.6-33.3]25.1 [17.8-33.1]...17.5 [10.9-25.3]14.0 [9.6-19.1]10.7 [5.6-17.4]16.9 [10.4-24.8]13.5 [9.1-18.6]10.2 [5.2-16.9]16.4 [9.8-24.4]12.9 [8.6-18.1]9.7 [4.7-16.3]15.8 [9.2-23.9]
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5 rows × 127 columns

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" + ], + "text/plain": [ + " Unnamed: 0 2016 2016.1 2016.2 \\\n", + "3 Afghanistan 5.5 [3.4-8.1] 3.2 [1.3-6.4] 7.6 [4.3-12.4] \n", + "4 Albania 21.7 [17.0-26.7] 21.6 [14.8-29.0] 21.8 [15.3-28.9] \n", + "5 Algeria 27.4 [22.5-32.7] 19.9 [13.6-27.1] 34.9 [27.6-42.7] \n", + "6 Andorra 25.6 [20.1-31.3] 25.9 [18.0-34.3] 25.3 [17.7-33.7] \n", + "7 Angola 8.2 [5.1-12.2] 4.0 [1.6-7.9] 12.1 [6.8-19.0] \n", + "\n", + " 2015 2015.1 2015.2 2014 \\\n", + "3 5.2 [3.3-7.7] 3.0 [1.3-6.0] 7.3 [4.1-11.8] 4.9 [3.1-7.3] \n", + "4 21.1 [16.6-26.0] 20.9 [14.4-28.1] 21.3 [15.1-28.1] 20.5 [16.2-25.1] \n", + "5 26.7 [21.9-31.8] 19.2 [13.2-26.1] 34.2 [27.1-41.7] 26.0 [21.4-30.9] \n", + "6 25.4 [20.1-31.0] 25.5 [17.8-33.8] 25.2 [17.7-33.4] 25.2 [20.0-30.7] \n", + "7 7.9 [4.9-11.7] 3.8 [1.5-7.3] 11.6 [6.5-18.2] 7.5 [4.7-11.2] \n", + "\n", + " 2014.1 2014.2 ... 1978.2 1977 \\\n", + "3 2.8 [1.2-5.6] 7.0 [4.0-11.3] ... 0.9 [0.3-2.2] 0.6 [0.2-1.2] \n", + "4 20.2 [13.9-27.3] 20.8 [14.9-27.4] ... 9.1 [4.6-15.5] 6.8 [4.0-10.7] \n", + "5 18.5 [12.7-25.0] 33.6 [26.7-40.7] ... 11.8 [6.5-18.6] 7.4 [4.3-11.3] \n", + "6 25.2 [17.6-33.3] 25.1 [17.8-33.1] ... 17.5 [10.9-25.3] 14.0 [9.6-19.1] \n", + "7 3.6 [1.4-6.9] 11.1 [6.2-17.5] ... 1.6 [0.5-3.7] 0.9 [0.3-2.0] \n", + "\n", + " 1977.1 1977.2 1976 1976.1 \\\n", + "3 0.2 [0.0-0.7] 0.9 [0.3-2.1] 0.5 [0.2-1.1] 0.2 [0.0-0.7] \n", + "4 4.8 [2.0-9.3] 8.9 [4.3-15.4] 6.7 [3.8-10.6] 4.6 [1.8-9.2] \n", + "5 3.1 [1.2-6.2] 11.4 [6.2-18.4] 7.2 [4.1-11.1] 2.9 [1.1-6.1] \n", + "6 10.7 [5.6-17.4] 16.9 [10.4-24.8] 13.5 [9.1-18.6] 10.2 [5.2-16.9] \n", + "7 0.3 [0.0-0.9] 1.5 [0.4-3.6] 0.9 [0.3-2.0] 0.3 [0.0-0.9] \n", + "\n", + " 1976.2 1975 1975.1 1975.2 \n", + "3 0.8 [0.2-2.0] 0.5 [0.2-1.1] 0.2 [0.0-0.6] 0.8 [0.2-2.0] \n", + "4 8.8 [4.1-15.4] 6.5 [3.6-10.5] 4.4 [1.7-9.2] 8.6 [3.9-15.4] \n", + "5 11.1 [5.8-18.2] 6.9 [3.9-10.9] 2.8 [1.0-6.0] 10.7 [5.5-18.0] \n", + "6 16.4 [9.8-24.4] 12.9 [8.6-18.1] 9.7 [4.7-16.3] 15.8 [9.2-23.9] \n", + "7 1.4 [0.4-3.5] 0.8 [0.3-1.9] 0.2 [0.0-0.8] 1.4 [0.4-3.4] \n", + "\n", + "[5 rows x 127 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = data.copy()\n", + "df.drop([0,1,2], inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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country20162016.12016.220152015.12015.220142014.12014.2...1978.219771977.11977.219761976.11976.219751975.11975.2
3Afghanistan5.5 [3.4-8.1]3.2 [1.3-6.4]7.6 [4.3-12.4]5.2 [3.3-7.7]3.0 [1.3-6.0]7.3 [4.1-11.8]4.9 [3.1-7.3]2.8 [1.2-5.6]7.0 [4.0-11.3]...0.9 [0.3-2.2]0.6 [0.2-1.2]0.2 [0.0-0.7]0.9 [0.3-2.1]0.5 [0.2-1.1]0.2 [0.0-0.7]0.8 [0.2-2.0]0.5 [0.2-1.1]0.2 [0.0-0.6]0.8 [0.2-2.0]
4Albania21.7 [17.0-26.7]21.6 [14.8-29.0]21.8 [15.3-28.9]21.1 [16.6-26.0]20.9 [14.4-28.1]21.3 [15.1-28.1]20.5 [16.2-25.1]20.2 [13.9-27.3]20.8 [14.9-27.4]...9.1 [4.6-15.5]6.8 [4.0-10.7]4.8 [2.0-9.3]8.9 [4.3-15.4]6.7 [3.8-10.6]4.6 [1.8-9.2]8.8 [4.1-15.4]6.5 [3.6-10.5]4.4 [1.7-9.2]8.6 [3.9-15.4]
5Algeria27.4 [22.5-32.7]19.9 [13.6-27.1]34.9 [27.6-42.7]26.7 [21.9-31.8]19.2 [13.2-26.1]34.2 [27.1-41.7]26.0 [21.4-30.9]18.5 [12.7-25.0]33.6 [26.7-40.7]...11.8 [6.5-18.6]7.4 [4.3-11.3]3.1 [1.2-6.2]11.4 [6.2-18.4]7.2 [4.1-11.1]2.9 [1.1-6.1]11.1 [5.8-18.2]6.9 [3.9-10.9]2.8 [1.0-6.0]10.7 [5.5-18.0]
6Andorra25.6 [20.1-31.3]25.9 [18.0-34.3]25.3 [17.7-33.7]25.4 [20.1-31.0]25.5 [17.8-33.8]25.2 [17.7-33.4]25.2 [20.0-30.7]25.2 [17.6-33.3]25.1 [17.8-33.1]...17.5 [10.9-25.3]14.0 [9.6-19.1]10.7 [5.6-17.4]16.9 [10.4-24.8]13.5 [9.1-18.6]10.2 [5.2-16.9]16.4 [9.8-24.4]12.9 [8.6-18.1]9.7 [4.7-16.3]15.8 [9.2-23.9]
7Angola8.2 [5.1-12.2]4.0 [1.6-7.9]12.1 [6.8-19.0]7.9 [4.9-11.7]3.8 [1.5-7.3]11.6 [6.5-18.2]7.5 [4.7-11.2]3.6 [1.4-6.9]11.1 [6.2-17.5]...1.6 [0.5-3.7]0.9 [0.3-2.0]0.3 [0.0-0.9]1.5 [0.4-3.6]0.9 [0.3-2.0]0.3 [0.0-0.9]1.4 [0.4-3.5]0.8 [0.3-1.9]0.2 [0.0-0.8]1.4 [0.4-3.4]
\n", + "

5 rows × 127 columns

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" + ], + "text/plain": [ + " country 2016 2016.1 2016.2 \\\n", + "3 Afghanistan 5.5 [3.4-8.1] 3.2 [1.3-6.4] 7.6 [4.3-12.4] \n", + "4 Albania 21.7 [17.0-26.7] 21.6 [14.8-29.0] 21.8 [15.3-28.9] \n", + "5 Algeria 27.4 [22.5-32.7] 19.9 [13.6-27.1] 34.9 [27.6-42.7] \n", + "6 Andorra 25.6 [20.1-31.3] 25.9 [18.0-34.3] 25.3 [17.7-33.7] \n", + "7 Angola 8.2 [5.1-12.2] 4.0 [1.6-7.9] 12.1 [6.8-19.0] \n", + "\n", + " 2015 2015.1 2015.2 2014 \\\n", + "3 5.2 [3.3-7.7] 3.0 [1.3-6.0] 7.3 [4.1-11.8] 4.9 [3.1-7.3] \n", + "4 21.1 [16.6-26.0] 20.9 [14.4-28.1] 21.3 [15.1-28.1] 20.5 [16.2-25.1] \n", + "5 26.7 [21.9-31.8] 19.2 [13.2-26.1] 34.2 [27.1-41.7] 26.0 [21.4-30.9] \n", + "6 25.4 [20.1-31.0] 25.5 [17.8-33.8] 25.2 [17.7-33.4] 25.2 [20.0-30.7] \n", + "7 7.9 [4.9-11.7] 3.8 [1.5-7.3] 11.6 [6.5-18.2] 7.5 [4.7-11.2] \n", + "\n", + " 2014.1 2014.2 ... 1978.2 1977 \\\n", + "3 2.8 [1.2-5.6] 7.0 [4.0-11.3] ... 0.9 [0.3-2.2] 0.6 [0.2-1.2] \n", + "4 20.2 [13.9-27.3] 20.8 [14.9-27.4] ... 9.1 [4.6-15.5] 6.8 [4.0-10.7] \n", + "5 18.5 [12.7-25.0] 33.6 [26.7-40.7] ... 11.8 [6.5-18.6] 7.4 [4.3-11.3] \n", + "6 25.2 [17.6-33.3] 25.1 [17.8-33.1] ... 17.5 [10.9-25.3] 14.0 [9.6-19.1] \n", + "7 3.6 [1.4-6.9] 11.1 [6.2-17.5] ... 1.6 [0.5-3.7] 0.9 [0.3-2.0] \n", + "\n", + " 1977.1 1977.2 1976 1976.1 \\\n", + "3 0.2 [0.0-0.7] 0.9 [0.3-2.1] 0.5 [0.2-1.1] 0.2 [0.0-0.7] \n", + "4 4.8 [2.0-9.3] 8.9 [4.3-15.4] 6.7 [3.8-10.6] 4.6 [1.8-9.2] \n", + "5 3.1 [1.2-6.2] 11.4 [6.2-18.4] 7.2 [4.1-11.1] 2.9 [1.1-6.1] \n", + "6 10.7 [5.6-17.4] 16.9 [10.4-24.8] 13.5 [9.1-18.6] 10.2 [5.2-16.9] \n", + "7 0.3 [0.0-0.9] 1.5 [0.4-3.6] 0.9 [0.3-2.0] 0.3 [0.0-0.9] \n", + "\n", + " 1976.2 1975 1975.1 1975.2 \n", + "3 0.8 [0.2-2.0] 0.5 [0.2-1.1] 0.2 [0.0-0.6] 0.8 [0.2-2.0] \n", + "4 8.8 [4.1-15.4] 6.5 [3.6-10.5] 4.4 [1.7-9.2] 8.6 [3.9-15.4] \n", + "5 11.1 [5.8-18.2] 6.9 [3.9-10.9] 2.8 [1.0-6.0] 10.7 [5.5-18.0] \n", + "6 16.4 [9.8-24.4] 12.9 [8.6-18.1] 9.7 [4.7-16.3] 15.8 [9.2-23.9] \n", + "7 1.4 [0.4-3.5] 0.8 [0.3-1.9] 0.2 [0.0-0.8] 1.4 [0.4-3.4] \n", + "\n", + "[5 rows x 127 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.rename(columns={'Unnamed: 0':'country'}, inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvalue
0Afghanistan20165.5 [3.4-8.1]
1Albania201621.7 [17.0-26.7]
2Algeria201627.4 [22.5-32.7]
3Andorra201625.6 [20.1-31.3]
4Angola20168.2 [5.1-12.2]
\n", + "
" + ], + "text/plain": [ + " country year value\n", + "0 Afghanistan 2016 5.5 [3.4-8.1]\n", + "1 Albania 2016 21.7 [17.0-26.7]\n", + "2 Algeria 2016 27.4 [22.5-32.7]\n", + "3 Andorra 2016 25.6 [20.1-31.3]\n", + "4 Angola 2016 8.2 [5.1-12.2]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.melt(id_vars=['country'], var_name='year')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvalue
81India20163.9 [3.0-5.0]
279India2016.12.7 [1.7-4.0]
477India2016.25.1 [3.6-6.9]
675India20153.7 [2.9-4.7]
873India2015.12.6 [1.7-3.7]
1071India2015.24.8 [3.5-6.5]
1269India20143.5 [2.7-4.4]
1467India2014.12.4 [1.6-3.4]
1665India2014.24.6 [3.3-6.1]
1863India20133.3 [2.6-4.1]
2061India2013.12.3 [1.5-3.2]
2259India2013.24.4 [3.2-5.7]
2457India20123.1 [2.5-3.8]
2655India2012.12.1 [1.5-2.9]
2853India2012.24.2 [3.1-5.4]
3051India20113.0 [2.4-3.6]
3249India2011.12.0 [1.4-2.7]
3447India2011.24.0 [3.0-5.1]
3645India20102.8 [2.3-3.4]
3843India2010.11.9 [1.3-2.5]
\n", + "
" + ], + "text/plain": [ + " country year value\n", + "81 India 2016 3.9 [3.0-5.0]\n", + "279 India 2016.1 2.7 [1.7-4.0]\n", + "477 India 2016.2 5.1 [3.6-6.9]\n", + "675 India 2015 3.7 [2.9-4.7]\n", + "873 India 2015.1 2.6 [1.7-3.7]\n", + "1071 India 2015.2 4.8 [3.5-6.5]\n", + "1269 India 2014 3.5 [2.7-4.4]\n", + "1467 India 2014.1 2.4 [1.6-3.4]\n", + "1665 India 2014.2 4.6 [3.3-6.1]\n", + "1863 India 2013 3.3 [2.6-4.1]\n", + "2061 India 2013.1 2.3 [1.5-3.2]\n", + "2259 India 2013.2 4.4 [3.2-5.7]\n", + "2457 India 2012 3.1 [2.5-3.8]\n", + "2655 India 2012.1 2.1 [1.5-2.9]\n", + "2853 India 2012.2 4.2 [3.1-5.4]\n", + "3051 India 2011 3.0 [2.4-3.6]\n", + "3249 India 2011.1 2.0 [1.4-2.7]\n", + "3447 India 2011.2 4.0 [3.0-5.1]\n", + "3645 India 2010 2.8 [2.3-3.4]\n", + "3843 India 2010.1 1.9 [1.3-2.5]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.country=='India'].head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Correct the format\n", + "\n", + "1. we will drop the first 3 row since its actually headers in the original forms.\n", + "2. correct year value\n", + "3. correct the gender value" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country object\n", + "year object\n", + "value object\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2015', '2']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[1000].year.split('.')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvaluegender
0Afghanistan20165.5 [3.4-8.1]None
1Albania201621.7 [17.0-26.7]None
2Algeria201627.4 [22.5-32.7]None
3Andorra201625.6 [20.1-31.3]None
4Angola20168.2 [5.1-12.2]None
\n", + "
" + ], + "text/plain": [ + " country year value gender\n", + "0 Afghanistan 2016 5.5 [3.4-8.1] None\n", + "1 Albania 2016 21.7 [17.0-26.7] None\n", + "2 Algeria 2016 27.4 [22.5-32.7] None\n", + "3 Andorra 2016 25.6 [20.1-31.3] None\n", + "4 Angola 2016 8.2 [5.1-12.2] None" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['year','gender']] = df.year.str.split('.', expand=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([None, '1', '2'], dtype=object)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.gender.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvaluegender
0Afghanistan20165.5 [3.4-8.1]Both
1Albania201621.7 [17.0-26.7]Both
2Algeria201627.4 [22.5-32.7]Both
3Andorra201625.6 [20.1-31.3]Both
4Angola20168.2 [5.1-12.2]Both
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" + ], + "text/plain": [ + " country year value gender\n", + "0 Afghanistan 2016 5.5 [3.4-8.1] Both\n", + "1 Albania 2016 21.7 [17.0-26.7] Both\n", + "2 Algeria 2016 27.4 [22.5-32.7] Both\n", + "3 Andorra 2016 25.6 [20.1-31.3] Both\n", + "4 Angola 2016 8.2 [5.1-12.2] Both" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['gender'] = df.gender.map({None:'Both', '1': 'Male', '2':'Female'})\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country Angola\n", + "year 2016\n", + "value 8.2 [5.1-12.2]\n", + "gender Both\n", + "Name: 4, dtype: object" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## correct the BMI value columns\n", + "\n", + "From the webpage of WHO we can know that the values in [] are actually estimation intervel，so we need to seperate them into 3 columns\n", + "\n", + "you can use str.matach() or str.findall() with regular expression to extract float number in this field,but we are gonna use str.split()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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012345
05.53.48.1NoneNoneNone
121.717.026.7NoneNoneNone
227.422.532.7NoneNoneNone
325.620.131.3NoneNoneNone
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.....................
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countryyearvaluegender
112Monaco2016No dataBoth
149San Marino2016No dataBoth
162South Sudan2016No dataBoth
165Sudan2016No dataBoth
307Monaco2016No dataMale
...............
24487Monaco1975No dataFemale
24524San Marino1975No dataFemale
24537South Sudan1975No dataFemale
24540Sudan1975No dataFemale
24541Sudan (former)19751.7 [0.5-4.1] 1.8 [0.6-4.1]Female
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" + ], + "text/plain": [ + " country year value gender\n", + "112 Monaco 2016 No data Both\n", + "149 San Marino 2016 No data Both\n", + "162 South Sudan 2016 No data Both\n", + "165 Sudan 2016 No data Both\n", + "307 Monaco 2016 No data Male\n", + "... ... ... ... ...\n", + "24487 Monaco 1975 No data Female\n", + "24524 San Marino 1975 No data Female\n", + "24537 South Sudan 1975 No data Female\n", + "24540 Sudan 1975 No data Female\n", + "24541 Sudan (former) 1975 1.7 [0.5-4.1] 1.8 [0.6-4.1] Female\n", + "\n", + "[584 rows x 4 columns]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.value.str.findall('\\d+\\.\\d+').apply(len).ne(3)]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvaluegenderBMIBMI_lowerBMI_upper
0Afghanistan20165.5 [3.4-8.1]Both5.53.48.1
1Albania201621.7 [17.0-26.7]Both21.717.026.7
2Algeria201627.4 [22.5-32.7]Both27.422.532.7
3Andorra201625.6 [20.1-31.3]Both25.620.131.3
4Angola20168.2 [5.1-12.2]Both8.25.112.2
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" + ], + "text/plain": [ + " country year value gender BMI BMI_lower BMI_upper\n", + "0 Afghanistan 2016 5.5 [3.4-8.1] Both 5.5 3.4 8.1\n", + "1 Albania 2016 21.7 [17.0-26.7] Both 21.7 17.0 26.7\n", + "2 Algeria 2016 27.4 [22.5-32.7] Both 27.4 22.5 32.7\n", + "3 Andorra 2016 25.6 [20.1-31.3] Both 25.6 20.1 31.3\n", + "4 Angola 2016 8.2 [5.1-12.2] Both 8.2 5.1 12.2" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['BMI','BMI_lower','BMI_upper']] = pd.DataFrame(df.value.str.findall('\\d+\\.\\d+').tolist()).drop(columns=[3,4,5])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(24570, 7)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check validity of all columns or fields\n", + "We now have a pretty clean data compared to the one we just got. but our job is still not done yet. we need to go through every columns or fields to make sure the data is reletively correct.\n", + "\n", + "**Country columns**\n", + "\n", + "### What we know：\n", + "\n", + "There is a country named country which need to be fixed\n", + "\n", + "There are Nones in country column which need to be fixed\n", + "\n", + "We have\n", + "\n", + "### What we do：\n", + "\n", + "We gonna drop those entries." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country 0\n", + "year 0\n", + "value 0\n", + "gender 0\n", + "BMI 504\n", + "BMI_lower 504\n", + "BMI_upper 504\n", + "dtype: int64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyearvaluegenderBMIBMI_lowerBMI_upper
112Monaco2016No dataBothNoneNoneNone
149San Marino2016No dataBothNoneNoneNone
162South Sudan2016No dataBothNoneNoneNone
165Sudan2016No dataBothNoneNoneNone
307Monaco2016No dataMaleNoneNoneNone
........................
24345Sudan1975No dataMaleNoneNoneNone
24487Monaco1975No dataFemaleNoneNoneNone
24524San Marino1975No dataFemaleNoneNoneNone
24537South Sudan1975No dataFemaleNoneNoneNone
24540Sudan1975No dataFemaleNoneNoneNone
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" + ], + "text/plain": [ + " country year value gender BMI BMI_lower BMI_upper\n", + "112 Monaco 2016 No data Both None None None\n", + "149 San Marino 2016 No data Both None None None\n", + "162 South Sudan 2016 No data Both None None None\n", + "165 Sudan 2016 No data Both None None None\n", + "307 Monaco 2016 No data Male None None None\n", + "... ... ... ... ... ... ... ...\n", + "24345 Sudan 1975 No data Male None None None\n", + "24487 Monaco 1975 No data Female None None None\n", + "24524 San Marino 1975 No data Female None None None\n", + "24537 South Sudan 1975 No data Female None None None\n", + "24540 Sudan 1975 No data Female None None None\n", + "\n", + "[504 rows x 7 columns]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.value=='No data']" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Monaco 126\n", + "San Marino 126\n", + "South Sudan 126\n", + "Sudan 126\n", + "Name: country, dtype: int64" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.value=='No data'].country.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "504" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "126*4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you missed dropping top 3 rows before melting\n", + "```python \n", + "df.dropna(subset=['country'], inplace=True)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BMI \\ BMI_upper_esti and BMI_lower_esti columns\n", + "\n", + "### What we know：\n", + "\n", + "1. 4 contries have no BMI data which are Monaca,Sudan,South Sudan and San Marino,hence they dont have estimations.\n", + "2. We have 191 countries that do have BMI data and each of them has 126 entries.\n", + "3. The descriptive statistics of BMI data seems OK, no outliers.\n", + "\n", + "### What we do：\n", + "\n", + "1. We gonna create a new dataframe without those 4 countries to analyze.\n", + "2. We gonna change the data type of BMI and estimations to float." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country 0\n", + "year 0\n", + "value 0\n", + "gender 0\n", + "BMI 504\n", + "BMI_lower 504\n", + "BMI_upper 504\n", + "dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country 0\n", + "year 0\n", + "value 0\n", + "gender 0\n", + "BMI 0\n", + "BMI_lower 0\n", + "BMI_upper 0\n", + "dtype: int64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dropna(inplace=True)\n", + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(24066, 7)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country object\n", + "year object\n", + "value object\n", + "gender object\n", + "BMI object\n", + "BMI_lower object\n", + "BMI_upper object\n", + "dtype: object" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country object\n", + "year int32\n", + "gender object\n", + "BMI float64\n", + "BMI_lower float64\n", + "BMI_upper float64\n", + "dtype: object" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(columns=['value'], inplace=True)\n", + "df['year'] = df.year.astype('int')\n", + "df[['BMI','BMI_lower','BMI_upper']] = df[['BMI','BMI_lower','BMI_upper']].astype('float')\n", + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(24066, 6)" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countryyeargenderBMIBMI_lowerBMI_upper
0Afghanistan2016Both5.53.48.1
1Albania2016Both21.717.026.7
2Algeria2016Both27.422.532.7
3Andorra2016Both25.620.131.3
4Angola2016Both8.25.112.2
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" + ], + "text/plain": [ + " country year gender BMI BMI_lower BMI_upper\n", + "0 Afghanistan 2016 Both 5.5 3.4 8.1\n", + "1 Albania 2016 Both 21.7 17.0 26.7\n", + "2 Algeria 2016 Both 27.4 22.5 32.7\n", + "3 Andorra 2016 Both 25.6 20.1 31.3\n", + "4 Angola 2016 Both 8.2 5.1 12.2" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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yearBMIBMI_lowerBMI_upper
count24066.0000024066.00000024066.00000024066.000000
mean1995.5000012.4489329.23724316.232112
std12.1211710.4074288.85428112.003078
min1975.000000.1000000.0000000.200000
25%1985.000003.9000002.2000006.300000
50%1995.5000010.6000007.00000014.800000
75%2006.0000018.17500013.80000023.000000
max2016.0000063.30000055.60000070.800000
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" + ], + "text/plain": [ + " year BMI BMI_lower BMI_upper\n", + "count 24066.00000 24066.000000 24066.000000 24066.000000\n", + "mean 1995.50000 12.448932 9.237243 16.232112\n", + "std 12.12117 10.407428 8.854281 12.003078\n", + "min 1975.00000 0.100000 0.000000 0.200000\n", + "25% 1985.00000 3.900000 2.200000 6.300000\n", + "50% 1995.50000 10.600000 7.000000 14.800000\n", + "75% 2006.00000 18.175000 13.800000 23.000000\n", + "max 2016.00000 63.300000 55.600000 70.800000" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity (15 minutes)\n", + "\n", + "## Visualization and EDA\n", + "Before you doing any EDA, come up with some questions first. Question orientated is always a good way to explore a set of data, you could easily fall into rabbit holes you enconter along the process otherwise.\n", + "\n", + "What question we could possibly answer through this data?" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.loc[(df.gender=='Female') & (df.country=='India'),['year','BMI']].plot.line(x='year',y='BMI', title='Indian females avg. BMI over the last 42 years')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## list of top 10 countries having obesity (BMI ≥ 30) issues" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
countryyeargenderBMIBMI_lowerBMI_upper
0Afghanistan2016Both5.53.48.1
1Albania2016Both21.717.026.7
2Algeria2016Both27.422.532.7
3Andorra2016Both25.620.131.3
4Angola2016Both8.25.112.2
\n", + "
" + ], + "text/plain": [ + " country year gender BMI BMI_lower BMI_upper\n", + "0 Afghanistan 2016 Both 5.5 3.4 8.1\n", + "1 Albania 2016 Both 21.7 17.0 26.7\n", + "2 Algeria 2016 Both 27.4 22.5 32.7\n", + "3 Andorra 2016 Both 25.6 20.1 31.3\n", + "4 Angola 2016 Both 8.2 5.1 12.2" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country\n", + "Nauru 53.042857\n", + "Palau 42.080952\n", + "Cook Islands 41.816667\n", + "Marshall Islands 40.454762\n", + "Tuvalu 35.080952\n", + "Niue 34.307143\n", + "Tonga 33.885714\n", + "Samoa 33.659524\n", + "Micronesia (Federated States of) 31.995238\n", + "Kiribati 31.204762\n", + "Name: BMI, dtype: float64" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_BMI_per_country_over_the_years = df.loc[df.gender=='Both'].groupby('country').BMI.mean()\n", + "avg_BMI_per_country_over_the_years[avg_BMI_per_country_over_the_years.ge(30)].sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.5452380952380953" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_BMI_per_country_over_the_years['India']" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2012, 2013, 2014, 2015, 2016])" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.year.sort_values().unique()[-5:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.year.ge(2012)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country\n", + "Nauru 60.32\n", + "Cook Islands 54.86\n", + "Palau 54.22\n", + "Marshall Islands 51.82\n", + "Tuvalu 50.12\n", + "Niue 48.40\n", + "Tonga 46.80\n", + "Samoa 46.02\n", + "Kiribati 44.72\n", + "Micronesia (Federated States of) 44.38\n", + "Kuwait 36.78\n", + "United States of America 34.92\n", + "Jordan 34.30\n", + "Saudi Arabia 34.08\n", + "Qatar 33.78\n", + "Libya 31.26\n", + "Lebanon 30.84\n", + "Turkey 30.82\n", + "Egypt 30.64\n", + "Bahamas 30.52\n", + "United Arab Emirates 30.34\n", + "Name: BMI, dtype: float64" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_BMI_per_country_over_last5_years = df.loc[(df.gender=='Both') & (df.year.isin(df.year.sort_values().unique()[-5:]))].groupby('country').BMI.mean()\n", + "avg_BMI_per_country_over_last5_years[avg_BMI_per_country_over_last5_years.ge(30)].sort_values(ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.5" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_BMI_per_country_over_last5_years['India']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BMI Trend of a particular country over the years" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [], + "source": [ + "def bmi_trend(df, some_country = 'New Zealand'): \n", + " \"\"\"\n", + " generating BMI trend plot for a given country\n", + " \"\"\"\n", + " sns.scatterplot(data= df[df.country==some_country] ,\n", + " x='year',\n", + " y='BMI',\n", + " hue='gender'\n", + " )\n", + " plt.title(f'BMI trend of {some_country} from {df.year.min()}-{df.year.max()}')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bmi_trend(df, some_country='Samoa')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try [bar_chart_race](https://github.com/dexplo/bar_chart_race)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting bar_chart_race\n", + " Downloading bar_chart_race-0.1.0-py3-none-any.whl (156 kB)\n", + "Requirement already satisfied: matplotlib>=3.1 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from bar_chart_race) (3.4.1)\n", + "Requirement already satisfied: pandas>=0.24 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from bar_chart_race) (1.2.4)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (8.1.2)\n", + "Requirement already satisfied: numpy>=1.16 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (1.20.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (0.10.0)\n", + "Requirement already satisfied: pyparsing>=2.2.1 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from matplotlib>=3.1->bar_chart_race) (2.8.1)\n", + "Requirement already satisfied: six in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from cycler>=0.10->matplotlib>=3.1->bar_chart_race) (1.15.0)\n", + "Requirement already satisfied: pytz>=2017.3 in c:\\program files\\anaconda3\\envs\\glabs_data_science_learn\\lib\\site-packages (from pandas>=0.24->bar_chart_race) (2021.1)\n", + "Installing collected packages: bar-chart-race\n", + "Successfully installed bar-chart-race-0.1.0\n" + ] + } + ], + "source": [ + "!pip install bar_chart_race" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting package metadata (current_repodata.json): ...working... done\n", + "Solving environment: ...working... done\n", + "\n", + "# All requested packages already installed.\n", + "\n" + ] + } + ], + "source": [ + "!conda install -c conda-forge ffmpeg -y" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [], + "source": [ + "import bar_chart_race as bcr\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + " \n", + " \n", + " View in Colab\n", + " \n", + "

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resources used in the session:\n", + "\n", + "- [Wiki Confusion Matrix](https://en.wikipedia.org/wiki/Confusion_matrix)\n", + "- [XKCD Machine Learning](https://xkcd.com/1838/)\n", + "- [Tuning Hyper Parameters](https://scikit-learn.org/stable/modules/grid_search.html#exhaustive-grid-search)\n", + "- [Model Specific Cross Validation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV)\n", + "- [Increase accuracy of Logistic regression](https://stackoverflow.com/questions/38077190/how-to-increase-the-model-accuracy-of-logistic-regression-in-scikit-python)\n", + "- [Order of feature/model selection and parameter tuning](https://stats.stackexchange.com/questions/264533/how-should-feature-selection-and-hyperparameter-optimization-be-ordered-in-the-m)\n", + "- Scikit-Learn Pipeline [[1]](https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html) [[2]](https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classifying Email as Spam or Non-Spam\n", + "\n", + "[Source: UCI ML Repo: Spambase Data Set](https://archive.ics.uci.edu/ml/datasets/spambase) \n", + "\n", + "Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word 'george' and the area code '650' are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter. \n", + "\n", + "- Number of Instances: 4601 (1813 Spam = 39.4%)\n", + "- Number of Attributes: 58 (57 continuous, 1 nominal class label)\n", + "\n", + "- Attribute Information:\n", + "\n", + " - The last column of 'spambase.data' denotes whether the e-mail was \n", + " considered spam (1) or not (0)\n", + " \n", + " - 48 attributes are continuous real [0,100] numbers of type `word freq WORD` i.e. percentage of words in the e-mail that match WORD\n", + "\n", + " - 6 attributes are continuous real [0,100] numbers of type `char freq CHAR` i.e. percentage of characters in the e-mail that match CHAR\n", + "\n", + " - 1 attribute is continuous real [1,...] numbers of type `capital run length average` i.e. average length of uninterrupted sequences of capital letters\n", + "\n", + " - 1 attribute is continuous integer \\[1,...\\] numbers of type\n", + "`capital run length longest` i.e. length of longest uninterrupted sequence of capital letters\n", + "\n", + " - 1 attribute is continuous integer \\[1,...\\] numbers of type `capital run length total` i.e.\n", + "sum of length of uninterrupted sequences of capital letters in the email\n", + "\n", + " - 1 attribute is nominal {0,1} class of type spam i.e denotes whether the e-mail was considered spam (1) or not (0), \n", + "\n", + "- Missing Attribute Values: None\n", + "\n", + "- Class Distribution: \n", + "\n", + "\n", + "\n", + "
Spam1813(39.4%)
Non-Spam2788(60.6%)
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.feature_selection import chi2\n", + "from sklearn.feature_selection import f_classif\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.decomposition import PCA\n", + "from sklearn.metrics import classification_report,confusion_matrix\n", + "from sklearn.pipeline import Pipeline\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Part A: Base Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "### Task 1: Load the data stored in `path` using `.read_csv()` api." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Also many word frequencies appear to be zero (looking at their quartile values), indicating these words maybe the ones that help decide spam from not-spam." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 3.1: Split the data into train and test set and fit the base logistic regression model on train set." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(random_state=101)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = df.iloc[:,:-1]\n", + "y = df.iloc[:,-1]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size= 0.3, random_state = 42)\n", + "lr = LogisticRegression(random_state=101)\n", + "lr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3.2 Compare predicted values and observed values" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Prediction for 10 observation: [0 0 0 0 0 1 0 0 0 0]\n", + "Actual values for 10 observation: [0 0 0 1 0 1 0 0 0 0]\n" + ] + } + ], + "source": [ + "# Compare observed value and Predicted value\n", + "print(\"Prediction for 10 observation: \",lr.predict(X_test[0:10]))\n", + "print(\"Actual values for 10 observation: \",y_test[0:10].values)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Fantastic, 9/10 are correct predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3.3 Find out the accuracy, print out the Classification report and Confusion Matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test data: 0.9210716871832005\n" + ] + } + ], + "source": [ + "print(\"Accuracy on test data:\", lr.score(X_test,y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: \n", + " [[750 54]\n", + " [ 55 522]]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "y_pred = lr.predict(X_test)\n", + "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n", + "\n", + "## see the plot\n", + "from sklearn.metrics import plot_confusion_matrix\n", + "plot_confusion_matrix(lr, X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification Report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.93 0.93 0.93 804\n", + " 1 0.91 0.90 0.91 577\n", + "\n", + " accuracy 0.92 1381\n", + " macro avg 0.92 0.92 0.92 1381\n", + "weighted avg 0.92 0.92 0.92 1381\n", + "\n" + ] + } + ], + "source": [ + "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Base model is jus the start, but this time its a pretty good start, lets see if we can improve on it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Part B: Feature Selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 4: Copy dataset df into df1 variable and apply correlation on df1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "df1 = df.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 5.1: As we have learned one of the assumptions of Logistic Regression model is that the independent features should not be correlated to each other (i.e no multicolinearity).\n", + "\n", + "So we have to find the features that have a correlation higher that 0.75 and remove the same so that the assumption for logistic regression model is satisfied. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns to be dropped: \n", + "[33, 39]\n" + ] + } + ], + "source": [ + "# Remove correlated features \n", + "## Adapted from \n", + "## https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/\n", + "corr_matrix = df1.drop(57, axis=1).corr().abs()\n", + "upper_mask = np.triu(np.ones(corr_matrix.shape),k=1).astype(np.bool)\n", + "upper = corr_matrix.where(upper_mask)\n", + "to_drop = [column for column in upper.columns if any(upper[column] > 0.75)]\n", + "print(\"Columns to be dropped: \")\n", + "print(to_drop)\n", + "df1.drop(to_drop,axis=1,inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 5.2: Split the new subset of the data acquired by feature selection into train and test set and fit the logistic regression model on train set." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(random_state=101)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = df1.iloc[:,:-1]\n", + "y = df1.iloc[:,-1]\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state = 42)\n", + "lr = LogisticRegression(random_state=101)\n", + "lr.fit(X_train,y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 5.3 Find out the accuracy, print out the Classification report and Confusion Matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy on test data: 0.9210716871832005\n", + "Confusion Matrix: \n", + " [[746 58]\n", + " [ 51 526]]\n", + "Classification Report: \n", + " precision recall f1-score support\n", + "\n", + " 0 0.94 0.93 0.93 804\n", + " 1 0.90 0.91 0.91 577\n", + "\n", + " accuracy 0.92 1381\n", + " macro avg 0.92 0.92 0.92 1381\n", + "weighted avg 0.92 0.92 0.92 1381\n", + "\n" + ] + } + ], + "source": [ + "print(\"Accuracy on test data:\", lr.score(X_test,y_test))\n", + "y_pred = lr.predict(X_test)\n", + "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))\n", + "print(\"Classification Report: \\n\",classification_report(y_test,y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: After removing highly correlated features, there is not much change in the score. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 6.1: Lets apply a feature selection technique (Chi Squared test) to see whether we can increase our accuracy score. \n", + "\n", + "Find the optimum number of features using Chi Square and fit the logistic model on train data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For no of features= 20 , score= 0.9015206372194062\n", + "For no of features= 25 , score= 0.9102099927588704\n", + "For no of features= 30 , score= 0.9116582186821144\n", + "For no of features= 35 , score= 0.9225199131064447\n", + "For no of features= 40 , score= 0.9210716871832005\n", + "For no of features= 50 , score= 0.9232440260680667\n", + "For no of features= 55 , score= 0.9210716871832005\n", + "High Score is: 0.9232440260680667 with features= 50\n" + ] + } + ], + "source": [ + "# let us try selecting different number of features using chi2 test\n", + "nof_list = [20,25,30,35,40,50,55]\n", + "high_score = 0\n", + "nof = 0\n", + "best_chi_model = None\n", + "best_chi_X_train = None\n", + "best_chi_X_test = None\n", + "\n", + "for n in nof_list:\n", + " test = SelectKBest(score_func=chi2 , k= n )\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state = 42)\n", + " X_train = test.fit_transform(X_train,y_train)\n", + " X_test = test.transform(X_test)\n", + " \n", + " chi_model = LogisticRegression(random_state=101)\n", + " chi_model.fit(X_train,y_train)\n", + " print(\"For no of features=\",n,\", score=\", chi_model.score(X_test,y_test))\n", + " if chi_model.score(X_test,y_test)>high_score:\n", + " high_score = chi_model.score(X_test,y_test)\n", + " nof = n \n", + " best_chi_model = chi_model\n", + " best_chi_X_train = X_train\n", + " best_chi_X_test = X_test\n", + "print(\"High Score is:\",high_score, \"with features=\",nof)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 6.2 Print out the Confusion Matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: \n", + " [[755 49]\n", + " [ 57 520]]\n" + ] + } + ], + "source": [ + "y_pred = best_chi_model.predict(best_chi_X_test)\n", + "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [ + "hide" + ] + }, + "outputs": [ + { + "data": { + "text/markdown": [ + "#### Insight: Using chi squared test there is no or very little change in the score and the optimum features that we got is 50." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Markdown as md\n", + "md(\"#### Insight: Using chi squared test there is no or very little change in \\\n", + "the score and the optimum features that we got is {}.\".format(nof))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 7.1 Now lets see if we can increase our score using another feature selection technique called Anova.\n", + "\n", + "Find the optimum number of features using Anova and fit the logistic model on train data." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For no of features= 20 , score= 0.8855901520637219\n", + "For no of features= 25 , score= 0.9015206372194062\n", + "For no of features= 30 , score= 0.9174511223750905\n", + "For no of features= 35 , score= 0.9181752353367125\n", + "For no of features= 40 , score= 0.9160028964518465\n", + "For no of features= 50 , score= 0.9246922519913107\n", + "For no of features= 55 , score= 0.9210716871832005\n", + "High Score is: 0.9246922519913107 with features= 50\n" + ] + } + ], + "source": [ + "# let us try selecting different number of features using anova test\n", + "nof_list = [20,25,30,35,40,50,55]\n", + "high_score = 0\n", + "nof = 0\n", + "best_anova_model = None\n", + "best_anova_X_train = None\n", + "best_anova_X_test = None\n", + "\n", + "for n in nof_list:\n", + " test = SelectKBest(score_func=f_classif , k= n )\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)\n", + " X_train = test.fit_transform(X_train,y_train)\n", + " X_test = test.transform(X_test)\n", + " anova_model = LogisticRegression()\n", + " anova_model.fit(X_train,y_train)\n", + " print(\"For no of features=\",n,\", score=\", anova_model.score(X_test,y_test))\n", + "\n", + " if anova_model.score(X_test,y_test)>high_score:\n", + " high_score = anova_model.score(X_test,y_test)\n", + " nof = n \n", + " best_anova_model = anova_model\n", + " best_anova_X_train = X_train\n", + " best_anova_X_test = X_test\n", + "print(\"High Score is:\",high_score, \"with features=\",nof)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 7.2 Print out the Confusion Matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: \n", + " [[754 50]\n", + " [ 54 523]]\n" + ] + } + ], + "source": [ + "y_pred = best_anova_model.predict(best_anova_X_test)\n", + "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: Number of selected features still seem to remain same." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 8.1: Let us apply PCA as our last feature selection method" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For no of features= 20 , score= 0.9022447501810282\n", + "For no of features= 25 , score= 0.9044170890658942\n", + "For no of features= 30 , score= 0.9058653149891384\n", + "For no of features= 35 , score= 0.9167270094134685\n", + "For no of features= 40 , score= 0.9196234612599565\n", + "For no of features= 50 , score= 0.9167270094134685\n", + "For no of features= 55 , score= 0.9174511223750905\n", + "High Score is: 0.9196234612599565 with features= 40\n" + ] + } + ], + "source": [ + "# Apply PCA and fit the logistic model on train data use df dataset\n", + "nof_list = [20,25,30,35,40,50,55]\n", + "high_score = 0\n", + "nof = 0\n", + "best_pca_lr_model = None\n", + "best_pca_lr_X_train = None\n", + "best_pca_lr_X_test = None\n", + "\n", + "for n in nof_list:\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state = 42)\n", + " pca = PCA(n_components=n)\n", + " pca.fit(X_train)\n", + " X_train = pca.transform(X_train)\n", + " X_test = pca.transform(X_test)\n", + " pca_lr_model = LogisticRegression(random_state=101)\n", + " pca_lr_model.fit(X_train, y_train)\n", + " print(\"For no of features=\",n,\", score=\", pca_lr_model.score(X_test,y_test))\n", + " \n", + " if pca_lr_model.score(X_test,y_test)>high_score:\n", + " high_score = pca_lr_model.score(X_test,y_test)\n", + " nof = n\n", + " best_pca_lr_model = pca_lr_model\n", + " best_pca_lr_X_train = X_train\n", + " best_pca_lr_X_test = X_test\n", + "print(\"High Score is:\",high_score, \"with features=\",nof)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 8.2 Print out the Confusion Matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix: \n", + " [[759 45]\n", + " [ 66 511]]\n" + ] + } + ], + "source": [ + "y_pred = best_pca_lr_model.predict(best_pca_lr_X_test)\n", + "print(\"Confusion Matrix: \\n\",confusion_matrix(y_test,y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: There is significant reduction in number of features selected but the score is not the best." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Part C: Hyper-parameter optimisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 9: Let us try to optimise the hyper-parameters of high scoring model with featuers selected with PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, estimator=LogisticRegression(random_state=101),\n", + " param_grid={'penalty': ['l2', 'l1', 'elasticnet'],\n", + " 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n", + " 'saga']})" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "param_grid = {\n", + " 'penalty': ['l2', 'l1', 'elasticnet'],\n", + " 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']}\n", + "search = GridSearchCV(best_pca_lr_model, param_grid, cv=5)\n", + "search.fit(best_pca_lr_X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'penalty': 'l1', 'solver': 'liblinear'}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9170807453416149" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search.best_score_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Insight: The score did not improve much." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### Task 10: Does the order matter? \n", + "**Method A:** \n", + "Try different feature selection techniques, choose the model with best score and finally optimise its hyper-parameters \n", + "\n", + "**Method B:** \n", + "Perform feature selection and hyper-parameters tuning for each model, then select the best model\n", + "\n", + "So far, we have been trying Method A, let us try Method B, first with Chi-square and Anova, then with all Chi-square, Anova and PCA put together\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'model__penalty': 'l1',\n", + " 'model__solver': 'liblinear',\n", + " 'select__k': 55,\n", + " 'select__score_func': },\n", + " Pipeline(steps=[('select', SelectKBest(k=55)),\n", + " ('model',\n", + " LogisticRegression(penalty='l1', solver='liblinear'))]),\n", + " 0.9134954916678467)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using Scikit-learn Pipeline method for Chi2 and Anova\n", + "nof_list = [20,25,30,35,40,50,55]\n", + "scoring_func_list = [f_classif, chi2]\n", + "penalty_list = ['l2', 'l1', 'elasticnet']\n", + "solver_list = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n", + "\n", + "pipe = Pipeline([\n", + " ('select', SelectKBest()),\n", + " ('model', LogisticRegression())\n", + "])\n", + "\n", + "param_grid = {\n", + " 'select__k': nof_list,\n", + " 'select__score_func': scoring_func_list,\n", + " 'model__penalty': penalty_list,\n", + " 'model__solver': solver_list}\n", + "\n", + "search = GridSearchCV(pipe, param_grid, cv=5)\n", + "best_model = search.fit(X, y)\n", + "\n", + "best_model.best_params_,best_model.best_estimator_,best_model.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'model__penalty': 'l1',\n", + " 'model__solver': 'liblinear',\n", + " 'select': SelectKBest(k=55, score_func=),\n", + " 'select__k': 55,\n", + " 'select__score_func': },\n", + " Pipeline(steps=[('select',\n", + " SelectKBest(k=55,\n", + " score_func=)),\n", + " ('model',\n", + " LogisticRegression(penalty='l1', solver='liblinear'))]),\n", + " 0.9139300382382098)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Using Scikit-learn Pipeline method for Chi2, Anova and PCA\n", + "nof_list = [20,25,30,35,40,50,55]\n", + "scoring_func_list = [f_classif, chi2]\n", + "penalty_list = ['l2', 'l1', 'elasticnet']\n", + "solver_list = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n", + "\n", + "pipe = Pipeline([\n", + " # select stage is populated by the param_grid\n", + " ('select', 'passthrough'),\n", + " ('model', LogisticRegression())\n", + "])\n", + "\n", + "param_grid = [\n", + " {\n", + " 'select':[SelectKBest()],\n", + " 'select__k': nof_list,\n", + " 'select__score_func': scoring_func_list,\n", + " 'model__penalty': penalty_list,\n", + " 'model__solver': solver_list\n", + " },\n", + " {\n", + " 'select':[PCA()],\n", + " 'select__n_components': nof_list,\n", + " 'model__penalty': penalty_list,\n", + " 'model__solver': solver_list\n", + " }\n", + "]\n", + "search = GridSearchCV(pipe, param_grid, cv=5)\n", + "best_model = search.fit(X, y)\n", + "\n", + "best_model.best_params_,best_model.best_estimator_,best_model.best_score_" + ] + } + ], + "metadata": { + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Getting_started_with_text_preprocessing/notebook/Getting_started_with_text_preprocessing_MK.ipynb b/Getting_started_with_text_preprocessing/notebook/Getting_started_with_text_preprocessing_MK.ipynb new file mode 100644 index 0000000..ed8b7c2 --- /dev/null +++ b/Getting_started_with_text_preprocessing/notebook/Getting_started_with_text_preprocessing_MK.ipynb @@ -0,0 +1,2372 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Text Data Preprocessing\n", + "\n", + "In any machine learning task, cleaning or preprocessing the data is as important as model building if not more. And when it comes to unstructured data like text, this process is even more important.\n", + "\n", + "Objective of this notebook is to understand the various text preprocessing steps with code examples.\n", + "\n", + "Some of the common text preprocessing / cleaning steps are:\n", + "\n", + "* Lower casing\n", + "* Removal of Punctuations\n", + "* Removal of Stopwords\n", + "* Removal of Frequent words\n", + "* Removal of Rare words\n", + "* Stemming\n", + "* Lemmatization\n", + "* Removal of emojis\n", + "* Removal of URLs\n", + "\n", + "\n", + "So these are the different types of text preprocessing steps which we can do on text data. But we need not do all of these all the times. We need to carefully choose the preprocessing steps based on our use case since that also play an important role.\n", + "\n", + "For example, in sentiment analysis use case, we need not remove the emojis as it will convey some important information about the sentiment. Similarly we need to decide based on our use cases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import re\n", + "import nltk\n", + "import spacy\n", + "import string\n", + "#pd.options.mode.chained_assignment = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('max_colwidth', 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.0.5'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(5000, 1)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('../data/text.csv', lineterminator='\\n')\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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text
0@161252 What's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.
\n", + "
" + ], + "text/plain": [ + " text\n", + "0 @161252 What's that egg website people talk about\n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq\n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...\n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins\n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced." + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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text
85@235730 and click on \" How to disable Automatic Restart\".\\nShould you need any assistance, DM th...
86@AmericanAir So disappointed in your service. How can you not keep customers and staff updated.
87@811677 Hey, we're unable to tell when an item will be back in stock when it is listed as \"Tempo...
88Woke up....hyped for a Sunday stream......................................................\\n\\nCo...
89@542004 We can take a look for you, Jessi. Please DM your booking reference, full name, home and...
\n", + "
" + ], + "text/plain": [ + " text\n", + "85 @235730 and click on \" How to disable Automatic Restart\".\\nShould you need any assistance, DM th...\n", + "86 @AmericanAir So disappointed in your service. How can you not keep customers and staff updated.\n", + "87 @811677 Hey, we're unable to tell when an item will be back in stock when it is listed as \"Tempo...\n", + "88 Woke up....hyped for a Sunday stream......................................................\\n\\nCo...\n", + "89 @542004 We can take a look for you, Jessi. Please DM your booking reference, full name, home and..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[85:90]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lower Casing\n", + "Lower casing is a common text preprocessing technique. The idea is to convert the input text into same casing format so that 'text', 'Text' and 'TEXT' are treated the same way.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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texttext_lower
0@161252 What's that egg website people talk about@161252 what's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.
\n", + "
" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['text_lower'] = df.text.str.lower()\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of Punctuations\n", + "\n", + "One another common text preprocessing technique is to remove the punctuations from the text data. This is again a text standardization process that will help to treat 'hurray' and 'hurray!' in the same way.\n", + "\n", + "We also need to carefully choose the list of punctuations to exclude depending on the use case. For example, the `string.punctuation` in python contains the following punctuation symbols \n", + "```\n", + "!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_{|}~`\n", + "```\n", + "\n", + "We can add or remove more punctuations as per our need." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\\\!\\\\\"\\\\#\\\\$\\\\%\\\\&\\\\\\'\\\\(\\\\)\\\\*\\\\+\\\\,\\\\-\\\\.\\\\/\\\\:\\\\;\\\\<\\\\=\\\\>\\\\?\\\\@\\\\[\\\\\\\\\\\\]\\\\^\\\\_\\\\`\\\\{\\\\|\\\\}\\\\~'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\".join([\"\\\\\"+c for c in string.punctuation])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\\!\\\"\\#\\$\\%\\&\\'\\(\\)\\*\\+\\,\\-\\.\\/\\:\\;\\<\\=\\>\\?\\@\\[\\\\\\]\\^\\_\\`\\{\\|\\}\\~]\n" + ] + } + ], + "source": [ + "print(\"[\" + \"\".join([\"\\\\\"+c for c in string.punctuation]) + \"]\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"text_wo_punct\"] = df.text_lower.str.replace(\"-\",\" \")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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texttext_lowertext_wo_punct
0@161252 What's that egg website people talk about@161252 what's that egg website people talk about@161252 what's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5 20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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texttext_lowertext_wo_punct
0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experienced
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"text_wo_punct\"] = df.text_wo_punct.str.replace( \"[\" + \"\".join([\"\\\\\"+c for c in string.punctuation]) + \"]\" , \"\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of stopwords\n", + "Stopwords are commonly occuring words in a language like 'the', 'a' and so on. They can be removed from the text most of the times, as they don't provide valuable information for downstream analysis. In cases like Part of Speech tagging, we should not remove them as provide very valuable information about the POS.\n", + "\n", + "These stopword lists are already compiled for different languages and we can safely use them. For example, the stopword list for english language from the nltk package can be seen below." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to\n", + "[nltk_data] C:\\Users\\kukre\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "nltk.download('stopwords')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"i, me, my, myself, we, our, ours, ourselves, you, you're, you've, you'll, you'd, your, yours, yourself, yourselves, he, him, his, himself, she, she's, her, hers, herself, it, it's, its, itself, they, them, their, theirs, themselves, what, which, who, whom, this, that, that'll, these, those, am, is, are, was, were, be, been, being, have, has, had, having, do, does, did, doing, a, an, the, and, but, if, or, because, as, until, while, of, at, by, for, with, about, against, between, into, through, during, before, after, above, below, to, from, up, down, in, out, on, off, over, under, again, further, then, once, here, there, when, where, why, how, all, any, both, each, few, more, most, other, some, such, no, nor, not, only, own, same, so, than, too, very, s, t, can, will, just, don, don't, should, should've, now, d, ll, m, o, re, ve, y, ain, aren, aren't, couldn, couldn't, didn, didn't, doesn, doesn't, hadn, hadn't, hasn, hasn't, haven, haven't, isn, isn't, ma, mightn, mightn't, mustn, mustn't, needn, needn't, shan, shan't, shouldn, shouldn't, wasn, wasn't, weren, weren't, won, won't, wouldn, wouldn't\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nltk.corpus import stopwords\n", + "\", \".join(stopwords.words('english'))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'161252 whats that egg website people talk about'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.text_wo_punct.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'161252 whats egg website people talk'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "text = df.text_wo_punct.iloc[0]\n", + "\n", + "\" \".join([word for word in text.split() if word not in stopwords.words('english')])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def remove_words(text, wordlist):\n", + " \"\"\" custome function to remove words from text present in the wordlist\"\"\"\n", + " return \" \".join([word for word in text.split() if word not in wordlist])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced
\n", + "
" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['text_wo_stop'] = df.text_wo_punct.apply(lambda text: remove_words(text, stopwords.words('english')))\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of Frequent words\n", + "In the previos preprocessing step, we removed the stopwords based on language information. But say, if we have a domain specific corpus, we might also have some frequent words which are of not so much importance to us.\n", + "\n", + "So this step is to remove the frequent words in the given corpus. If we use something like tfidf, this is automatically taken care of.\n", + "\n", + "Let us get the most common words adn then remove them in the next step" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"i, me, my, myself, we, our, ours, ourselves, you, you're, you've, you'll, you'd, your, yours, yourself, yourselves, he, him, his, himself, she, she's, her, hers, herself, it, it's, its, itself, they, them, their, theirs, themselves, which, who, whom, this, that, that'll, these, those, am, is, are, was, were, be, been, being, have, has, had, having, do, does, did, doing, a, an, the, and, but, if, or, because, as, until, while, of, at, by, for, with, about, against, between, into, through, during, before, after, above, below, to, from, up, down, in, out, on, off, over, under, again, further, then, once, here, there, where, why, how, all, any, both, each, few, more, most, other, some, such, no, nor, not, only, own, same, so, than, too, very, s, t, can, will, just, don, don't, should, should've, now, d, ll, m, o, re, ve, y, ain, aren, aren't, couldn, couldn't, didn, didn't, doesn, doesn't, hadn, hadn't, hasn, hasn't, haven, haven't, isn, isn't, ma, mightn, mightn't, mustn, mustn't, needn, needn't, shan, shan't, shouldn, shouldn't, wasn, wasn't, weren, weren't, won, won't, wouldn, wouldn't\"" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stop_word_list = stopwords.words('english')\n", + "stop_word_list.remove('when')\n", + "stop_word_list.remove('what')\n", + "\n", + "\", \".join(stop_word_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('us', 836),\n", + " ('please', 747),\n", + " ('dm', 633),\n", + " ('help', 460),\n", + " ('thanks', 405),\n", + " ('hi', 404),\n", + " ('get', 352),\n", + " ('sorry', 314),\n", + " ('like', 281),\n", + " ('send', 276)]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from collections import Counter\n", + "cnt = Counter()\n", + "\n", + "for text in df.text_wo_stop.values:\n", + " for word in text.split():\n", + " cnt[word] += 1\n", + " \n", + "cnt.most_common(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['us', 'please', 'dm', 'help', 'thanks', 'hi', 'get', 'sorry', 'like', 'send']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[w for w,_ in cnt.most_common(10)]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk161252 whats egg website people talk
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...693975 assist recommend updating ios 1111 havent chance also following link futher support https...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreq \n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['text_wo_stopfreq'] = df.text_wo_stop.apply(lambda text: remove_words(text, [w for w,_ in cnt.most_common(10)] ))\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of Rare words\n", + "This is very similar to previous preprocessing step but we will remove the rare words from the corpus." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('custom', 1),\n", + " ('puma', 1),\n", + " ('inr1400', 1),\n", + " ('170844', 1),\n", + " ('bricked', 1),\n", + " ('implementing', 1),\n", + " ('407091', 1),\n", + " ('reunion', 1),\n", + " ('gravity', 1),\n", + " ('319396', 1),\n", + " ('684726', 1),\n", + " ('hotmail', 1),\n", + " ('sean', 1),\n", + " ('457844', 1),\n", + " ('703576', 1),\n", + " ('598743', 1),\n", + " ('hk', 1),\n", + " ('313942', 1),\n", + " ('httpstcobqcl3gv57t', 1)]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cnt.most_common()[:-20:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk161252 whats egg website people talk161252 whats egg website people talk
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...693975 assist recommend updating ios 1111 havent chance also following link futher support https...693975 assist recommend updating ios 1111 havent chance also following link futher support https...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreq \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreqrare \n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['text_wo_stopfreqrare'] = df.text_wo_stopfreq.apply(lambda text: remove_words(text, [w for w,_ in cnt.most_common()[:-10:-1]] ))\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " word count\n", + "4230 161252 1\n", + "4231 why🤷🏻‍♀️ 1\n", + "4232 httpstcobxrvfeixxq 1\n", + "4233 693975 1\n", + "4234 futher 1\n", + "... ... ...\n", + "13472 bricked 1\n", + "13473 170844 1\n", + "13474 inr1400 1\n", + "13475 puma 1\n", + "13476 custom 1\n", + "\n", + "[9247 rows x 2 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(cnt.most_common(), columns=['word','count']).query('count == 1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stemming\n", + "Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form (From Wikipedia)\n", + "\n", + "For example, if there are two words in the corpus walks and walking, then stemming will stem the suffix to make them walk. But say in another example, we have two words console and consoling, the stemmer will remove the suffix and make them consol which is not a proper english word.\n", + "\n", + "There are several type of stemming algorithms available and one of the famous one is porter stemmer which is widely used. We can use nltk package for the same." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about@161252 what' that egg websit peopl talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 that better than have an unstabl connect that drop everi 5-20 min
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.@virginamerica is probabl one of the best airlin i'v ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_stemmed \n", + "0 @161252 what' that egg websit peopl talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so... \n", + "3 @331912 @115955 that better than have an unstabl connect that drop everi 5-20 min \n", + "4 @virginamerica is probabl one of the best airlin i'v ever experienced. " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nltk.stem.porter import PorterStemmer\n", + "\n", + "\n", + "stemmer = PorterStemmer()\n", + "def stem_words(text):\n", + " return \" \".join([stemmer.stem(word) for word in text.split()])\n", + "\n", + "df['text_stemmed'] = df.text_lower.apply(stem_words)\n", + "df.head()[['text','text_lower','text_stemmed']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'ppl' -> # normailisation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lemmatization\n", + "Lemmatization is similar to stemming in reducing inflected words to their word stem but differs in the way that it makes sure the root word (also called as lemma) belongs to the language.\n", + "\n", + "As a result, this one is generally slower than stemming process. So depending on the speed requirement, we can choose to use either stemming or lemmatization.\n", + "\n", + "Let us use the WordNetLemmatizer in nltk to lemmatize our sentences" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package wordnet to\n", + "[nltk_data] C:\\Users\\kukre\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "nltk.download('wordnet')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about@161252 what' that egg websit peopl talk about@161252 what's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so...@693975 we can assist you. we recommend updating to io 11.1.1 if you haven't had the chance to d...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 that better than have an unstabl connect that drop everi 5-20 min@331912 @115955 thats better than having an unstable connection that drop every 5-20 min
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.@virginamerica is probabl one of the best airlin i'v ever experienced.@virginamerica is probably one of the best airline i've ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_stemmed \\\n", + "0 @161252 what' that egg websit peopl talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so... \n", + "3 @331912 @115955 that better than have an unstabl connect that drop everi 5-20 min \n", + "4 @virginamerica is probabl one of the best airlin i'v ever experienced. \n", + "\n", + " text_lemmatized \n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to io 11.1.1 if you haven't had the chance to d... \n", + "3 @331912 @115955 thats better than having an unstable connection that drop every 5-20 min \n", + "4 @virginamerica is probably one of the best airline i've ever experienced. " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nltk.stem import WordNetLemmatizer\n", + "\n", + "\n", + "lemmatizer = WordNetLemmatizer()\n", + "def lemmatize_words(text):\n", + " return \" \".join([lemmatizer.lemmatize(word) for word in text.split()])\n", + "\n", + "df['text_lemmatized'] = df.text_lower.apply(lemmatize_words)\n", + "df.head()[['text','text_lower','text_stemmed', 'text_lemmatized']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Redo the lemmatization process with POS tag for our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'having'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lemmatizer.lemmatize('having')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'have'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lemmatizer.lemmatize('having', 'v') " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from nltk.corpus import wordnet\n", + "wordnet_map = {\"N\":wordnet.NOUN, \"V\":wordnet.VERB, \"J\":wordnet.ADJ, \"R\":wordnet.ADV}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] C:\\Users\\kukre\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "nltk.download('averaged_perceptron_tagger')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk161252 whats egg website people talk161252 whats egg website people talk@161252 what' that egg websit peopl talk about@161252 What's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqWhy!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...693975 assist recommend updating ios 1111 havent chance also following link futher support https...693975 assist recommend updating ios 1111 havent chance also following link futher support https...@693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so...@693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins@331912 @115955 that better than have an unstabl connect that drop everi 5-20 min@331912 @115955 Thats good than have an unstable connection that drop every 5-20 min
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced@virginamerica is probabl one of the best airlin i'v ever experienced.@VirginAmerica be probably one of the best airline I've ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreq \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreqrare \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_stemmed \\\n", + "0 @161252 what' that egg websit peopl talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so... \n", + "3 @331912 @115955 that better than have an unstabl connect that drop everi 5-20 min \n", + "4 @virginamerica is probabl one of the best airlin i'v ever experienced. \n", + "\n", + " text_lemmatized \n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d... \n", + "3 @331912 @115955 Thats good than have an unstable connection that drop every 5-20 min \n", + "4 @VirginAmerica be probably one of the best airline I've ever experienced. " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from nltk.corpus import wordnet\n", + "\n", + "lemmatizer = WordNetLemmatizer()\n", + "wordnet_map = {\"N\":wordnet.NOUN, \"V\":wordnet.VERB, \"J\":wordnet.ADJ, \"R\":wordnet.ADV}\n", + "\n", + "def lemmatize_words(text):\n", + " pos_tagged_text = nltk.pos_tag(text.split())\n", + " return \" \".join([lemmatizer.lemmatize(word, wordnet_map.get(pos[0], wordnet.NOUN)) for word, pos in pos_tagged_text])\n", + "\n", + "df[\"text_lemmatized\"] = df[\"text\"].apply(lambda text: lemmatize_words(text))\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of Emojis\n", + "\n", + "With more and more usage of social media platforms, there is an explosion in the usage of emojis in our day to day life as well. Probably we might need to remove these emojis for some of our textual analysis.\n", + "\n", + "Thanks to [this code](https://stackoverflow.com/a/58356570/8210613), please find below a helper function to remove emojis from our text." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk161252 whats egg website people talk161252 whats egg website people talk@161252 what' that egg websit peopl talk about@161252 What's that egg website people talk about@161252 What's that egg website people talk about@161252 What's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqWhy!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqWhy! #iOS11 @AppleSupport https://t.co/BXrVfeIXxqWhy!🤷🏻‍♀️ #iOS11 @AppleSupport
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...693975 assist recommend updating ios 1111 havent chance also following link futher support https...693975 assist recommend updating ios 1111 havent chance also following link futher support https...@693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so...@693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d...@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins@331912 @115955 that better than have an unstabl connect that drop everi 5-20 min@331912 @115955 Thats good than have an unstable connection that drop every 5-20 min@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced@virginamerica is probabl one of the best airlin i'v ever experienced.@VirginAmerica be probably one of the best airline I've ever experienced.@VirginAmerica is probably one of the best airlines I've ever experienced.@VirginAmerica is probably one of the best airlines I've ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreq \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreqrare \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_stemmed \\\n", + "0 @161252 what' that egg websit peopl talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so... \n", + "3 @331912 @115955 that better than have an unstabl connect that drop everi 5-20 min \n", + "4 @virginamerica is probabl one of the best airlin i'v ever experienced. \n", + "\n", + " text_lemmatized \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d... \n", + "3 @331912 @115955 Thats good than have an unstable connection that drop every 5-20 min \n", + "4 @VirginAmerica be probably one of the best airline I've ever experienced. \n", + "\n", + " text_no_emoji \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why! #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_no_url \n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#https://stackoverflow.com/a/58356570/8210613\n", + "def remove_emoji(data):\n", + " emoj = re.compile(\"[\"\n", + " u\"\\U0001F600-\\U0001F64F\" # emoticons\n", + " u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n", + " u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n", + " u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n", + " u\"\\U00002500-\\U00002BEF\" # chinese char\n", + " u\"\\U00002702-\\U000027B0\"\n", + " u\"\\U00002702-\\U000027B0\"\n", + " u\"\\U000024C2-\\U0001F251\"\n", + " u\"\\U0001f926-\\U0001f937\"\n", + " u\"\\U00010000-\\U0010ffff\"\n", + " u\"\\u2640-\\u2642\" \n", + " u\"\\u2600-\\u2B55\"\n", + " u\"\\u200d\"\n", + " u\"\\u23cf\"\n", + " u\"\\u23e9\"\n", + " u\"\\u231a\"\n", + " u\"\\ufe0f\" # dingbats\n", + " u\"\\u3030\"\n", + " \"]+\", re.UNICODE)\n", + " return re.sub(emoj, '', data)\n", + "\n", + "df[\"text_no_emoji\"] = df[\"text\"].apply(remove_emoji)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Removal of URLs\n", + "\n", + "Next preprocessing step is to remove any URLs present in the data. For example, if we are doing a twitter analysis, then there is a good chance that the tweet will have some URL in it. Probably we might need to remove them for our further analysis.\n", + "\n", + "We can use the below code snippet to do that" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0@161252 What's that egg website people talk about@161252 what's that egg website people talk about161252 whats that egg website people talk about161252 whats egg website people talk161252 whats egg website people talk161252 whats egg website people talk@161252 what' that egg websit peopl talk about@161252 What's that egg website people talk about@161252 What's that egg website people talk about@161252 What's that egg website people talk about
1Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxqwhy!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxqWhy!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxqWhy! #iOS11 @AppleSupport https://t.co/BXrVfeIXxqWhy!🤷🏻‍♀️ #iOS11 @AppleSupport
2@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ...693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so...693975 assist recommend updating ios 1111 havent chance also dm us following link futher support...693975 assist recommend updating ios 1111 havent chance also following link futher support https...693975 assist recommend updating ios 1111 havent chance also following link futher support https...@693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so...@693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d...@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...@693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ...
3@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 thats better than having an unstable connection that drops every 5-20 mins331912 115955 thats better than having an unstable connection that drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins331912 115955 thats better unstable connection drops every 5 20 mins@331912 @115955 that better than have an unstabl connect that drop everi 5-20 min@331912 @115955 Thats good than have an unstable connection that drop every 5-20 min@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins@331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins
4@VirginAmerica is probably one of the best airlines I've ever experienced.@virginamerica is probably one of the best airlines i've ever experienced.virginamerica is probably one of the best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experiencedvirginamerica probably one best airlines ive ever experienced@virginamerica is probabl one of the best airlin i'v ever experienced.@VirginAmerica be probably one of the best airline I've ever experienced.@VirginAmerica is probably one of the best airlines I've ever experienced.@VirginAmerica is probably one of the best airlines I've ever experienced.
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" + ], + "text/plain": [ + " text \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_lower \\\n", + "0 @161252 what's that egg website people talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updating to ios 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @virginamerica is probably one of the best airlines i've ever experienced. \n", + "\n", + " text_wo_punct \\\n", + "0 161252 whats that egg website people talk about \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 we can assist you we recommend updating to ios 1111 if you havent had the chance to do so... \n", + "3 331912 115955 thats better than having an unstable connection that drops every 5 20 mins \n", + "4 virginamerica is probably one of the best airlines ive ever experienced \n", + "\n", + " text_wo_stop \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also dm us following link futher support... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreq \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_wo_stopfreqrare \\\n", + "0 161252 whats egg website people talk \n", + "1 why🤷🏻‍♀️ ios11 applesupport httpstcobxrvfeixxq \n", + "2 693975 assist recommend updating ios 1111 havent chance also following link futher support https... \n", + "3 331912 115955 thats better unstable connection drops every 5 20 mins \n", + "4 virginamerica probably one best airlines ive ever experienced \n", + "\n", + " text_stemmed \\\n", + "0 @161252 what' that egg websit peopl talk about \n", + "1 why!🤷🏻‍♀️ #ios11 @applesupport https://t.co/bxrvfeixxq \n", + "2 @693975 we can assist you. we recommend updat to io 11.1.1 if you haven't had the chanc to do so... \n", + "3 @331912 @115955 that better than have an unstabl connect that drop everi 5-20 min \n", + "4 @virginamerica is probabl one of the best airlin i'v ever experienced. \n", + "\n", + " text_lemmatized \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend update to iOS 11.1.1 if you haven't have the chance to d... \n", + "3 @331912 @115955 Thats good than have an unstable connection that drop every 5-20 min \n", + "4 @VirginAmerica be probably one of the best airline I've ever experienced. \n", + "\n", + " text_no_emoji \\\n", + "0 @161252 What's that egg website people talk about \n", + "1 Why! #iOS11 @AppleSupport https://t.co/BXrVfeIXxq \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. \n", + "\n", + " text_no_url \n", + "0 @161252 What's that egg website people talk about \n", + "1 Why!🤷🏻‍♀️ #iOS11 @AppleSupport \n", + "2 @693975 We can assist you. We recommend updating to iOS 11.1.1 if you haven't had the chance to ... \n", + "3 @331912 @115955 Thats better than having an unstable connection that drops every 5-20 mins \n", + "4 @VirginAmerica is probably one of the best airlines I've ever experienced. " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def remove_urls(text):\n", + " url_pattern = re.compile(r'https?://\\S+|www\\.\\S+')\n", + " return url_pattern.sub(r'', text)\n", + "\n", + "\n", + "df[\"text_no_url\"] = df[\"text\"].apply(remove_urls)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion activity:\n", + "\n", + "* What usecases can you think for NLP?\n", + " - analysis of speech - news article, transcription of speeches -- topic modelling vs topic classification \n", + "\n", + "* What role does preprocessing play in the application of NLP?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.3" + }, + "toc-autonumbering": true + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/IPL Dataset analysis using more loops and conditionals/notebook/IPL_data_analysis-MK.ipynb b/IPL Dataset analysis using more loops and conditionals/notebook/IPL_data_analysis-MK.ipynb new file mode 100644 index 0000000..dbcccd9 --- /dev/null +++ b/IPL Dataset analysis using more loops and conditionals/notebook/IPL_data_analysis-MK.ipynb @@ -0,0 +1,1549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IPL Dataset Analysis\n", + "\n", + "## Problem Statement\n", + "We want to know as to what happens during an IPL match which raises several questions in our mind with our limited knowledge about the game called cricket on which it is based. This analysis is done to know as which factors led one of the team to win and how does it matter.\n", + "\n", + "## About the Dataset :\n", + "The Indian Premier League (IPL) is a professional T20 cricket league in India contested during April-May of every year by teams representing Indian cities. It is the most-attended cricket league in the world and ranks sixth among all the sports leagues. It has teams with players from around the world and is very competitive and entertaining with a lot of close matches between teams.\n", + "\n", + "The IPL and other cricket related datasets are available at [cricsheet.org](https://cricsheet.org). Feel free to visit the website and explore the data by yourself as exploring new sources of data is one of the interesting activities a data scientist gets to do.\n", + "\n", + "## About the dataset:\n", + "Snapshot of the data you will be working on:
\n", + "
\n", + "The dataset 136522 data points and 23 features
\n", + "\n", + "|Features|Description|\n", + "|-----|-----|\n", + "|match_code|Code pertaining to individual match|\n", + "|date|Date of the match played|\n", + "|city|City where the match was played|\n", + "|venue|Stadium in that city where the match was played|\n", + "|team1|team1|\n", + "|team2|team2|\n", + "|toss_winner|Who won the toss out of two teams|\n", + "|toss_decision|toss decision taken by toss winner|\n", + "|winner|Winner of that match between two teams|\n", + "|win_type|How did the team won(by wickets or runs etc.)|\n", + "|win_margin|difference with which the team won| \n", + "|inning|inning type(1st or 2nd)|\n", + "|delivery|ball delivery|\n", + "|batting_team|current team on batting|\n", + "|batsman|current batsman on strike|\n", + "|non_striker|batsman on non-strike|\n", + "|bowler|Current bowler|\n", + "|runs|runs scored|\n", + "|extras|extra run scored|\n", + "|total|total run scored on that delivery including runs and extras|\n", + "|extras_type|extra run scored by wides or no ball or legby|\n", + "|player_out|player that got out|\n", + "|wicket_kind|How did the player got out|\n", + "|wicket_fielders|Fielder who caught out the player by catch|\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(136522, 24)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read the data using pandas module.\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "df_ipl = pd.read_csv('../data/ipl_data.csv')\n", + "df_ipl.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "match_code 392203\n", + "date 2009-05-01\n", + "city East London\n", + "venue Buffalo Park\n", + "team1 Kolkata Knight Riders\n", + "team2 Mumbai Indians\n", + "toss_winner Mumbai Indians\n", + "toss_decision bat\n", + "winner Mumbai Indians\n", + "win_type runs\n", + "win_margin 9\n", + "inning 1\n", + "delivery 0.1\n", + "batting_team Mumbai Indians\n", + "batsman ST Jayasuriya\n", + "non_striker SR Tendulkar\n", + "bowler I Sharma\n", + "runs 0\n", + "extras 1\n", + "total 1\n", + "extras_type wides\n", + "player_out NaN\n", + "wicket_kind NaN\n", + "wicket_fielders NaN\n", + "Name: 0, dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.iloc[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## There are matches being played all around the world. Find the list of unique cities where matches are being played throughout the world." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cities these matches were played at ['East London' 'Port Elizabeth' 'Centurion' 'neutral_venue' 'Chennai'\n", + " 'Jaipur' 'Kolkata' 'Delhi' 'Chandigarh' 'Hyderabad' 'Ranchi' 'Mumbai'\n", + " 'Bangalore' 'Dharamsala' 'Pune' 'Rajkot' 'Durban' 'Cuttack' 'Cape Town'\n", + " 'Ahmedabad' 'Johannesburg' 'Visakhapatnam' 'Abu Dhabi' 'Raipur' 'Kochi'\n", + " 'Kimberley' 'Nagpur' 'Bloemfontein' 'Indore' 'Kanpur']\n" + ] + } + ], + "source": [ + "print('Cities these matches were played at',\n", + " df_ipl.city.unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find columns containing null values if any." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 136522 entries, 0 to 136521\n", + "Data columns (total 24 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 match_code 136522 non-null int64 \n", + " 1 date 136522 non-null object \n", + " 2 city 136522 non-null object \n", + " 3 venue 136522 non-null object \n", + " 4 team1 136522 non-null object \n", + " 5 team2 136522 non-null object \n", + " 6 toss_winner 136522 non-null object \n", + " 7 toss_decision 136522 non-null object \n", + " 8 winner 134704 non-null object \n", + " 9 win_type 134704 non-null object \n", + " 10 win_margin 134704 non-null float64\n", + " 11 inning 136522 non-null int64 \n", + " 12 delivery 136522 non-null float64\n", + " 13 batting_team 136522 non-null object \n", + " 14 batsman 136522 non-null object \n", + " 15 non_striker 136522 non-null object \n", + " 16 bowler 136522 non-null object \n", + " 17 runs 136522 non-null int64 \n", + " 18 extras 136522 non-null int64 \n", + " 19 total 136522 non-null int64 \n", + " 20 extras_type 7458 non-null object \n", + " 21 player_out 6715 non-null object \n", + " 22 wicket_kind 6715 non-null object \n", + " 23 wicket_fielders 4865 non-null object \n", + "dtypes: float64(2), int64(5), object(17)\n", + "memory usage: 25.0+ MB\n" + ] + } + ], + "source": [ + "df_ipl.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Columns with null values are ['winner', 'win_type', 'win_margin', 'extras_type', 'player_out', 'wicket_kind', 'wicket_fielders']\n" + ] + } + ], + "source": [ + "nulls = df_ipl.isnull().sum()\n", + "print(\"Columns with null values are\",\n", + " nulls[nulls>0].index.to_list())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Matches are played throughout the world in different countries but they may or may not have multiple venues(stadiums where matches are played). Find the top 5 venues where the most matches are played.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Chandigarh': array(['Punjab Cricket Association Stadium, Mohali',\n", + " 'Punjab Cricket Association IS Bindra Stadium, Mohali'],\n", + " dtype=object),\n", + " 'Mumbai': array(['Wankhede Stadium', 'Brabourne Stadium',\n", + " 'Dr DY Patil Sports Academy'], dtype=object),\n", + " 'Pune': array(['Maharashtra Cricket Association Stadium',\n", + " 'Subrata Roy Sahara Stadium'], dtype=object),\n", + " 'neutral_venue': array(['Dubai International Cricket Stadium', 'Sharjah Cricket Stadium'],\n", + " dtype=object)}" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "city_venue = df_ipl.groupby('city').venue.nunique()\n", + "multi_stadium_cities = city_venue[city_venue>1].index.to_list()\n", + "\n", + "df_ipl.loc[df_ipl.city.isin(multi_stadium_cities),].groupby('city').venue.unique().to_dict()\n", + "#.value_counts().index.to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "city\n", + "Mumbai 77\n", + "Bangalore 58\n", + "Kolkata 54\n", + "Delhi 53\n", + "Chennai 48\n", + "Name: match_code, dtype: int64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.groupby(['city']).match_code.nunique().nlargest(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "city venue \n", + "Bangalore M Chinnaswamy Stadium 58\n", + "Kolkata Eden Gardens 54\n", + "Delhi Feroz Shah Kotla 53\n", + "Mumbai Wankhede Stadium 49\n", + "Chennai MA Chidambaram Stadium, Chepauk 48\n", + "Name: match_code, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.groupby(['city','venue']).match_code.nunique().nlargest(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find out how the runs were scored that is the runs count frequency table( number of singles, doubles, boundaries, sixes etc were scored)." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(df_ipl\n", + " runs.value_counts(sort=False)\n", + " .plot.barh(figsize=(12,8))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://stackoverflow.com/a/30874820/8210613" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "(df_ipl\n", + " .groupby(['inning'])\n", + " .runs.value_counts(sort=False)\n", + " .unstack().T\n", + " .plot.barh(figsize=(12,8))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IPL seasons are held every year now let's look at our data and extract how many seasons and which year were they played?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2009\n", + "1 2009\n", + "2 2009\n", + "3 2009\n", + "4 2009\n", + " ... \n", + "136517 2008\n", + "136518 2008\n", + "136519 2008\n", + "136520 2008\n", + "136521 2008\n", + "Name: date, Length: 136522, dtype: int64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.date.astype('datetime64[ns]').dt.year" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2009\n", + "1 2009\n", + "2 2009\n", + "3 2009\n", + "4 2009\n", + " ... \n", + "136517 2008\n", + "136518 2008\n", + "136519 2008\n", + "136520 2008\n", + "136521 2008\n", + "Name: year, Length: 136522, dtype: object" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl['year'] = df_ipl.date.apply(lambda row: row[:4])\n", + "df_ipl['year']" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.year.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',\n", + " '2016'], dtype=object)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ipl.year.sort_values().unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find out the total number of matches played in each season also find the total number of runs scored in each season.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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OXAFsad22AGtanyfb2/WOjB8nyTLgtKr6s6oq4L8cPUaSNDtm5Z5FkhHgQuBuYGlVHYBBoADndBy+HNg/bnt/a5vse9YlGU0y+sxTh55r2ZKkpvewSHIqcDuwsaoeP5EhJmmryTpW1eaqWl1VqxcsWnwCXyVJmkyvYZFkIYOguK2q7mjNj7VLS0cvMR3sGGY/cO647XOBR2e6VknS1Pp8GirArcBYVd08btc2YG1bXwvceaxx2qWqw0kubmP+465jJEkzq89fcF8CXA3sTrKrtd0A3ARsTXIN8DBw5dEDkuwDTgNOTrIGuKyq7gPeCfw+8OPA/2gfSdIs6S0s2pNNU/0e4tIpjhmZon0UeMXMVCZJOl7zdm6o85cvZtQ5YyRpRjjdhySpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE7zdiLB3Y8cYmTTXXNdhqTjtM8JQIeSZxaSpE59vilvRZKdScaS7EmyobWfkWR7kr1tuWTcMdcneTDJA0neNK79Hyb5Whvng33VLEmaXJ9nFk8D11bVecDFwPokq4BNwI6qWgnsaNu0fVcBLwcuB343yYIkZwK/CVxaVS8HliaZ9OVJkqR+9BYWVXWgqu5t64eBMWA5cAWwpXXbAqxp61cAn6yq71XVN4EHgYuAnwa+UVXfbv2+ALytr7olSc82K/cskowAFwJ3A0ur6gAMAgU4p3VbDnxr3GH7W9uDwMuSjCQ5iUG4rJiNuiVJA72HRZJTgduBjVX1+LG6TtJWVfVXwDuB/w78L2Afg0tck33XuiSjSUafeerQcytckvQ3eg2LJAsZBMVtVXVHa34sybK2fxlwsLXv50fPGM4FHgWoqs9W1c9X1WuAB4C9k31fVW2uqtVVtXrBosUz/weSpBeoPp+GCnArMFZVN4/btQ1Y29bXAneOa78qySlJXgKsBL7SxjqnLZcA/xz4WF91S5Kerc8f5V0CXA3sTrKrtd0A3ARsTXIN8DBwJUBV7UmyFbiPwWWm9VX1TDvuw0le2db/XVV9o8e6JUkT9BYWVfVlJr8PATDpo69V9QHgA5O0v30GS5MkHSd/wS1J6jRv54Y6f/liRp1jRpJmhGcWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSeo0b+eG2v3IIUY23TXXZUh6jvY5x9tQ8MxCktTJsJAkderztaorkuxMMpZkT5INrf2MJNuT7G3LJeOOuT7Jg0keSPKmce1vT7I7ydeSfC7JWX3VLUl6tj7PLJ4Grq2q84CLgfVJVgGbgB1VtRLY0bZp+64CXg5cDvxukgVJTgI+DLy+qn4O+BrwL3qsW5I0QW9hUVUHquretn4YGAOWA1cAW1q3LcCatn4F8Mmq+l5VfRN4ELiIwatZA7w4SYDTgEf7qluS9Gyzcs8iyQhwIXA3sLSqDsAgUIBzWrflwLfGHbYfWF5V3wfeCexmEBKrgFun+J51SUaTjD7z1KEe/iSS9MLUe1gkORW4HdhYVY8fq+skbZVkIYOwuBD4SQaXoa6fbICq2lxVq6tq9YJFi59j5ZKko3oNi/Yf+tuB26rqjtb8WJJlbf8y4GBr3w+sGHf4uQzOJC4AqKq/qKoCtgK/0GfdkqQf1efTUGFwuWisqm4et2sbsLatrwXuHNd+VZJTkrwEWAl8BXgEWJXk7NbvjQzuf0iSZkmfv+C+BLga2J1kV2u7AbgJ2JrkGuBh4EqAqtqTZCtwH4MnqdZX1TPAo0neD3wpyfeBh4B39Fi3JGmC3sKiqr7M5PchAC6d4pgPAB+YpP2jwEdnrjpJ0vGYt3NDnb98MaPOKSNJM8LpPiRJnQwLSVInw0KS1MmwkCR1MiwkSZ0y+FH0/JPkMPDAXNcxDWcB35nrIqbBOmfW86HO50ONYJ0z7aeq6uyJjfP20VnggapaPddFdEkyap0zxzpnzvOhRrDO2eJlKElSJ8NCktRpPofF5rkuYJqsc2ZZ58x5PtQI1jkr5u0NbknSzJnPZxaSpBliWEiSOs27sEhyeZIHkjyYZNNc13NUkv+c5GCSr49rOyPJ9iR723LJXNbYalqRZGeSsSR7kmwYxlqT/FiSryT5aqvz/cNY51FJFiT58yR/1LaHrs4k+5LsTrIryegQ13l6kk8nub/9e/qaYaszyUvb3+PRz+NJNg5bncdjXoVFkgXAfwTeDKwC3p5k1dxW9Td+H7h8QtsmYEdVrQR2tO259jRwbVWdB1wMrG9/h8NW6/eAN1TVKxm8evfyJBczfHUetYEffcPjsNb5+qq6YNzvAYaxzg8Dn6uqlwGvZPD3OlR1VtUD7e/xAuDVwFPAZxiyOo9LVc2bD/Aa4PPjtq8Hrp/rusbVMwJ8fdz2A8Cytr6MwQ8J57zOCTXfyeBVtkNbK7AIuBf4+WGsk8H75HcAbwD+aFj/2QP7gLMmtA1VncBpwDdpD+cMa50TarsM+NNhr7PrM6/OLIDlwLfGbe9vbcNqaVUdAGjLc+a4nh+RZAS4ELibIay1XdrZBRwEtlfVUNYJfAh4F/CDcW3DWGcBf5zkniTrWtuw1fnTwLeBj7fLeh9L8mKGr87xrgI+0daHuc5jmm9hMdlrXH02+AQkORW4HdhYVY/PdT2TqapnanCafy5wUZJXzHVNEyV5K3Cwqu6Z61qm4ZKqehWDy7jrk/ziXBc0iZOAVwEfqaoLgScZ4ks5SU4Gfgn41FzX8lzNt7DYD6wYt30u8Ogc1TIdjyVZBtCWB+e4HgCSLGQQFLdV1R2teShrBaiq/wd8kcE9oWGr8xLgl5LsAz4JvCHJHzB8dVJVj7blQQbX1y9i+OrcD+xvZ5EAn2YQHsNW51FvBu6tqsfa9rDW2Wm+hcX/AVYmeUlL9KuAbXNc07FsA9a29bUM7g/MqSQBbgXGqurmcbuGqtYkZyc5va3/OPB3gfsZsjqr6vqqOreqRhj8+/gnVfWPGLI6k7w4yU8cXWdwnf3rDFmdVfWXwLeSvLQ1XQrcx5DVOc7b+eElKBjeOrvN9U2Tmf4AbwG+AfwF8J65rmdcXZ8ADgDfZ/D/jq4BzmRw43NvW54xBHW+lsGlu68Bu9rnLcNWK/BzwJ+3Or8OvLe1D1WdE2p+HT+8wT1UdTK4F/DV9tlz9H87w1Znq+kCYLT9s/9DYMmQ1rkI+C6weFzb0NU53Y/TfUiSOs23y1CSpB4YFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSp0/8Hk584+DzZ508AAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_ipl.groupby('year').match_code.nunique().plot.barh()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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yearmatch_codeinningteam1team2toss_winnertoss_decisiontotal
70120135980271Royal Challengers BangalorePune WarriorsPune Warriorsfield263
111820169809871Royal Challengers BangaloreGujarat LionsGujarat Lionsfield248
29220104191371Chennai Super KingsRajasthan RoyalsChennai Super Kingsbat246
220083359831Kings XI PunjabChennai Super KingsChennai Super Kingsbat240
100220158297951Mumbai IndiansRoyal Challengers BangaloreRoyal Challengers Bangalorebat235
47220115012601Kings XI PunjabRoyal Challengers BangaloreKings XI Punjabbat232
39820115012231Delhi DaredevilsKings XI PunjabKings XI Punjabfield231
85120147339871Kings XI PunjabChennai Super KingsChennai Super Kingsfield231
103820169809071Royal Challengers BangaloreSunrisers HyderabadSunrisers Hyderabadfield227
91120147340471Chennai Super KingsKings XI PunjabChennai Super Kingsfield226
\n", + "
" + ], + "text/plain": [ + " year match_code inning team1 \\\n", + "701 2013 598027 1 Royal Challengers Bangalore \n", + "1118 2016 980987 1 Royal Challengers Bangalore \n", + "292 2010 419137 1 Chennai Super Kings \n", + "2 2008 335983 1 Kings XI Punjab \n", + "1002 2015 829795 1 Mumbai Indians \n", + "472 2011 501260 1 Kings XI Punjab \n", + "398 2011 501223 1 Delhi Daredevils \n", + "851 2014 733987 1 Kings XI Punjab \n", + "1038 2016 980907 1 Royal Challengers Bangalore \n", + "911 2014 734047 1 Chennai Super Kings \n", + "\n", + " team2 toss_winner toss_decision \\\n", + "701 Pune Warriors Pune Warriors field \n", + "1118 Gujarat Lions Gujarat Lions field \n", + "292 Rajasthan Royals Chennai Super Kings bat \n", + "2 Chennai Super Kings Chennai Super Kings bat \n", + "1002 Royal Challengers Bangalore Royal Challengers Bangalore bat \n", + "472 Royal Challengers Bangalore Kings XI Punjab bat \n", + "398 Kings XI Punjab Kings XI Punjab field \n", + "851 Chennai Super Kings Chennai Super Kings field \n", + "1038 Sunrisers Hyderabad Sunrisers Hyderabad field \n", + "911 Kings XI Punjab Chennai Super Kings field \n", + "\n", + " total \n", + "701 263 \n", + "1118 248 \n", + "292 246 \n", + "2 240 \n", + "1002 235 \n", + "472 232 \n", + "398 231 \n", + "851 231 \n", + "1038 227 \n", + "911 226 " + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_scores = df_ipl.groupby(['year','match_code','inning','team1','team2','toss_winner','toss_decision']).total.sum().reset_index()\n", + "high_scores = total_scores[total_scores.total>200]\n", + "high_scores.nlargest(10,columns='total')" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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match_codeinningteam1team2total
7015980271Royal Challengers BangalorePune Warriors263
11189809871Royal Challengers BangaloreGujarat Lions248
2924191371Chennai Super KingsRajasthan Royals246
23359831Kings XI PunjabChennai Super Kings240
10028297951Mumbai IndiansRoyal Challengers Bangalore235
4725012601Kings XI PunjabRoyal Challengers Bangalore232
3985012231Delhi DaredevilsKings XI Punjab231
8517339871Kings XI PunjabChennai Super Kings231
10389809071Royal Challengers BangaloreSunrisers Hyderabad227
9117340471Chennai Super KingsKings XI Punjab226
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" + ], + "text/plain": [ + " match_code inning team1 \\\n", + "701 598027 1 Royal Challengers Bangalore \n", + "1118 980987 1 Royal Challengers Bangalore \n", + "292 419137 1 Chennai Super Kings \n", + "2 335983 1 Kings XI Punjab \n", + "1002 829795 1 Mumbai Indians \n", + "472 501260 1 Kings XI Punjab \n", + "398 501223 1 Delhi Daredevils \n", + "851 733987 1 Kings XI Punjab \n", + "1038 980907 1 Royal Challengers Bangalore \n", + "911 734047 1 Chennai Super Kings \n", + "\n", + " team2 total \n", + "701 Pune Warriors 263 \n", + "1118 Gujarat Lions 248 \n", + "292 Rajasthan Royals 246 \n", + "2 Chennai Super Kings 240 \n", + "1002 Royal Challengers Bangalore 235 \n", + "472 Royal Challengers Bangalore 232 \n", + "398 Kings XI Punjab 231 \n", + "851 Chennai Super Kings 231 \n", + "1038 Sunrisers Hyderabad 227 \n", + "911 Kings XI Punjab 226 " + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_scores = df_ipl.groupby(['match_code','inning','team1','team2']).total.sum().reset_index()\n", + "high_scores = total_scores[total_scores.total>=200]\n", + "high_scores.nlargest(10,columns='total')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chasing a 200+ target is difficulty in T-20 format. What are the chances that a team scoring runs above 200 in their 1st inning is chased by the opposition in 2nd inning.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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team1team2total_inn1total_inn2
match_code
335983Kings XI PunjabChennai Super Kings240207
335989Chennai Super KingsMumbai Indians208202
335990Deccan ChargersRajasthan Royals214217
336033Chennai Super KingsRajasthan Royals211201
419107Mumbai IndiansRajasthan Royals212208
419112Royal Challengers BangaloreKings XI Punjab203204
419137Chennai Super KingsRajasthan Royals246223
419139Kolkata Knight RidersKings XI Punjab200204
501223Delhi DaredevilsKings XI Punjab231202
548318Chennai Super KingsRoyal Challengers Bangalore205208
729283Chennai Super KingsKings XI Punjab205206
734007Sunrisers HyderabadKings XI Punjab205211
734047Chennai Super KingsKings XI Punjab226202
981019Royal Challengers BangaloreSunrisers Hyderabad208200
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" + ], + "text/plain": [ + " team1 team2 \\\n", + "match_code \n", + "335983 Kings XI Punjab Chennai Super Kings \n", + "335989 Chennai Super Kings Mumbai Indians \n", + "335990 Deccan Chargers Rajasthan Royals \n", + "336033 Chennai Super Kings Rajasthan Royals \n", + "419107 Mumbai Indians Rajasthan Royals \n", + "419112 Royal Challengers Bangalore Kings XI Punjab \n", + "419137 Chennai Super Kings Rajasthan Royals \n", + "419139 Kolkata Knight Riders Kings XI Punjab \n", + "501223 Delhi Daredevils Kings XI Punjab \n", + "548318 Chennai Super Kings Royal Challengers Bangalore \n", + "729283 Chennai Super Kings Kings XI Punjab \n", + "734007 Sunrisers Hyderabad Kings XI Punjab \n", + "734047 Chennai Super Kings Kings XI Punjab \n", + "981019 Royal Challengers Bangalore Sunrisers Hyderabad \n", + "\n", + " total_inn1 total_inn2 \n", + "match_code \n", + "335983 240 207 \n", + "335989 208 202 \n", + "335990 214 217 \n", + "336033 211 201 \n", + "419107 212 208 \n", + "419112 203 204 \n", + "419137 246 223 \n", + "419139 200 204 \n", + "501223 231 202 \n", + "548318 205 208 \n", + "729283 205 206 \n", + "734007 205 211 \n", + "734047 226 202 \n", + "981019 208 200 " + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_scores1 = high_scores.loc[high_scores.inning==1,:]\n", + "high_scores2 = high_scores.loc[high_scores.inning==2,:]\n", + "\n", + "\n", + "(high_scores1.set_index('match_code').drop(columns='inning')\n", + " .join(high_scores2.set_index('match_code')[['total']],\n", + " lsuffix='_inn1',\n", + " rsuffix='_inn2',\n", + " how=\"inner\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "42.86" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_score_matches = high_scores1.drop(columns='inning').merge(high_scores2[['match_code','total']],on='match_code',suffixes=('_inn1','_inn2'))\n", + "\n", + "high_score_matches['is_score_chased'] = high_score_matches.total_inn2 > high_score_matches.total_inn1\n", + "high_score_matches.is_score_chased.value_counts(normalize=True).multiply(100).round(2)[True]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False 8\n", + "True 6\n", + "Name: is_score_chased, dtype: int64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_score_matches.is_score_chased.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Every season has that one team which is outperforming others and is in great form. Which team has the highest win counts in their respective seasons ?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "year winner \n", + "2008 Rajasthan Royals 13\n", + " Kings XI Punjab 10\n", + " Chennai Super Kings 9\n", + " Delhi Daredevils 7\n", + " Mumbai Indians 7\n", + " ..\n", + "2016 Kolkata Knight Riders 8\n", + " Delhi Daredevils 7\n", + " Mumbai Indians 7\n", + " Rising Pune Supergiants 5\n", + " Kings XI Punjab 4\n", + "Name: winner, Length: 76, dtype: int64" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "match_wise_data = df_ipl.drop_duplicates(subset = 'match_code', keep='first').reset_index(drop=True)\n", + "match_wise_data.groupby('year')['winner'].value_counts(ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://stackoverflow.com/a/22720517/8210613" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "2008 (Rajasthan Royals, 13)\n", + "2009 (Delhi Daredevils, 10)\n", + "2010 (Mumbai Indians, 11)\n", + "2011 (Chennai Super Kings, 11)\n", + "2012 (Kolkata Knight Riders, 12)\n", + "2013 (Mumbai Indians, 13)\n", + "2014 (Kings XI Punjab, 12)\n", + "2015 (Mumbai Indians, 10)\n", + "2016 (Sunrisers Hyderabad, 11)\n", + "dtype: object" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df_ipl\n", + " .drop_duplicates(subset='match_code')\n", + " .reset_index(drop=True)\n", + " .groupby('year')\n", + " .apply(lambda group: (group['winner'].value_counts().index[0],group['winner'].value_counts()[0] ))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://stackoverflow.com/a/10762516/8210613" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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winnercount
year
2008Rajasthan Royals13
2009Delhi Daredevils10
2010Mumbai Indians11
2011Chennai Super Kings11
2012Kolkata Knight Riders12
2013Mumbai Indians13
2014Kings XI Punjab12
2015Mumbai Indians10
2016Sunrisers Hyderabad11
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" + ], + "text/plain": [ + " winner count\n", + "year \n", + "2008 Rajasthan Royals 13\n", + "2009 Delhi Daredevils 10\n", + "2010 Mumbai Indians 11\n", + "2011 Chennai Super Kings 11\n", + "2012 Kolkata Knight Riders 12\n", + "2013 Mumbai Indians 13\n", + "2014 Kings XI Punjab 12\n", + "2015 Mumbai Indians 10\n", + "2016 Sunrisers Hyderabad 11" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df_ipl\n", + " .drop_duplicates(subset='match_code')\n", + " .reset_index(drop=True)\n", + " .groupby('year')\n", + " .apply(lambda group: pd.Series( (group['winner'].value_counts().index[0],group['winner'].value_counts()[0]),\n", + " index=['winner','count']))\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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winnercount
year
2008Rajasthan Royals13
2009Delhi Daredevils10
2010Mumbai Indians11
2011Chennai Super Kings11
2012Kolkata Knight Riders12
2013Mumbai Indians13
2014Kings XI Punjab12
2015Mumbai Indians10
2016Sunrisers Hyderabad11
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" + ], + "text/plain": [ + " winner count\n", + "year \n", + "2008 Rajasthan Royals 13\n", + "2009 Delhi Daredevils 10\n", + "2010 Mumbai Indians 11\n", + "2011 Chennai Super Kings 11\n", + "2012 Kolkata Knight Riders 12\n", + "2013 Mumbai Indians 13\n", + "2014 Kings XI Punjab 12\n", + "2015 Mumbai Indians 10\n", + "2016 Sunrisers Hyderabad 11" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_max(group):\n", + " vc = group['winner'].value_counts()\n", + " return pd.Series([vc.index[0], vc[0]], index=['winner', 'count'])\n", + " \n", + "(df_ipl\n", + " .drop_duplicates(subset='match_code')\n", + " .reset_index(drop=True)\n", + " .groupby('year')\n", + " .apply(get_max)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Indian_rainfall_analysis/notebook/Indian_rainfall_analysis-MK.ipynb b/Indian_rainfall_analysis/notebook/Indian_rainfall_analysis-MK.ipynb new file mode 100644 index 0000000..663ff11 --- /dev/null +++ b/Indian_rainfall_analysis/notebook/Indian_rainfall_analysis-MK.ipynb @@ -0,0 +1,2581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Indian Rainfall Analysis\n", + "\n", + "The 2019 Indian floods were a series of floods that affected over thirteen states in late July and early August 2019, due to incessant rains. At least 200 people died and about a million people were displaced. Karnataka and Maharashtra were the most severely affected states.\n", + "\n", + "It was the heaviest monsoon in the last 25 years. More than 1600 people died between June and October 2019.\n", + "\n", + "India being an agriculturally driven economy, it will be interesting to study the rainfall in India in the past decade to give us an idea of the changes in the pattern if there are any.\n", + "\n", + "\n", + "Source: [Open Gov Data Platform India - data.gov.in](https://data.gov.in/resources/subdivision-wise-rainfall-and-its-departure-1901-2015)\n", + "\n", + "Let us work on the INDIAN RAINFALL DATA!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SUBDIVISIONYEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-Dec
0ANDAMAN & NICOBAR ISLANDS190149.287.129.22.3528.8517.5365.1481.1332.6388.5558.233.63373.2136.3560.31696.3980.3
1ANDAMAN & NICOBAR ISLANDS19020.0159.812.20.0446.1537.1228.9753.7666.2197.2359.0160.53520.7159.8458.32185.9716.7
2ANDAMAN & NICOBAR ISLANDS190312.7144.00.01.0235.1479.9728.4326.7339.0181.2284.4225.02957.4156.7236.11874.0690.6
3ANDAMAN & NICOBAR ISLANDS19049.414.70.0202.4304.5495.1502.0160.1820.4222.2308.740.13079.624.1506.91977.6571.0
4ANDAMAN & NICOBAR ISLANDS19051.30.03.326.9279.5628.7368.7330.5297.0260.725.4344.72566.71.3309.71624.9630.8
\n", + "
" + ], + "text/plain": [ + " SUBDIVISION YEAR JAN FEB MAR APR MAY JUN \\\n", + "0 ANDAMAN & NICOBAR ISLANDS 1901 49.2 87.1 29.2 2.3 528.8 517.5 \n", + "1 ANDAMAN & NICOBAR ISLANDS 1902 0.0 159.8 12.2 0.0 446.1 537.1 \n", + "2 ANDAMAN & NICOBAR ISLANDS 1903 12.7 144.0 0.0 1.0 235.1 479.9 \n", + "3 ANDAMAN & NICOBAR ISLANDS 1904 9.4 14.7 0.0 202.4 304.5 495.1 \n", + "4 ANDAMAN & NICOBAR ISLANDS 1905 1.3 0.0 3.3 26.9 279.5 628.7 \n", + "\n", + " JUL AUG SEP OCT NOV DEC ANNUAL Jan-Feb Mar-May \\\n", + "0 365.1 481.1 332.6 388.5 558.2 33.6 3373.2 136.3 560.3 \n", + "1 228.9 753.7 666.2 197.2 359.0 160.5 3520.7 159.8 458.3 \n", + "2 728.4 326.7 339.0 181.2 284.4 225.0 2957.4 156.7 236.1 \n", + "3 502.0 160.1 820.4 222.2 308.7 40.1 3079.6 24.1 506.9 \n", + "4 368.7 330.5 297.0 260.7 25.4 344.7 2566.7 1.3 309.7 \n", + "\n", + " Jun-Sep Oct-Dec \n", + "0 1696.3 980.3 \n", + "1 2185.9 716.7 \n", + "2 1874.0 690.6 \n", + "3 1977.6 571.0 \n", + "4 1624.9 630.8 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('../data/rainfall in india 1901-2015.csv')\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4116 entries, 0 to 4115\n", + "Data columns (total 19 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 SUBDIVISION 4116 non-null object \n", + " 1 YEAR 4116 non-null int64 \n", + " 2 JAN 4112 non-null float64\n", + " 3 FEB 4113 non-null float64\n", + " 4 MAR 4110 non-null float64\n", + " 5 APR 4112 non-null float64\n", + " 6 MAY 4113 non-null float64\n", + " 7 JUN 4111 non-null float64\n", + " 8 JUL 4109 non-null float64\n", + " 9 AUG 4112 non-null float64\n", + " 10 SEP 4110 non-null float64\n", + " 11 OCT 4109 non-null float64\n", + " 12 NOV 4105 non-null float64\n", + " 13 DEC 4106 non-null float64\n", + " 14 ANNUAL 4090 non-null float64\n", + " 15 Jan-Feb 4110 non-null float64\n", + " 16 Mar-May 4107 non-null float64\n", + " 17 Jun-Sep 4106 non-null float64\n", + " 18 Oct-Dec 4103 non-null float64\n", + "dtypes: float64(17), int64(1), object(1)\n", + "memory usage: 611.1+ KB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SUBDIVISION 0\n", + "YEAR 0\n", + "JAN 4\n", + "FEB 3\n", + "MAR 6\n", + "APR 4\n", + "MAY 3\n", + "JUN 5\n", + "JUL 7\n", + "AUG 4\n", + "SEP 6\n", + "OCT 7\n", + "NOV 11\n", + "DEC 10\n", + "ANNUAL 26\n", + "Jan-Feb 6\n", + "Mar-May 9\n", + "Jun-Sep 10\n", + "Oct-Dec 13\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Finding years where all months data is missing" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Series([], Name: YEAR, dtype: int64)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.YEAR[data.iloc[:,2:14].isnull().all(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is no year (or row) in the dataset where all the measurements from Jan-Dec are missing" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# can might handle missing values in ANNUAL column by adding it ourselves\n", + "data['MY_ANNUAL'] = data.iloc[:,2:14].sum(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SUBDIVISION 0\n", + "YEAR 0\n", + "JAN 4\n", + "FEB 3\n", + "MAR 6\n", + "APR 4\n", + "MAY 3\n", + "JUN 5\n", + "JUL 7\n", + "AUG 4\n", + "SEP 6\n", + "OCT 7\n", + "NOV 11\n", + "DEC 10\n", + "ANNUAL 26\n", + "Jan-Feb 6\n", + "Mar-May 9\n", + "Jun-Sep 10\n", + "Oct-Dec 13\n", + "MY_ANNUAL 0\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, a better way in this analysis would be to replace it mean." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspect the data, find missing values and replace them with appropriate values" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-DecMY_ANNUAL
SUBDIVISION
ANDAMAN & NICOBAR ISLANDS1958.91818252.63727327.99454531.82407472.223148357.056881471.580556400.042593400.047222439.482243290.264815233.744444153.1448602927.43942380.632727462.2495331706.687850675.4168222876.093636
ARUNACHAL PRADESH1965.82474247.29791791.116667153.527368263.836082358.522680647.373958694.544792495.229897432.134021194.68631635.69684224.5021053418.857143138.416667777.6863162271.422105254.5138303414.786598
ASSAM & MEGHALAYA1958.00000016.97478331.44173979.026957203.115652341.539130510.161739495.102609404.593043310.734783152.11826126.9382618.9513042580.69565248.413043623.6878261720.590435188.0156522580.698261
BIHAR1958.00000013.38608714.39391310.12434816.91826153.081739174.315652324.441739299.643478217.38434863.0747837.1782613.6947831197.63391327.77652280.1269571015.78608773.9539131197.637391
CHHATTISGARH1958.00000014.20695719.25913015.26695716.77304321.048696198.266087398.577391389.873043217.78000063.66000011.7721745.2486961371.72869633.46260953.0921741204.50087080.6747831371.732174
COASTAL ANDHRA PRADESH1958.0000007.48347812.92347813.22173926.74087062.549565123.693913173.824348175.923478181.707826185.51130477.90347811.4200001052.90434820.404348102.515652655.141739274.8356521052.903478
COASTAL KARNATAKA1958.0000001.9377191.5182616.35739130.916522122.787826841.3260871127.028696713.618261299.652174184.55217463.60782612.6139133408.4096493.371053160.0513042981.618261260.7756523405.900000
EAST MADHYA PRADESH1958.00000019.40173918.69391313.6373917.1886969.273043141.029565371.378261369.368696194.23652239.68608712.7052178.4043481205.00000038.09478330.0965221076.01826160.8000001205.003478
EAST RAJASTHAN1958.0000006.4226095.4173914.5165223.1443489.82000063.399130223.347826218.27739197.97826114.3608704.8739133.651304655.21565211.83739117.487826602.99826122.887826655.209565
EAST UTTAR PRADESH1958.00000016.01217415.8739138.9078266.43043517.211304110.712174290.568696275.613913184.59130442.9208704.5904355.776522979.21304331.88782632.553913861.48608753.293913979.209565
GANGETIC WEST BENGAL1958.00000012.59565222.45217429.09043544.885217107.787826247.196522326.377391311.382609245.710435115.74608721.5791305.6904351490.48782635.042609181.7660871130.657391143.0182611490.493913
GUJARAT REGION1958.0000001.7860871.1913041.2208701.1165225.809565121.284348348.920870259.193043148.84173920.5652176.9286961.339130918.2304352.9773918.161739878.25130428.834783918.197391
HARYANA DELHI & CHANDIGARH1958.00000016.88956517.43391312.9356527.63391314.53391348.626087150.015652150.84087088.30695712.8234783.2643487.186087530.49652234.33130435.112174437.78695723.270435530.490435
HIMACHAL PRADESH1958.00000084.18956590.894783101.14608762.42869658.15652291.220870280.284348273.933043130.21913031.27826116.69565239.8930431260.345217175.082609221.726957775.66434887.8713041260.340000
JAMMU & KASHMIR1958.000000102.030435115.450435131.37826193.70260967.47652264.234783179.837719180.97304389.28956534.16695724.13333355.4254391139.684211217.482609292.552174515.428070113.9596491135.843478
JHARKHAND1958.00000017.62173924.18608718.42347819.36695748.317391194.588696336.975652325.524348227.42173980.01565211.9234784.9391301309.30347841.80956586.0956521084.51043596.8808701309.304348
KERALA1958.00000012.24695715.49652236.814783110.573913229.881739654.302609700.953043421.977391245.619130294.122609163.56000039.9504352925.48782627.739130377.2539132022.840870497.6365222925.499130
KONKAN & GOA1958.0000001.2626090.5469571.3747834.26608733.515652688.5695651073.030435682.756522349.780000113.38695724.6713044.5165222977.6860871.81304339.1617392794.130435142.5791302977.677391
LAKSHADWEEP1958.35087727.49464315.83451314.35089345.163393163.893750327.627679281.928829207.993750163.170270166.727928124.84074160.8109091590.88640842.500000223.822727983.554545355.3870371561.094737
MADHYA MAHARASHTRA1958.0000003.0547831.4678263.5965229.14695722.943478147.426087248.980000184.397391157.22173970.19478325.9452175.848696880.2330434.52608735.692174738.025217101.986087880.223478
MATATHWADA1958.0000005.0008704.4434787.1052177.59478315.646957136.957391180.648696166.484348178.47652258.58000022.4365227.302609790.6921749.44869630.352174662.56782688.319130790.677391
NAGA MANI MIZO TRIPURA1958.00000014.02521736.65217477.199130170.733043290.839130445.633913438.684348411.281739314.350435175.00608746.83391312.3991302433.61913050.669565538.7695651609.941739234.2400002433.638261
NORTH INTERIOR KARNATAKA1958.0000003.0130433.1721747.12347824.30087047.035652100.993043138.531304119.459130142.94087095.68869629.2078266.327826717.7956526.18434878.460870501.927826131.223478717.793913
ORISSA1958.00000012.32956519.71913021.13478334.16000064.886087210.860870351.173043355.382609241.403478113.59217427.9617395.5678261458.16956532.047826120.1843481158.817391147.1226091458.171304
PUNJAB1958.00000025.24608726.78695723.65130412.66000014.13652246.466957168.963478158.16782686.78956513.8365224.14000012.694783593.53565252.03043550.440000460.39217430.669565593.540000
RAYALSEEMA1958.0000009.8678265.6800008.07652219.80869650.47565264.74260996.081739107.511304131.720000135.327826102.65391334.260000766.20608715.54521778.358261400.058261272.241739766.206087
SAURASHTRA & KUTCH1958.0000001.1391301.6156521.2965221.1834784.66260974.371304194.970435118.77043575.41826114.5104356.0965221.108696495.1617392.7521747.140870463.53652221.718261495.143478
SOUTH INTERIOR KARNATAKA1958.0000002.9286964.1634789.48521742.28087092.100000141.417391231.359130174.239130137.313913139.14347854.43130411.5173911040.3913047.087826143.861739684.338261205.0939131040.380000
SUB HIMALAYAN WEST BENGAL & SIKKIM1958.00000014.08347822.97478343.135652110.681739269.143478537.881739646.402609520.763478421.341739143.64608716.0886966.0600002752.21739137.060870422.9686962126.391304165.8017392752.203478
TAMIL NADU1958.00000023.81913013.42260919.47565244.99565269.92087052.05652271.31478395.887826111.597391183.196522176.90347881.137391943.71304337.239130134.386087330.847826441.234783943.727826
TELANGANA1958.0000007.7026099.68869612.61478318.18521725.373913142.126087247.499130215.059130175.50347874.22695720.2504355.141739953.37826117.39652256.173043780.18695799.620870953.372174
UTTARAKHAND1958.00000053.79739163.45217457.27217435.16608755.338261162.551304390.698261382.023478196.09652239.0739138.18782622.0356521465.696522117.251304147.7739131131.36782669.3060871465.693043
VIDARBHA1958.00000010.56347811.98260911.8721749.43565211.551304173.578261329.428696285.949565175.44956552.14869615.5747837.9278261095.45913022.54434832.851304964.40347875.6547831095.462609
WEST MADHYA PRADESH1958.0000009.2417396.3078955.1730432.3756527.657391111.781739302.982609288.108696161.16869628.08695712.3408706.296522944.35877215.63859615.217391864.04347846.724348941.466957
WEST RAJASTHAN1958.0000003.3278264.9304353.9860873.5713049.44347828.63739195.17130494.55565240.3426095.1278261.6669571.902609292.6730438.25565217.006087258.7078268.700870292.663478
WEST UTTAR PRADESH1958.00000017.66608717.89391311.4617396.25304312.30608777.597391246.520000251.299130146.25478328.7773913.9660877.114783827.11478335.55478330.026087721.67652239.858261827.110435
\n", + "
" + ], + "text/plain": [ + " YEAR JAN FEB \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 1958.918182 52.637273 27.994545 \n", + "ARUNACHAL PRADESH 1965.824742 47.297917 91.116667 \n", + "ASSAM & MEGHALAYA 1958.000000 16.974783 31.441739 \n", + "BIHAR 1958.000000 13.386087 14.393913 \n", + "CHHATTISGARH 1958.000000 14.206957 19.259130 \n", + "COASTAL ANDHRA PRADESH 1958.000000 7.483478 12.923478 \n", + "COASTAL KARNATAKA 1958.000000 1.937719 1.518261 \n", + "EAST MADHYA PRADESH 1958.000000 19.401739 18.693913 \n", + "EAST RAJASTHAN 1958.000000 6.422609 5.417391 \n", + "EAST UTTAR PRADESH 1958.000000 16.012174 15.873913 \n", + "GANGETIC WEST BENGAL 1958.000000 12.595652 22.452174 \n", + "GUJARAT REGION 1958.000000 1.786087 1.191304 \n", + "HARYANA DELHI & CHANDIGARH 1958.000000 16.889565 17.433913 \n", + "HIMACHAL PRADESH 1958.000000 84.189565 90.894783 \n", + "JAMMU & KASHMIR 1958.000000 102.030435 115.450435 \n", + "JHARKHAND 1958.000000 17.621739 24.186087 \n", + "KERALA 1958.000000 12.246957 15.496522 \n", + "KONKAN & GOA 1958.000000 1.262609 0.546957 \n", + "LAKSHADWEEP 1958.350877 27.494643 15.834513 \n", + "MADHYA MAHARASHTRA 1958.000000 3.054783 1.467826 \n", + "MATATHWADA 1958.000000 5.000870 4.443478 \n", + "NAGA MANI MIZO TRIPURA 1958.000000 14.025217 36.652174 \n", + "NORTH INTERIOR KARNATAKA 1958.000000 3.013043 3.172174 \n", + "ORISSA 1958.000000 12.329565 19.719130 \n", + "PUNJAB 1958.000000 25.246087 26.786957 \n", + "RAYALSEEMA 1958.000000 9.867826 5.680000 \n", + "SAURASHTRA & KUTCH 1958.000000 1.139130 1.615652 \n", + "SOUTH INTERIOR KARNATAKA 1958.000000 2.928696 4.163478 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 1958.000000 14.083478 22.974783 \n", + "TAMIL NADU 1958.000000 23.819130 13.422609 \n", + "TELANGANA 1958.000000 7.702609 9.688696 \n", + "UTTARAKHAND 1958.000000 53.797391 63.452174 \n", + "VIDARBHA 1958.000000 10.563478 11.982609 \n", + "WEST MADHYA PRADESH 1958.000000 9.241739 6.307895 \n", + "WEST RAJASTHAN 1958.000000 3.327826 4.930435 \n", + "WEST UTTAR PRADESH 1958.000000 17.666087 17.893913 \n", + "\n", + " MAR APR MAY \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 31.824074 72.223148 357.056881 \n", + "ARUNACHAL PRADESH 153.527368 263.836082 358.522680 \n", + "ASSAM & MEGHALAYA 79.026957 203.115652 341.539130 \n", + "BIHAR 10.124348 16.918261 53.081739 \n", + "CHHATTISGARH 15.266957 16.773043 21.048696 \n", + "COASTAL ANDHRA PRADESH 13.221739 26.740870 62.549565 \n", + "COASTAL KARNATAKA 6.357391 30.916522 122.787826 \n", + "EAST MADHYA PRADESH 13.637391 7.188696 9.273043 \n", + "EAST RAJASTHAN 4.516522 3.144348 9.820000 \n", + "EAST UTTAR PRADESH 8.907826 6.430435 17.211304 \n", + "GANGETIC WEST BENGAL 29.090435 44.885217 107.787826 \n", + "GUJARAT REGION 1.220870 1.116522 5.809565 \n", + "HARYANA DELHI & CHANDIGARH 12.935652 7.633913 14.533913 \n", + "HIMACHAL PRADESH 101.146087 62.428696 58.156522 \n", + "JAMMU & KASHMIR 131.378261 93.702609 67.476522 \n", + "JHARKHAND 18.423478 19.366957 48.317391 \n", + "KERALA 36.814783 110.573913 229.881739 \n", + "KONKAN & GOA 1.374783 4.266087 33.515652 \n", + "LAKSHADWEEP 14.350893 45.163393 163.893750 \n", + "MADHYA MAHARASHTRA 3.596522 9.146957 22.943478 \n", + "MATATHWADA 7.105217 7.594783 15.646957 \n", + "NAGA MANI MIZO TRIPURA 77.199130 170.733043 290.839130 \n", + "NORTH INTERIOR KARNATAKA 7.123478 24.300870 47.035652 \n", + "ORISSA 21.134783 34.160000 64.886087 \n", + "PUNJAB 23.651304 12.660000 14.136522 \n", + "RAYALSEEMA 8.076522 19.808696 50.475652 \n", + "SAURASHTRA & KUTCH 1.296522 1.183478 4.662609 \n", + "SOUTH INTERIOR KARNATAKA 9.485217 42.280870 92.100000 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 43.135652 110.681739 269.143478 \n", + "TAMIL NADU 19.475652 44.995652 69.920870 \n", + "TELANGANA 12.614783 18.185217 25.373913 \n", + "UTTARAKHAND 57.272174 35.166087 55.338261 \n", + "VIDARBHA 11.872174 9.435652 11.551304 \n", + "WEST MADHYA PRADESH 5.173043 2.375652 7.657391 \n", + "WEST RAJASTHAN 3.986087 3.571304 9.443478 \n", + "WEST UTTAR PRADESH 11.461739 6.253043 12.306087 \n", + "\n", + " JUN JUL AUG \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 471.580556 400.042593 400.047222 \n", + "ARUNACHAL PRADESH 647.373958 694.544792 495.229897 \n", + "ASSAM & MEGHALAYA 510.161739 495.102609 404.593043 \n", + "BIHAR 174.315652 324.441739 299.643478 \n", + "CHHATTISGARH 198.266087 398.577391 389.873043 \n", + "COASTAL ANDHRA PRADESH 123.693913 173.824348 175.923478 \n", + "COASTAL KARNATAKA 841.326087 1127.028696 713.618261 \n", + "EAST MADHYA PRADESH 141.029565 371.378261 369.368696 \n", + "EAST RAJASTHAN 63.399130 223.347826 218.277391 \n", + "EAST UTTAR PRADESH 110.712174 290.568696 275.613913 \n", + "GANGETIC WEST BENGAL 247.196522 326.377391 311.382609 \n", + "GUJARAT REGION 121.284348 348.920870 259.193043 \n", + "HARYANA DELHI & CHANDIGARH 48.626087 150.015652 150.840870 \n", + "HIMACHAL PRADESH 91.220870 280.284348 273.933043 \n", + "JAMMU & KASHMIR 64.234783 179.837719 180.973043 \n", + "JHARKHAND 194.588696 336.975652 325.524348 \n", + "KERALA 654.302609 700.953043 421.977391 \n", + "KONKAN & GOA 688.569565 1073.030435 682.756522 \n", + "LAKSHADWEEP 327.627679 281.928829 207.993750 \n", + "MADHYA MAHARASHTRA 147.426087 248.980000 184.397391 \n", + "MATATHWADA 136.957391 180.648696 166.484348 \n", + "NAGA MANI MIZO TRIPURA 445.633913 438.684348 411.281739 \n", + "NORTH INTERIOR KARNATAKA 100.993043 138.531304 119.459130 \n", + "ORISSA 210.860870 351.173043 355.382609 \n", + "PUNJAB 46.466957 168.963478 158.167826 \n", + "RAYALSEEMA 64.742609 96.081739 107.511304 \n", + "SAURASHTRA & KUTCH 74.371304 194.970435 118.770435 \n", + "SOUTH INTERIOR KARNATAKA 141.417391 231.359130 174.239130 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 537.881739 646.402609 520.763478 \n", + "TAMIL NADU 52.056522 71.314783 95.887826 \n", + "TELANGANA 142.126087 247.499130 215.059130 \n", + "UTTARAKHAND 162.551304 390.698261 382.023478 \n", + "VIDARBHA 173.578261 329.428696 285.949565 \n", + "WEST MADHYA PRADESH 111.781739 302.982609 288.108696 \n", + "WEST RAJASTHAN 28.637391 95.171304 94.555652 \n", + "WEST UTTAR PRADESH 77.597391 246.520000 251.299130 \n", + "\n", + " SEP OCT NOV \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 439.482243 290.264815 233.744444 \n", + "ARUNACHAL PRADESH 432.134021 194.686316 35.696842 \n", + "ASSAM & MEGHALAYA 310.734783 152.118261 26.938261 \n", + "BIHAR 217.384348 63.074783 7.178261 \n", + "CHHATTISGARH 217.780000 63.660000 11.772174 \n", + "COASTAL ANDHRA PRADESH 181.707826 185.511304 77.903478 \n", + "COASTAL KARNATAKA 299.652174 184.552174 63.607826 \n", + "EAST MADHYA PRADESH 194.236522 39.686087 12.705217 \n", + "EAST RAJASTHAN 97.978261 14.360870 4.873913 \n", + "EAST UTTAR PRADESH 184.591304 42.920870 4.590435 \n", + "GANGETIC WEST BENGAL 245.710435 115.746087 21.579130 \n", + "GUJARAT REGION 148.841739 20.565217 6.928696 \n", + "HARYANA DELHI & CHANDIGARH 88.306957 12.823478 3.264348 \n", + "HIMACHAL PRADESH 130.219130 31.278261 16.695652 \n", + "JAMMU & KASHMIR 89.289565 34.166957 24.133333 \n", + "JHARKHAND 227.421739 80.015652 11.923478 \n", + "KERALA 245.619130 294.122609 163.560000 \n", + "KONKAN & GOA 349.780000 113.386957 24.671304 \n", + "LAKSHADWEEP 163.170270 166.727928 124.840741 \n", + "MADHYA MAHARASHTRA 157.221739 70.194783 25.945217 \n", + "MATATHWADA 178.476522 58.580000 22.436522 \n", + "NAGA MANI MIZO TRIPURA 314.350435 175.006087 46.833913 \n", + "NORTH INTERIOR KARNATAKA 142.940870 95.688696 29.207826 \n", + "ORISSA 241.403478 113.592174 27.961739 \n", + "PUNJAB 86.789565 13.836522 4.140000 \n", + "RAYALSEEMA 131.720000 135.327826 102.653913 \n", + "SAURASHTRA & KUTCH 75.418261 14.510435 6.096522 \n", + "SOUTH INTERIOR KARNATAKA 137.313913 139.143478 54.431304 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 421.341739 143.646087 16.088696 \n", + "TAMIL NADU 111.597391 183.196522 176.903478 \n", + "TELANGANA 175.503478 74.226957 20.250435 \n", + "UTTARAKHAND 196.096522 39.073913 8.187826 \n", + "VIDARBHA 175.449565 52.148696 15.574783 \n", + "WEST MADHYA PRADESH 161.168696 28.086957 12.340870 \n", + "WEST RAJASTHAN 40.342609 5.127826 1.666957 \n", + "WEST UTTAR PRADESH 146.254783 28.777391 3.966087 \n", + "\n", + " DEC ANNUAL Jan-Feb \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 153.144860 2927.439423 80.632727 \n", + "ARUNACHAL PRADESH 24.502105 3418.857143 138.416667 \n", + "ASSAM & MEGHALAYA 8.951304 2580.695652 48.413043 \n", + "BIHAR 3.694783 1197.633913 27.776522 \n", + "CHHATTISGARH 5.248696 1371.728696 33.462609 \n", + "COASTAL ANDHRA PRADESH 11.420000 1052.904348 20.404348 \n", + "COASTAL KARNATAKA 12.613913 3408.409649 3.371053 \n", + "EAST MADHYA PRADESH 8.404348 1205.000000 38.094783 \n", + "EAST RAJASTHAN 3.651304 655.215652 11.837391 \n", + "EAST UTTAR PRADESH 5.776522 979.213043 31.887826 \n", + "GANGETIC WEST BENGAL 5.690435 1490.487826 35.042609 \n", + "GUJARAT REGION 1.339130 918.230435 2.977391 \n", + "HARYANA DELHI & CHANDIGARH 7.186087 530.496522 34.331304 \n", + "HIMACHAL PRADESH 39.893043 1260.345217 175.082609 \n", + "JAMMU & KASHMIR 55.425439 1139.684211 217.482609 \n", + "JHARKHAND 4.939130 1309.303478 41.809565 \n", + "KERALA 39.950435 2925.487826 27.739130 \n", + "KONKAN & GOA 4.516522 2977.686087 1.813043 \n", + "LAKSHADWEEP 60.810909 1590.886408 42.500000 \n", + "MADHYA MAHARASHTRA 5.848696 880.233043 4.526087 \n", + "MATATHWADA 7.302609 790.692174 9.448696 \n", + "NAGA MANI MIZO TRIPURA 12.399130 2433.619130 50.669565 \n", + "NORTH INTERIOR KARNATAKA 6.327826 717.795652 6.184348 \n", + "ORISSA 5.567826 1458.169565 32.047826 \n", + "PUNJAB 12.694783 593.535652 52.030435 \n", + "RAYALSEEMA 34.260000 766.206087 15.545217 \n", + "SAURASHTRA & KUTCH 1.108696 495.161739 2.752174 \n", + "SOUTH INTERIOR KARNATAKA 11.517391 1040.391304 7.087826 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 6.060000 2752.217391 37.060870 \n", + "TAMIL NADU 81.137391 943.713043 37.239130 \n", + "TELANGANA 5.141739 953.378261 17.396522 \n", + "UTTARAKHAND 22.035652 1465.696522 117.251304 \n", + "VIDARBHA 7.927826 1095.459130 22.544348 \n", + "WEST MADHYA PRADESH 6.296522 944.358772 15.638596 \n", + "WEST RAJASTHAN 1.902609 292.673043 8.255652 \n", + "WEST UTTAR PRADESH 7.114783 827.114783 35.554783 \n", + "\n", + " Mar-May Jun-Sep Oct-Dec \\\n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 462.249533 1706.687850 675.416822 \n", + "ARUNACHAL PRADESH 777.686316 2271.422105 254.513830 \n", + "ASSAM & MEGHALAYA 623.687826 1720.590435 188.015652 \n", + "BIHAR 80.126957 1015.786087 73.953913 \n", + "CHHATTISGARH 53.092174 1204.500870 80.674783 \n", + "COASTAL ANDHRA PRADESH 102.515652 655.141739 274.835652 \n", + "COASTAL KARNATAKA 160.051304 2981.618261 260.775652 \n", + "EAST MADHYA PRADESH 30.096522 1076.018261 60.800000 \n", + "EAST RAJASTHAN 17.487826 602.998261 22.887826 \n", + "EAST UTTAR PRADESH 32.553913 861.486087 53.293913 \n", + "GANGETIC WEST BENGAL 181.766087 1130.657391 143.018261 \n", + "GUJARAT REGION 8.161739 878.251304 28.834783 \n", + "HARYANA DELHI & CHANDIGARH 35.112174 437.786957 23.270435 \n", + "HIMACHAL PRADESH 221.726957 775.664348 87.871304 \n", + "JAMMU & KASHMIR 292.552174 515.428070 113.959649 \n", + "JHARKHAND 86.095652 1084.510435 96.880870 \n", + "KERALA 377.253913 2022.840870 497.636522 \n", + "KONKAN & GOA 39.161739 2794.130435 142.579130 \n", + "LAKSHADWEEP 223.822727 983.554545 355.387037 \n", + "MADHYA MAHARASHTRA 35.692174 738.025217 101.986087 \n", + "MATATHWADA 30.352174 662.567826 88.319130 \n", + "NAGA MANI MIZO TRIPURA 538.769565 1609.941739 234.240000 \n", + "NORTH INTERIOR KARNATAKA 78.460870 501.927826 131.223478 \n", + "ORISSA 120.184348 1158.817391 147.122609 \n", + "PUNJAB 50.440000 460.392174 30.669565 \n", + "RAYALSEEMA 78.358261 400.058261 272.241739 \n", + "SAURASHTRA & KUTCH 7.140870 463.536522 21.718261 \n", + "SOUTH INTERIOR KARNATAKA 143.861739 684.338261 205.093913 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 422.968696 2126.391304 165.801739 \n", + "TAMIL NADU 134.386087 330.847826 441.234783 \n", + "TELANGANA 56.173043 780.186957 99.620870 \n", + "UTTARAKHAND 147.773913 1131.367826 69.306087 \n", + "VIDARBHA 32.851304 964.403478 75.654783 \n", + "WEST MADHYA PRADESH 15.217391 864.043478 46.724348 \n", + "WEST RAJASTHAN 17.006087 258.707826 8.700870 \n", + "WEST UTTAR PRADESH 30.026087 721.676522 39.858261 \n", + "\n", + " MY_ANNUAL \n", + "SUBDIVISION \n", + "ANDAMAN & NICOBAR ISLANDS 2876.093636 \n", + "ARUNACHAL PRADESH 3414.786598 \n", + "ASSAM & MEGHALAYA 2580.698261 \n", + "BIHAR 1197.637391 \n", + "CHHATTISGARH 1371.732174 \n", + "COASTAL ANDHRA PRADESH 1052.903478 \n", + "COASTAL KARNATAKA 3405.900000 \n", + "EAST MADHYA PRADESH 1205.003478 \n", + "EAST RAJASTHAN 655.209565 \n", + "EAST UTTAR PRADESH 979.209565 \n", + "GANGETIC WEST BENGAL 1490.493913 \n", + "GUJARAT REGION 918.197391 \n", + "HARYANA DELHI & CHANDIGARH 530.490435 \n", + "HIMACHAL PRADESH 1260.340000 \n", + "JAMMU & KASHMIR 1135.843478 \n", + "JHARKHAND 1309.304348 \n", + "KERALA 2925.499130 \n", + "KONKAN & GOA 2977.677391 \n", + "LAKSHADWEEP 1561.094737 \n", + "MADHYA MAHARASHTRA 880.223478 \n", + "MATATHWADA 790.677391 \n", + "NAGA MANI MIZO TRIPURA 2433.638261 \n", + "NORTH INTERIOR KARNATAKA 717.793913 \n", + "ORISSA 1458.171304 \n", + "PUNJAB 593.540000 \n", + "RAYALSEEMA 766.206087 \n", + "SAURASHTRA & KUTCH 495.143478 \n", + "SOUTH INTERIOR KARNATAKA 1040.380000 \n", + "SUB HIMALAYAN WEST BENGAL & SIKKIM 2752.203478 \n", + "TAMIL NADU 943.727826 \n", + "TELANGANA 953.372174 \n", + "UTTARAKHAND 1465.693043 \n", + "VIDARBHA 1095.462609 \n", + "WEST MADHYA PRADESH 941.466957 \n", + "WEST RAJASTHAN 292.663478 \n", + "WEST UTTAR PRADESH 827.110435 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## We can either replace subdivision means or means across all subdivision\n", + "data.groupby('SUBDIVISION').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YEARJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-DecMY_ANNUAL
0190149.287.129.22.3528.8517.5365.1481.1332.6388.5558.233.63373.2136.3560.31696.3980.33373.2
119020.0159.812.20.0446.1537.1228.9753.7666.2197.2359.0160.53520.7159.8458.32185.9716.73520.7
2190312.7144.00.01.0235.1479.9728.4326.7339.0181.2284.4225.02957.4156.7236.11874.0690.62957.4
319049.414.70.0202.4304.5495.1502.0160.1820.4222.2308.740.13079.624.1506.91977.6571.03079.6
419051.30.03.326.9279.5628.7368.7330.5297.0260.725.4344.72566.71.3309.71624.9630.82566.7
............................................................
411120115.12.83.185.9107.2153.6350.2254.0255.2117.4184.314.91533.77.9196.21013.0316.61533.7
4112201219.20.11.676.821.2327.0231.5381.2179.8145.912.48.81405.519.399.61119.5167.11405.5
4113201326.234.437.55.388.3426.2296.4154.4180.072.878.126.71426.360.6131.11057.0177.61426.3
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411520152.20.53.787.1133.1296.6257.5146.4160.4165.4231.0159.01642.92.7223.9860.9555.41642.9
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4116 rows × 19 columns

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" + ], + "text/plain": [ + " YEAR JAN FEB MAR APR MAY JUN JUL AUG SEP \\\n", + "0 1901 49.2 87.1 29.2 2.3 528.8 517.5 365.1 481.1 332.6 \n", + "1 1902 0.0 159.8 12.2 0.0 446.1 537.1 228.9 753.7 666.2 \n", + "2 1903 12.7 144.0 0.0 1.0 235.1 479.9 728.4 326.7 339.0 \n", + "3 1904 9.4 14.7 0.0 202.4 304.5 495.1 502.0 160.1 820.4 \n", + "4 1905 1.3 0.0 3.3 26.9 279.5 628.7 368.7 330.5 297.0 \n", + "... ... ... ... ... ... ... ... ... ... ... \n", + "4111 2011 5.1 2.8 3.1 85.9 107.2 153.6 350.2 254.0 255.2 \n", + "4112 2012 19.2 0.1 1.6 76.8 21.2 327.0 231.5 381.2 179.8 \n", + "4113 2013 26.2 34.4 37.5 5.3 88.3 426.2 296.4 154.4 180.0 \n", + "4114 2014 53.2 16.1 4.4 14.9 57.4 244.1 116.1 466.1 132.2 \n", + "4115 2015 2.2 0.5 3.7 87.1 133.1 296.6 257.5 146.4 160.4 \n", + "\n", + " OCT NOV DEC ANNUAL Jan-Feb Mar-May Jun-Sep Oct-Dec \\\n", + "0 388.5 558.2 33.6 3373.2 136.3 560.3 1696.3 980.3 \n", + "1 197.2 359.0 160.5 3520.7 159.8 458.3 2185.9 716.7 \n", + "2 181.2 284.4 225.0 2957.4 156.7 236.1 1874.0 690.6 \n", + "3 222.2 308.7 40.1 3079.6 24.1 506.9 1977.6 571.0 \n", + "4 260.7 25.4 344.7 2566.7 1.3 309.7 1624.9 630.8 \n", + "... ... ... ... ... ... ... ... ... \n", + "4111 117.4 184.3 14.9 1533.7 7.9 196.2 1013.0 316.6 \n", + "4112 145.9 12.4 8.8 1405.5 19.3 99.6 1119.5 167.1 \n", + "4113 72.8 78.1 26.7 1426.3 60.6 131.1 1057.0 177.6 \n", + "4114 169.2 59.0 62.3 1395.0 69.3 76.7 958.5 290.5 \n", + "4115 165.4 231.0 159.0 1642.9 2.7 223.9 860.9 555.4 \n", + "\n", + " MY_ANNUAL \n", + "0 3373.2 \n", + "1 3520.7 \n", + "2 2957.4 \n", + "3 3079.6 \n", + "4 2566.7 \n", + "... ... \n", + "4111 1533.7 \n", + "4112 1405.5 \n", + "4113 1426.3 \n", + "4114 1395.0 \n", + "4115 1642.9 \n", + "\n", + "[4116 rows x 19 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby('SUBDIVISION').fillna(data.groupby('SUBDIVISION').mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YEAR 1958.218659\n", + "JAN 18.957320\n", + "FEB 21.805325\n", + "MAR 27.359197\n", + "APR 43.127432\n", + "MAY 85.745417\n", + "JUN 230.234444\n", + "JUL 347.214334\n", + "AUG 290.263497\n", + "SEP 197.361922\n", + "OCT 95.507009\n", + "NOV 39.866163\n", + "DEC 18.870580\n", + "ANNUAL 1411.008900\n", + "Jan-Feb 40.747786\n", + "Mar-May 155.901753\n", + "Jun-Sep 1064.724769\n", + "Oct-Dec 154.100487\n", + "MY_ANNUAL 1414.379252\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "## lets just replace it with mean across all subdivisions, since our aim would be to create a model, \n", + "## however, any imputaion like this should happen before the train/test split and seperately for train and test sets,\n", + "## to avoid data-or-information-leak\n", + "\n", + "data.fillna(data.mean(), inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the mean annual rainfall and note down your observations regarding the same" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mean_annual_rainfall = (data.groupby('YEAR').ANNUAL.mean())\n", + "#mean_annual_rainfall.name = 'Mean Annual Rainfall'\n", + "mean_annual_rainfall.plot(legend=True, label='Mean Annual Rainfall', figsize=(12,10))\n", + "\n", + "ma10 = mean_annual_rainfall.rolling(10).mean()\n", + "#ma10.name = 'Moving Avg. for last 10 years'\n", + "ma10.plot(legend=True,label='Moving Avg. for last 10 years')\n", + "plt.xlabel('Year',fontsize=20)\n", + "plt.ylabel('Annual Rainfall (in mm)',fontsize=20)\n", + "plt.title('Annual Rainfall in India from year 1901-2015', fontsize=25)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insights:\n", + "* Highest average rainfall in India was recored in the year 1961.This was because in 1961 India received multiple cyclones.City of Pune was flooded in the year 1961 which is remembered as Panshet Flood.\n", + "\n", + "* Year 1965-66 were twin drought years and there was food scarcity in India.Prime Minister Lal Bahadur Shastri gave the Slogan Jai Jawan Jai Kissan to people of India.This lead to green revolution in India making India a food surplus country in the coming decades.\n", + "\n", + "* The red line is the 10 year moving average of the rainfall in India.It seems since 1960s there is slight dip in the rainfall in India.Now a days due to global warming the period of Monsoon season has shortned.We see more of erratic rainfall pattern.This needs more study." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Similarly analyze the seasonal rainfall as per subdivisions and note down your observations" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(data\n", + " .loc[:,['SUBDIVISION','Jan-Feb','Mar-May', 'Jun-Sep','Oct-Dec']]\n", + " .groupby('SUBDIVISION')\n", + " .mean()\n", + " .sort_values('Jun-Sep')\n", + " .plot.barh(stacked=True,figsize=(16,10), title='Rainfall in subdivision in India'))\n", + "plt.xlabel('Rainfall in mm')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insights:\n", + "\n", + "* From the above graph we can see that majority of rainfall is received in the month of Jun-Sep which is the Monsoon season.Oct-Dec is time of return monsoon.Jan-Feb are the winter months.Mar-May is time for Summer rains.\n", + "\n", + "* Coastal Karnataka,Arunachal Pradesh,Konkan Goa and Kerala receive highest rainfall.\n", + "\n", + "* Rajastan,Gujrat,Haryana and Punjab receives low rainfall.Interesting thing is that Punjab and Haryana have high agricultural output despite low rainfall.Their water requirnments are met by rivers and canals.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## With boxplot analyze the distribution of rainfall in various states and onote down your observations" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(23,10))\n", + "ax=sns.boxplot(y='SUBDIVISION', x='ANNUAL', data=data, width=0.8)\n", + "ax.set_xlabel('Annual Rainfall in mm',fontsize=30)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insights:\n", + "We can see Subdivision Arunachal Pradesh shows highest highest difference between Maximum and Minimum rainfall received.Costal Karnataka receives close to 3400 mm of Annual rainfall which is the highest in India.West Rajastan receives the least amount of rainfall.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyze the rainfall through years in Kerala and note down your observations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Kerala = data.loc[data.SUBDIVISION=='KERALA',:]\n", + "Kerala.groupby(\"YEAR\").ANNUAL.mean().plot(legend=True, label='Kerala')\n", + "mean_annual_rainfall.plot(figsize=(12,8), \n", + " title='Annual rainfall in Kerala compared to the whole of India',\n", + " legend=True, label='India')\n", + "plt.ylabel('Annual rainfall in mm')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "Prior to 2018 Kerala had major flood in the year 1924 which is evident in the data.Contrary to popular belief Kerala received maximum annual rainfall in year 1961(4257 mm) and not 1924(4226 mm).In 2018 Kerala has received 2226.4 mm of rain in the monsoon season.This is 40% more than the average rainfall." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Districts of Kerala](../graph2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the district wise rainfall data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATE_UT_NAMEDISTRICTJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECANNUALJan-FebMar-MayJun-SepOct-Dec
0ANDAMAN And NICOBAR ISLANDSNICOBAR107.357.965.2117.0358.5295.5285.0271.9354.8326.0315.2250.92805.2165.2540.71207.2892.1
1ANDAMAN And NICOBAR ISLANDSSOUTH ANDAMAN43.726.018.690.5374.4457.2421.3423.1455.6301.2275.8128.33015.769.7483.51757.2705.3
2ANDAMAN And NICOBAR ISLANDSN & M ANDAMAN32.715.98.653.4343.6503.3465.4460.9454.8276.1198.6100.02913.348.6405.61884.4574.7
3ARUNACHAL PRADESHLOHIT42.280.8176.4358.5306.4447.0660.1427.8313.6167.134.129.83043.8123.0841.31848.5231.0
4ARUNACHAL PRADESHEAST SIANG33.379.5105.9216.5323.0738.3990.9711.2568.0206.929.531.74034.7112.8645.43008.4268.1
\n", + "
" + ], + "text/plain": [ + " STATE_UT_NAME DISTRICT JAN FEB MAR APR \\\n", + "0 ANDAMAN And NICOBAR ISLANDS NICOBAR 107.3 57.9 65.2 117.0 \n", + "1 ANDAMAN And NICOBAR ISLANDS SOUTH ANDAMAN 43.7 26.0 18.6 90.5 \n", + "2 ANDAMAN And NICOBAR ISLANDS N & M ANDAMAN 32.7 15.9 8.6 53.4 \n", + "3 ARUNACHAL PRADESH LOHIT 42.2 80.8 176.4 358.5 \n", + "4 ARUNACHAL PRADESH EAST SIANG 33.3 79.5 105.9 216.5 \n", + "\n", + " MAY JUN JUL AUG SEP OCT NOV DEC ANNUAL Jan-Feb \\\n", + "0 358.5 295.5 285.0 271.9 354.8 326.0 315.2 250.9 2805.2 165.2 \n", + "1 374.4 457.2 421.3 423.1 455.6 301.2 275.8 128.3 3015.7 69.7 \n", + "2 343.6 503.3 465.4 460.9 454.8 276.1 198.6 100.0 2913.3 48.6 \n", + "3 306.4 447.0 660.1 427.8 313.6 167.1 34.1 29.8 3043.8 123.0 \n", + "4 323.0 738.3 990.9 711.2 568.0 206.9 29.5 31.7 4034.7 112.8 \n", + "\n", + " Mar-May Jun-Sep Oct-Dec \n", + "0 540.7 1207.2 892.1 \n", + "1 483.5 1757.2 705.3 \n", + "2 405.6 1884.4 574.7 \n", + "3 841.3 1848.5 231.0 \n", + "4 645.4 3008.4 268.1 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Dist = pd.read_csv('../data/district wise rainfall normal.csv')\n", + "Dist.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annual rainfall in different districts of Kerala" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Kerala_Dist = Dist.loc[Dist.STATE_UT_NAME=='KERALA',:]\n", + "(Kerala_Dist\n", + " .sort_values('ANNUAL')\n", + " .plot.barh(x='DISTRICT',\n", + " y='ANNUAL', \n", + " title=\"Rainfall in Districts of Kerala\",\n", + " figsize=(12,8),\n", + " legend=False)\n", + ")\n", + "plt.xlabel('Annual Rainfall in mm')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "North west districts are among the highest receiving rainfall places annually. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find out the districts with least rainfall" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(Dist\n", + " .sort_values('ANNUAL',ascending=False)\n", + " .tail(10)\n", + " .plot.barh(x='DISTRICT',y='ANNUAL',\n", + " legend=False,\n", + " title='Districts with Minumum Rainfall in India')\n", + ")\n", + "plt.xlabel('Annual Rainfall (in mm)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## If there are multiple Districts with same name, then we can be explicit by grouping State and District\n", + "(Dist\n", + " .groupby(['STATE_UT_NAME', 'DISTRICT'])\n", + " .ANNUAL.mean()\n", + " .sort_values(ascending=False)\n", + " .tail(10)\n", + " .plot.barh(x='DISTRICT',y='ANNUAL',\n", + " legend=False,\n", + " title='Districts with Minumum Rainfall in India')\n", + ")\n", + "plt.xlabel('Annual Rainfall (in mm)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "Ladak which is part of Jammu and Kashmir receives 94.6 mm. Ladak and Kargil which receive less rainfall are part of Indian State Jammu and Kashmir.Jaisalmer,Sri Ganganaga and Barmer are part of Rajastan State.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Similarly, find districts with maximum rainfall" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "(Dist\n", + " .groupby(['STATE_UT_NAME', 'DISTRICT'])\n", + " .ANNUAL.mean()\n", + " .sort_values(ascending=True)\n", + " .tail(10)\n", + " .plot.barh(x='DISTRICT',y='ANNUAL',\n", + " legend=False)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insights:\n", + "Districts from North eastern states - Manipur, Meghalaya, Arunachal Pradesh and also Southern state of Karnataka receive max rainfall " + ] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/San_Fransisco_salary_analysis/notebook/SF_salary_data_analysis-MK.ipynb b/San_Fransisco_salary_analysis/notebook/SF_salary_data_analysis-MK.ipynb new file mode 100644 index 0000000..8682a82 --- /dev/null +++ b/San_Fransisco_salary_analysis/notebook/SF_salary_data_analysis-MK.ipynb @@ -0,0 +1,2056 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Ow2UdpIAXhtq" + }, + "source": [ + "# City Salary Data Analyis\n", + "One way to understand how a city government works is by looking at who it employs and how its employees are compensated. This data contains the names, job title, and compensation for San Francisco city employees on an annual basis from 2011 to 2014.\n", + "\n", + "# Exploration Ideas\n", + "\n", + "* How have salaries changed over time between different groups of people?\n", + "* How are base pay, overtime pay, and benefits allocated between different groups?\n", + "* Is there any evidence of pay discrimination based on gender in this dataset?\n", + "* How is budget allocated based on different groups and responsibilities?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wwkCDpBoX6cy" + }, + "source": [ + "## Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xn6L-niwV9Kp" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tm2tW8YTYAVw" + }, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ccLpmOX2YDVP" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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IdEmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearNotesAgencyStatus
01NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.0400184.25NaN567595.43567595.432011NaNSan FranciscoNaN
12GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011NaNSan FranciscoNaN
23ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.6NaN335279.91335279.912011NaNSan FranciscoNaN
34CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.056120.71198306.9NaN332343.61332343.612011NaNSan FranciscoNaN
45PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.69737.0182234.59NaN326373.19326373.192011NaNSan FranciscoNaN
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" + ], + "text/plain": [ + " Id EmployeeName JobTitle \\\n", + "0 1 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 2 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 3 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 4 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 5 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.0 400184.25 NaN 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 NaN 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.6 NaN 335279.91 335279.91 \n", + "3 77916.0 56120.71 198306.9 NaN 332343.61 332343.61 \n", + "4 134401.6 9737.0 182234.59 NaN 326373.19 326373.19 \n", + "\n", + " Year Notes Agency Status \n", + "0 2011 NaN San Francisco NaN \n", + "1 2011 NaN San Francisco NaN \n", + "2 2011 NaN San Francisco NaN \n", + "3 2011 NaN San Francisco NaN \n", + "4 2011 NaN San Francisco NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries = pd.read_csv('../data/Salaries.csv', low_memory=False)\n", + "salaries.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ONgXgAFbYErb" + }, + "source": [ + "## 1. Basic data study" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "swpD4k8uYIe5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 148654 entries, 0 to 148653\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 148654 non-null int64 \n", + " 1 EmployeeName 148654 non-null object \n", + " 2 JobTitle 148654 non-null object \n", + " 3 BasePay 148049 non-null object \n", + " 4 OvertimePay 148654 non-null object \n", + " 5 OtherPay 148654 non-null object \n", + " 6 Benefits 112495 non-null object \n", + " 7 TotalPay 148654 non-null float64\n", + " 8 TotalPayBenefits 148654 non-null float64\n", + " 9 Year 148654 non-null int64 \n", + " 10 Notes 0 non-null float64\n", + " 11 Agency 148654 non-null object \n", + " 12 Status 38119 non-null object \n", + "dtypes: float64(3), int64(2), object(8)\n", + "memory usage: 14.7+ MB\n" + ] + } + ], + "source": [ + "salaries.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Id TotalPay TotalPayBenefits Year Notes\n", + "count 148654.000000 148654.000000 148654.000000 148654.000000 0.0\n", + "mean 74327.500000 74768.321972 93692.554811 2012.522643 NaN\n", + "std 42912.857795 50517.005274 62793.533483 1.117538 NaN\n", + "min 1.000000 -618.130000 -618.130000 2011.000000 NaN\n", + "25% 37164.250000 36168.995000 44065.650000 2012.000000 NaN\n", + "50% 74327.500000 71426.610000 92404.090000 2013.000000 NaN\n", + "75% 111490.750000 105839.135000 132876.450000 2014.000000 NaN\n", + "max 148654.000000 567595.430000 567595.430000 2014.000000 NaN" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "351Gd6SrYKJp" + }, + "source": [ + "#### What are your observations about the basic statistics of data?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insight:\n", + "- data cleaning - Pay cols have str data, there -ve salaries\n", + "- Notes, Id, no value\n", + "- benfits play role\n", + "- data is from 2011 to 2014" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "O3PSYxkHYQ9T" + }, + "source": [ + "## 2. What columns do you think do not add value to our analysis? \n", + "Drop those columns." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(148654, 13)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RO-dE2B9YP-3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Id 148654\n", + "EmployeeName 110811\n", + "JobTitle 2159\n", + "BasePay 109724\n", + "OvertimePay 66162\n", + "OtherPay 84243\n", + "Benefits 98647\n", + "TotalPay 138486\n", + "TotalPayBenefits 142098\n", + "Year 4\n", + "Notes 0\n", + "Agency 1\n", + "Status 2\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.nunique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Id`, `Notes`, `Status`, `Agency` seems empty or have unqiue value for each row, it is safe to assume these do not add value and that we can drop them" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYear
0NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.0400184.25NaN567595.43567595.432011
1GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011
2ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.6NaN335279.91335279.912011
3CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.056120.71198306.9NaN332343.61332343.612011
4PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.69737.0182234.59NaN326373.19326373.192011
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" + ], + "text/plain": [ + " EmployeeName JobTitle \\\n", + "0 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.0 400184.25 NaN 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 NaN 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.6 NaN 335279.91 335279.91 \n", + "3 77916.0 56120.71 198306.9 NaN 332343.61 332343.61 \n", + "4 134401.6 9737.0 182234.59 NaN 326373.19 326373.19 \n", + "\n", + " Year \n", + "0 2011 \n", + "1 2011 \n", + "2 2011 \n", + "3 2011 \n", + "4 2011 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.drop(['Id', 'Notes', 'Status' , 'Agency'], axis = 1, inplace=True)\n", + "salaries.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TPYk-QPVYVUo" + }, + "source": [ + "## 3. Check for missing values in the data. \n", + "What is the strategy you will apply to deal with missing values?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Typically we can either impute or remove the null values, lets inspect which columns have null values, however it is often best to **check with the business team**, if you are going to make any assumptions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7cJw9WgMYcCg" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName 0\n", + "JobTitle 0\n", + "BasePay 605\n", + "OvertimePay 0\n", + "OtherPay 0\n", + "Benefits 36159\n", + "TotalPay 0\n", + "TotalPayBenefits 0\n", + "Year 0\n", + "dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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81391Kevin P CashmanDeputy Chief 3NaN0.0149934.110.0149934.11149934.112013
84506Demetrya MullensLicensed Vocational NurseNaN0.0110485.4120779.0110485.41131264.412013
84960Michael M HoranPark Patrol OfficerNaN0.0120000.08841.48120000.00128841.482013
90525Thomas TangPolice Officer 3NaN0.0106079.310.0106079.31106079.312013
90786Michael C HillDeputy SheriffNaN0.081299.0223877.5381299.02105176.552013
..............................
110526Arthur L CurryPS Aide Health ServicesNaN0.010.670.010.6710.672013
110527Nereida VegaSenior ClerkNaN0.05.560.05.565.562013
110528Timothy E GibsonPolice Officer 3NaN0.00.0-2.730.00-2.732013
110529Mark E LahertyPolice Officer 3NaN0.00.0-8.20.00-8.202013
110530David P KuciaPolice Officer 3NaN0.00.0-33.890.00-33.892013
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" + ], + "text/plain": [ + " EmployeeName JobTitle BasePay OvertimePay \\\n", + "81391 Kevin P Cashman Deputy Chief 3 NaN 0.0 \n", + "84506 Demetrya Mullens Licensed Vocational Nurse NaN 0.0 \n", + "84960 Michael M Horan Park Patrol Officer NaN 0.0 \n", + "90525 Thomas Tang Police Officer 3 NaN 0.0 \n", + "90786 Michael C Hill Deputy Sheriff NaN 0.0 \n", + "... ... ... ... ... \n", + "110526 Arthur L Curry PS Aide Health Services NaN 0.0 \n", + "110527 Nereida Vega Senior Clerk NaN 0.0 \n", + "110528 Timothy E Gibson Police Officer 3 NaN 0.0 \n", + "110529 Mark E Laherty Police Officer 3 NaN 0.0 \n", + "110530 David P Kucia Police Officer 3 NaN 0.0 \n", + "\n", + " OtherPay Benefits TotalPay TotalPayBenefits Year \n", + "81391 149934.11 0.0 149934.11 149934.11 2013 \n", + "84506 110485.41 20779.0 110485.41 131264.41 2013 \n", + "84960 120000.0 8841.48 120000.00 128841.48 2013 \n", + "90525 106079.31 0.0 106079.31 106079.31 2013 \n", + "90786 81299.02 23877.53 81299.02 105176.55 2013 \n", + "... ... ... ... ... ... \n", + "110526 10.67 0.0 10.67 10.67 2013 \n", + "110527 5.56 0.0 5.56 5.56 2013 \n", + "110528 0.0 -2.73 0.00 -2.73 2013 \n", + "110529 0.0 -8.2 0.00 -8.20 2013 \n", + "110530 0.0 -33.89 0.00 -33.89 2013 \n", + "\n", + "[605 rows x 9 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.loc[salaries.BasePay.isnull(), :]\n", + "# salaries[salaries.BasePay.isnull()]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYear
0NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.0400184.25NaN567595.43567595.432011
1GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.38NaN538909.28538909.282011
2ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.6NaN335279.91335279.912011
3CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.056120.71198306.9NaN332343.61332343.612011
4PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.69737.0182234.59NaN326373.19326373.192011
..............................
36154SERENA HUGHESSWIMMING INSTRUCTOR/POOL LIFEGUARD0.00.04.17NaN4.174.172011
36155JOE BROWN JRTRANSIT OPERATOR0.00.00.3NaN0.300.302011
36156PAULETTE ADAMSSTATIONARY ENGINEER, WATER TREATMENT PLANT0.00.00.0NaN0.000.002011
36157KAUKAB MOHSINTRANSIT OPERATOR0.00.00.0NaN0.000.002011
36158JOSEPHINE MCCREARYMANAGER IV0.00.00.0NaN0.000.002011
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36159 rows × 9 columns

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" + ], + "text/plain": [ + " EmployeeName JobTitle \\\n", + "0 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "... ... ... \n", + "36154 SERENA HUGHES SWIMMING INSTRUCTOR/POOL LIFEGUARD \n", + "36155 JOE BROWN JR TRANSIT OPERATOR \n", + "36156 PAULETTE ADAMS STATIONARY ENGINEER, WATER TREATMENT PLANT \n", + "36157 KAUKAB MOHSIN TRANSIT OPERATOR \n", + "36158 JOSEPHINE MCCREARY MANAGER IV \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.0 400184.25 NaN 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 NaN 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.6 NaN 335279.91 335279.91 \n", + "3 77916.0 56120.71 198306.9 NaN 332343.61 332343.61 \n", + "4 134401.6 9737.0 182234.59 NaN 326373.19 326373.19 \n", + "... ... ... ... ... ... ... \n", + "36154 0.0 0.0 4.17 NaN 4.17 4.17 \n", + "36155 0.0 0.0 0.3 NaN 0.30 0.30 \n", + "36156 0.0 0.0 0.0 NaN 0.00 0.00 \n", + "36157 0.0 0.0 0.0 NaN 0.00 0.00 \n", + "36158 0.0 0.0 0.0 NaN 0.00 0.00 \n", + "\n", + " Year \n", + "0 2011 \n", + "1 2011 \n", + "2 2011 \n", + "3 2011 \n", + "4 2011 \n", + "... ... \n", + "36154 2011 \n", + "36155 2011 \n", + "36156 2011 \n", + "36157 2011 \n", + "36158 2011 \n", + "\n", + "[36159 rows x 9 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.loc[salaries.Benefits.isnull(), :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### It is safe to assume, that not all roles would get benefits, so we can impute null values with `0` " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.Benefits.fillna(0, inplace=True)\n", + "salaries.Benefits.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName 0\n", + "JobTitle 0\n", + "BasePay 605\n", + "OvertimePay 0\n", + "OtherPay 0\n", + "Benefits 0\n", + "TotalPay 0\n", + "TotalPayBenefits 0\n", + "Year 0\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Also, since the percentage of records with missing `BasePay` is very low, we can safely remove these from our analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName 0\n", + "JobTitle 0\n", + "BasePay 0\n", + "OvertimePay 0\n", + "OtherPay 0\n", + "Benefits 0\n", + "TotalPay 0\n", + "TotalPayBenefits 0\n", + "Year 0\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.dropna(inplace=True)\n", + "salaries.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(148049, 9)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GEMD4kMSYcs6" + }, + "source": [ + "## 4. Convert the object values of all the types of pays to numeric, use to_numeric method of pandas to convert. \n", + "Is it as straightforward or there is some descripency? if yes, how will you overcome it?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName object\n", + "JobTitle object\n", + "BasePay object\n", + "OvertimePay object\n", + "OtherPay object\n", + "Benefits object\n", + "TotalPay float64\n", + "TotalPayBenefits float64\n", + "Year int64\n", + "dtype: object" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qJMPW47DY1Qr" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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EmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYear
148646Not providedNot providedNot ProvidedNot ProvidedNot ProvidedNot Provided0.00.02014
148650Not providedNot providedNot ProvidedNot ProvidedNot ProvidedNot Provided0.00.02014
148651Not providedNot providedNot ProvidedNot ProvidedNot ProvidedNot Provided0.00.02014
148652Not providedNot providedNot ProvidedNot ProvidedNot ProvidedNot Provided0.00.02014
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" + ], + "text/plain": [ + " EmployeeName JobTitle BasePay OvertimePay OtherPay \\\n", + "148646 Not provided Not provided Not Provided Not Provided Not Provided \n", + "148650 Not provided Not provided Not Provided Not Provided Not Provided \n", + "148651 Not provided Not provided Not Provided Not Provided Not Provided \n", + "148652 Not provided Not provided Not Provided Not Provided Not Provided \n", + "\n", + " Benefits TotalPay TotalPayBenefits Year \n", + "148646 Not Provided 0.0 0.0 2014 \n", + "148650 Not Provided 0.0 0.0 2014 \n", + "148651 Not Provided 0.0 0.0 2014 \n", + "148652 Not Provided 0.0 0.0 2014 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.loc[salaries.Benefits == 'Not Provided', :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To convert one column with mixed type, use `pd.to_numeric` \n", + "pass `errors='coerce'` to replace any non-numeric value to `NaN`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + " ... \n", + "148649 0.0\n", + "148650 NaN\n", + "148651 NaN\n", + "148652 NaN\n", + "148653 0.0\n", + "Name: Benefits, Length: 148049, dtype: float64" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.to_numeric(salaries.Benefits, errors='coerce')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lets convert all the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "for column in ['BasePay','OvertimePay','OtherPay','Benefits','TotalPay','TotalPayBenefits']:\n", + " salaries[column] = pd.to_numeric(salaries[column], errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName object\n", + "JobTitle object\n", + "BasePay float64\n", + "OvertimePay float64\n", + "OtherPay float64\n", + "Benefits float64\n", + "TotalPay float64\n", + "TotalPayBenefits float64\n", + "Year int64\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "a2rQ7KbEY2CX" + }, + "source": [ + "## 5. Find the job titles of the 10 highest median base pay.\n", + "\n", + "Base Pay -> The most basic sum of money or hourly rate paid to an employee of a business in compensation for their work efforts or time spent on the job" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "salaries['jobtitle'] = salaries.JobTitle.str.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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EmployeeNameJobTitleBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitsYearjobtitle
0NATHANIEL FORDGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY167411.180.00400184.250.0567595.43567595.432011general manager-metropolitan transit authority
1GARY JIMENEZCAPTAIN III (POLICE DEPARTMENT)155966.02245131.88137811.380.0538909.28538909.282011captain iii (police department)
2ALBERT PARDINICAPTAIN III (POLICE DEPARTMENT)212739.13106088.1816452.600.0335279.91335279.912011captain iii (police department)
3CHRISTOPHER CHONGWIRE ROPE CABLE MAINTENANCE MECHANIC77916.0056120.71198306.900.0332343.61332343.612011wire rope cable maintenance mechanic
4PATRICK GARDNERDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)134401.609737.00182234.590.0326373.19326373.192011deputy chief of department,(fire department)
\n", + "
" + ], + "text/plain": [ + " EmployeeName JobTitle \\\n", + "0 NATHANIEL FORD GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY \n", + "1 GARY JIMENEZ CAPTAIN III (POLICE DEPARTMENT) \n", + "2 ALBERT PARDINI CAPTAIN III (POLICE DEPARTMENT) \n", + "3 CHRISTOPHER CHONG WIRE ROPE CABLE MAINTENANCE MECHANIC \n", + "4 PATRICK GARDNER DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT) \n", + "\n", + " BasePay OvertimePay OtherPay Benefits TotalPay TotalPayBenefits \\\n", + "0 167411.18 0.00 400184.25 0.0 567595.43 567595.43 \n", + "1 155966.02 245131.88 137811.38 0.0 538909.28 538909.28 \n", + "2 212739.13 106088.18 16452.60 0.0 335279.91 335279.91 \n", + "3 77916.00 56120.71 198306.90 0.0 332343.61 332343.61 \n", + "4 134401.60 9737.00 182234.59 0.0 326373.19 326373.19 \n", + "\n", + " Year jobtitle \n", + "0 2011 general manager-metropolitan transit authority \n", + "1 2011 captain iii (police department) \n", + "2 2011 captain iii (police department) \n", + "3 2011 wire rope cable maintenance mechanic \n", + "4 2011 deputy chief of department,(fire department) " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Jq-6G__LY6zi" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "jobtitle\n", + "chief of police 305014.02\n", + "chief, fire department 302068.00\n", + "gen mgr, public trnsp dept 294000.18\n", + "chief of department, (fire department) 285262.00\n", + "dep dir for investments, ret 276153.76\n", + "dept head v 270616.27\n", + "adm, sfgh medical center 268946.02\n", + "controller 267914.00\n", + "deputy chief 3 263408.55\n", + "dep chf of dept (fire dept) 260728.00\n", + "Name: BasePay, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(salaries\n", + " .groupby('jobtitle')['BasePay']\n", + " .median()\n", + " .round(2)\n", + " .nlargest(10)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wMMqnYoSY77K" + }, + "source": [ + "## 6. Find the job titles of the 10 highest median Overtime Pay.\n", + "\n", + "\n", + "Overtime Pay -> Additional financial compensation for any hours worked by nonexempt staff over the amount of forty hours per week." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "NZUvy9HjZAaK" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "salaries.groupby('jobtitle')['OvertimePay'].median().round(2).nlargest(10).plot.barh(title='Top 10 overtime')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "T6muLiSWZHBv" + }, + "source": [ + "## 7. Plot the sectors which have provided the maximum number of employment" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EmployeeName NATHANIEL FORD\n", + "JobTitle GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY\n", + "BasePay 167411\n", + "OvertimePay 0\n", + "OtherPay 400184\n", + "Benefits 0\n", + "TotalPay 567595\n", + "TotalPayBenefits 567595\n", + "Year 2011\n", + "jobtitle general manager-metropolitan transit authority\n", + "Name: 0, dtype: object" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.loc[0,:]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tG-rwzIGZ2rw" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(salaries\n", + " .jobtitle\n", + " .value_counts()\n", + " .nlargest(30)\n", + " .sort_values()\n", + " .plot.barh(figsize=(10,10))\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JKmQo5SLZ-Cq" + }, + "source": [ + "## 8. Plot the top 10 Job titles with highest mean TotalPayBenefits" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qBBjyNazZ_vb" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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YearEmployeeNameTotalPay
361592012Gary Altenberg362844.66
361672012John Goldberg350403.41
361632012Frederick Binkley337204.86
361612012Khoa Trinh336393.73
361732012Mark Kearney327689.78
729272013Samson Lai347102.32
729252013Gregory P Suhr339282.07
729262013Joanne M Hayes-White336922.01
729282013Ellen G Moffatt335537.96
729302013David L Franklin333888.32
1105312014David Shinn471952.64
1105322014Amy P Hart390111.98
1105362014Ellen G Moffatt344187.46
1105332014William J Coaker Jr.339653.70
1105392014Samson Lai335484.96
\n", + "
" + ], + "text/plain": [ + " Year EmployeeName TotalPay\n", + "36159 2012 Gary Altenberg 362844.66\n", + "36167 2012 John Goldberg 350403.41\n", + "36163 2012 Frederick Binkley 337204.86\n", + "36161 2012 Khoa Trinh 336393.73\n", + "36173 2012 Mark Kearney 327689.78\n", + "72927 2013 Samson Lai 347102.32\n", + "72925 2013 Gregory P Suhr 339282.07\n", + "72926 2013 Joanne M Hayes-White 336922.01\n", + "72928 2013 Ellen G Moffatt 335537.96\n", + "72930 2013 David L Franklin 333888.32\n", + "110531 2014 David Shinn 471952.64\n", + "110532 2014 Amy P Hart 390111.98\n", + "110536 2014 Ellen G Moffatt 344187.46\n", + "110533 2014 William J Coaker Jr. 339653.70\n", + "110539 2014 Samson Lai 335484.96" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_paid_emp = (salaries\n", + " .loc[salaries.Year.isin([2012,2013,2014]),:] # filter required years \n", + " .groupby(['Year'])['TotalPay']\n", + " .nlargest() # by default top 5\n", + " .index\n", + " .get_level_values(-1) # index values of last level, in this case row index of highest salaries individuals\n", + " )\n", + "salaries.loc[high_paid_emp, ['Year','EmployeeName','TotalPay']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UCenIjdfaQGB" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "SF_salary_data_analysis.ipynb", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Vehicle_insurane/notebook/Vehicle_insurance_classification-MK.ipynb b/Vehicle_insurane/notebook/Vehicle_insurance_classification-MK.ipynb new file mode 100644 index 0000000..9fa75e8 --- /dev/null +++ b/Vehicle_insurane/notebook/Vehicle_insurance_classification-MK.ipynb @@ -0,0 +1,1502 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vehicle Insurance Interest Response Classification\n", + "\n", + "Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.\n", + "\n", + "Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer.\n", + "\n", + "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.\n", + "\n", + "## Problem Statement \n", + "\n", + "**An insurance company has provided Health Insurance to its customers now they want a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company.**\n", + "\n", + "## Data\n", + "\n", + "|Variable|Definition|\n", + "|-----|-----|\n", + "|id\t|Unique ID for the customer|\n", + "|Gender\t|Gender of the customer|\n", + "|Age\t|Age of the customer|\n", + "|Driving_License\t|0 : Customer does not have DL, 1 : Customer already has DL|\n", + "|Region_Code\t|Unique code for the region of the customer|\n", + "|Previously_Insured\t|1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance|\n", + "|Vehicle_Age\t|Age of the Vehicle|\n", + "|Vehicle_Damage\t|1 : Customer got his/her vehicle damaged in the past. 0 : Customer didn't get his/her vehicle damaged in the past.|\n", + "|Annual_Premium\t|The amount customer needs to pay as premium in the year|\n", + "|PolicySalesChannel\t|Anonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.|\n", + "|Vintage\t|Number of Days, Customer has been associated with the company|\n", + "|Response\t|1 : Customer is interested, 0 : Customer is not interested|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Source: [Kaggle](https://www.kaggle.com/anmolkumar/health-insurance-cross-sell-prediction) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Resources:\n", + "- [Handle imbalance classes](https://elitedatascience.com/imbalanced-classes)\n", + "- [One-Hot Encoding Pandas](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html)\n", + " - [drop_first=True discussion#1](https://datascience.stackexchange.com/questions/28353/always-drop-the-first-column-after-performing-one-hot-encoding)\n", + " - [drop_first=True discussion#2](https://www.kaggle.com/c/instant-gratification/discussion/92817)\n", + "- [Seaborn percentage in countplot](https://github.com/mwaskom/seaborn/issues/1027)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score,roc_auc_score,confusion_matrix,classification_report\n", + "from sklearn.utils import resample,shuffle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('../data/data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(381109, 12)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 381109 entries, 0 to 381108\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 381109 non-null int64 \n", + " 1 Gender 381109 non-null object \n", + " 2 Age 381109 non-null int64 \n", + " 3 Driving_License 381109 non-null int64 \n", + " 4 Region_Code 381109 non-null float64\n", + " 5 Previously_Insured 381109 non-null int64 \n", + " 6 Vehicle_Age 381109 non-null object \n", + " 7 Vehicle_Damage 381109 non-null object \n", + " 8 Annual_Premium 381109 non-null float64\n", + " 9 Policy_Sales_Channel 381109 non-null float64\n", + " 10 Vintage 381109 non-null int64 \n", + " 11 Response 381109 non-null int64 \n", + "dtypes: float64(3), int64(6), object(3)\n", + "memory usage: 34.9+ MB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "Seems like data is clean, without any missing values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary of the data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idAgeDriving_LicenseRegion_CodePreviously_InsuredAnnual_PremiumPolicy_Sales_ChannelVintageResponse
count381109.000000381109.000000381109.000000381109.000000381109.000000381109.000000381109.000000381109.000000381109.000000
mean190555.00000038.8225840.99786926.3888070.45821030564.389581112.034295154.3473970.122563
std110016.83620815.5116110.04611013.2298880.49825117213.15505754.20399583.6713040.327936
min1.00000020.0000000.0000000.0000000.0000002630.0000001.00000010.0000000.000000
25%95278.00000025.0000001.00000015.0000000.00000024405.00000029.00000082.0000000.000000
50%190555.00000036.0000001.00000028.0000000.00000031669.000000133.000000154.0000000.000000
75%285832.00000049.0000001.00000035.0000001.00000039400.000000152.000000227.0000000.000000
max381109.00000085.0000001.00000052.0000001.000000540165.000000163.000000299.0000001.000000
\n", + "
" + ], + "text/plain": [ + " id Age Driving_License Region_Code \\\n", + "count 381109.000000 381109.000000 381109.000000 381109.000000 \n", + "mean 190555.000000 38.822584 0.997869 26.388807 \n", + "std 110016.836208 15.511611 0.046110 13.229888 \n", + "min 1.000000 20.000000 0.000000 0.000000 \n", + "25% 95278.000000 25.000000 1.000000 15.000000 \n", + "50% 190555.000000 36.000000 1.000000 28.000000 \n", + "75% 285832.000000 49.000000 1.000000 35.000000 \n", + "max 381109.000000 85.000000 1.000000 52.000000 \n", + "\n", + " Previously_Insured Annual_Premium Policy_Sales_Channel \\\n", + "count 381109.000000 381109.000000 381109.000000 \n", + "mean 0.458210 30564.389581 112.034295 \n", + "std 0.498251 17213.155057 54.203995 \n", + "min 0.000000 2630.000000 1.000000 \n", + "25% 0.000000 24405.000000 29.000000 \n", + "50% 0.000000 31669.000000 133.000000 \n", + "75% 1.000000 39400.000000 152.000000 \n", + "max 1.000000 540165.000000 163.000000 \n", + "\n", + " Vintage Response \n", + "count 381109.000000 381109.000000 \n", + "mean 154.347397 0.122563 \n", + "std 83.671304 0.327936 \n", + "min 10.000000 0.000000 \n", + "25% 82.000000 0.000000 \n", + "50% 154.000000 0.000000 \n", + "75% 227.000000 0.000000 \n", + "max 299.000000 1.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- average customer age is ~38 years old\n", + "- almost all have driving license\n", + "- around half have previous vehicle insurance cover\n", + "- looks like a class imbalance data set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get a count of the target variable and note down your observations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=data.Response)\n", + "plt.title('Vehicle insurance survey response distribution')\n", + "plt.xticks(ticks=[0,1],labels=['Not interested','Interested'])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- class imbalance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the ratio of male and female in our dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=data.Gender)\n", + "plt.title('Gender Distribution in data')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight\n", + "- fairly equal distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check the gender ratio in the interested customers, what are your observations?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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g5iQ3j6Y76UcWj7oAaSFIspret7GPrKrHkuxO7zbpl1bVJUneA1zAzD9dcAiwmt49pr7Svd4FSX4fOMYjFs0FHrFIs+MNwJU7/uGvqsfp/eDTP3TLL6N365CZrK2qLdW78eEGYMUQapVeFINFmh1h5tug71j+DN3fze5mhUv61tnWN70dzzpoDjJYpNlxI/DrSfaA3u/BA7fTu6s2wDuBL3fTDwNruunjgJ0GeP0n6P1srjRy/m9HmgVVdU+SjwK3JtkOfB04Dbg4yYeAR4Hf6la/CLg6yVp6gfTkAG9xIXB9kq1VdUz7DqTBebmxJKkpT4VJkpoyWCRJTRkskqSmDBZJUlMGiySpKYNFktSUwSJJaur/AP97ZkXQY9j6AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "filter_df = data.Gender.loc[data.Response==1]\n", + "sns.countplot(y=filter_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# use of estimator\n", + "sns.barplot(y=filter_df,\n", + " x=filter_df.index,\n", + " estimator=lambda grp: len(grp) / float(len(filter_df)) * 100)\n", + "plt.xlabel('percentage')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- Males are more likely to be interested in Vehicle Insurance than Females" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Find out the distribution of customers age" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.kdeplot(x=\"Age\",\n", + " hue='Response',\n", + " data=data,\n", + " shade=True)\n", + "plt.title('Age distribution according to the Response')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "if you are facing issues with kdeplot ensure you have latest version of seaborn library installed `pip install -U seaborn`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "seaborn==0.11.0\n" + ] + } + ], + "source": [ + "!pip freeze | grep seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- young people below 30 years are not interested, second group 40-50 years are not interested \n", + "- middle aged people 30-60 years are interested\n", + "\n", + "Inshort, age plays a role in determining interest, below 30 is a strong no, but between 40-50 there may be a yes or a no." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Which regions have people applied from more?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.Region_Code.value_counts(ascending=True).plot.barh(figsize=(12,10))\n", + "plt.title('Customer base by region')\n", + "plt.xlabel('count')\n", + "plt.ylabel('Region code')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- Region code 28 is the region with high number of existing customers\n", + "- its a diminishing curve" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.loc[data.Region_Code==28.0,'Response'].value_counts().plot.barh(title='Class imbalance for region code 28')\n", + "plt.ylabel('Response')\n", + "plt.xlabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's still an imbalance in region coded as `28`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check the ratio of previously insured, note down your observations" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.crosstab(data.Response, data.Previously_Insured).plot.barh(title='Interest for buying vehicle insurance if they already have a insurance')\n", + "plt.xlabel('Count')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight\n", + "- people who are not insured previsouly are mostly like to be interested" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How old are most of the vehicles? Does vehicle damage has any effect on the Response variable?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(data\n", + " .Vehicle_Age\n", + " .value_counts()\n", + " .plot.pie(startangle=50,\n", + " figsize=(8,8),\n", + " autopct=\"%0.1f%%\",\n", + " title='Distribution of customers based on their vehicle age')\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x=data.Vehicle_Age,\n", + " hue=data.Response)\n", + "plt.title('Interest in buying vehicle insurance by vehicle age')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "More than half of the customer in the data have vehicles aged 1-2 years old and they are the ones who are mostly interested" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.crosstab(data.Response, data.Vehicle_Damage).plot.barh(title='Interest in buying vehicle insurance by previous vehicle damage')\n", + "plt.xlabel('count')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "- customer whose vehicle had past damages are mostly interested" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot a correlation matrix, remove the two least correlated features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,10))\n", + "\n", + "sns.heatmap(data.corr(), annot=True, cmap='Spectral')\n", + "plt.title('Correlation Heat Map')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Previously_Insured -0.341170\n", + "Policy_Sales_Channel -0.139042\n", + "id -0.001368\n", + "Vintage -0.001050\n", + "Driving_License 0.010155\n", + "Region_Code 0.010570\n", + "Annual_Premium 0.022575\n", + "Age 0.111147\n", + "Response 1.000000\n", + "Name: Response, dtype: float64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.corr()['Response'].sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 334399\n", + "1 46710\n", + "Name: Response, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_model = data.copy()\n", + "df_model.drop(['id', 'Vintage'], axis=1, inplace=True)\n", + "df_model.Response.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the data into train and test, to avoid data leakage" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "X = df_model.drop(['Response'],axis=1) \n", + "\n", + "y = df_model.loc[:,'Response']\n", + "\n", + "X_train, X_test , y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## One Hot Encoding the categorical variables" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "train = pd.get_dummies(data = X_train, columns= ['Gender', 'Vehicle_Damage', 'Vehicle_Age'], drop_first=True)\n", + "test = pd.get_dummies(data = X_test, columns= ['Gender', 'Vehicle_Damage', 'Vehicle_Age'], drop_first=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets fit a base model of Logistic regression, calculate accuracy, auc_roc score and print classification report.\n", + "\n", + "## What are your observations? Are the results satisfactory?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8733698932066858\n" + ] + } + ], + "source": [ + "logisticRegression = LogisticRegression(max_iter=1000)\n", + "\n", + "logisticRegression.fit(train, y_train)\n", + "\n", + "predictions = logisticRegression.predict(test)\n", + "\n", + "print(accuracy_score(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5038488355619435\n" + ] + } + ], + "source": [ + "print(roc_auc_score(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.88 1.00 0.93 66699\n", + " 1 0.31 0.01 0.02 9523\n", + "\n", + " accuracy 0.87 76222\n", + " macro avg 0.59 0.50 0.48 76222\n", + "weighted avg 0.81 0.87 0.82 76222\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let us upsample the class with less data so that our model can learn about the minority class" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeDriving_LicenseRegion_CodePreviously_InsuredAnnual_PremiumPolicy_Sales_ChannelGender_MaleVehicle_Damage_YesVehicle_Age_< 1 YearVehicle_Age_> 2 YearsResponse
33280339115.0052906.055.001001
11624838111.0023038.026.011000
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31747423141.0129132.0151.000100
34421256148.002630.0154.011010
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" + ], + "text/plain": [ + " Age Driving_License Region_Code Previously_Insured Annual_Premium \\\n", + "332803 39 1 15.0 0 52906.0 \n", + "116248 38 1 11.0 0 23038.0 \n", + "255005 22 1 30.0 1 45318.0 \n", + "317474 23 1 41.0 1 29132.0 \n", + "344212 56 1 48.0 0 2630.0 \n", + "\n", + " Policy_Sales_Channel Gender_Male Vehicle_Damage_Yes \\\n", + "332803 55.0 0 1 \n", + "116248 26.0 1 1 \n", + "255005 152.0 1 0 \n", + "317474 151.0 0 0 \n", + "344212 154.0 1 1 \n", + "\n", + " Vehicle_Age_< 1 Year Vehicle_Age_> 2 Years Response \n", + "332803 0 0 1 \n", + "116248 0 0 0 \n", + "255005 1 0 0 \n", + "317474 1 0 0 \n", + "344212 0 1 0 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.concat([train,y_train],axis=1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeDriving_LicenseRegion_CodePreviously_InsuredAnnual_PremiumPolicy_Sales_ChannelGender_MaleVehicle_Damage_YesVehicle_Age_< 1 YearVehicle_Age_> 2 YearsResponse
11624838111.0023038.026.011000
25500522130.0145318.0152.010100
31747423141.0129132.0151.000100
34421256148.002630.0154.011010
2622930118.0135118.0152.010100
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" + ], + "text/plain": [ + " Age Driving_License Region_Code Previously_Insured Annual_Premium \\\n", + "116248 38 1 11.0 0 23038.0 \n", + "255005 22 1 30.0 1 45318.0 \n", + "317474 23 1 41.0 1 29132.0 \n", + "344212 56 1 48.0 0 2630.0 \n", + "26229 30 1 18.0 1 35118.0 \n", + "\n", + " Policy_Sales_Channel Gender_Male Vehicle_Damage_Yes \\\n", + "116248 26.0 1 1 \n", + "255005 152.0 1 0 \n", + "317474 151.0 0 0 \n", + "344212 154.0 1 1 \n", + "26229 152.0 1 0 \n", + "\n", + " Vehicle_Age_< 1 Year Vehicle_Age_> 2 Years Response \n", + "116248 0 0 0 \n", + "255005 1 0 0 \n", + "317474 1 0 0 \n", + "344212 0 1 0 \n", + "26229 1 0 0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# separating the two classes\n", + "df_majority = df[df['Response']==0]\n", + "df_minority = df[df['Response']==1]\n", + "\n", + "df_majority.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(37187, 11)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_minority.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(267700, 11)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_minority_upsampled = resample(df_minority,replace=True,n_samples=y_train.value_counts()[0],random_state = 123)\n", + "df_minority_upsampled.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(267700, 11)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_majority.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(535400, 11)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "balanced_df = pd.concat([df_minority_upsampled,df_majority])\n", + "balanced_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "balanced_df = shuffle(balanced_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the predictors and the target variables" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "X_train = balanced_df.drop('Response',axis=1)\n", + "y_train = balanced_df['Response']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Again fit a logistic regression model, find accuracy, auc_roc score and observe the results, have they improved?\n", + "## What are your observations?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6439741806827425\n" + ] + } + ], + "source": [ + "logisticRegression2 = LogisticRegression()\n", + "\n", + "logisticRegression2.fit(X_train, y_train)\n", + "\n", + "predictions = logisticRegression2.predict(test)\n", + "\n", + "print(accuracy_score(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7841039196693164\n" + ] + } + ], + "source": [ + "print(roc_auc_score(y_test, predictions))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.99 0.60 0.75 66699\n", + " 1 0.26 0.97 0.41 9523\n", + "\n", + " accuracy 0.64 76222\n", + " macro avg 0.62 0.78 0.58 76222\n", + "weighted avg 0.90 0.64 0.70 76222\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, predictions))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Upsampling of minority class improved the model performace and lifted (increased) auroc, recall and f1-scores" + ] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Video_game_sales_analysis/notebook/Video_Game_Sales-MK.ipynb b/Video_game_sales_analysis/notebook/Video_Game_Sales-MK.ipynb new file mode 100644 index 0000000..a3f60f8 --- /dev/null +++ b/Video_game_sales_analysis/notebook/Video_Game_Sales-MK.ipynb @@ -0,0 +1,2246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Exploring Video Game Sales\n", + "\n", + "This dataset contains a list of video games with sales greater than 100,000 copies.\n", + "\n", + "In this project I will deal only with exploratory analysis, where the objective is to understand how the data is distributed and generate insight for future decision-making, this analysis aims to explore as much as possible the data in a simple, intuitive and informative way. The data used in this project contains information only from 1980 to 2016." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Data information : \n", + "- Rank - Ranking of overall sales, integer\n", + "- Name - The games name\n", + "- Platform - Platform of the games release (i.e. PC,PS4, etc.), object\n", + "- Year - Year of the game's release, float\n", + "- Genre - Genre of the game ,object\n", + "- Publisher - Publisher of the game, object\n", + "- NA_Sales - Sales in North America (in millions), float\n", + "- EU_Sales - Sales in Europe (in millions), float\n", + "- JP_Sales - Sales in Japan (in millions), float\n", + "- Other_Sales - Sales in the rest of the world (in millions), float\n", + "- Global_Sales - Total worldwide sales, float\n", + "\n", + "## Source:\n", + "- Hosted on [Kaggle](https://www.kaggle.com/gregorut/videogamesales), web [scraped](https://github.com/GregorUT/vgchartzScrape) from [VGChartz.com](http://www.vgchartz.com/gamedb/)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Import necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import seaborn as sns\n", + "sns.set_style('whitegrid')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See many other pandas options [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html#available-options)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Read the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RankNamePlatformYearGenrePublisherNA_SalesEU_SalesJP_SalesOther_SalesGlobal_Sales
01Wii SportsWii2006.0SportsNintendo41.4929.023.778.4682.74
12Super Mario Bros.NES1985.0PlatformNintendo29.083.586.810.7740.24
23Mario Kart WiiWii2008.0RacingNintendo15.8512.883.793.3135.82
34Wii Sports ResortWii2009.0SportsNintendo15.7511.013.282.9633.00
45Pokemon Red/Pokemon BlueGB1996.0Role-PlayingNintendo11.278.8910.221.0031.37
\n", + "
" + ], + "text/plain": [ + " Rank Name Platform Year Genre Publisher \\\n", + "0 1 Wii Sports Wii 2006.0 Sports Nintendo \n", + "1 2 Super Mario Bros. NES 1985.0 Platform Nintendo \n", + "2 3 Mario Kart Wii Wii 2008.0 Racing Nintendo \n", + "3 4 Wii Sports Resort Wii 2009.0 Sports Nintendo \n", + "4 5 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n", + "\n", + " NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales \n", + "0 41.49 29.02 3.77 8.46 82.74 \n", + "1 29.08 3.58 6.81 0.77 40.24 \n", + "2 15.85 12.88 3.79 3.31 35.82 \n", + "3 15.75 11.01 3.28 2.96 33.00 \n", + "4 11.27 8.89 10.22 1.00 31.37 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"../data/vgsales.csv\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 16598 entries, 0 to 16597\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Rank 16598 non-null int64 \n", + " 1 Name 16598 non-null object \n", + " 2 Platform 16598 non-null object \n", + " 3 Year 16327 non-null float64\n", + " 4 Genre 16598 non-null object \n", + " 5 Publisher 16540 non-null object \n", + " 6 NA_Sales 16598 non-null float64\n", + " 7 EU_Sales 16598 non-null float64\n", + " 8 JP_Sales 16598 non-null float64\n", + " 9 Other_Sales 16598 non-null float64\n", + " 10 Global_Sales 16598 non-null float64\n", + "dtypes: float64(6), int64(1), object(4)\n", + "memory usage: 1.4+ MB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 1. Which genre have the most games been made for?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Top Game Genre')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = sns.countplot(y='Genre',\n", + " data=data, \n", + " order=data.Genre.value_counts().index)\n", + "ax.set_title('Top Game Genre')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Resources:\n", + "- https://seaborn.pydata.org/generated/seaborn.countplot.html\n", + "- https://github.com/mwaskom/seaborn/issues/1029#issuecomment-342365439" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Insight:\n", + "Action is the most popular genre followed by Sports" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 2. Which year had the most game releases?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Game releases by year')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,10))\n", + "sns.countplot(y='Year', data=data).set_title('Game releases by year')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Insight:\n", + "- Most games were released between 2008 and 2011\n", + "- Some areas to explore would be to look whether this trend of releasing fewer games is due to the fact there are more in-game-purchases or due to continous development trends?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 3. What are the top game genres produced for the five years with maximum game production?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2009.0 1431\n", + "2008.0 1428\n", + "2010.0 1259\n", + "2007.0 1202\n", + "2011.0 1139\n", + "Name: Year, dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lets get the five years with max game production\n", + "max_gp = data.Year.value_counts().nlargest(5)\n", + "max_gp" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## lets try using pandas and matplotlib\n", + "(data\n", + " .loc[data.Year.isin(max_gp.index),['Year','Genre']]\n", + " .groupby(['Year','Genre'])\n", + " .Genre\n", + " .count()\n", + " .unstack()\n", + " .plot.bar(figsize=(30,10), title='Distribution of genres for five max production years')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribution of genres for five max production years')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# with seaborn this type of plot is very easy to make\n", + "plt.figure(figsize=(30,10))\n", + "sns.countplot(x='Year',\n", + " data=data,\n", + " order=data.Year.value_counts().nlargest(5).index.sort_values(),\n", + " hue='Genre').set_title('Distribution of genres for five max production years')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Insight:\n", + "Nothing interesting in particular, except that Action is the dominant genre" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 4. Which genre has sold the most games per year?\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "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", + "
YearGenreGlobal_Sales
01980.0Shooter7.07
11981.0Action14.84
21982.0Puzzle10.03
31983.0Platform6.93
41984.0Shooter31.10
\n", + "
" + ], + "text/plain": [ + " Year Genre Global_Sales\n", + "0 1980.0 Shooter 7.07\n", + "1 1981.0 Action 14.84\n", + "2 1982.0 Puzzle 10.03\n", + "3 1983.0 Platform 6.93\n", + "4 1984.0 Shooter 31.10" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## lets find the global sales for each genre per year\n", + "sales_by_year = (data\n", + " .groupby(by=['Year','Genre'])\n", + " .Global_Sales\n", + " .sum()\n", + " .reset_index())\n", + "\n", + "## Lets find the max global sales per year\n", + "sales_by_year['Max_Global_Sales'] = (sales_by_year\n", + " .groupby(['Year'])\n", + " .Global_Sales\n", + " .transform(max)\n", + " )\n", + "\n", + "## Lets filter out the most selling genre per year\n", + "max_sales_by_year = (sales_by_year\n", + " .loc[sales_by_year.Global_Sales == sales_by_year.Max_Global_Sales]\n", + " .drop(columns=['Max_Global_Sales'])\n", + " .reset_index(drop=True)\n", + ")\n", + "\n", + "max_sales_by_year.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(30,10))\n", + "ax = sns.barplot(x='Year',\n", + " y='Global_Sales',\n", + " hue='Genre',\n", + " palette=\"Set2\",\n", + " dodge=False,\n", + " data=max_sales_by_year)\n", + "ax.set_title('Highest selling genres by year, globally')\n", + "## adding annotaions on bars\n", + "for index in range(0,max_sales_by_year.shape[0]):\n", + " ax.text(index, max_sales_by_year.Global_Sales[index]+1,\n", + " str(max_sales_by_year.Genre[index] + '---' + \n", + " str(round(max_sales_by_year.Global_Sales[index],2))),\n", + " rotation=90)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Resources:\n", + "- https://stackoverflow.com/a/59683826/8210613\n", + "- https://python-graph-gallery.com/46-add-text-annotation-on-scatterplot/\n", + "- https://thepythonguru.com/python-string-formatting/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Insight:\n", + "Action has dominated the market in the last 15 years or so." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 5. Which platform has the highest sales globally?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 1.0, 'Global Sales by Platform')]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "df_plat_sales = (data\n", + " .groupby('Platform')\n", + " .Global_Sales.sum()\n", + " .sort_values(ascending=False)\n", + " .reset_index()\n", + ")\n", + "plt.figure(figsize=(15,10))\n", + "sns.barplot(x='Global_Sales',\n", + " y='Platform',\n", + " data=df_plat_sales).set(title='Global Sales by Platform')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 6. Which individual game has the highest sales globally?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameGlobal_Sales
0Wii Sports82.74
1Super Mario Bros.40.24
2Mario Kart Wii35.82
3Wii Sports Resort33.00
4Pokemon Red/Pokemon Blue31.37
5Tetris30.26
6New Super Mario Bros.30.01
7Wii Play29.02
8New Super Mario Bros. Wii28.62
9Duck Hunt28.31
10Nintendogs24.76
11Mario Kart DS23.42
12Pokemon Gold/Pokemon Silver23.10
13Wii Fit22.72
14Wii Fit Plus22.00
15Kinect Adventures!21.82
16Grand Theft Auto V21.40
17Grand Theft Auto: San Andreas20.81
18Super Mario World20.61
19Brain Age: Train Your Brain in Minutes a Day20.22
\n", + "
" + ], + "text/plain": [ + " Name Global_Sales\n", + "0 Wii Sports 82.74\n", + "1 Super Mario Bros. 40.24\n", + "2 Mario Kart Wii 35.82\n", + "3 Wii Sports Resort 33.00\n", + "4 Pokemon Red/Pokemon Blue 31.37\n", + "5 Tetris 30.26\n", + "6 New Super Mario Bros. 30.01\n", + "7 Wii Play 29.02\n", + "8 New Super Mario Bros. Wii 28.62\n", + "9 Duck Hunt 28.31\n", + "10 Nintendogs 24.76\n", + "11 Mario Kart DS 23.42\n", + "12 Pokemon Gold/Pokemon Silver 23.10\n", + "13 Wii Fit 22.72\n", + "14 Wii Fit Plus 22.00\n", + "15 Kinect Adventures! 21.82\n", + "16 Grand Theft Auto V 21.40\n", + "17 Grand Theft Auto: San Andreas 20.81\n", + "18 Super Mario World 20.61\n", + "19 Brain Age: Train Your Brain in Minutes a Day 20.22" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## it seems like the data set is already sorted by Global_Sales\n", + "data.loc[:19,['Name','Global_Sales']]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.Global_Sales.head(20).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Need for Speed: Most Wanted 12\n", + "Madden NFL 07 9\n", + "LEGO Marvel Super Heroes 9\n", + "FIFA 14 9\n", + "Ratatouille 9\n", + " ..\n", + "Haze 1\n", + "Digging for Dinosaurs 1\n", + "Age of Empires III: Gold Edition 1\n", + "Classic Action: Devilish 1\n", + "Spore Hero 1\n", + "Name: Name, Length: 11493, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# however the games seem to repeat, maybe due to be re-released on different plaform or regions?\n", + "data.Name.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 1.0, 'Top 20 selling games globally')]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# so lets group them together and calculate the total global_sales before plotting\n", + "df_name_sales = (data\n", + " .groupby('Name')\n", + " .Global_Sales.sum()\n", + " .sort_values(ascending=False)\n", + " .reset_index()\n", + " .head(20)\n", + ")\n", + "plt.figure(figsize=(15,10))\n", + "sns.barplot(x='Global_Sales',\n", + " y='Name',\n", + " data=df_name_sales).set(title='Top 20 selling games globally')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Insight:\n", + "Wii Sports is by far the most top selling game in the world, followed by GTA V and Super Mario Bros." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 7. Find the total revenue by region." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "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": [ + "(data\n", + " .loc[:,['NA_Sales','EU_Sales','JP_Sales','Other_Sales']]\n", + " .sum()\n", + " .reset_index()\n", + " .rename(columns={'index':'region',0:'sales'})\n", + " .set_index('region')\n", + " .plot.pie(y='sales', startangle=270,figsize=(10,8),\n", + " autopct='%.1f%%', title=\"Sales by region\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Task 8. What other questions you would want to get answered?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "8.1. Distribution of genres for top five producing years" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Distribution of genres for top five producing years')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(25,10))\n", + "\n", + "sns.countplot(x='Genre',\n", + " data=data.loc[data.Year.isin(data.Year.value_counts().head(5).index),:],\n", + " hue='Year',\n", + " ).set_title('Distribution of genres for top five producing years')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "8.2 Animate pie-chart in Task 7" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "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", + " \n", + "
NA_SalesEU_SalesJP_SalesOther_Sales
Year
198010.590.670.000.12
198133.401.960.000.32
198226.921.650.000.31
19837.760.808.100.14
198433.282.1014.270.70
\n", + "
" + ], + "text/plain": [ + " NA_Sales EU_Sales JP_Sales Other_Sales\n", + "Year \n", + "1980 10.59 0.67 0.00 0.12\n", + "1981 33.40 1.96 0.00 0.32\n", + "1982 26.92 1.65 0.00 0.31\n", + "1983 7.76 0.80 8.10 0.14\n", + "1984 33.28 2.10 14.27 0.70" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert data into year-wise\n", + "df1 = (data\n", + " .loc[:,['Year','NA_Sales','EU_Sales','JP_Sales','Other_Sales']]\n", + " .dropna()\n", + " .astype({'Year':int})\n", + " .groupby('Year')\n", + " .sum()\n", + ")\n", + "df1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "from matplotlib import animation, rc\n", + "from IPython.display import HTML, Image\n", + "\n", + "def update(i):\n", + " if(df1.index.min() == df1.index[i]):\n", + " # when we have no data 1980-1980\n", + " plot,_ = ax.pie(x=np.zeros(0))\n", + " else:\n", + " ax.clear()\n", + " ax.axis('equal')\n", + " plot = (df1\n", + " .head(i)\n", + " .sum()\n", + " .plot.pie(y=df1.columns,\n", + " startangle=270,\n", + " autopct='%.1f%%', \n", + " title=\"Sales by region {}-{}\".format(df1.index.min(),df1.index[i]),\n", + " label='')\n", + " )\n", + "\n", + " return(plot)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# equivalent to rcParams['animation.html'] = 'html5'\n", + "rc('animation', html='html5')\n", + "fig, ax = plt.subplots()\n", + "animator = animation.FuncAnimation(fig, update, frames=df1.shape[0], repeat=False)\n", + "animator" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "## Resources: \n", + "- You will need to install FFmpeg and setup PATH variables before you can use it\n", + "- https://medium.com/@suryadayn/error-requested-moviewriter-ffmpeg-not-available-easy-fix-9d1890a487d3\n", + "- https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-full.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What useful insights would you like to take to the stakeholders planning to come up with a new game?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Take away activity : Understand the data more, complete the analysis keeping in mind the above business stakeholder question or any other and share your notebook on slack" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Visualize Indian Election Data Analysis using Pandas/notebook/Visualization_Code_Along_MK.ipynb b/Visualize Indian Election Data Analysis using Pandas/notebook/Visualization_Code_Along_MK.ipynb new file mode 100644 index 0000000..6d41d0d --- /dev/null +++ b/Visualize Indian Election Data Analysis using Pandas/notebook/Visualization_Code_Along_MK.ipynb @@ -0,0 +1,749 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Indian Election Analysis\n", + "\n", + "India's lower house of Parliament, the Lok Sabha, has 543 seats in total. Members of Lok Sabha (House of the People) or the lower house of India's Parliament are elected by being voted upon by all adult citizens of India, from a set of candidates who stand in their respective constituencies. Every adult citizen of India can vote only in their constituency. Candidates who win the Lok Sabha elections are called 'Member of Parliament' and hold their seats for five years or until the body is dissolved by the President on the advice of the council of ministers.\n", + "\n", + "There are more than 700 million voters with more than 800,000 polling stations.\n", + "\n", + "The Lok Sabha election is a very complex affair as it involves a lot of factors. It is this very fact that makes it a perfect topic to analyze.\n", + "\n", + "Currently there are two major parties in India, Bhartiya Janta Party(BJP) and Indian National Congress(INC).\n", + "\n", + "As India is country of diversities, and each region is very different from every other region, there are a lot of regional or state parties having major influences. These parties can either support any of the alliance to make a government or can stay independent.\n", + "\n", + "There are two major alliances, the NDA led by BJP and the UPA led by INC.\n", + "\n", + "## About the data set\n", + "\n", + "### There are two datasets:\n", + "\n", + "#### 1. 2009 Candidate dataset:\n", + "\n", + "The candidate dataset has 15 features namely 'ST_CODE', 'State_name', 'Month', 'Year', 'PC_Number', 'PC_name', 'PC_Type', 'Candidate_Name', 'Candidate_Sex', 'Candidate_Category', 'Candidate_Age', 'Party_Abbreviation', 'Total_Votes_Polled', 'Position','Alliance'.\n", + "\n", + "#### 2. 2009 Electors dataset\n", + "\n", + "The elector dataset consist of 8 features namely 'STATE CODE', 'STATE', 'PC NO', 'PARLIAMENTARY CONSTITUENCY','Total voters', 'Total_Electors', 'TOT_CONTESTANT', 'POLL PERCENTAGE'." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading necessary Libraries and dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "STATE CODE 0\nSTATE 0\nPC NO 0\nPARLIAMENTARY CONSTITUENCY 0\nTotal voters 0\nTotal_Electors 0\nTOT_CONTESTANT 0\nPOLL PERCENTAGE 0\ndtype: int64" + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# Read both the datasets\n", + "electors_2009 = pd.read_csv('../data/LS2009Electors.csv')\n", + "# quick check for any null values\n", + "electors_2009.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "(8070, 15)" + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "candidate_2009 = pd.read_csv(\"../data/candidate09.csv\")\n", + "# see the shape \n", + "candidate_2009.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 1 : Plot a bar chart to compare the number of male and female candidates in the election" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Finding the value counts of both the genders\n", + "# Plotting a bar graph\n", + "candidate_2009.Candidate_Sex.value_counts().plot(kind='bar',rot=0, title='Gender comparison 2009 elections')\n", + "plt.xticks(ticks=(0,1), labels=('Male Candidates', 'Female Candidates'))\n", + "plt.ylabel('No. of candidates')\n", + "plt.xlabel('Gender')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Female candidates are significantly less. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 2 : Plot a histogram of the age of all the candidates as well as of the winner amongst them. Compare them and note an observation" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " winner all\ncount 541.000000 8070.000000\nmean 53.059150 45.837673\nstd 11.215739 11.831528\nmin 26.000000 25.000000\n25% 45.000000 37.000000\n50% 53.000000 45.000000\n75% 60.000000 54.000000\nmax 88.000000 99.000000", + "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
winnerall
count541.0000008070.000000
mean53.05915045.837673
std11.21573911.831528
min26.00000025.000000
25%45.00000037.000000
50%53.00000045.000000
75%60.00000054.000000
max88.00000099.000000
\n
" + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "# Selecting the subset of the data with winner candidates\n", + "# looking at their summary statistics\n", + "pd.DataFrame({'winner':candidate_2009[candidate_2009.Position == 1].Candidate_Age.describe(),\n", + " 'all':candidate_2009.Candidate_Age.describe()})\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Meidan age is 53 for winners whereas it is 45 for all candidates, indicating winners are bit older (or experienced)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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uMzOzFlWknf+BwI4R8RqApPGkfvhPKzMwM6uvr+32zWoVbec/rGZ4gzICMTOzxily5P9t4EFJt5Gae+6Fj/rNzFpakQu+l0q6HXg3KfmfGhHPlh2YmZmVp1DfPhExD7i25FjMzKxB3LePmVkFOfmbmVVQ3eQvaZCkGY0KxszMGqNu8s9t+x+SNLJB8ZiZWQMUueA7HHhE0v3Ai52FEXFwaVGZmVmpiiT/M0uPwszMGqpIO//JkkYB20TErZLWAQaXH5qZmZWlSMdunwUmABfmoi2A35YZlJmZlatIU8/PA7sDiwEiYhawaZlBmZlZuYok/5cj4pXOEUlrkJ7kZWZmLapI8p8s6SvA2pL2B34DXFduWGZmVqYiyX8ssAD4A3ACcCPw1d4WkrSlpNskzZT0iKSTcvlGkiZKmpXfN1yZHTAzs74r0trntfwAl/tI1T2PR0SRap9lwJciYpqkocBUSROBTwGTIuJsSWNJPy6n9nsPzMysz4q09jkQeBL4T+B84AlJB/S2XETMi4hpeXgJMJPUUugQYHyebTxwaP9CNzOz/ipyk9d3gfdHxBMAkt4C3ADcVHQjktqBnUhnD5vlLqKJiHmS3HLIzKzBitT5z+9M/NlsYH7RDUhaD7gSODkiFvdhuTGSpkiasmDBgqKLmZlZAT0e+Us6LA8+IulG4ApSnf8RwANFVi5pCCnx/zoirsrFz0kano/6h9PDD0lEjAPGAXR0dLhpqZnZKlSv2ucjNcPPAe/LwwuAXlvoSBJwMTAzIv6jZtK1wGjg7Px+TV8CNjOzlddj8o+I41Zy3bsDnwT+IGl6LvsKKelfIel44CnSmYSZmTVQrxd8JW0F/CvQXjt/b106R8RdpAe+d2ff4iGamdmqVqS1z29J1TfXAa+VG46ZmTVCkeT/UkT8Z+mRmJlZwxRJ/j+Q9HXgd8DLnYWdN3CZmQG0j72hT/PPOfvAkiKxIook/3eQLtzuw4pqn8jjZmbWgook/48CW9d262xmZq2tyB2+DwHDyg7EzMwap8iR/2bAY5Ie4PV1/nWbelrz9LXu1cyqp0jy/3rpUZiZWUMV6c9/ciMCMTOzxilyh+8SVjyzd01gCPBiRKxfZmBmZlaeIkf+Q2vHJR0K7FJaRGZmVroirX1eJyJ+i9v4m5m1tCLVPofVjA4COlhRDWRmZi2oSGuf2n79lwFzSM/hNTOzFlWkzn9l+/U3M7MBpt5jHE+vs1xExFklxGNmZg1Q78j/xW7K1gWOBzYGnPzNzFpUvcc4frdzWNJQ4CTgOOAy4Ls9LWdmZgNf3Tp/SRsBXwSOBcYDO0fEC40IzMzMylOvzv87wGHAOOAdEbG0YVGZmVmp6t3k9SXgzcBXgbmSFufXEkmLGxOemZmVoV6df5/v/jUzs9ZQ5CYvW8Xc376ZNZuP7s3MKsjJ38ysgpz8zcwqyHX+ZtYtX5tavfnI38ysgpz8zcwqyMnfzKyCnPzNzCqotOQv6aeS5kuaUVO2kaSJkmbl9w3L2r6ZmfWszCP/nwMf6lI2FpgUEdsAk/K4mZk1WGnJPyLuAJ7vUnwIqWto8vuhZW3fzMx61uh2/ptFxDyAiJgnadOeZpQ0BhgDMHLkyAaF13duC21mrWjAXvCNiHER0RERHW1tbc0Ox8xstdLo5P+cpOEA+X1+g7dvZmY0PvlfC4zOw6OBaxq8fTMzo9ymnpcC9wLbSnpG0vHA2cD+kmYB++dxMzNrsNIu+EbE0T1M2resbZqZWTED9oKvmZmVx8nfzKyCnPzNzCrIyd/MrIKc/M3MKsiPcTSzltHX7lTmnH1gSZG0Ph/5m5lVkJO/mVkFOfmbmVWQ6/zNrCncHXpz+cjfzKyCnPzNzCrIyd/MrIKc/M3MKsjJ38ysgpz8zcwqyMnfzKyCVvt2/m5LbGb2Rj7yNzOrICd/M7MKcvI3M6ug1b7O38ysqCo9L8BH/mZmFeTkb2ZWQU7+ZmYV5ORvZlZBTv5mZhXk5G9mVkFO/mZmFeR2/ma22hpofXsNpPsImnLkL+lDkh6X9ISksc2Iwcysyhqe/CUNBi4ADgC2A46WtF2j4zAzq7JmHPnvAjwREbMj4hXgMuCQJsRhZlZZzajz3wJ4umb8GeA9XWeSNAYYk0eXSnq8AbEVsQnwl2YHsZK8DwOD92Fg6Pc+6JxVHEn/19/TPozqaYFmJH91UxZvKIgYB4wrP5y+kTQlIjqaHcfK8D4MDN6HgaGq+9CMap9ngC1rxkcAc5sQh5lZZTUj+T8AbCNpK0lrAkcB1zYhDjOzymp4tU9ELJP0BeAWYDDw04h4pNFxrIQBVxXVD96HgcH7MDBUch8U8YbqdjMzW825ewczswpy8jczqyAn/x5IWkvS/ZIekvSIpDNz+VaS7pM0S9Ll+aL1gCZpsKQHJV2fx1tqHyTNkfQHSdMlTcllG0mamPdhoqQNmx1nPZKGSZog6TFJMyXt1oL7sG3+DDpfiyWd3IL7cUr+Ts+QdGn+rrfad+KkHP8jkk7OZX36HJz8e/YysE9E7ADsCHxI0q7AOcD3ImIb4AXg+CbGWNRJwMya8Vbch/dHxI41bZnHApPyPkzK4wPZD4CbI+JtwA6kz6Ol9iEiHs+fwY7Au4C/AVfTQvshaQvgRKAjIrYnNTo5ihb6TkjaHvgsqbeEHYCDJG1DXz+HiPCrlxewDjCNdCfyX4A1cvluwC3Njq+X2Efkf4R9gOtJN9m12j7MATbpUvY4MDwPDwceb3acdeJfH/gTuYFFK+5DN/v0AeDuVtsPVvQwsBGpteP1wAdb6TsBHAFcVDP+NeDLff0cfORfR64umQ7MByYCTwILI2JZnuUZ0j/TQPZ90j/Ga3l8Y1pvHwL4naSpudsPgM0iYh5Aft+0adH1bmtgAfCzXP12kaR1aa196Ooo4NI83DL7ERF/Bs4DngLmAYuAqbTWd2IGsJekjSWtA3yYdONsnz4HJ/86ImJ5pFPcEaRTrH/sbrbGRlWcpIOA+RExtba4m1kH7D5ku0fEzqSeYD8vaa9mB9RHawA7Az+OiJ2AFxnAVSO9yfXhBwO/aXYsfZXrwQ8BtgLeDKxL+r/qasB+JyJiJqmaaiJwM/AQsKzuQt1w8i8gIhYCtwO7AsMkdd4cN9C7ptgdOFjSHFLvqfuQzgRaaR+IiLn5fT6pjnkX4DlJwwHy+/zmRdirZ4BnIuK+PD6B9GPQSvtQ6wBgWkQ8l8dbaT/2A/4UEQsi4lXgKuC9tN534uKI2Dki9gKeB2bRx8/Byb8HktokDcvDa5P+aWYCtwGH59lGA9c0J8LeRcRpETEiItpJp+n/HRHH0kL7IGldSUM7h0l1zTNIXYKMzrMN6H2IiGeBpyVtm4v2BR6lhfahi6NZUeUDrbUfTwG7SlpHkljxWbTMdwJA0qb5fSRwGOnz6NPn4Dt8eyDpncB4UmuAQcAVEfENSVuTjqI3Ah4EPhERLzcv0mIk7Q38W0Qc1Er7kGO9Oo+uAVwSEd+StDFwBTCS9IU+IiKeb1KYvZK0I3ARsCYwGziO/H9Fi+wDQK5jfhrYOiIW5bJW+yzOBD5Oqip5EPgMqY6/Jb4TAJLuJF2/exX4YkRM6uvn4ORvZlZBrvYxM6sgJ38zswpy8jczqyAnfzOzCnLyNzOrICd/axhJH5UUkt7WgG215V4aH5S0Z5dpJ+cmi53jS1dyW7tIukPS47nXzotq178S6/2UpPPz8Ock/VM387RLmtHLetolHbOy8djqxcnfGulo4C7SDWdl2xd4LCJ2iog7u0w7mdRZ30qTtBmpm4NTI2JbUhcgNwNDV8X6O0XETyLiF/1cvB1w8rfXcfK3hpC0Hqm7ieOpSf6SBkn6Ue6X/HpJN0o6PE97l6TJuUO3WzpvXe+y3lGSJkl6OL+PzDdUnQt8OPc7v3bN/CeS+nS5TdJtNeXfUnp2w+9zQu88e7hS0gP5tXs3u/Z5YHxE3AsQyYSIeC6fEdyTzz7u6bzDNx/RXyXp5tz3+rk1cRwn6Y+SJue/V2f5GZL+rebv8pCke/P2O+dpl3SnpGn59d486Wxgz/y3OEWpw8Lv5H16WNIJefnh+QxmulJf8a87Y7LVTLO7J/WrGi/gE8DFefgeYOc8fDhwI+lAZHNSX+qHA0PyfG15vo8DP+1mvdcBo/Pwp4Hf5uFPAef3EMscarqIJnXi9ZE8fC7w1Tx8CbBHHh4JzOxmXVcBh/SwnfVZ0U3wfsCVNbHNBjYA1gL+h9Qr43DSnZltpDuB7+7cB+AM0h3aAA8D78vD3wFm5OF1gLXy8DbAlDy8N3B9TVxjavbxTcAUUkdnXwL+fy4fDAxt9v+NX+W9OjsyMivb0aRO5SDdRn806RkJewC/iYjXgGdrjsa3BbYHJqYuWBhM6oK3q91IfZsA/JKUvPvqFVK/7pC6990/D+8HbJe3D7C+pKERsaTgejcAxis9aCNIP2idJsWK7hEeBUYBmwC3R8SCXH458A+1K5S0ATAsIibnol+yolfKIcD5+cxneddla3wAeGfnGVaOcxvgAeCnkoaQfkSnF9xPa0FO/la63OfIPsD2koKUyEPSl+m+i2ly+SMRsVsfN9ef/kpejXy4S0qand+LQcBuEfH3Oss+QnqqVXedaJ0F3BYRH5XUTuoZtlNtvzG12+wtftWZ5xTgOdLTnQYBL9VZx79GxC1vmJC6yz4Q+KWk70T/rzPYAOc6f2uEw4FfRMSoiGiPiC1JT7bag3QB+GO57n8zUhUFpKcStUnaDUDSEElv72bd97DiGsKxeX29WUKxC7K/A77QOZKPqLs6Hxgt6T01831C0uakI+o/5+JPFdjefcDeSg/pGEJ6YtPrROpefJGkPXLRsTWTNwDm5bOoT5J+ZOGN+3sL8M95G0j6B6XeU0eRnv/wX8DFpG6nbTXl5G+NcDQreubsdCWpBcqVpP7uZwAXkhLgooh4hfSjcY6kh4DppH7XuzoROE7Sw6SEd1KBeMYBN9Ve8O3BiUBHvij6KPC5rjNE6tP+KOC83NRzJrAnsJhUBfVtSXezIhH3KNLTl84A7gVuJVWLdec44IJ8wbf2rORHpB+i35OqfF7M5Q8Dy/JF4lNIvYs+CkzLzUQvJJ157A1Ml/Qg8DHSc4dtNeVePa3pJK0XEUtz9dD9pCd3PdvsuMxWZ67zt4HgeqUH56wJnOXEb1Y+H/mbmVWQ6/zNzCrIyd/MrIKc/M3MKsjJ38ysgpz8zcwq6H8BBfpTF19uYd8AAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Lets plot the winners\n", + "winner = candidate_2009[candidate_2009.Position == 1].Candidate_Age\n", + "winner.plot.hist(bins=25, title='Winner Candidates')\n", + "plt.xlabel('Age of the Candidates')\n", + "plt.ylabel('Number of Candidates')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Seems like Age is normally distribution for winning candidates" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Lets plot them side by side\n", + "\n", + "# Histogram of the age of all the candidates\n", + "fig,ax = plt.subplots(nrows=1,ncols=2,tight_layout = True)\n", + "\n", + "ax[0].hist(list(candidate_2009.Candidate_Age),bins = 25)\n", + "ax[0].set_xlabel('Age of the Candidates')\n", + "ax[0].set_ylabel('Number of Candidates')\n", + "ax[0].set_title('All the Candidates')\n", + "\n", + "ax[1].hist(list(winner),bins = 25,color = 'red')\n", + "ax[1].set_xlabel('Age of the Candidates')\n", + "ax[1].set_ylabel('Number of Candidates')\n", + "ax[1].set_title('Winner Candidates')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Most of the candidates are of the in the age bracket of 40 - 50 but the age bracket of winner candidates is between 50-70." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# How about we overlap these histogram to see the proportions\n", + "bins =25\n", + "plt.figure(figsize=(8,8))\n", + "\n", + "candidate_2009.Candidate_Age.plot.hist(bins=bins,label='all')\n", + "candidate_2009[candidate_2009.Position == 1].Candidate_Age.plot.hist(bins=bins,label='winners')\n", + "plt.legend(loc='upper right')\n", + "plt.title('Age comparison winners vs all')\n", + "plt.xlabel('Age')\n", + "plt.ylabel('No of candidates')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight: Mostly older candidates are the winners (50-65), but the proportion of candidates participating vs winning is drastically different, indicating only a handful of people get selected. It would be interesting to see which parties have the highest conversion rate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 3 : Plot a bar graph to get the vote shares of different parties" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " Party_Abbreviation Total_Votes_Polled\n0 TDP 372268.0\n1 INC 257181.0\n2 PRAP 112930.0\n3 BJP 57931.0\n4 BSP 16471.0\n... ... ...\n8065 IND 422.0\n8066 IND 378.0\n8067 IND 378.0\n8068 IND 375.0\n8069 IND 298.0\n\n[8070 rows x 2 columns]", + "text/html": "
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Party_AbbreviationTotal_Votes_Polled
0TDP372268.0
1INC257181.0
2PRAP112930.0
3BJP57931.0
4BSP16471.0
.........
8065IND422.0
8066IND378.0
8067IND378.0
8068IND375.0
8069IND298.0
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" + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "# Lets see the features required for this plot\n", + "candidate_2009[['Party_Abbreviation', 'Total_Votes_Polled']]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Group the dataframe by 'Party_Abbreviation' and sum the 'Total _Votes_Polled'\n", + "# Plot the vote share of top 10 parties\n", + "candidate_2009.groupby('Party_Abbreviation')['Total_Votes_Polled'].\\\n", + " sum().sort_values(ascending=False)[:10].\\\n", + " plot.bar(rot=0, title='Vote shares')\n", + "plt.ylabel('No of votes in millions')\n", + "plt.xlabel('Party')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: The vote share of Indian National Congres(INC) is highest followed by the Bhartiya Janta Party(BJP). The intresting observation here is the Bahujan Samaj Party(BSP) despite being a regional party has the third highest number of vote share. Indicating the state of UP is deciding factor." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 4 : Plot a barplot to compare the mean poll percentage of all the states" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "## Adapted from https://stackoverflow.com/a/56780852/8210613 to show values on the bars\n", + "def show_values_on_bars(axs, h_v=\"v\", xspace=0.4, yspace=0.4, unit='%'):\n", + " def _show_on_single_plot(ax):\n", + " if h_v == \"v\":\n", + " for p in ax.patches:\n", + " _x = p.get_x() + p.get_width() / 2\n", + " _y = p.get_y() + p.get_height()\n", + " value = str(round(float(p.get_height()),2)) + unit\n", + " ax.text(_x, _y, value, ha=\"center\") \n", + " elif h_v == \"h\":\n", + " for p in ax.patches:\n", + " _x = p.get_x() + p.get_width() + float(xspace)\n", + " _y = p.get_y() + p.get_height() + float(yspace)\n", + " value = str(round(float(p.get_width()),2)) + unit\n", + " ax.text(_x, _y, value, ha=\"left\")\n", + "\n", + " if isinstance(axs, np.ndarray):\n", + " for idx, ax in np.ndenumerate(axs):\n", + " _show_on_single_plot(ax)\n", + " else:\n", + " _show_on_single_plot(axs)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "# Mean POLL PERCENTAGE of all the STATES\n", + "polls = electors_2009.groupby('STATE')['POLL PERCENTAGE'].mean().sort_values(ascending=False)\n", + "# Generating a bar plot\n", + "plt.figure(figsize=(6,20))\n", + "sns_t = sns.barplot(polls,polls.index)\n", + "show_values_on_bars(sns_t, \"h\", -10.2,-0.3 ,'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insigt: Top 3 (out of top 5) are Northeastern states. UP is voting less than 50%, still their regional parties BSP and SP are among top-six most voted parties, imagine what would happen if more no. of people in UP participate in elections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 5 : Plot a bar plot to compare the seats won by different parties in Uttar Pradesh" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Find winners in UP and count the party affiliation\n", + "ax = candidate_2009[ (candidate_2009.Position == 1 ) & ( candidate_2009.State_name == 'Uttar Pradesh') ].\\\n", + " Party_Abbreviation.value_counts().plot.barh(title='Winning Seats distributions by party in UP')\n", + "\n", + "# to show in descending order\n", + "ax.invert_yaxis()\n", + "\n", + "plt.xlabel('No. of seats')\n", + "plt.ylabel('Party')\n", + "\n", + "# [optional] to save the figure to be included in external reports\n", + "plt.savefig('up.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Regional parties have major influencies, the highest number of seats won in UP are by Samajwadi Party(SP). Also Bahujan Samaj Party(BSP) is trailing behind INC only by a few seats." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 6 : Plot a stacked bar chart to compare the number of seats won by different `Alliances` in Gujarat, Madhya Pradesh and Maharashtra. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Subset the the dataset for the states of Gujarat, Maharashtra and Madhya Pradesh\n", + "states_list = ['Gujarat', 'Madhya Pradesh', 'Maharashtra']\n", + "states = candidate_2009[candidate_2009.State_name.isin(states_list)][candidate_2009.Position ==1]\n", + "\n", + "# Stacked bar plot\n", + "states.groupby(['State_name', 'Alliance']).size().unstack().\\\n", + " plot.bar(stacked=True,figsize=(10,10),rot=0, \n", + " title='Alliance-wise seat distributions in three states')\n", + "plt.xlabel('States')\n", + "plt.ylabel('No. of seats')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Maharashtra is dynamic state with various parties winning seats in the election with UPA getting the highest seats followed by NDA." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 7 : Plot a grouped bar chart to compare the number of winner candidates on the basis of their caste in the states of Andhra Pradesh, Kerala, Tamil Nadu and Karnataka" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Subset the data with the winner of each constituency of the mentioned states\n", + "states_list = ['Andhra Pradesh', 'Kerala', 'Tamil Nadu', 'Karnataka']\n", + "states = candidate_2009[candidate_2009.State_name.isin(states_list)][candidate_2009.Position ==1]\n", + "\n", + "# Plotting the grouped bar\n", + "states.groupby(['Alliance', 'Candidate_Category']).size().unstack().\\\n", + " plot.bar(figsize=(8,8),rot=0, title =\"2009 Winning Category\")\n", + "plt.xlabel(\"Party-wise Candidate Category\", fontsize=12)\n", + "plt.ylabel(\"No. of seats\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Most of the winner candidates are from general category with UPA having the highest number of SC candidates." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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mz59PZjb91aXOlps2bRpXXnklN9xwA6uuuioTJ07s9PvLmckRRxzB17/+9aZe26233sorr7yyqKu8p7bXHvjNaHy9q6222qL7G264Ieuvvz5XXXUVN954I+eff37T65QkvTbLzTHud77znbz00kv84Ac/WDTtz3/+Mw8++CDrrbcegwYN4uqrr+bBB7scKQ2ANddck6FDh3LdddcBdBlCO++886J5l19+OU8//TQAc+fOZa211mLVVVflrrvu4k9/+tOi5wwaNIiXX34ZgEmTJnHhhRfyxBNPAPDUU091WduoUaOYMGECJ5988qJj6ffccw+XXnrpMm9v++2355prruHpp59m4cKFXHTRRYuev+OOO3LBBRcs2g877bRTl/vt6KOP5rDDDuOggw5a1BKXJPW8XgvuiPhRRDwREbd1Mu+zEZERsW4Pbo9LLrmEK664YtFXpiZPnswee+zBjBkzmDBhAueffz5vetOblrius88+m49+9KPssMMOrLLKKp0uc/LJJzN9+nS22WYb/vCHP7DRRhsBsPvuu7Nw4ULGjRvHl770Jd761rcues6xxx7LuHHjOPTQQ9lqq6049dRT2W233Rg3bhy77rors2fP7rKmH/7whzz22GNsuummjB07lmOOOYbhw4cv8/Y22GADvvCFL/CWt7yFd73rXWy11VYMHToUqLrgzz77bMaNG8e5557LGWec0WV9++yzD/PmzbObXJJ6WbS34Hp8xRE7A/OAH2fmmIbpGwI/BN4EbJuZSzz9eMKECTljxozFpt15551sueWWPVv0CmrevHmsvvrqLFy4kP3335+jjjqK/fff/zWtY8aMGXzqU5/i2muv7XIZ3zNJak5EzMzMCZ3N67UWd2ZOB57qZNa3gROB3vnEoNds8uTJjB8/njFjxrDJJpuw3377vabnf+Mb3+CAAw5o6ni9JGnZ9OnJaRGxD/BIZt7qtas795a3vIWXXnppsWnnnnsuY8eO7bVtnn766cv0/JNOOomTTjqph6qRJHWnz4I7IlYF/h+wW5PLHwscCyw6frwiWNJlTCVJtclDe2g9c3tmPX2kL88qHwVsAtwaEQ8AI4CbI+L1nS2cmVMyc0JmThg2bFgflilJUv/VZy3uzPwrsOgKI3V4T2jm5DRJklTpza+D/RS4AdgiImZFxId6a1uSJK0oevOs8kMy8w2ZOSgzR2TmWR3mj7S1vfS++tWvMnr0aMaNG8f48eO58cYbefnllznppJPYbLPNGDNmDNtvv32nl2uVJJVrubnkacv01MkRi9a35JMkbrjhBi677DJuvvlmVl55ZZ588kkWLFjAl770JWbPns1tt93GyiuvzOOPP84111zTs/VJklrK4C7Q7NmzWXfddRdda33dddflhRde4Ac/+AH333//ounrr78+Bx10UCtLlST1sOXmWuUrkt12242HH36YzTffnI985CNcc801/P3vf2ejjTZijTXWaHV5kqReZHAXaPXVV2fmzJlMmTKFYcOG8f73v59p06a1uixJUh+wq7xQAwYMYOLEiUycOJGxY8fy/e9/n4ceeojnnnuOIUOGtLo8SVIvscVdoLvvvpt77rln0eNbbrmFLbbYgg996EMcf/zxLFiwAKiOhZ933nmtKlOS1AtscRdo3rx5fPzjH+eZZ55h4MCBbLrppkyZMoU11liDL37xi2y11VYMHjyY1VZbja985SutLleS1IMM7mXVgmvcbrvttlx//fWdzjvttNM47bTT+rgiSVJfsatckqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJB/DpYob761a/yk5/8hAEDBrDSSiux1lpr8fTTTzNv3jza2trYZJNNAPjv//5vdtxxxxZXK0nqKQb3Mho7dWyPru+vR/x1ict0Nazn8OHDmTZtGqeffjqXXXZZj9YlSeofDO4CdTaspyRpxeAx7gJ1NqynJGnFYHAXqLNhPc8555xWlyVJ6gN2lReq47CeU6dO5YMf/GCry5Ik9TJb3AXqbFjPjTfeuIUVSZL6ii3uAnU1rKckaflncC+jZr6+1dO6G9azvftckrR8sqtckqSCGNySJBXE4JYkqSBFB3dmtroENcn3SpJ6RrHBPXjwYObMmWMgFCAzmTNnDoMHD251KZJUvGLPKh8xYgSzZs2ira2t1aWoCYMHD2bEiBGtLkOSildscA8aNGjR0JWSJK0oiu0qlyRpRWRwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFaTXgjsifhQRT0TEbQ3TvhkRd0XE/0XEJRGxZm9tX5Kk5VFvtrjPAXbvMO0KYExmjgP+Bny+F7cvSdJyp9eCOzOnA091mPaHzFxYP/wTMKK3ti9J0vKolce4jwIub+H2JUkqTkuCOyL+H7AQOL+bZY6NiBkRMaOtra3vipMkqR/r8+COiCOAvYBDMzO7Wi4zp2TmhMycMGzYsL4rUJKkfmxgX24sInYHPgfskpkv9OW2JUlaHvTm18F+CtwAbBERsyLiQ8B/AUOAKyLiloj4Xm9tX5Kk5VGvtbgz85BOJp/VW9uTJGlF4JXTJEkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBei24I+JHEfFERNzWMG3tiLgiIu6pf67VW9uXJGl51Jst7nOA3TtMOwn4Y2ZuBvyxfixJkprUa8GdmdOBpzpM3heYWt+fCuzXW9uXJGl51NfHuNfPzNkA9c/1+nj7kiQVrd+enBYRx0bEjIiY0dbW1upyJEnqF/o6uB+PiDcA1D+f6GrBzJySmRMyc8KwYcP6rEBJkvqzvg7uXwFH1PePAC7t4+1LklS03vw62E+BG4AtImJWRHwI+Aawa0TcA+xaP5YkSU0a2FsrzsxDupg1qbe2KUnS8q7fnpwmSZJezeCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBlhjcEXFgRAyp738xIi6OiG16vzRJktRRMy3uL2XmcxGxE/BuYCrwP71bliRJ6kwzwf2P+ueewP9k5qXA63qvJEmS1JVmgvuRiPg+cBDw24hYucnnSZKkHtZMAB8E/B7YPTOfAdYGTujVqiRJUqeaCe7vZ+bFmXkPQGbOBg5flo1GxKci4vaIuC0ifhoRg5dlfZIkrSiaCe7RjQ8iYgCw7dJuMCI2AI4HJmTmGGAAcPDSrk+SpBVJl8EdEZ+PiOeAcRHxbH17DngCuHQZtzsQWCUiBgKrAo8u4/okSVohdBncmfn1zBwCfDMz16hvQzJzncz8/NJuMDMfAU4HHgJmA3Mz8w9Luz5JklYkS+wqz8zPR8RaEbF9ROzcflvaDUbEWsC+wCbAcGC1iDisk+WOjYgZETGjra1taTcnSdJypZkrpx0NTKc6s/yU+ufkZdjmu4D7M7MtM18GLgZ27LhQZk7JzAmZOWHYsGHLsDlJkpYfzZyc9glgO+DBzHwHsDWwLE3gh4C3RsSqERHAJODOZVifJEkrjGaC+8XMfBEgIlbOzLuALZZ2g5l5I3AhcDPw17qGKUu7PkmSViQDm1hmVkSsCfwSuCIinmYZzwLPzJOBk5dlHZIkrYiWGNyZuX99d3JEXA0MBX7Xq1VJkqRONdPiph4ZbLPMPDsihgEbAPf3amWSJOlVmjmr/GTgc0D7d7cHAef1ZlGSJKlzzZyctj+wD/A8QGY+CgzpzaIkSVLnmgnuBZmZQAJExGq9W5IkSepKM8H983o87jUj4hjgSuAHvVuWJEnqTDNnlZ8eEbsCz1J9f/vLmXlFr1cmSZJeZYnBXXeNX5WZV0TEFsAWETGovlypJEnqQ810lU8HVq7H0b4SOBI4pzeLkiRJnWsmuCMzXwDeC3y3viDLVr1bliRJ6kxTwR0ROwCHAr+ppzV14RZJktSzmh0d7PPAJZl5e0S8Ebi6d8uSJEmdaeas8ulUx7nbH98HHN+bRUmSpM410+KWJEn9hMEtSVJBugzuiPj3+ueBfVeOJEnqTnct7j0iYhD/HBVMkiS1WHcnp/0OeBJYLSKeBYJqoJEAMjPX6IP6JElSgy5b3Jl5QmYOBX6TmWtk5pDGn31YoyRJqjXzdbB9I2J9YLt60o2Z2da7ZUmSpM4s8azy+uS0m4ADgYOAmyLifb1dmCRJerVmLl36RWC7zHwCICKGUQ02cmFvFiZJkl6tme9xr9Qe2rU5TT5PkiT1sGZa3L+LiN8DP60fvx/4be+VJEmSutLMyWknRMR7gZ2ovgo2JTMv6fXKJEnSqzQ1PGdmXgxc3Mu1SJKkJfBYtSRJBTG4JUkqiMEtSVJBliq4I2JyD9chSZKasLQt7pk9WoUkSWrKUgV3Zv66pwuRJElL1sy1ykdExCUR0RYRj0fERRExoi+KkyRJi2umxX028CvgDcAGwK/raZIkqY81E9zDMvPszFxY384BhvVyXZIkqRPNBPeTEXFYRAyob4dRDTQiSZL6WDPBfRTVONyPAbOB99XTJElSH2tmkJGHgH36oBZJkrQEXQZ3RHy5m+dlZv5bL9QjSZK60V2L+/lOpq0GfAhYBzC4JUnqY10Gd2Z+q/1+RAwBPgEcCVwAfKur50mSpN7T7THuiFgb+DRwKDAV2CYzn+6LwiRJ0qt1d4z7m8B7gSnA2Myc12dVSZKkTnX3dbDPAMOBLwKPRsSz9e25iHi2b8qTJEmNujvG7VjdkiT1M4azJEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCtCS4I2LNiLgwIu6KiDsjYodW1CFJUmmWOKxnLzkD+F1mvi8iXges2qI6JEkqSp8Hd0SsAewMfBAgMxcAC/q6DkmSStSKrvI3Am3A2RHxl4j4YUSs1nGhiDg2ImZExIy2tra+r1KSpH6oFcE9ENgG+J/M3Jpq3O+TOi6UmVMyc0JmThg2bFhf1yhJUr/UiuCeBczKzBvrxxdSBbkkSVqCPg/uzHwMeDgitqgnTQLu6Os6JEkqUavOKv84cH59Rvl9wJEtqkOSpKK0JLgz8xZgQiu2LUlSybxymiRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqSMuCOyIGRMRfIuKyVtUgSVJpWtni/gRwZwu3L0lScVoS3BExAtgT+B8XcyoAABKcSURBVGErti9JUqla1eL+T+BE4JWuFoiIYyNiRkTMaGtr67vKJEnqx/o8uCNiL+CJzJzZ3XKZOSUzJ2TmhGHDhvVRdZIk9W+taHG/DdgnIh4ALgDeGRHntaAOSZKK0+fBnZmfz8wRmTkSOBi4KjMP6+s6JEkqkd/jliSpIANbufHMnAZMa2UNkiSVxBa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSpInwd3RGwYEVdHxJ0RcXtEfKKva5AkqVQDW7DNhcBnMvPmiBgCzIyIKzLzjhbUIklSUfq8xZ2ZszPz5vr+c8CdwAZ9XYckSSVq6THuiBgJbA3c2Mo6JEkqRSu6ygGIiNWBi4BPZuazncw/FjgWYKONNuq5DU8e2kPrmdsz65FWFD30tzd2k577f/DXI/7aY+tSucZOHdtj6+qL36mWtLgjYhBVaJ+fmRd3tkxmTsnMCZk5YdiwYX1boCRJ/VQrzioP4Czgzsz8j77eviRJJWtFi/ttwOHAOyPilvq2RwvqkCSpOH1+jDszrwOir7crSdLywCunSZJUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCjKw1QWon5o8tAfXNbfn1tXfuJ/U03rwd2rsJhv1yHr+esRfe2Q96hm2uCVJKojBLUlSQQxuSZIKYnBLklQQg1uSpIIY3JIkFcTgliSpIAa3JEkFMbglSSqIwS1JUkEMbkmSCmJwS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqiMEtSVJBDG5JkgpicEuSVBCDW5KkghjckiQVxOCWJKkgBrckSQUxuCVJKkhLgjsido+IuyPi7xFxUitqkCSpRH0e3BExADgTeA+wFXBIRGzV13VIklSiVrS4twf+npn3ZeYC4AJg3xbUIUlScVoR3BsADzc8nlVPkyRJSxCZ2bcbjDgQeHdmHl0/PhzYPjM/3mG5Y4Fj64dbAHf3aaFLti7wZKuLKIT7qjnup+a4n5rnvmpOf9xPG2fmsM5mDOzrSqha2Bs2PB4BPNpxocycAkzpq6Jeq4iYkZkTWl1HCdxXzXE/Ncf91Dz3VXNK20+t6Cr/M7BZRGwSEa8DDgZ+1YI6JEkqTp+3uDNzYUR8DPg9MAD4UWbe3td1SJJUolZ0lZOZvwV+24pt96B+243fD7mvmuN+ao77qXnuq+YUtZ/6/OQ0SZK09LzkqSRJBTG4gYjYPyIyIt5UPx4ZEfMj4i8RcWdE3BQRRzQs/8F6+UmdrON99eNpETGhYX33RMS7+/q19ZWIeH1EXBAR90bEHRHx24jYvN6Pt9TTvhcRK9X747ZW19yXlmL/ZET8W8Pz142IlyPiv1r5OnpT/Zq/1fD4sxExub4/OSIeqffVPRFxcccrLkbEsHofHdfHpfe5iFin3he3RMRjDfvmlno/3hIRt0fErRHx6YhYqX7exIiY2/C/7eRWv5be1tn/m/r36bMRcU5E3F/vr5sjYoeGZQZGxJMR8fW+r7p7BnflEOA6qjPc292bmVtn5pb19E9FxJEN8/9aP6/dwcCtHVccESOoTsT7TGb+vscr7wciIoBLgGmZOSoztwK+AKxPtR/HA+OoLnG7X+sqbY2l3D/3AXs1rOZAYHk/ifMl4L0RsW4X87+dmeMzczPgZ8BVEdH4PdcDgT+x+N/lcikz59T7YjzwPf65b8YDz9f3RwO7AnsAjQF9bWZuDUwADouIbfv8BfQvJ9T77STg+w3Td6O6fshB9d9wv7HCB3dErA68DfgQiwf3Ipl5H/Bp4PiGydcC20fEoHodmwK3dHjq64E/AF/MzOX5K2/vAF7OzO+1T8jMW2i4Ql5mLgSup9pPK5ql2T/zgTvbe22A9wM/75tyW2Yh1UlCn1rSgpn5M6q/rX9pmHwI8BlgRER4NUYgM5+gupDVxzqGT2Y+D8wERrWitn5oOov/fzoEOAN4CHhrSyrqwgof3FQtnN9l5t+ApyJimy6Wuxl4U8PjBK4E3k11rfXOgvnHwH9l5i96sN7+aAzVP4AuRcSqwCSqnooVzdLunwuAg+tem3/QyYWKlkNnAodGxNAmll30NxkRGwKvz8ybqD7gvL/3SixL3fBYCVivcXpErEMVSMt7T06z9qb++4uIVaj+Hi8Dfko/68UxuKs35IL6/gV0/QZ11lVyAVUr/WCqN7ejK4HD63/KK6pREXEL8L/AbzLz8lYX1M90t39+R9XVeQhV1/ByLzOfpfrAe/ySlmXxv8mD+WePRHd/xyuqxn319oj4C1WPxTdWgOtodPXVqfbp36z/Bo+l6nmF6jDV1Zn5AnARsH9UI1v2Cy35Hnd/UX/ifCcwJiKS6oIwCfx3J4tvDdzZOCEzb4qIMcD8zPxbJ4dBTgMOA34REfvW3aHLo9uB93Uxr/0Y7opsqfZPZi6IiJlU3b+jqVoEK4L/pGpNn72E5bYGZtT3DwHWj4hD68fDI2KzzLynl2osRkS8karH5glgS6pj3Ht1/6zlyhxgrQ7T1gbur++fkJkXdph/CPC2iHigfrwO1SGvK3uryNdiRW9xvw/4cWZunJkjM3NDqjdzRONCETESOB34bifr+DzViUZd+RTwLHBWfzvBoQddBawcEce0T4iI7YCNW1dSv7Is++dbwOcyc05vFdffZOZTVK3nD3W1TEQcQHXy0E8jYgtgtczcoP47Hgl8nS7OWVmR1CfvfY/qkN0KedGOzJwHzI76W0ARsTawO9UJya8SEWsAOwEbNfw+fZR+1Iuzogf3IVRn+za6iCqIR7V/ZYLqn8h3M/NVLYDMvDwzr+5qA/UfyxHAG6ha4Mud+jXuD+xaf93pdmAyK8Yx2SValv2Tmbdn5tReLrE/+hbViE2NPtX+dTCqnqx3ZmYbXf8d95t/tH1slfavg1G1EP8AnNLimlrtA8AX6y7xq4BTMvPeLpZ9L3BVZr7UMO1SYJ+IWLmX62yKV06TJKkgK3qLW5KkohjckiQVxOCWJKkgBrckSQUxuCVJKojBLfWiiNgoIub1p6suvVZRjYZ3XcPjefVFPZa4rKSeZ3CrGBHxQFTDYM6LiMcj4ux6gJelWdfEiJjV0zV2lJkPZebqmfmPnlxvRLyuHprwnoh4vt43P6ovFtSr6tdz37Kup67/vGVcx+YR8Yt6+MW5EfF/9TCWS/ygVA/peOqybF9qBYNbpdk7M1cHtgG2A774WlcQEcvDpX4vBPahGh1rKPBmqoFMJnX3pOVJRIwCbqQaZW1sZg6lGtpzAjCklbUtSck9MGo9g1tFysxHgMupRt4iIo6MiDsj4rmIuC8ijmtftr11HRGfi4jHqAaEuZzqetbz6tvwiHihvn59+/O2jYi2iBjUcfsRcUpEfLe+P6hu9Z5WP14lIl6MiLUiYmREZPuHhbor+b66zvsbrq1NRBxVv4anI+L3EdHpJVEj4l1Ug4/sm5l/zsyFmTk3M8/MzLNew/74TEQ8ERGzo2Gs+YhYJyJ+FRHPRsRNdBj2sX49mza57BkR8XA9f2ZEvL2evjvVFQrfX+//W+vpQyPirLqmRyLi1G5C7hTg+sz8dGbOBsjMuzPzXzLzmXp9v4iIx+rW+PSIGF1PPxY4FDix3v6v6+nDI+Ki+n2/PyIWDXZSv69T6/fnzog4sbHXJiK2jIhpEfFMRNweEfs0zDsnIv4nIn4bEc8Dn46q12hgwzIHRHVlL6lbBreKFNUwjnsAf6knPUE1os8awJHAt2PxIVpfTzWwwMZUlz98D/Bo3e27emY+CkwDDmp4zmHABZn5ciclXANMrO9vBzwG7FI/3gG4OzOf7lDzasB3gPdk5hBgR+ox3CNiP6ogey8wjGq8985GnAN4F3BTZj7cxXxobn8MBTaguib4mRHRPhDDmcCLVJfpPaq+dWVJy/4ZGE+1739CNeDO4Mz8HfA14Gf1/n9zvfxUqnG5N6UaRGQ34Ogutv0uqp6H7lwObEY1pOXNwPkAmTmlvn9avf29I2Il4NfArVT7ZRLwyYh4d72uk4GRwBupPjgd1r6R+sPdr6kuL7oe8HHg/Kiuo97uX4CvUvUGfJdq8ItdG+YfBpy7hNcjQWZ681bEDXgAmAc8AzxINYrbKl0s+0vgE/X9icACYHDD/InArA7PeT/wv/X9AVRhvH0X61+FKrDWAU6iCt1ZwOpULcHv1MuNpBpxbiCwWl37AR3rpgqYDzU8Xgl4Adi4k23/gOoDxWvZdx33x3xgYMP8J6jGZh4AvAy8qWHe14DrGh4nVbAucdlO6ngaeHN9fzJwXsO89YGXGvcN1fXGr+5iXS8Du7+GfbBmXfvQ+vE5wKkN898CPNThOZ8Hzq7v3we8u2He0e2/Q8Db69+XlRrm/xSY3LCtH3dY9+eA8+v7a9fv9xta/Xfmrf/fbHGrNPtl5ppZjej2kcycDxAR74mIP0XEUxHxDFVrvHGQirbMfHEJ674U2CqqM6Z3BeZm5k31+m9v6FZ/e73dGVSt7J2pWuDXA2+rp13TceWZ+TzVh4MPU41W9JuIeFM9e2PgjLqb9RngKaoxlDfopM45VC3cLjWxP+bk4sPMvkD1oWMY1YeMxtb8g11sZonL1t3xd9Zd1c9QtfI7Dh7SbmNgENW+ad8P36dqwXam2/0QEQMi4htRDezyLNUHP5aw/eHt2663/wWqDxQAwzu81sb7w4GHM/OVhmkPsvj717GH5Dxg76hOsDyIarjN2V29Hqmdwa3iRTViz0VUQ6+un5lrAr+lCr52HUfTedXoOnWw/5zq2OfhNHRbZubo/Ge3+rX15GuoxnPfmqpL+Brg3cD2wPTOas3M32fmrlSBcxdV6xmqf+rH1R9K2m+rZOb1nazmSmD7iBjRybxm90dX2qi6qjdsmLbR0ixbH8/+HFUorVXXMbehjo7vwcNULe51G/bBGpk5uovtX0nVe9GVfwH2pepSH0rV+8EStn9/h/dgSGbuUc+fzeJD/ja+7keBDevu9nYbAY80PF5se1mdp3ED1chxi/2+Sd0xuLU8eB2wMnWQRMR7qI6NdudxYJ2IGNph+o+BD1Kdsb2krypdQ3W8/I7MXEB1jPxoqn/+bR0Xjoj1I2Kf+lj3S1Td/u1fE/se8PmGk6eGRsSBnW00M68ErgAuieoEuoERMSQiPhwRR7F0+6N93f8ALgYmR8SqEbEV1bC0S7PsEKpgbwMGRsSXqY65t3scGNkednVr8w/AtyJijYhYKSJGRcQudO5kYMeI+GZEvB4gIjaNiPMiYs16+y9RtcxXperGb/Q41fHqdjcBz0Z1EuMqdYt9TFRjp0P1oe7zUZ10uAHwsYbn3gg8T3Wy26CImAjsDVzQRe3tfgycCIzl1UOTSp0yuFW8zHwOOJ7qH+vTVC2tXy3hOXdRHYO8r+4WHV5P/1/gFeDmzHxgCZu+nupYd3vr+g6q496dtrap/t4+Q9U6e4qqS/0j9XYvAf4duKDu1r2N6gS6rryPqhX9M6pW7G1UX4O6cmn2Rwcfo+o2f4zq2OyrxqFvctnfUx27/xtVt/GLLN5d/Iv655yIuLm+/wGqDx531LVfSBfd4VmNp7wDVUv69oiYS9XTMAN4jioUH6Rq9d4B/KnDKs6iOjTyTET8sv4gsjfVyXT3A08CP6RqrQN8heo8hvupWvsXUn0woP7gtg/Ve/Yk1fkXH6h/z7pzCVUX/SX1oRRpiRyPW+ogIq4CfpKZP2x1Leq/IuJfgYMzs6segWbXcy/VYZIre6YyLe9scUsN6m7RbahastIiEfGGiHhb3YW/BVXvyTJ1b0fEAVTHvq/qiRq1YlgeriAl9YiImArsR/W1qedaXY/6nddRneW+CdXX+i6g6hJfKhExDdgKOLzD2ehSt+wqlySpIHaVS5JUEINbkqSCGNySJBXE4JYkqSAGtyRJBTG4JUkqyP8HfhV0tda0xroAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# But if we remove GEN category and only focus on SC, ST we might see a different picture\n", + "\n", + "# Plotting the grouped bar\n", + "states[states.Candidate_Category!='GEN'].\\\n", + " groupby(['Alliance', 'Candidate_Category']).size().unstack().\\\n", + " plot.bar(figsize=(8,8),rot=0, title =\"2009 Winning Category excl. GEN\",\n", + " color=['tab:orange','tab:green'])\n", + "plt.xlabel(\"Party-wise Candidate Category\", fontsize=12)\n", + "plt.ylabel(\"No. of seats\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: SC and ST are under-represented in the lower house of the parliament." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 8 : Plot a horizontal bar graph of the Parliamentary constituency with total voters less than 100000" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Constituency with less than 100000 voters\n", + "# Plot a horizontal bar graph to compare constituencies with less than 100000 voters\n", + "electors_2009[electors_2009.Total_Electors < 100000].\\\n", + " sort_values(ascending=True,by='Total_Electors').\\\n", + " plot.barh(x='PARLIAMENTARY CONSTITUENCY',y='Total_Electors',\n", + " figsize=(10,10), \n", + " title=\"Parliamentary constituencies with less than 1 lakh total voters\")\n", + "\n", + "plt.xlabel('Number of Voters')\n", + "plt.ylabel('Parliamentary Constituencies')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Only two constituencies which have electors strength less than one lakh." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 9: Plot a pie chart with the top 10 parties with majority seats in the elections" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Candidates with 1st position in their respective constituiency\n", + "all_winners = candidate_2009[candidate_2009.Position ==1].Party_Abbreviation.value_counts()\n", + "top_10_winners = all_winners[:9] \n", + "# count of other regional parties\n", + "top_10_winners['Others'] = all_winners.sum() - top_10_winners.sum()\n", + "# Pie chart\n", + "top_10_winners.plot.pie(autopct='%.f%%', \n", + " figsize=(10,10), \n", + " title='Top 10 parties with majority seats')\n", + "plt.legend(loc='upper right')\n", + "plt.ylabel('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: INC have won almost 38% of the total seats followed by BJP with 21% seats." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task 10 : Plot a pie diagram for top 10 states with most number of seats" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Top 9 states with maximum number of seats\n", + "top_10_seats = electors_2009.STATE.value_counts()[:9]\n", + "\n", + "# Sum of other states\n", + "top_10_seats['Others'] = electors_2009.STATE.value_counts().sum() - top_10_seats.sum()\n", + "\n", + "# Function to convert percentages into actual values\n", + "def autopct_format(values):\n", + " def my_format(pct):\n", + " total = sum(values)\n", + " val = int(round(pct*total/100.0))\n", + " return '{val:d} ({pct:.0f}%)'.format(val=val,pct=pct)\n", + " return my_format\n", + "\n", + "# PLotting the pie chart\n", + "top_10_seats.plot.pie(autopct=autopct_format(top_10_seats.values), \n", + " figsize=(10,10), \n", + " title=\"Top 10 States with most number of seats\")\n", + "plt.ylabel('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Insight: Uttar Pradesh has the highest number of seats (80 or 15%) followed by Maharashtra (48 or 9%).\n", + "\n" + ] + } + ], + "metadata": { + "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": "3.7.0-final" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/sales_campaign_analysis/notebook/Manipulating data with Numpy - Code Walkthrough-MK.ipynb b/sales_campaign_analysis/notebook/Manipulating data with Numpy - Code Walkthrough-MK.ipynb new file mode 100644 index 0000000..da6b267 --- /dev/null +++ b/sales_campaign_analysis/notebook/Manipulating data with Numpy - Code Walkthrough-MK.ipynb @@ -0,0 +1,1515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sales Campaign analysis\n", + "\n", + "__An introduction to the Facebook advertising platform__
\n", + "Along with Google's search and display networks, Facebook is one of the big players when it comes to online advertising. As Facebook users interact with the platform, adding demographic information, liking particular pages and commenting on specific posts, Facebook builds a profile of that user based on who they are and what they're interested in.
\n", + "This fact makes Facebook very attractive for advertisers. Advertisers can create Facebook adverts, then create an 'Audience' for that advert or group of adverts. Audiences can be built from a range of attributes including gender, age, location and interests. This specific targetting means advertisers can tailor content appropriately for a specific audience, even if the product being marketed is the same.
\n", + "\n", + "__What do we need from our Facebook ads analysis?__
\n", + "When it comes to analysing the Facebook adverts dataset, there are a lot of questions we can ask, and a lot of insight we can generate. However, from a business perspective we want to ask questions that will give us answers we can use to improve business performance.
\n", + "Without knowing anything of the company's marketing strategy or campaign objectives, we do not know which key performance indicators (KPIs) are the most important. For example, a new company may be focussed on brand awareness and may want to maximise the amount of impressions, being less concerned about how well these adverts perform in terms of generating clicks and revenue. Another company may simply want to maximise the amount of revenue, while minimising the amount it spends on advertising.
\n", + "As these two objectives are very different, it is important to work with the client to understand exactly what they are hoping to achieve from their marketing campaigns before beginning any analysis in order to ensure that our conclusions are relevant and, in particular, actionable. There's not much point in producing a report full of insight, if there's nothing the client can do about it.\n", + "\n", + "\n", + "\n", + "__Understanding the dataset__
\n", + "The data used in this project is from an anonymous organisation’s social media ad campaign. The data contains 1143 observations in 11 variables. Below are the descriptions of the variables. Since you are working with numpy, refer the `Feature Index` column for the indices of every feature.\n", + "\n", + "|Feature Index|Features|Description|\n", + "|----|----|----|\n", + "|0|ad_id| unique ID for each ad|\n", + "|1|xyz_campaign_id| an ID associated with each ad campaign of XYZ company|\n", + "|2|fb_campaign_id| an ID associated with how Facebook tracks each campaign|\n", + "|3|age| age of the person to whom the ad is shown|\n", + "|4|gender| gender of the person to whom the add is shown|\n", + "|5|interest| a code specifying the category to which the person’s interest belongs (interests are as mentioned in the person’s Facebook public profile)|\n", + "|6|Impressions| the number of times the ad was shown|\n", + "|7|Clicks| number of clicks on for that ad|\n", + "|8|Spent| Amount paid by company xyz to Facebook, to show that ad|\n", + "|9|Total conversion| Total number of people who enquired about the product after seeing the ad|\n", + "|10|Approved conversion| Total number of people who bought the product after seeing the ad|" + ] + }, + { + "attachments": { + "NumPy_code_along.PNG": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is a snapshot of the data you will be working with![NumPy_code_along.PNG](attachment:NumPy_code_along.PNG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Command to display all the columns of a numpy array\n", + "np.set_printoptions(threshold=sys.maxsize)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's load the data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ad_idxyz_campaign_idfb_campaign_idagegenderinterestImpressionsClicksSpentTotal_ConversionApproved_Conversion
070874691610391630-34M15735011.4321
170874991610391730-34M161786121.8220
270877191610392030-34M2069300.0010
370881591610392830-34M28425911.2510
470881891610392830-34M28413311.2911
\n", + "
" + ], + "text/plain": [ + " ad_id xyz_campaign_id fb_campaign_id age gender interest \\\n", + "0 708746 916 103916 30-34 M 15 \n", + "1 708749 916 103917 30-34 M 16 \n", + "2 708771 916 103920 30-34 M 20 \n", + "3 708815 916 103928 30-34 M 28 \n", + "4 708818 916 103928 30-34 M 28 \n", + "\n", + " Impressions Clicks Spent Total_Conversion Approved_Conversion \n", + "0 7350 1 1.43 2 1 \n", + "1 17861 2 1.82 2 0 \n", + "2 693 0 0.00 1 0 \n", + "3 4259 1 1.25 1 0 \n", + "4 4133 1 1.29 1 1 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import csv\n", + "# Load the data\n", + "conversion_data = pd.read_csv('../data/KAG_conversion_data.csv')\n", + "\n", + "# Remove the header\n", + "\n", + "# Convert the data into a numpy array and store it in sales_data\n", + "conversion_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Let's delve into the data to find the answers to some questions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How many unique ad campaigns (xyz_campaign_id) does this data contain ? And for how many times was each campaign run ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total number of campaigns" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of campaigns are: 3\n", + "They are: [ 916 936 1178]\n" + ] + } + ], + "source": [ + "print('The total number of campaigns are:',conversion_data.xyz_campaign_id.nunique())\n", + "print('They are:',conversion_data.xyz_campaign_id.unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Total times each campaign ran" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of time each campaign ran is given as: {916: 54, 936: 464, 1178: 625}\n" + ] + } + ], + "source": [ + "print('The number of time each campaign ran is given as:',\n", + " conversion_data.xyz_campaign_id.value_counts(sort=False).to_dict())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What are the age groups that were targeted through these ad campaigns ?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The age groups targeted are: ['30-34' '35-39' '40-44' '45-49']\n" + ] + } + ], + "source": [ + "# Age groups are categorized as bins. So get a unique count of the bin\n", + "print('The age groups targeted are:',conversion_data.age.unique())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What was the average, minimum and maximum amount spent on the ads ?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mean 51.360656\n", + "min 0.000000\n", + "max 639.949998\n", + "Name: Spent, dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversion_data.Spent.agg(['mean','min','max'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum amt spent on ads was 0.0\n", + "Maximum amt spent on ads was 639.95\n", + "Average amt spent on ads was 51.36\n" + ] + } + ], + "source": [ + "avg_amt, min_amt, max_amt = conversion_data.Spent.describe()[['mean','min','max']].round(2)\n", + "print('Minimum amt spent on ads was ',min_amt)\n", + "print('Maximum amt spent on ads was ',max_amt)\n", + "print('Average amt spent on ads was ',avg_amt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the id of the ad having the maximum number of clicks ?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What were the maximum number of clicks" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The maximum number of clicks were 421\n" + ] + } + ], + "source": [ + "print('The maximum number of clicks were',conversion_data.Clicks.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Which was the ad having the maximum number of clicks" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The advertisement with the maximum number of clicks was the one with id 1121814\n" + ] + } + ], + "source": [ + "print('The advertisement with the maximum number of clicks was the one with id',\n", + " conversion_data.ad_id[conversion_data.Clicks.argmax()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How many people bought the product after seeing the ad with most clicks ? Is that the maximum number of purchases in this dataset ?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of people who bought the product having maximum ad clicks is 13\n", + "The maximum number of purchases was 21\n" + ] + } + ], + "source": [ + "# Max value of the Approved_Conversion column\n", + "print('Number of people who bought the product having maximum ad clicks is',\n", + " conversion_data.Approved_Conversion[conversion_data.Clicks.argmax()])\n", + "\n", + "if conversion_data.Approved_Conversion[conversion_data.Clicks.argmax()] == \\\n", + " conversion_data.Approved_Conversion.max():\n", + " print('The maximum sales were on this product')\n", + "else:\n", + " print('The maximum number of purchases was',\n", + " conversion_data.Approved_Conversion.max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## So the ad with the most clicks didn't fetch the maximum number of purchases. Let's find the details of the product having maximum number of purchases" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The ad id for the product having maximum number of purchases is: 1121104\n", + "The record for this product is as shown below\n", + "ad_id 1121104\n", + "xyz_campaign_id 1178\n", + "fb_campaign_id 144533\n", + "age 30-34\n", + "gender M\n", + "interest 16\n", + "Impressions 2080666\n", + "Clicks 202\n", + "Spent 360.15\n", + "Total_Conversion 40\n", + "Approved_Conversion 21\n", + "Name: 528, dtype: object\n" + ] + } + ], + "source": [ + "print(\"The ad id for the product having maximum number of purchases is:\",\n", + " conversion_data.ad_id[conversion_data.Approved_Conversion.argmax()])\n", + "print('The record for this product is as shown below')\n", + "print(conversion_data.iloc[conversion_data.Approved_Conversion.argmax()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating additional features\n", + "\n", + "Let's add some additional features that will represent some additional standard metrics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Click Through Rate (CTR)\n", + "This is the percentage of how many of our impressions became clicks. A high CTR is often seen as a sign of good creative being presented to a relevant audience. A low click through rate is suggestive of less-than-engaging adverts (design and / or messaging) and / or presentation of adverts to an inappropriate audience. What is seen as a good CTR will depend on the type of advert (website banner, Google Shopping ad, search network test ad etc.) and can vary across sectors, but 2% would be a reasonable benchmark.\n", + "\n", + "### Create a new feature `Click Through Rate` (CTR) and then concatenate it to the original numpy array \n", + "\n", + "CTR = $\\frac{Clicks}{Impressions}$x100" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ad_idxyz_campaign_idfb_campaign_idagegenderinterestImpressionsClicksSpentTotal_ConversionApproved_ConversionCTR
070874691610391630-34M15735011.43210.013605
170874991610391730-34M161786121.82200.011198
270877191610392030-34M2069300.00100.000000
370881591610392830-34M28425911.25100.023480
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" + ], + "text/plain": [ + " ad_id xyz_campaign_id fb_campaign_id age gender interest \\\n", + "0 708746 916 103916 30-34 M 15 \n", + "1 708749 916 103917 30-34 M 16 \n", + "2 708771 916 103920 30-34 M 20 \n", + "3 708815 916 103928 30-34 M 28 \n", + "4 708818 916 103928 30-34 M 28 \n", + "\n", + " Impressions Clicks Spent Total_Conversion Approved_Conversion CTR \n", + "0 7350 1 1.43 2 1 0.013605 \n", + "1 17861 2 1.82 2 0 0.011198 \n", + "2 693 0 0.00 1 0 0.000000 \n", + "3 4259 1 1.25 1 0 0.023480 \n", + "4 4133 1 1.29 1 1 0.024195 " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversion_data['CTR'] = (conversion_data.Clicks\n", + " .divide(conversion_data.Impressions)\n", + " .multiply(100)\n", + " )\n", + "conversion_data.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, 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ad_idxyz_campaign_idfb_campaign_idagegenderinterestImpressionsClicksSpentTotal_ConversionApproved_ConversionCTRCVR
070874691610391630-34M15735011.43210.0136050.027211
170874991610391730-34M161786121.82200.0111980.011198
270877191610392030-34M2069300.00100.0000000.144300
370881591610392830-34M28425911.25100.0234800.023480
470881891610392830-34M28413311.29110.0241950.024195
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" + ], + "text/plain": [ + " ad_id xyz_campaign_id fb_campaign_id age gender interest \\\n", + "0 708746 916 103916 30-34 M 15 \n", + "1 708749 916 103917 30-34 M 16 \n", + "2 708771 916 103920 30-34 M 20 \n", + "3 708815 916 103928 30-34 M 28 \n", + "4 708818 916 103928 30-34 M 28 \n", + "\n", + " Impressions Clicks Spent Total_Conversion Approved_Conversion \\\n", + "0 7350 1 1.43 2 1 \n", + "1 17861 2 1.82 2 0 \n", + "2 693 0 0.00 1 0 \n", + "3 4259 1 1.25 1 0 \n", + "4 4133 1 1.29 1 1 \n", + "\n", + " CTR CVR \n", + "0 0.013605 0.027211 \n", + "1 0.011198 0.011198 \n", + "2 0.000000 0.144300 \n", + "3 0.023480 0.023480 \n", + "4 0.024195 0.024195 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversion_data['CVR'] = (conversion_data.Total_Conversion\n", + " .divide(conversion_data.Impressions)\n", + " .multiply(100)\n", + " )\n", + "conversion_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "xyz_campaign_id\n", + "916 54\n", + "936 464\n", + "1178 625\n", + "dtype: int64" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversion_data.groupby(['xyz_campaign_id']).size()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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ad_idxyz_campaign_idfb_campaign_idagegenderinterestImpressionsClicksSpentTotal_ConversionApproved_ConversionCTRCVRCPM
070874691610391630-34M15735011.43210.0136050.0272110.194558
170874991610391730-34M161786121.82200.0111980.0111980.101898
270877191610392030-34M2069300.00100.0000000.1443000.000000
370881591610392830-34M28425911.25100.0234800.0234800.293496
470881891610392830-34M28413311.29110.0241950.0241950.312122
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" + ], + "text/plain": [ + " ad_id xyz_campaign_id fb_campaign_id age gender interest \\\n", + "0 708746 916 103916 30-34 M 15 \n", + "1 708749 916 103917 30-34 M 16 \n", + "2 708771 916 103920 30-34 M 20 \n", + "3 708815 916 103928 30-34 M 28 \n", + "4 708818 916 103928 30-34 M 28 \n", + "\n", + " Impressions Clicks Spent Total_Conversion Approved_Conversion \\\n", + "0 7350 1 1.43 2 1 \n", + "1 17861 2 1.82 2 0 \n", + "2 693 0 0.00 1 0 \n", + "3 4259 1 1.25 1 0 \n", + "4 4133 1 1.29 1 1 \n", + "\n", + " CTR CVR CPM \n", + "0 0.013605 0.027211 0.194558 \n", + "1 0.011198 0.011198 0.101898 \n", + "2 0.000000 0.144300 0.000000 \n", + "3 0.023480 0.023480 0.293496 \n", + "4 0.024195 0.024195 0.312122 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conversion_data['CPM'] = (conversion_data.Spent\n", + " .divide(conversion_data.Impressions)\n", + " .multiply(1000)\n", + " )\n", + "conversion_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bonus: Cost per acquisition (CPA)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ad_idxyz_campaign_idfb_campaign_idagegenderinterestImpressionsClicksSpentTotal_ConversionApproved_ConversionCTRCVRCPMCPA
070874691610391630-34M15735011.43210.0136050.0272110.1945581.43
170874991610391730-34M161786121.82200.0111980.0111980.101898inf
270877191610392030-34M2069300.00100.0000000.1443000.000000NaN
370881591610392830-34M28425911.25100.0234800.0234800.293496inf
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SNoDateStartupNameIndustryVerticalSubVerticalCityInvestorsNameInvestmentTypeAmountInUSDRemarksyearyearmonthCleanedAmount
0109/01/2020BYJU’SE-TechE-learningBangaloreTiger Global ManagementPrivate Equity Round20,00,00,000NaN20202020-01-01200000000.0
1213/01/2020ShuttlTransportationApp based shuttle serviceNCRSusquehanna Growth EquitySeries C80,48,394NaN20202020-01-018048394.0
2309/01/2020MamaearthE-commerceRetailer of baby and toddler productsBangaloreSequoia Capital IndiaSeries B1,83,58,860NaN20202020-01-0118358860.0
3402/01/2020https://www.wealthbucket.in/FinTechOnline InvestmentNCRVinod KhatumalPreseries A30,00,000NaN20202020-01-013000000.0
4502/01/2020FashorFashion and ApparelEmbroiled Clothes For WomenMumbaiSprout Venture PartnersSeed Funding18,00,000NaN20202020-01-011800000.0
\n", + "
" + ], + "text/plain": [ + " SNo Date StartupName IndustryVertical \\\n", + "0 1 09/01/2020 BYJU’S E-Tech \n", + "1 2 13/01/2020 Shuttl Transportation \n", + "2 3 09/01/2020 Mamaearth E-commerce \n", + "3 4 02/01/2020 https://www.wealthbucket.in/ FinTech \n", + "4 5 02/01/2020 Fashor Fashion and Apparel \n", + "\n", + " SubVertical City \\\n", + "0 E-learning Bangalore \n", + "1 App based shuttle service NCR \n", + "2 Retailer of baby and toddler products Bangalore \n", + "3 Online Investment NCR \n", + "4 Embroiled Clothes For Women Mumbai \n", + "\n", + " InvestorsName InvestmentType AmountInUSD Remarks \\\n", + "0 Tiger Global Management Private Equity Round 20,00,00,000 NaN \n", + "1 Susquehanna Growth Equity Series C 80,48,394 NaN \n", + "2 Sequoia Capital India Series B 1,83,58,860 NaN \n", + "3 Vinod Khatumal Preseries A 30,00,000 NaN \n", + "4 Sprout Venture Partners Seed Funding 18,00,000 NaN \n", + "\n", + " year yearmonth CleanedAmount \n", + "0 2020 2020-01-01 200000000.0 \n", + "1 2020 2020-01-01 8048394.0 \n", + "2 2020 2020-01-01 18358860.0 \n", + "3 2020 2020-01-01 3000000.0 \n", + "4 2020 2020-01-01 1800000.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('../data/startup_data.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task1: Number Of Fundings\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Can we get an overview of the number of fundings that has changed over time?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# lets begin by plotting using matplotlib and pandas\n", + "df.year.value_counts().sort_index(ascending=True).plot.bar(rot=0)\n", + "plt.title('Number of funding deals over the years')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## In this notebook we will try to use plotly as much as we can" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2015 936\n", + "2016 993\n", + "2017 687\n", + "2018 310\n", + "2019 111\n", + "2020 7\n", + "Name: year, dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_of_funding_rounds = df.year.value_counts().sort_index(ascending=True)\n", + "num_of_funding_rounds" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(data = go.Bar(x=num_of_funding_rounds.index,\n", + " y=num_of_funding_rounds.values),\n", + " layout_title_text='Number of funding deals by year')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "\n", + "\n", + "* Years 2015 & 2016 has got more number of fundings compared to the recent years\n", + "\n", + "* We can see a clear decling trend in the number of funding deals from 2016. \n", + "\n", + "Not sure of the exact reason. One thing could be that not all the funding deals are captured in the recent days.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.yearmonth.value_counts().sort_index().plot(figsize=(12,5))\n", + "plt.title('Number of funding deals - month on month')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "We can see a steady decline here as well but seems to be increasing in the last few months.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lets try to see if the decrease in deals has any impact on amount being invested?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.ticker as ticker\n", + "ax1 = df.yearmonth.value_counts().sort_index().plot( figsize=(15,8), color='tab:red', rot=90)\n", + "ax1.tick_params(axis='y', labelcolor='tab:red')\n", + "ax1.set_ylabel('no. of funding deals', color='tab:red')\n", + "\n", + "#https://matplotlib.org/gallery/api/two_scales.html\n", + "ax2 = ax1.twinx()\n", + "\n", + "# https://matplotlib.org/3.1.0/gallery/ticks_and_spines/custom_ticker1.html\n", + "def billions(x, pos):\n", + " 'The two args are the value and tick position'\n", + " return '$%1.1fB' % (x * 1e-9)\n", + "formatter_billions = ticker.FuncFormatter(billions)\n", + "\n", + "#https://stackoverflow.com/a/38152510/8210613\n", + "formatter = ticker.StrMethodFormatter('${x:,.0f}')\n", + "\n", + "ax2.yaxis.set_major_formatter(formatter_billions)\n", + "\n", + "df.groupby('yearmonth').CleanedAmount.sum().plot.bar(ax=ax2)\n", + "ax2.set_ylabel('total amount invested (in billions)')\n", + "plt.title('Funding deals against the amount invested')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight\n", + "Inspite of less number of deals, in the recent past, the total investments have seen exponential growth?? or is it??" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task2: Funding Values\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Can we get an overview of the funding values investors usually invest?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# lets convert the amount from string to numeric\n", + "df['CleanedAmount'] = pd.to_numeric(df.AmountInUSD.str.replace(',',''),errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.CleanedAmount.sort_values(ignore_index=True).plot(style='.')\n", + "plt.title('Distribution of funding in USD')\n", + "plt.xlabel('index')\n", + "plt.ylabel('Funding amount in USD')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "\n", + "\n", + "There are some extreme values at the right. Let us see who are these very well funded startups.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SNoDateStartupNameIndustryVerticalSubVerticalCityInvestorsNameInvestmentTypeAmountInUSDRemarksyearyearmonthCleanedAmount
606127/08/2019Rapido Bike TaxiTransportationBike TaxiBangaloreWestbridge CapitalSeries B3,90,00,00,000NaN20192019-08-013.900000e+09
65165211/08/2017FlipkartE-CommerceOnline MarketplaceBangaloreSoftbankPrivate Equity2,50,00,00,000NaN20172017-08-012.500000e+09
83083118/05/2017PaytmE-CommerceMobile Wallet & ECommerce platformBangaloreSoftBank GroupPrivate Equity1,40,00,00,000NaN20172017-05-011.400000e+09
96696721/03/2017FlipkartE-CommerceECommerce MarketplaceBangaloreMicrosoft, eBay, Tencent HoldingsPrivate Equity1,40,00,00,000NaN20172017-03-011.400000e+09
313225/11/2019PaytmFinTechMobile WalletNCRVijay Shekhar SharmaFunding Round1,00,00,00,000NaN20192019-11-011.000000e+09
2648264928/07/2015Flipkart.comOnline MarketplaceNaNBangaloreSteadview Capital and existing investorsPrivate Equity70,00,00,000Late Stage, 10th Round More here20152015-07-017.000000e+08
2459246029/09/2015PaytmE-Commerce & M-Commerce platformNaNNCRAlibaba Group, Ant FinancialPrivate Equity68,00,00,000Late Stage (Alibaba @ 40% equity)20152015-09-016.800000e+08
18818930/08/2018True NorthFinancePrivate Equity FirmMumbaiNaNPrivate Equity60,00,00,000NaN20182018-08-016.000000e+08
333402/10/2019UdaanB2BBusiness developmentBangaloreAltimeter Capital, DST GlobalSeries D58,50,00,000NaN20192019-10-015.850000e+08
2244224518/11/2015OlaCar Aggregator & Retail Mobile AppNaNBangaloreBaillie Gifford, Falcon Edge Capital, Tiger Gl...Private Equity50,00,00,000Series F ( More Details Here)20152015-11-015.000000e+08
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20203.902073e+085.574389e+07
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Out of the 10B in 2017, 5.5B is raised by Flipkart and PayTM in 3 deals which we can see in the table above the plot.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "type": "bar", + "x": [ + 2015, + 2016, + 2017, + 2018, + 2019, + 2020 + ], + "y": [ + 13261502.091743119, + 6532574.416382252, + 22871293.26754386, + 19329691.958490565, + 56120928.22326923, + 55743893.428571425 + ] + } + ], + "layout": { + "autosize": true, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(data = go.Bar(x = amt_df.index,\n", + " y= amt_df['mean']),\n", + " layout_title_text = 'Avg. investments by year')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "* When it comes to the mean value of funding, 2020 leads the pack with an average of 55 Million USD.\n", + "* But the year has just started, should the mean funding of 2020 be considered or there is something we are missing? Check the number of funds raised in the year 2020, it is pretty less.\n", + "* We will consider 2019 data as valid data for mean funding." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task3: Investment Type\n", + "\n", + "Now let us explore the investment type of the funding deals like whether it is seed funding, private equity funding or so on.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Can we get an idea about the number and value of funding deals with respect to the investment type?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "43" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.InvestmentType.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Seed Funding 1393\n", + 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sizesummean
InvestmentType
Seed Funding13937.757209e+081.038448e+06
Private Equity13572.672787e+102.493271e+07
Seed Angel Funding1412.256960e+082.051782e+06
Debt Funding251.509204e+086.288348e+06
Series A242.032000e+089.236364e+06
Series B209.491957e+084.745979e+07
Series C141.044718e+097.462274e+07
Series D121.481799e+091.234832e+08
PreSeries A84.137200e+075.910286e+06
Seed45.280000e+071.320000e+07
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(data = go.Bar(y = top10_inv_type_amt.index[::-1],\n", + " x= top10_inv_type_amt['mean'][::-1],\n", + " orientation='h'),\n", + " layout_title_text = 'Top 10 Investment types against their avg. amount')\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "We can see a clear increase in the mean funding value as we go up the funding round ladder from Seed funding to Series D as expected." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task4: Location" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Find about the major start up hubs in India.\n", + "\n", + "Now let us explore the location of the startups that got funded. This can help us to understand the startup hubs of India.\n", + "\n", + "Since there are multiple locations in the data, let us plot the top 10 locations. We will also club New Delhi, Gurgaon & Noida together to form NCR for the below chart." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "102" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.City.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NCR 892\n", + "Bangalore 842\n", + "Mumbai 568\n", + "Pune 105\n", + "Hyderabad 99\n", + "Chennai 97\n", + "Ahmedabad 38\n", + "Jaipur 30\n", + "Kolkata 21\n", + "Indore 13\n", + "Name: City, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "top10_cities = df.City.value_counts()[:10]\n", + "top10_cities" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + 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Gkem+z7UR0yRJkmScYlpMLxthMd0EavO5boxubu0OiunNrd2p10Vvbu1eGdvt67/Ja6a74dw9T/O5JsDFNEmSJBmnmBbTy0boa6a7cdx9CvaQmC5l+qnWfW9INuv6b/Ju3n1Hobvv5u0NyEiSJMmPo5gW08vGaDH9sdnZO7jyDc4+NmKaJEmSjFNMi+llI01Md48Kt49KLwNimiRJkoxTTIvpZSNNTC87YpokSZKMU0yL6WVDTI+EmCZJkiTjFNNietkQ0yMhpkmSJMk4xbSYXjbE9Eg8+oOL8ulvTHhH/XWS6fz0weLvA0ny+v77/3ghpsX0UiGmR+LlV9+Xz87fc2SfvZqUZ68mC78f83z6Ip/PX03K81eThd+PGvys4+X3bc9pvJ3PXo647fPFf+8sk89e3q1/Ez7m9+3nf7/toh8/NXrTbRf9/ZfBz19Oyosl+zfh81fvFx6fYhptxPSILPrBUKPffDcp3/zy+4Xfjxr9O9uGbvsL28Zs+61tbZvPn387Kd++s61tc/m3307Kd7aN2VZMV4OYHpFFPxhqVEzHKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0NYnpEFv1gqFExHaeYjt1WlARtK/hsm1DBZ9uMiunAbcV0NYjpEVn0g6FGxXScYjp2W1EStK3gs21CBZ9tMyqmA7cV09Ugpkfi1ZeT8vTFO47s85cX5fmri4Xfjxp9ZlvbJtS2th3LL75+99F+WBV8ts2omA7cVkxXg5geid3HF+WTBxOSJLnk/vbvXZSn52K6Bm0bp5gO3FZMV4OYHomHjy4W/ofsSZLkfLd++1dH4D/WD6uCz7YZFdOB24rpahDTIyGmSZLMoZiuR9vGKaYDtxXT1SCmR0JMkySZQzFdj7aNU0wHbiumq0FMj4SYJkkyh2K6Hm0bp5gO3FZMV8Odjum19e1yfHJ6o8sen5yWldWNy4/FNEmSORTT9WjbOMV04LZiuhqWJqbX1ren4rRhZXWjrK1vh92mmCZJ8m4ppuvRtnGK6cBtxXQ1LFVMb27tlv3Do8vP7R8elc2t3aWM6S5imiTJHIrperRtnGI6cFsxXQ1LFdNPzs6njvaurG6U45PTqZjuBnD39JXVjbKzd1BWVjfKyupG2T88mvr4ydn51HXtHx5dntY9vTn63Ni+ne59FdMkSeZQTNejbeMU04HbiulqWLqY3tk7KPuHR5feNKab05qgLqWUnb2Dsrm1O3Vd7ct2n7rdXE/7/M3nxDRJkjkV0/Vo2zjFdOC2Yroali6mX795O3UU+CYx3Rxdbq7r9Zu317qu7uW7NE87L0VMkySZVTFdj7aNU0wHbiumq2HpYrqUcnl0upTFx3TzxmiNYpokydyK6Xq0bZxiOnBbMV0NSxnTbRYZ081rqhscmSZJMr9iuh5tG6eYDtxWTFdDupje3Nqdei1z93XPt4npnb2DmdfV3LaYJkkyt2K6Hm0bp5gO3FZMV0O6mG4Cuf1u3beJ6Vnv1t2cv/sUbzFNkmRuxXQ92jZOMR24rZiuhqWJ6eyIaZIkcyim69G2cYrpwG3FdDWI6ZEQ0yRJ5lBM16Nt4xTTgduK6WoQ0yMhpkmSzKGYrkfbximmA7cV09UgpkdCTJMkmUMxXY+2jVNMB24rpqtBTI+EmCZJModiuh5tG6eYDtxWTFeDmB6JR48vyqcPJiRJcsnd+b2LcnYupmvQtnGK6cBtxXQ1iOmRePXV9+Xp+XuO7PNXk/L81WTh96NGbWvbjD5/aVvb3t6zn74vX/zs4/2wKvhsm1ExHbitmK4GMT0ii34w1Og3303KN7/0D3mEf2fb0G1/YduYbb+1rW3zKfhsm1ExHbitmK4GMT0ii34w1KiYjlNMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0iCz6wVCjYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yOy6AdDjYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPxKsvJ+Xpi3cc2ecvL8rzVxeXH3/24l356Vcf7x1Ya1ZMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0SOw+viifPJgw2H+8Myn/9UxMj6GYjt1WlARtK/hsm1DBZ9uMiunAbcV0NYjpkXj46KLcuz9hsP9we1L+81MxPYZiOnZbURK0reCzbUIFn20zKqYDtxXT1SCmR0JMi+lsiunYbUVJ0LaCz7YJFXy2zaiYDtxWTFeDmB4JMS2msymmY7cVJUHbCj7bJlTw2TajYjpwWzFdDWJ6JMS0mM6mmI7dVpQEbSv4bJtQwWfbjIrpwG3FdDWI6ZEQ02I6m2I6dltRErSt4LNtQgWfbTMqpgO3FdPVUE1M7+wdlJXVjSmfnJ1/tNsX02I6m2I6dltRErSt4LNtQgWfbTMqpgO3FdPVUFVM7+wdXH785Oz8owa1mBbT2RTTsduKkqBtBZ9tEyr4bJtRMR24rZiuhmpjupRSNrd2y/7hUSmllLX17XJ8cnp52vHJaVlb3778uDm9fWT79Zu3U9e3f3g083QxLaazKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0NYvrvWVvfLiurG1PXt7m1e/nx/uHR1Pm7lxfTYjqbYjp2W1EStK3gs21CBZ9tMyqmA7cV09VQbUw3R5mbo8fXPTJ91endp4y3n0YupsV0NsV07LaiJGhbwWfbhAo+22ZUTAduK6aroaqY7r4BWftp2LeN6e51d9/kTEyL6WyK6dhtRUnQtoLPtgkVfLbNqJgO3FZMV0NVMd19mnebMWL6qjczE9NiOptiOnZbURK0reCzbUIFn20zKqYDtxXT1XBnYnpza3fq9LX17UExvbN3MPVx87nm6LeYFtPZFNOx24qSoG0Fn20TKvhsm1ExHbitmK6GOxPTr9+8nXp6dvcNxebFdCkfvpu3NyAT05kV07HbipKgbQWfbRMq+GybUTEduK2YroZqYnrRiGkxnU0xHbutKAnaVvDZNqGCz7YZFdOB24rpahDTIyGmxXQ2xXTstqIkaFvBZ9uECj7bZlRMB24rpqtBTI+EmBbT2RTTsduKkqBtBZ9tEyr4bJtRMR24rZiuBjE9EmJaTGdTTMduK0qCthV8tk2o4LNtRsV04LZiuhrE9EiIaTGdTTEdu60oCdpW8Nk2oYLPthkV04HbiulqENMj8ejxRfn0wYTB/pOdSfkvZ2J6DMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPxKuvvi9Pz99zZJ+/mpTnryZTn/vpz8T0GIrp2G1FSdC2gs+2CRV8ts2omA7cVkxXg5gekUU/GGr0G8EXppiO3VaUBG0r+GybUMFn24yK6cBtxXQ1iOkRWfSDoUbFdJxiOnZbURK0reCzbUIFn20zKqYDtxXT1SCmR2TRD4YaFdNxiunYbUVJ0LaCz7YJFXy2zaiYDtxWTFeDmB6RRT8YalRMxymmY7cVJUHbCj7bJlTw2TajYjpwWzFdDWJ6JF59OSlPX7zjiJ6dvyv/528vBF+QYjp2W1EStK3gs21CBZ9tMyqmA7cV09Ugpkdi9/FF+eTBhCP6O78/KV98LaajFNOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0SDx9dlHv3JxzRB78rpiMV07HbipKgbQWfbRMq+GybUTEduK2YrgYxPRJiWkxnU0zHbitKgrYVfLZNqOCzbUbFdOC2YroaxPRIiGkxnU0xHbutKAnaVvDZNqGCz7YZFdOB24rpahDTIyGmxXQ2xXTstqIkaFvBZ9uECj7bZlRMB24rpqthKWL6+OS0rK1vh97G2vp2OT45He36uvdZTIvpbIrp2G1FSdC2gs+2CRV8ts2omA7cVkxXw41iem19u+wfHk19bv/wqGxu7d7oTohpiumPr5iO3VaUBG0r+GybUMFn24yK6cBtxXQ1iOkbIqbFdHbFdOy2oiRoW8Fn24QKPttmVEwHbiumqyEkpvcPjz6I482t3anLbG7tlpXVjUu7598/PJo6/fWbt5enraxulOOT06nT2h/3XV9zn9vneXJ2fnn6vMvPu89iWkxnU0zHbitKgrYVfLZNqOCzbUbFdOC2Yroawo5Mt2P19Zu3ZWV14/K0nb2DqfN2j/J2Y7x7el/s7uwdfHAf259bW9/+4Dq79+mqy8+7z2JaTGdTTMduK0qCthV8tk2o4LNtRsV04LZiuhpuHNPtI7SN7djc2Tu4jNH2/y6l9B4Vbofp2vr21Ondy3Qv30c37vue5n3V9Vz1y4G++yymxXQ2xXTstqIkaFvBZ9uECj7bZlRMB24rpqsh7Mh0+2h031Hq9tO2+4489zkvpruRPzSmZ13+OvdZTIvpbIrp2G1FSdC2gs+2CRV8ts2omA7cVkxXQ+gbkG1u7V7aZt5R3nlHnvtO796noUem513ekWkxXZtiOnZbURK0reCzbUIFn20zKqYDtxXT1RAa083rkrsRu7m1O/W0782t3akw3dk76H1NdHNkuC+mu5/rRnw3pru3Me/y8+6zmBbT2RTTsduKkqBtBZ9tEyr4bJtRMR24rZiuhtCY7r7xWJv206n74rn7ztvzjlx33427L6averfueZefd5/FtJjOppiO3VaUBG0r+GybUMFn24yK6cBtxXQ13Cimr8vO3sEH0V0rYlpMZ1NMx24rSoK2FXy2Tajgs21GxXTgtmK6GsJiuu9Nu2pGTIvpbIrp2G1FSdC2gs+2CRV8ts2omA7cVkxXQ+iR6buEmBbT2RTTsduKkqBtBZ9tEyr4bJtRMR24rZiuBjE9EmJaTGdTTMduK0qCthV8tk2o4LNtRsV04LZiuhrE9EiIaTGdTTEdu60oCdpW8Nk2oYLPthkV04HbiulqENMj8ejxRfn0wYQj+ru/L6YjFdOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0Sr776vjw9f88RPTt/X/7654IvSjEdu60oCdpW8Nk2oYLPthkV04HbiulqENMjsugHQ41+I/jCFNOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0ii34w1KiYjlNMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0iCz6wVCjYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yOy6AdDjYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPxKsvJ+Xpi3ccyZc/+9UDXEzHKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0NYnokdh9flE8eTDiCW789Kad/8a58/TdiOlIxHbutKAnaVvDZNqGCz7YZFdOB24rpahDTI/Hw0UW5d3/CEfwH/2hS/pOYDldMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0SIhpMZ1NMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yMhpsV0NsV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPhJgW09kU07HbipKgbQWfbRMq+GybUTEduK2Yroaljum19e1yfHK66LvRy/HJaVlZ3bj8WEyL6WyK6dhtRUnQtoLPtgkVfLbNqJgO3FZMV8PCY3ptfbusrG5M2T5NTN89xfTHUUzHbitKgrYVfLZNqOCzbUbFdOC2YroaFhbTr9+8LSurG2X/8Gjq8/uHR5efW+aY7iKmxXQ2xXTstqIkaFvBZ9uECj7bZlRMB24rpqthYTG9ubVbdvYOrjxPE9Pto9av37ydOs/+4dHM0+ddft7p3dPW1rcvT3tydu7ItJhOrZiO3VaUBG0r+GybUMFn24yK6cBtxXQ1LCymV1Y3ypOz8yvP0zwFvGFn76Bsbu1efrx/eDQVuMcnp1Mfz7v8vNO7sb+2vn35OTEtprMrpmO3FSVB2wo+2yZU8Nk2o2I6cFsxXQ0LienmKd7do8xduk/z7ovlbpC3I/06l7/q9C77h0eXsS2mxXR2xXTstqIkaFvBZ9uECj7bZlRMB24rpqth6Y9MXxW73TcuaxwzprtvkCamxXQtik3FL3sAABpcSURBVOnYbUVJ0LaCz7YJFXy2zaiYDtxWTFdDitdMN/TF9FVBftuYXlvfnnqDNEemxXRNiunYbUVJ0LaCz7YJFXy2zaiYDtxWTFfDwmK6idHuu3kfn5zOfDfvbuzu7B18cCR5Z+/g8unjt43pbqxvbu2KaTFdjWI6dltRErSt4LNtQgWfbTMqpgO3FdPVsPC/M919ivbQp2F338176JHnq07vvpu3mBbTNSmmY7cVJUHbCj7bJlTw2TajYjpwWzFdDQuP6VoQ02I6m2I6dltRErSt4LNtQgWfbTMqpgO3FdPVIKZHQkyL6WyK6dhtRUnQtoLPtgkVfLbNqJgO3FZMV4OYHgkxLaazKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0NYnokxLSYzqaYjt1WlARtK/hsm1DBZ9uMiunAbcV0NYjpkRDTYjqbYjp2W1EStK3gs21CBZ9tMyqmA7cV09Ugpkfi0eOL8umDCUdw+59NyulTMR2tmI7dVpQEbSv4bJtQwWfbjIrpwG3FdDWI6ZF49dX35en5e47ky5+J6WjFdOy2oiRoW8Fn24QKPttmVEwHbiumq0FMj8iiHww1KqbjFNOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0ii34w1KiYjlNMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0iCz6wVCjYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yOy6AdDjYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPxKsvJ+Xpi3cL83+cv1v4gzFCMR2nmI7dVpQEbSv4bJtQwWfbjIrpwG3FdDWI6ZHYfXxRPnkwWZj/8t9clC9eL/4BObZiOk4xHbutKAnaVvDZNqGCz7YZFdOB24rpahDTI/Hw0UW5d3+yMH/798Q0hymmY7cVJUHbCj7bJlTw2TajYjpwWzFdDWJ6JMR0jGI6TjEdu60oCdpW8Nk2oYLPthkV04HbiulqENMjIaZjFNNxiunYbUVJ0LaCz7YJFXy2zaiYDtxWTFeDmB4JMR2jmI5TTMduK0qCthV8tk2o4LNtRsV04LZiuhpuFdNr69vl+OR0rPsyiOOT07K2vn3t8499X7u3L6ZjFNNxiunYbUVJ0LaCz7YJFXy2zaiYDtxWTFfDtWJ6bX27N1zFtJiOVkzHKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0Nc2P6ydl52dzaLWvr2+XJ2fnUaWJaTEcrpuMU07HbipKgbQWfbRMq+GybUTEduK2Yroa5Mb2zd1COT07Lzt5B2dk7mDqtCdSV1Y1LX795e3n6yupG2dk7uDxt//Bo6uNunO8fHs28rlJK2dzanTq9HbPd+9EN7bX17Q+uv3378y4/7/bFdIxiOk4xHbutKAnaVvDZNqGCz7YZFdOB24rpapgb003UPjk77w3UldWNy4939g7K5tbu1GWbAG9idf/wqPe8+4dHH8Rx++Pu+ftO79639ue6T1Vv7s91Lz/v9sV0jGI6TjEdu60oCdpW8Nk2oYLPthkV04HbiulquDKmm6d4N3SP5nafOt0NzPb5X795O3W0uXvevqeRty/fdyT5qqd57x8eTd33vqd59x0dn3X5ebcvpmMU03GK6dhtRUnQtoLPtgkVfLbNqJgO3FZMV8OVMd08xbv9cfdo71gx3X76dPep2N3L9l2+uT/tyw6N6VmXv87ti+kYxXScYjp2W1EStK3gs21CBZ9tMyqmA7cV09VwZUzPCtyGsWN61lHivtP7jmw3TyEvZfiR6XmXd2R6MYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arYWZM971GupQPA3SsmN7ZO/jg9nb2Di7Pv7m1O3VUvHmH8b7bak6/Kqa7tzfv8vNuX0zHKKbjFNOx24qSoG0Fn20TKvhsm1ExHbitmK6GmTHdjceG9lO9x4zpUj58N+/u6e3TujHcfTfuvpi+6rrnXX7e7YvpGMV0nGI6dltRErSt4LNtQgWfbTMqpgO3FdPVMPfdvHE9xHSMYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yMhpmMU03GK6dhtRUnQtoLPtgkVfLbNqJgO3FZMV4OYHgkxHaOYjlNMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0SIjpGMV0nGI6dltRErSt4LNtQgWfbTMqpgO3FdPVIKZHQkzHKKbjFNOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0Sjx5flE8fTBbm7/1YTHOYYjp2W1EStK3gs21CBZ9tMyqmA7cV09Ugpkfi1Vffl6fn7xfm2U/fL/zBGKGYjlNMx24rSoK2FXy2Tajgs21GxXTgtmK6GsT0iCz6wVCjYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WoQ0yOy6AdDjYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPyKIfDDUqpuMU07HbipKgbQWfbRMq+GybUTEduK2YrgYxPSKLfjDUqJiOU0zHbitKgrYVfLZNqOCzbUbFdOC2YroaxPRIvHg1KU8+fxfqT579snz9dvEPuo+pmI5TTMduK0qCthV8tk2o4LNtRsV04LZiuhrE9EjsPr4onzyYhPrP/9VF+cv/vfgH3cdUTMcppmO3FSVB2wo+2yZU8Nk2o2I6cFsxXQ1ieiQeProo9+5PQv2tH4lpjqeYjt1WlARtK/hsm1DBZ9uMiunAbcV0NYjpkRDTMYrpOMV07LaiJGhbwWfbhAo+22ZUTAduK6arQUyPhJiOUUzHKaZjtxUlQdsKPtsmVPDZNqNiOnBbMV0NYnokxHSMYjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6Wq4EzF9fHJaVlY3Qm9DTMcopuMU07HbipKgbQWfbRMq+GybUTEduK2YroaUMb2yunHp6zdv555fTOdVTMcppmO3FSVB2wo+2yZU8Nk2o2I6cFsxXQ0pY7qUUp6cnV87pj8GYjpGMR2nmI7dVpQEbSv4bJtQwWfbjIrpwG3FdDVUE9PN0efGtfXtD87bsLa+XfYPj6bO/+TsfOr045PTy4+PT06nrm9ldWPq9l6/eSumgxTTcYrp2G1FSdC2gs+2CRV8ts2omA7cVkxXQzUxvbN3MHX62vr25ef6Yrodx01Yt0+fF9Ptj0txZDpKMR2nmI7dVpQEbSv4bJtQwWfbjIrpwG3FdDVUE9Nd9g+PyubW7tR5G7qxXEqZOjp9nZhuH8kuRUxHKabjFNOx24qSoG0Fn20TKvhsm1ExHbitmK6GqmJ6bX176qnbYjq/YjpOMR27rSgJ2lbw2Tahgs+2GRXTgduK6WqoJqab10E3ODJdh2I6TjEdu60oCdpW8Nk2oYLPthkV04HbiulqSBvT8wJ3c2v32jG9s3cwdV2bW7tTr8HuvsZaTH88xXScYjp2W1EStK3gs21CBZ9tMyqmA7cV09WQLqY3t3Z734G7+27e82J61jt/l1LK6zdvp07fPzwS0wtSTMcppmO3FSVB2wo+2yZU8Nk2o2I6cFsxXQ3pYnoM+p7mfVvEdIxiOk4xHbutKAnaVvDZNqGCz7YZFdOB24rpahDTIyGmYxTTcYrp2G1FSdC2gs+2CRV8ts2omA7cVkxXw52M6QjEdIxiOk4xHbutKAnaVvDZNqGCz7YZFdOB24rpahDTIyGmYxTTcYrp2G1FSdC2gs+2CRV8ts2omA7cVkxXg5geCTEdo5iOU0zHbitKgrYVfLZNqOCzbUbFdOC2YroaxPRIPHp8UT59MAn1X/xrMc3xFNOx24qSoG0Fn20TKvhsm1ExHbitmK4GMT0S5198X558/j7Unzx/X75+u/gH3cdUTMcppmO3FSVB2wo+2yZU8Nk2o2I6cFsxXQ1iekQW/WCoUTEdp5iO3VaUBG0r+GybUMFn24yK6cBtxXQ1iOkRWfSDoUbFdJxiOnZbURK0reCzbUIFn20zKqYDtxXT1SCmR2TRD4YaFdNxiunYbUVJ0LaCz7YJFXy2zai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sizesummean
City
NCR8928.461949e+091.492407e+07
Bangalore8421.462637e+102.508811e+07
Mumbai5684.940535e+091.228989e+07
Pune1056.330820e+088.916648e+06
Hyderabad994.010762e+085.570503e+06
Chennai977.187670e+089.583560e+06
Ahmedabad381.136360e+084.208741e+06
Jaipur301.527350e+081.090964e+07
Kolkata211.598300e+071.598300e+06
Indore134.672000e+069.344000e+05
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Cityyearsizemean
112NCR20163247.845871e+06
10Bangalore20162836.202401e+06
111NCR20152541.716481e+07
11Bangalore20172264.606586e+07
9Bangalore20152002.150281e+07
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" + ], + "text/plain": [ + " City year size mean\n", + "112 NCR 2016 324 7.845871e+06\n", + "10 Bangalore 2016 283 6.202401e+06\n", + "111 NCR 2015 254 1.716481e+07\n", + "11 Bangalore 2017 226 4.606586e+07\n", + "9 Bangalore 2015 200 2.150281e+07" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_df=(df.groupby(['City','year'])\n", + " .CleanedAmount.agg(['size','mean'])\n", + " .reset_index()\n", + " .sort_values('size',ascending=False)\n", + ")\n", + "temp_df = temp_df.loc[temp_df.City.isin([\"Bangalore\", \"NCR\", \"Mumbai\", \"Chennai\", \"Pune\", \"Hyderabad\", \"Jaipur\"])]\n", + "temp_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "City=%{y}
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0L5xL94ZKypi2Hv5nafu8IC6KlXoozVJ5+Zu3Q2LamrlPvKOHxeU8HjhgyW7L/PyCVHl9giklpotF7mJ60+btsmHjlj7zAjHdb7NqjTwh0Epi2tnzqHhOw/jjwrW14fpSf/l32cb0ya/5yLUps1lviPPMY2sb0zP3SfP8OY6TLYi+XRfrp8ufS0bFtDVzn3jHPmJ+4JKu5UoQxJJcPCuB35ZG1ZI5mzfkcLzEdLHIZUwfPHxs6euZXXtl67adBkd0B2L6jmUnEL9aS/XiNW1MWzP3SdsPpMQfqqmy7bfFuv8fMo3p+ovPcDzsFFmy2tJuBan//TUxXX/5efHqnN172nWclniHXh/+xu2YmLYe+LE0q+zujW1pVGpS/+NesWa+/Zv0k/vE3n2/+HNlmWOXbxwjMV0sch/TBw8fk02bt/d93bvVuvd7vT/fe51Sxe67ztZtO0fexrjbHxbTvbc1OJYNG7f03V53HHv2zY4cn+kFlVa7EYn77qE1j+nG58ek6rJVYFqccwJpWY4qjFcS09YjP5FWg+Omp8WaG0rjm5OZxLT10D/xkXwF0G/4Yj/2c31Mz9wn/m0+JaDoupYr9ddevPPa5NuYtmbuE+vhfxGfN29xjMR0sch9TG/dtlP27Jtd+npm195l1++9rLubeO/1e7dsD349GOvjbn8wpofddu9tDQvsPftml8V779emF1Ra681I3DdeXfOY9j58X2x2sZsaq/VQ/Bs3s4/pf90uQRAbf7y4NlpeKI3P/pxNTP/khxJEHeOPEbN13J4Nk2LaO/K22A1OVFdkg7D/U0r6YnrmPvGvXeONfxxpYDsS1t11cy4XWB25jOnBY6Z7Y3qQPftm+4J20pbtDRu3yPFTF0Z+f9ztD8b04G2VKnbflubB73fHN3hZ7/U6nYX1YbMprT/8Vhr3/2iizZ//SJoprte4/0fSfuMl84/NpPPTZ3Lm61T/9oNzRvszHUkH0DUAACAASURBVNsy/lhxbZxfEAkOvaKaL2mfYxbnSs34Y1xr22HH+BhyZenG6Pky4TmmPftHmTc9fjRqcuns2L9Jwduvy/yCGB8n5s/5hQUJjxyS4NArMh/Hd+d+V/naE1ZHLmO6N4ZF7uwu3Xud3thOG9ODsTv4/Um33xvTw26rO9ZuGA+L6cE3Crp2rzfntNeFXisWd/alxXdqJ7i0ZTrFdRsfvit1PzL++IxpT5c1N5Dmtaup/u272j/p2QqQ2h9KECXGHy+ujU4zksbHR1L/+y9tmU55/TDqGH+Ma66VgzHkxLLTlqAVjPz3X9oyPerv0JG3pd6MjD8OzM/8adz/I7F7vvYvnBPLC42PFfNn2WlL5LoSe56U79brulW+9oTVsS5iundX702bt/dtqV7rLdPjbn8ttkwPu6wX07t6pNVuROK+fWDNd/NufHFMauw6NTWWnUBalWr2u3nvmpF2k2Omp8WaF0rzfLqPxVLv5v3QP0nbN/8YMVt9xxPrwf+1ot28Gye/5u9QwW1YdbGf2jlyN+9WzVo3u/Di3dfzY/GD9XM4EayO3Md0N1C7lw3G6NZtO1UxvXXbzr7jordu2zk2tntvfyXHTA+G8+B1upd1A9z0gkprpR6Kf/v2msd0220IHzsxXfo1O/OYdn77C2lUHeOPFdfGObstbdfLJKad5/dIw20af4yYrXXLk/pLwz+Sb+IJyGqczbvoluy2+JWq2L/82bKY9i9dkgqfOY1j5ARkxSKXMT24C/RgHA/ugq2JaZH+Xa2HnZBs1O2v5Gzew7ZCD57Nez2egOy21ZZWwxfrgR+vWUzbTzwovs0ZMqfNptsU5/lfZxrT3rGPOGHQlOl7TXGefGjNY7rx9Zd8JnkBLNltaTda6pj2PnxXnDp7uWBbSnYgvtuU1q1bErw7K/7lS9Ks1DjTO06UmC4WuYvpu83Mrr1jT3B2tzG9oDTW3ZY0Pnx3zWK6eYpd66ZRywvF++SDTGPav32bz/2cMi0vFO/D99c8pn2Hj8UqipbdlOa5M6lj2nn2CfHZawEHrNQDmV9YENvjDVtMJzFdLAoX04NbhXu3SucB0wtKq++4Yv38H1cd0/Yv7xffco0/HszGlh+ItfOfM4np+su/k4brG3+MuPa2/bZYD0+eN2lj2vv4fbY6FsxayZLGieMTY9p57lfSrLJ7Nw63M78gZd6wxZQS08WicDGdd0wvKK2V+uQTTKWJ6XazxbHSU6zlRdI89XUmMd1u+GyVnlKr9UAaZ5dvWVxJTNu//Jk0ORa2kFp2Q4KoI42j74iz5xFxHpkR78Efi737fnEPvCzN8+fErbHHAo6WmEaNxHSxIKZzhukFtRLLFVf8a1dXHNOt2yViqAA26g1xXh5+QqCVxnTzxHGxbHbLnGZtx5fm6ZOri+mf/kiCNoeQFF27EYlXtSVqtSXxPGnUHPGcJpGEEyWmUSMxXSyI6ZxhekGt1Kobin/rljh7n0wd0/UXnxHfcfkDVSBbblOcZx5bk5huHPtA3Cpbk4pgteZJ89zZlcX04zskCBMp5eBxYD60vFCCeN74OHD9SEyjRmK6WBDTOcP0glqNZSeQptOQxqkT4r72wtKZvvtieuf/FvfAS9I8f048x+NzGgtoy2uK89Jzq4rpxmcfizNXM/5Y8O5ZsxriW/bic0uamH7gx9I88ZV4ZeYJ9ktMo1ZiGjUS08WCmM4ZphfUWlh1Q3Hrvvg1W4JWIFF5TkLblnYrEN92pe62+aNUcBtOQ5rfnBTr4X9RxbTz7GPSvHBebIcTjhXRkt0Wt+5Lq1KR5tdfivPcU+LseVQav/iJ1J/YIc6zT0j99RfFv3pVGjWbj8DCoRLTqJWYRo3EdLEgpnOG6QW11pbstrTCRLxWzG6W2GfNDaXVbIv36cdSf+FpsX7yw+ExvfN/i/unP0jj5NfSrDd4QYMyZwdiNSJxa6403aYkbl2CZksajid1t83nwOJYiWnUSkyjRmK6WBDTOcP0gspCP0jE9WPj48B8anmRNOoN8au2tB1XWrduSXz2hPilOWl7TWl5vrhOU6p8BjmOMIzn2QqNqSWmUSsxjRqJ6WJBTOcM0wsqC4lpTOucE4jlhdKZX5BKPeDj0jCVxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUWHYC6cwvGB8Hrh+JadRITKNWYho1EtPFgpjOGaYXVBYS06iRmEatxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEgpnOG6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYENM5w/SCykJiGjUS06iVmEaNxDRqJaZRIzFdLIjpnGF6QWUhMY0aiWnUSkyjRmIatRLTqJGYLhbEdM4wvaCykJhGjcQ0aiWmUSMxjVqJadRITBcLYjpnmF5QWUhMo0ZiGrUS06iRmEatxDRqJKaLBTGdM0wvqCwkplEjMY1aiWnUSEyjVmIaNRLTxYKYzhmmF1QWEtOokZhGrcQ0aiSmUSsxjRqJ6WJBTOcM0wsqC4lp1EhMo1ZiGjUS06iVmEaNxHSxIKZzhukFlYXENKa17ARieaF05hekUg+kZJsfE+ZfYho1EtOolZhGjcR0sSCmc4bpBbVWluxAal4orh9LNWyLFYZSb8ZieZHxsWG+nLPbUm0G4gaxfNOoy+mGIx80SnK+6cpJ15ZaOxC7wbzB0RLTqJGYRq3ENGokposFMZ0zTC+o1Vqy21L1A3HDSGZr1+SRWyfkn29+Kj+9+YU8cuuEHLFLUm61ZK7JHyVsy61GSyrttvzr9S/l/zz3ptxzer/8xZn98t0Ls3LP6f1yz+n98l8uvi9PlU5LK0zkVr1lfMyYD6tuKOVmW2qtQCpRW7wokuuNptiNUOZ40YtjJKZRKzGNGonpYkFM5wzTC2o13nJbcr7hysz1z5dC6J7T++U75w7IX5490HfZrltfy+VmQyp1/jgV1Wo7kJ03v+qbF8NiemkenTkgr1avyO2G+bGjwXnjhlJrh3LEvi0/u/6F/P2lo/LXl96W/3zxXfnB5aPyYuWSnPBsmWOe4AiJadRKTKNGYrpYENM5w/SCWqnX6758Vq8uC6BRMd31YtOVq1bT+Pjx7nqt2ZQfXv546JwYFdNdX61dkYuuZ/wxoAHdlrxn35b/fPG9vjmx6fys/Nsz/c8xD938StyAwwNwucQ0aiWmUSMxXSyI6ZxhekGtxFt1Xz5zh4f0pJi+5/R+tlAXzLlmS/6/yx+NnA+TYvqe0/vl1coVmfPMPxa8e96q+/KBMzd0PgyL6XtO75f/eO5Nueo3jI8d8+GcvXj+hWo7kFoUSKUZSNXlWHucLDGNGonpYkFMrwGbNm+Xg4ePrehnDx4+Jhs2bln62vSC0jrnBHKp6Y0Nn0kx/ZdnDsgN3zf+WDB7LS+Uw/bNsfMlTUzfc3q/WAFn+y6KVTeUt6zR82ZUTN9zer98//zbcrPJ80vRnfPacs1vyO8rF2TmxufyDzf+LD+5/rm8a9+W6w32jsLxEtOokZguFlMX05s2b++L0y4bNm6RTZu3Z3afRY3pcjOQn934clUxfc/p/fLM3DmpNtlCMO3ebPry78+8sSYx/S/XP5cSx8UWwnoQyffOv7WimL7n9H75Y+USezIU2DN2XfZb15bmw789c0A2nb/zHPPfLh2VG02CGkdLTKNGYrpYTGVMb922U/bsm126bM++Wdm6bWcuY3oQ0wtKazNMJkZPmpj+D2ffkEbEZ1FPs1U3lK+82sT5kjam7zm9X4KoY/xxYbbWvFBerl0eOw8mxfSmM7Ny0+dM8EXU8iJ5vXqlbz4MxvQ9p/fL3118X640OCQAl1uyFmO6xiEBmFJiulhMZUwfP3Whb2vvho1b5ODhY30xPRjAg9/fsHGLzOzaKxs2bpENG7fInn2zfV8fP3Wh77b27Jtd+t7g97tbn7v23s/gWE0vKI01N5RZ6/qaxPQ9p/fL5/Uqx05PsdVGIP9644s1jen3ndsc8zjlVlqB/I9rx1YV0/ec3i8nPJu5UkCvN5vyV2cOTozpe07vl1drVwkm7POK1ZCLTU8+bs7JSc+SSw5vuOBkieliMbUxPbNrr+zZN7vkSmO6+71uUIuIzOzaK1u37ey7rd6fHdx1u3s7vdfvXraeY9ppRvLE7W/WLKZfqlwSu8HZd6fVuWZbfnhl+Bm8VxrTz5fPM2em3HoQyX/89jPIVxPTz86dY64UzGo9lCN2adlcGBXT/+PaMbnZ4Ph6XLRcv3NOmO9emJW/OLNfjjpzUuKQEZyg58fiB+tnzzlYHVMb06WK3bcVeCUx3d263L2tUsVOdVuDPz9Id7dzkeUx7QfJutGNIrn/1pfynXMHxvrd87Oy6fzsxOs9Vf5Gwnje+OPCbPTiSP7u8nsT58F3zh+Qv7oweb5859wBebR0QsK4Y/yxYYYm8eTnmAuTn2Omfa402+bHkDeDqCP77avL5sKm87Py3SHPMf/1yhGpR5HxcWM+DON5ebL8jXzn3Ld/k84fkP/n6odihYHxsWG+rUQtuRW1pBWuj785sDqmNqZFZGnrtIj5mO6eGK3rqJh2/XjdWA8j2Xn7K/nLswfGuunbmJ50vWcq56QVdow/LsxGL4rl768cnTgP/vLcAfmrCwcnX+/sAXly7htpR8yZaTbuzMt3zo5//uiGUZHnitM0P4a82Wwn8n799rK50H0DZvDy/37lQ3GjyPi4MR/6QSJ7K+fkL89++zfp3AHZfv3PYgWB8bFhvi2HLZkLW+K1EuNjSSOsjqmO6V5MxnT3mOou47ZMm97VQ6PTWNvdvF+tXhaL3TCn1lKzLduv/nlNd/N+oXKRXXen3Fu+vya7ef+6dJq5UjAr9UBOeXbq3bx/euMLPiEA+6y02/L982/Ldy/MynfPzMopz5ZKnePqcbwcM10sChvTW7ft7DuWefC459XE9MyuvSNvq3vf0xDTVTeUz+vVNYvpa36Tj56YYiuNQB6/dWpNY/orz5IqL2ym2kozkH+d8PF7aWL6it/gBIcF9GqjIZvPv5Mqpj9w5pgj2GfJasuNpi9XQ09utnzmB6aSmC4WhY3pbiD3nq17NTE96mzd3esP7uI9DTF922pLtR3K/3F6/IvYNDH9n86/Lbd9TvwyzVZ6TuayFjH97868IY2Aj1ObdqtuKAdr4z81YFJMf+/cW3K1yVl4i2ilHsjHTnliTD9864Tc4ORjOEI+Zxo1EtPFYupier1jekFprTQCeXru7Kpj+pB1g10wC2DFD+T/vvj+msT0U6UzUmnw4qYI1lqh/NdLR1cc0+/bt8XyeH4pqtdsX/7slJcOFxiM6Udvn5Rzrmt8nJhfiWnUSEwXC2I6Z5heUCvxqt9cVUz/zfm35XqzafxxYPamOTQgTUz/xen94gTEUVGccwK51HRXFNP/cOUTKTVbxh8DmrXqhlJqteTzelWer5yXx8vfyIuVS/JNw5FbDeYHjpeYRo3EdLEgpnOG6QW1Esv1QG61WiuOaS+KpZSDx4F3x1KjJb+dO7eqmP7Kq0nV5VjpInnVbsqphqOK6f957VO50PCMjx3zY7UeSKOdSNxZkJobypxNIOFkiWnUSEwXC2I6Z5heUCv1stWUUqsl/+7MG6lj+t7z70gj4pjXInrFbYwM6kkx/blbles2ezIU0Uo9kDNeXX5y/fOxMf2dMwfkDeuGlFvmx4z50/JCCeJ54+PA9SMxjRqJ6WJBTOcM0wtqNZadQG75LXmxfFG+f/7tkTF97/l35C37plxv+lKyzY8bzXir4csr1SvyH84eShXTP7h8VD6rV+WWyy6ZRXbODqTqB3K52ZCXqpdlx42v5B9vfCIzNz6Xn9/4Ur50a3LN98Xy2HMBh0tMo1ZiGjUS08WCmM4ZphfUWlhuBFIPIjnbcOWwfVOeq56TF6sX5bB9U261WnK71eLzpFFuW22puaF4YSyvVK/IP179RDadme2L6X9/5g25/8aX8rZ1U6qtgI8lwSVL9mIUWX4o1SiQuVZbnEYkVeYITpCYRq3ENGokposFMZ0zTC+otbTsBIsvWqKOtMKOWF7EHyMcas0LpeIHcsv3pRnGciX0pBUlcrnhScUPOD4axxrG82yJxtQS06iVmEaNxHSxIKZzhukFlYV+kIjrc2w0prNSD2R+YcH4OHD9SEyjRmIatRLTqJGYLhbEdM4wvaCykJhGjWUnkM48MY3pJaZRIzGNWolp1EhMFwtiOmeYXlBZSEyjRmIatRLTqJGYRq3ENGokposFMZ0zTC+oLCSmUSMxjVqJadRITKNWYho1EtPFgpjOGaYXVBYS06iRmEatxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEgpnOG6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYENM5w/SCykJiGjUS06iVmEaNxDRqJaZRIzFdLMbG9IaNW2TPvtm7NRYQYhqRmEatxDRqJKZRKzGNGonpYjE2pg8ePiYbNm5ZcmbX3rs1rsJiekFlITGNGolp1EpMo0ZiGrUS06iRmC4Wqt28CevsMb2gspCYRo3ENGolplEjMY1aiWnUSEwXi9QxXarYfTHd69ZtO7McY6EwvaCykJhGjcQ0aiWmUSMxjVqJadRITBeLsTE9s2tvqq3RGzZuyWJshcT0gspCYho1EtOolZhGjcQ0aiWmUSMxXSw4AVnOML2gspCYRo3ENGolplEjMY1aiWnUSEwXCz4aK2eYXlBZSEyjRmIatRLTqJGYRq3ENGokpovFxC3Tx09dWHb5wcPHZNPm7ZkNqsiYXlBZSEyjRmIatRLTqJGYRq3ENGokpovFimL6+KkLHCedEaYXVBYS06iRmEatxDRqJKZRKzGNGonpYrGimN6zb5Yt0xlhekFlITGNGolp1EpMo0ZiGrUS06iRmC4Wy2K6u9V5ksMiG1aP6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYrGjLNGSH6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYcDbvnGF6QWUhMY1pnbMDsbxQ5hcWpFInjjCdxDRqJKZRKzGNGonpYkFMDzCza6/RXdpNL6gsJKYxjTUrlurtRCpXE7n6cSLW7UQcK5FyjUjC0ZbsQOJkQZxmZHwsuD4kplErMY0aieliMTSmN2zcIgcPH5t43PQ0MrNrr8zs2rv0dfcY8rsV1KYX1FprO7GE7XkJgnmxbYIah1utRHL1eCKHHwrl3Z2RfPjot/+/KxTrdiJzVYIal2vVI3FqHbGudsSzO+LUEpmzzY8L8y0xjVqJadRITBcLtkwPMBjTIiJbt+2UPftmRURk0+btcvDwsaXvDX7mdvf7vW86lCp23+3t2Tc78vumF9RaWbIDqVuJfP6HUD56MpaPfx3Lif2x2LX18+SCd8daPZJrX8Vy+KFwWUx3de0OL2Swz/LcnXlz9JFY3ns4kvd3h1K7RVDjaMtOIA23I+3mglh19mbAdBLTqJGYLhbE9ABrEdO9W+1ndu2Vrdt2Ln09+LFigz9vekGtlY6dyMe/XQyhI7tieX/X4oveL1+OxKmzhRrv6DqJvPtIODamv/hDxJ4NuGTVDeTa13fegOnG9OGHQnn/l6E41vp5EYN3z7ITSO1WIseejeSTPbFc/iyWcpmgxvGW7LYkyYLUXPaQwnQS08ViaEx3t6z2RmOa700DgzHdfbzdrcdpt0yP+/7gLuO9u5HbjWjd6zQjafsdee/hSN57OJKjj8Ry5NsXu+/tiiSO542PEfNhvRlJsz6/NFfeeziS93fF8uEvkr7LPv51JG2feYOL+q2OnHnrzhz54NFEjuyKl75u1uel3oyNjxPzZdPryKfPRXJkVywfPLo4f+zbHan7zBUcbdPtyJnZjrhlnlcwne2wI0G0fl6zwOoYGtNbt+1ctnW2l8GtrdPEsBOQ9e6GvdqYnvS53UHUWfeGcUfqpXk5+kgsRx+J5YNHF+1+HTQWjI8R82EYd8S5ubA0N7p++Iuk7+sPdsfSbjJv8Fv9eTn52p058uGjiXzQM1/s6/MSxjkY513UDxLjY8i9zQU59ptYPvh2znTnShTPmx8b5tIw7kjTmpcPf5FI+WxHooS5gpONk3lJOutnrsDqGHkCsnEn3OqelGsaGbabdy9rEdPjfremd/VYKx2rI4d3Lt/N++gToVhVdtfFOwbhfN8u3cN28/7s95G49Y7xsWI+tBuRnP8gGbqb9+GHFnfzLuVgnJgvrXokV79IlvaY+vBXodTt9bMrJpqxXAul7S6IVeO1C6aT3byLBTE9wKSYHtxqv2nzdlVMz+za2/d197Lu1m/TC2qtrLqhzF1OlsV09WYilTon8cA7WrVYPn8hGhvTFz+JpepwbCPe0fc78uenl8f0jVOJ2C5zBYdbngvFupWIfX1e3Pr6ebGLZuUEZKiRmC4WQ2N62HG9vRw/dWFZEE4Lk2K6VLH7ds8ePKHYpJgWWX4272k8Adlta/Hd3FopEevqvNi3O2JXE/4Y4VAbbkc+/PXwmD77fiy1MlsEcLm+35HzHyVy6vVEzh6O5eaZRKpVQhrH6zQiCRM+GgvTS0yjRmK6WAyN6T37ZsceEz3pmGpYOaYX1Fo7ZwfSjjrr6kkF774lO5CGm8i5I7F8/Xoknz+byMmDkVz5IhaH3TBxjJYXSuAvSNNPpMpeL5hCPmcatRLTqJGYLhYjPxprcPfl3sundRfvPGB6QWWhHyTi+mxZxMlaXih1J5GoLWI7CR9FgqkM43mxPOYKppOYRq3ENGokpovF2M+ZHtwdecPGLWyRzhjTCyoLiWnUWHYC6cwvGB8Hrh+JadRITKNWYho1EtPFYmxMw93H9ILKQmIaNRLTqJWYRo3ENGolplEjMV0siOmcYXpBZSExjRqJadRKTKNGYhq1EtOokZguFsR0zjC9oLKQmEaNxDRqJaZRIzGNWolp1EhMFwtiOmeYXlBZSEyjRmIatRLTqJGYRq3ENGokposFMZ0zTC+oLCSmUSMxjVqJadRITKNWYho1EtPFgpjOGaYXVBYS06iRmEatxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEgpnOG6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYENM5w/SCykJiGjUS06iVmEaNxDRqJaZRIzFdLIjpnGF6QWUhMY0aiWnUSkyjRmIatRLTqJGYLhbEdM4wvaCykJhGjcQ0aiWmUSMxjVqJadRITBcLYjpnmF5QWUhMo0ZiGrUS06iRmEatxDRqJKaLBTGdM0wvqCwkplEjMY1aiWnUSEyjVmIaNRLTxYKYzhmmF1QWEtOokZhGrcQ0aiSmUSsxjRqJ6WJBTOcM0wsqC4lp1EhMo1ZiGjUS06iVmEaNxHSxIKZzhukFlYXENGokplErMY0aiWnUSkyjRmK6WBDTOcP0gspCYho1EtOolZhGjcQ0aiWmUSMxXSyI6ZxhekFlITGNGolp1EpMo0ZiGrUS06iRmC4WxHTOML2gspCYRo3ENGolplEjMY1aiWnUSEwXC2I6Z5heUFlITKNGYhq1EtOokZhGrcQ0aiSmiwUxnTNML6gsJKZRIzGNWolp1EhMo1ZiGjUS08WCmM4ZphdUFhLTqJGYRq3ENGokplErMY0aieliQUznDNMLKguJadRITKNWYho1EtOolZhGjcR0sSCmc4bpBZWFxDRqJKZRKzGNGolp1EpMo0ZiulgQ0znD9ILKQmIaNRLTqJWYRo3ENGolplEjMV0spiqmDx4+Jps2b8/0PjZt3i4HDx9bs9sbHLPpBZWFxDRqrNRDmSemMaWVeihJZ0E8P5ZynRe7OFliGrUS06iRmC4WRmN60+btsmffbN9le/bNytZtO1d0e8R0/qx7bYmDloTtltiNyPh4ML+W7LY0XU+CIJR566oEYSye1zI+LsynJastraYv7dot6Zx/X6Jb34hfvi71esP42DDfEtOolZhGjcR0sSCmlRDT6W01G9I+cUiST/ZK/OnvJTj9rjTrdePjwvxZsgNpubYEHz4t4aEHZOHwgxIcekCCb96VplU1Pj7Mny23LsGHT0tw6AHpvPOgRG8+IMGhB6R97aQ4Ncv4+DC/un4sUWdB5mziCNNJTKNGYrpY5Dqm9+ybXRbHW7ft7PuZrdt2yoaNW5YcvP6efbN93y9V7KXvbdi4RQ4ePtb3vd6vh91ed8y91zl+6sLS9yf9/KQxm15Qa6XvtyQ49nsJDj0gyds7JH5rx2IcnXhDGg3f+PgwX/qeJ8HRPRIceqA/pg89IMHZo2xtxD4bXlOCP/9uaY70xnRw6AFp3b4oVZdjqHG5Dasq4c1vZP7qMWl7dSnZ5seE+Xd+QYhpTC0xXSxyHdMi0herpYotGzZuWfrezK69fdcd3Mo7GOOD3x8WuzO79i4bY+9lmzZvX3abg2Ma9/OTxmx6Qa2FlXog7Xpt6YVtX0wfekCCVostArhkuR5I4NWX5seymH73MWm3mS94xyCM78yPITEdnHhDPJc3YLDfuteS4PQRid5cnDPBoQfFdx3j48J86zmOLFQuSLvhGh8Lrg+J6WJhPKZ7t9B27Y3NmV17l2K09/9FZOhW4d4w3bR5e9/3B39m8OeHMRj3w3bzHnc7494cGDbmqeHW1yKHHxxu9bzp0UHemDs9er4cflCkZU++DSgO1uXx8+XYsyJRy/Qo7ypRMm96CPkn8kU+e65/rri3TY8K8o59bXGunH/X9EgAIIcYj+lJW6Z7t0YP20rdu9v2sC3Pw5wU04ORr43pUT+fZsym351aC8tOIG1n9JbpdqvNrnW4ZKot0y3z48T8OHnL9CFxm+zNgP06bluCs0fvbJl+8yHxXdv4uDDferYtC9WL0vY45wumky3TxSL3MS2yeIxx114mbeWdtOV5EPuUCAAAFwtJREFU2PcHx6TdMj3p5yeN2fSCWiv9pi/Bpy8uj+mTb0rDaxofH+ZLv9GU4IPfjDhm+ojUPfNjxPzoeQ0JPtnHMdOo1rNtCW+ekflrny0eM52DMWH+5QRkqJGYLhbrIqa7xyUPRuzWbTv7dvveum1nX5jO7No79Jjo7pbhYTE9eNlgxA/G9OB9TPr5SWM2vaDWypL17UmlTr4pyae/k/izFyT45rA0Xd7ZxeWWrLa0PEeCj57tj+lzH0jD4czM2O/ifHEl+OjZ5TF9/ZTYNsc24mhdP5IoWSCkMbXENGokpovFuojpwROP9dK7O/WweB488/akLdeDZ+MeFtPjztY96ecnjdn0glprHa8tcdiWsMXnTONkG64rYRjLgn1NgqgjrsteDDjckt3zOdMXjkp0+7y0qjfYiwEnyudMo1ZiGjUS08XCaEynZWbX3mXRPa2YXlBZ6AeJuH5sfBy4PqzUA5lfmM61gGtvpR5KnCyI14p5sYupJKZRKzGNGonpYpH7mB520q5pxvSCykJiGjWWnUA68wvGx4HrxzCeF8vjGGlMJzGNWolp1EhMF4vcx3TRML2gspCYRo3ENGolplEjMY1aiWnUSEwXC2I6Z5heUFlITKNGYhq1EtOokZhGrcQ0aiSmiwUxnTNML6gsJKZRIzGNWolp1EhMo1ZiGjUS08WCmM4ZphdUFhLTqJGYRq3ENGokplErMY0aieliQUznDNMLKguJadRITKNWYho1EtOolZhGjcR0sSCmc4bpBZWFxDRqJKZRKzGNGolp1EpMo0ZiulgQ0znD9ILKQmIaNRLTqJWYRo3ENGolplEjMV0siOmcYXpBZSExjRqJadRKTKNGYhq1EtOokZguFsR0zjC9oLKQmEaNxDRqJaZRIzGNWolp1EhMFwtiOmeYXlBZSEyjRmIatRLTqJGYRq3ENGokposFMZ0zTC+oLCSmUSMxjVqJadRITKNWYho1EtPFgpjOGaYXVBYS06iRmEatxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEgpnOG6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYENM5w/SCykJiGjUS06iVmEaNxDRqJaZRIzFdLIjpnGF6QWUhMY0aiWnUSkyjRmIatRLTqJGYLhbEdM4wvaCykJhGjcQ0aiWmUSMxjVqJadRITBcLYjpnmF5QWUhMo0ZiGrUS06iRmEatxDRqJKaLBTGdM0wvqCwkplEjMY1aiWnUSEyjVmIaNRLTxYKYzhmmF1QWEtOokZhGrcQ0aiSmUSsxjRqJ6WJBTOcM0wsqC4lp1EhMo1ZiGjUS06iVmEaNxHSxIKZzhukFlYXENGokplErMY0aiWnUSkyjRmK6WBDTOcP0gspCYho1EtOolZhGjcQ0aiWmUSMxXSyI6ZxhekFlITGNGolp1EpMo0ZiGrUS06iRmC4WxHTOML2gspCYRo3ENGolplEjMY1aiWnUSEwXi0LE9KbN2+Xg4WOmhzGUg4ePyYaNW5a+Nr2gspCYRo3ENGolplEjMY1aiWnUSEwXi6mJ6U2bt8uGjVv67P0eMW3GSj2UMJ6XVtCRSp0/RDjeuWYkXphIOV6QetCROQIJJ1h1Q4mTefFasczZPMfgZIlp1EpMo0Ziulis+5guVWzZsHGL7Nk323f5nn2zS5flOaYHMb2g1lQvkk/qsfzRCuUNJ5avvFhKXmR+XJhL5/xEdt1qy3+57MvfXm3Lf7/iy5t2JNdcghqHO+cn8lE9kmdrobxlR3LFj+U6L3hxjFU3FDtIpJ4sSLnB3yNMJzGNGonpYrHuY3rrtp0ys2vv2Ot0Y7p3q3WpYvddZ8++2ZHfn/Tzk74/+L1Nm7cvfe/4qQtTuWW61IjkDSuU711oyr2XW/LXl1ryvQtN+diNpExQ44C1dkf+6friHPlPFxdj+nsXmvK9C02ZtUKp8KIXByz5seyZW5wnm6+05PsXffnehaacacRS5Q0YHOL1eigXm7H8/HZb/uFmIM+UA7nA3yNMITGNGonpYrHuY3rDxi1y/NSFsdfp7gLeZWbXXtm6befS13v2zfYF7sHDx/q+nvTzk74/GPubNm9fumwaY7pcD+R0I16Kod6Y/t6Fptz0YynlYJyYDyv1QE70zJfBmP6bi02xg/XzRwmzt+aGctSJluZIb0z/3aWm3PSZL9jvnBPISW9xznz/oi/3Xln8m/R0OZAqb9bhBIlp1NhoxeKvo9ctsDrWdUx3d/Ee3Mo8yOBu3sNieTDIeyM9zc+P+/4ge/bNLsX2YEwHUWfdGyXz8lEjkXuvtOTeKy3526st2fzt/997pSWnWh0JY/PjxHwYJ/Pysh0tzY/NV1ryt1fbS1/fe6UlF9vMGbxjnMzL3lrY8xzT7nuOmQvNj/Fu67cT42PIs1EyL+948bLnmH+4GUg9mjc+PsyvzWhePml2xImZJ5jOUjwv18MFCdfJnIHVsa5jWiT9lulxsTt44rKuaxnTgydIGxXTlheue+vNSM41Y/n+RX9pC8C9l1tLX5eDjjgN8+PEfOj5sXzk3Zkvf31pcct09+vvX/TFizpiM2fwWxutWN6yo6X5sflKS/7m0uL//81FX6Jk3vgY77Y11/wY8qzrR3K6mSzOkUv+0t4MP7/dllqQGB8f5lenGYmdLMjtRmx8LLg+rIXzUo07xseRVlgd6z6mNcdMdxkW0+OCfLUxvWnz9r4TpI3bMm16V4+1sGS15WYrlu8P2c377y/7cpuPycIey04gJ9zRu3n/l8tNudFcP7tLYfaWnUDO9xwa0Lub9z/faEmlyXMMLtcJOvK/rrf6dvO+2OQYe5wsu3mjRo6ZLhbrPqa7MTp4Nu+Dh4+NPJv3YOzO7Nq7bEvyzK69S7uPrzamB2N967adUx3Tt63F49MuN2P5+8t3Yvr/veLL5WbCx9fgMsteJO99ewzsYExf9GMp87FqOOB1J5TTzVj+r0t3Yvqfr7fkNsdL4xidoCOnm4kc9hL5xovlukNI42SJadRITBeLdR/TXQZ30dbuhj14Nm/tludx3x88m3cRYvq2tbj16FozlkayIGEyLzcbESGNI73uhvKFG8sfq6E8Ug7lDSsUL+zwAgZHWnVDudWMZS5akCjuyO0mJ5LCybp+JFFnQeZs82PB9SExjRqJ6WIxNTE9LZheUFnoB4m47NqNKazUA6k3I4kWFsTyQs76jqmMksX5YnocuD60vFCCeN74OHD9SEyjRmK6WBDTOcP0gspCYho1lp1AOvMLxseB68cwniemMbXENGolplEjMV0siOmcYXpBZSExjRqJadRKTKNGYhq1EtOokZguFsR0zjC9oLKQmEaNxDRqJaZRIzGNWolp1EhMFwtiOmeYXlBZSEyjRmIatRLTqJGYRq3ENGokposFMZ0zTC+oLCSmUSMxjVqJadRITKNWYho1EtPFgpjOGaYXVBYS06iRmEatxDRqJKZRKzGNGonpYkFM5wzTCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEgpnOG6QWVhcQ0aiSmUSsxjRqJadRKTKNGYrpYENM5w/SCykJiGjUS06iVmEaNxDRqJaZRIzFdLIjpnGF6QWUhMY0aiWnUSkyjRmIatRLTqJGYLhbEdM4wvaCykJhGjcQ0aiWmUSMxjVqJadRITBcLYjpnmF5QWUhMo0ZiGrUS06iRmEatxDRqJKaLBTGdM0wvqCwkplEjMY1aiWnUSEyjVmIaNRLTxYKYzhmmF1QWEtOokZhGrcQ0aiSmUSsxjRqJ6WJBTOcM0wsqC4lp1EhMo1ZiGjUS06iVmEaNxHSxIKZzhukFlYXENGokplErMY0aiWnUSkyjRmK6WBDTOcP0gspCYho1EtOolZhGjcQ0aiWmUSMxXSyI6ZxhekFlITGNGolp1EpMo0ZiGrUS06iRmC4WxHTOML2gspCYRo3ENGolplEjMY1aiWnUSEwXC2I6Z5heUFlITKNGYhq1EtOokZhGrcQ0aiSmiwUxnTNML6gsJKZRIzGNWolp1EhMo1ZiGjUS08WCmM4ZphdUFhLTqJGYRq3ENGokplErMY0aieliQUznDNMLKguJadRITKNWYho1EtOolZhGjcR0sSCmc4bpBZWFxDRqJKZRKzGNGolp1EpMo0ZiulgQ0znD9ILKQmIaNRLTqJWYRo3ENGolplEjMV0siOmcYXpBZSExjRqJadRKTKNGYhq1EtOokZguFsS0goOHj8mGjVsyvQ/TCyoLiWnUSEyjVmIaNRLTqJWYRo3EdLEodExv2LhlyVLFnnh9YnplEtOokZhGrcQ0aiSmUSsxjRqJ6WJR6JgWETl+6kLqmL4bmF5QWUhMo0bbi2R+gRcumF5iGjUS06iVmEaNxHSxIKYHYrq79bnrps3bl123y6bN22XPvtm+6x8/daHv+wcPH1v6+uDhY323t2Hjlr77K1Vs4wsqC9tRRxqt9fOkguZsVJsSXJqThc/OS+vSnNjlhvExYf4lplEjMY1aiWnUSEwXC2J6IKZndu3t+/6mzduXLhsW071x3A3r3u9Piuner0Wmb8t0vexJcqMq0U1L7FuO8fFgfm04LQkOfy3hrtdk4Zf7JXj4NWlfKIntmB8b5le74sl81ZXA8aVkmx8P5t+GF0jiR8wXTC0xjRqJ6WJBTE/YzXvPvlnZum1n33W7DMayiPRtnU4T071bskVEXD+eGptBIvE3N2T+l3+Szi/+JNH1qjRa5seF+TQJOxI/+rokj/5JFnbvl/jR1yX+/9u7f55G8jMO4O+HN0Ht1+CCjneAe3rL0u4pFZWlkCLdbiAVpygpTigncXeiWRHtpcmRJTkIu/wxYMMvxWmQ7TXGD55hZuzPR/oWxh4zxTNjfz3j8R/+mm6vbktfN6lmLq776e7DL+n+7fvU/92f0u3ZZenrVFbOLspfhzrk4rqfBj9/Svff/pRuzq9KXx+pfr5c99PD+VW6Pr8ufV2kHundDtLN3X3p6zFrmI8yPaFMN5qtkVO3X7NMX/b6C5Pe7SD1f/iY7t++S/dv3qX+x0/p+mZQ+npJ9XJ1M0j9L73Uf/PbBy8P37xP/TfvUr/7bbq77JW+flLN9G4Hqf/jz+nh7fs0+OZ96p9flb5OZeXiuvx1qENuev00+Omf6f6Pf0v9i5vS10eqn97lbUr/Ok13ny9LXxepR27v7tNd/7709Zg1zEeZHivT2fegM699ZLrsUz3yzsW/z9Pg+6N0d/Axnf/novT1kerm4uwy9X7/l5HTvHvffUhnTvOWKfl88iUNjo7TzfFZOvlkHyPP5+rkSxp8vkrHp07bldniNG+JxGney2Xpy/RzBXd9oz1zmd7sdEeea32jPfId7PHvWC9Dmf7l1+vUux2ky96g9PWQauf41+t0dXaVbr77kB7+/H26/vs/0vmJC5DJ87nrP6SzL7elr4fUIy5AJtEo0xKJMr1clrZMr2+0J16Be/xq3s+V6aeu/J1SSscnpyP3b23vLGWZvuz5aSyZPf+7uE33/fv033NXZ5bZ4mreEokyLdEo0xKJMr1clrZM52HSad7zKnuDKiLKtETy6ayXBvcPpa+H1CfKtESiTEs0yrREokwvF2V6Dsr0bFGmJRJlWqJRpiUSZVqiUaYlEmV6uSjTFVP2BlVElGmJRJmWaJRpiUSZlmiUaYlEmV4uynTFlL1BFRFlWiJRpiUaZVoiUaYlGmVaIlGml4syXTFlb1BFRJmWSJRpiUaZlkiUaYlGmZZIlOnlokxXTNkbVBFRpiUSZVqiUaYlEmVaolGmJRJlerko0xVT9gZVRJRpiUSZlmiUaYlEmZZolGmJRJleLsp0xZS9QRURZVoiUaYlGmVaIlGmJRplWiJRppeLMl0xZW9QRUSZlkiUaYlGmZZIlGmJRpmWSJTp5aJMV0zZG1QRUaYlEmVaolGmJRJlWqJRpiUSZXq5KNMVU/YGVUSUaYlEmZZolGmJRJmWaJRpiUSZXi7KdMWUvUEVEWVaIlGmJRplWiJRpiUaZVoiUaaXizJdMWVvUEVEmZZIlGmJRpmWSJRpiUaZlkiU6eWiTAMAAECQMg0AAABByjQAAAAEKdMAAAAQpEwzs81ON62srj1mfaP91WPWN9pT708ppd29/dRotr76+9b2zsjzZ6Geip6XzPD/2N3bz239eX1FzszxyenE/Yt9TH29xj6m0WyZlQXy2jPz1PLUw7zzkte8UW3KNDMbf+FoNFtpa3vn8fZmpzuyI1jfaKfNTvfx9sHh0eMO46kybUeyOIqel6wcKdCLo+iZGbe1vTOyPPVS9LyMP378+aifomdm/PkazZZ9TI3NOy/zLk89KNO82Hj5bTRb6eDw6PH2weHRxBebaUemvVFZXHnPy2anO/KixOLJe2bGrayupeOT03xWltLlPS+NZmvkw7pZ54r6yHNmsqL93N+or5fOS17LU03KNC+2vtF+LDPZUcLhN6aT/pbS7Kd526EslrznJZuR4ZlRjBZL3jMzzFHpxVPUa1L2nMPPz2LIc2YmFeenlqeeXjoveS1PNSnTvEj2JiOT9xvdlH7b6ThSvRjynpfsscOf6I7/D+qt6H2MNyyLpYh5yR4//KEdi6OImRn+8GXa8tTPPPOSx/JUlzJN2O7e/kw7gHnLtNOjFkMR8/LUY8cLNvVU9D5ms9N1VHqBFDUvPrBbXEXNzFMXOqTe8piXeZan2pRpQqa9mcj7+4zKdP0VOS+TirMyXX9F72O8WVksRc2LN7qL6zXfx/g6Sf3NOy95zBvVpkwzs+dOu571qoTTLvYyftuLUH0VPS+bne7I37e2d7wI1VzRM5M9h/3KYih6XlZW10Yebx9Tf6+xj8lkBwR8+FJf885LXvNGtSnTzGTab7QOf6o27ffyhn9SIsvwd4uGlx1/E0O9vMa8pDT6G47e5Nbba8zMpFPtqKfX2scM32cfU2+vuY9xenf9zTsvecwb9aBMAwAAQJAyDQAAAEHKNAAAAAQp0wAAABCkTAMAAECQMg0AAABByjQAAAAEKdMAAAAQpEwDAABAkDINAAAAQco0AAAABCnTAAAAEKRMAwAAQJAyDQAAAEHKNAAAAAQp0wAAABCkTAMAAECQMg0AAABByjQAAAAEKdMAAAAQpEwDQEVsbe+kldW1r/6+srqWtrZ3Hm9vdrppZXXtMccnp0/eN/586xvttNnpjjxueHkAYDbKNABUyMrqWtrd23+8vbu3P1KINzvdtL7Rnnr/cDle32iPPH59o/3V/wAA4pRpAKiQ8bKcHUlOKaXjk9OJR5IbzdaT5Xh3bz81mq2JzwcAvJwyDQAVcnB4lFZW19LB4dFjeT44PBq5b1KGy3Sj2XryVG9lGgDyoUwDQMU0mq202emmre2dkaPKWZmeZmV1baQsj58GrkwDQD6UaQComOxCZI1ma+TCY+NHqsdNKtvKNAAUQ5kGgIrJSvOk70dnV+EetrW9M/G08JSS07wBoCDKNABU0LTSO+3nr7Kj2sPfpVamASB/yjQAVNC007kBgPIp0wBQMeM/ZwUAVI8yDQAVM37hMQCgepRpAAAACFKmAQAAIEiZBgAAgCBlGgAAAIKUaQAAAAhSpgEAACBImQYAAIAgZRoAAACClGkAAAAIUqYBAAAgSJkGAACAIGUaAAAAgpRpAAAACFKmAQAAIEiZBgAAgCBlGgAAAIKUaQAAAAhSpgEAACBImQYAAIAgZRoAAACClGkAAAAIUqYBAAAgSJkGAACAIGUaAAAAgpRpAAAACFKmAQAAIEiZBgAAgCBlGgAAAIKUaQAAAAhSpgEAACBImQYAAIAgZRoAAACClGkAAAAIUqYBAAAgSJkGAACAIGUaAAAAgv4PRMFHwN5eQ00AAAAASUVORK5CYII=", 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year=%{x}
mean=%{marker.size}", + "legendgroup": "NCR", + "marker": { + "color": "#636efa", + "size": [ + 7845871.052631579, + 17164814.814814813, + 10882127.192982456, + 15527416.614285715, + 60785843.41214286, + 53682798 + ], + "sizemode": "area", + "sizeref": 275000, + "symbol": "circle" + }, + "mode": "markers", + "name": "NCR", + "orientation": "h", + "showlegend": true, + "type": "scatter", + "x": [ + 2016, + 2015, + 2017, + 2018, + 2019, + 2020 + ], + "xaxis": "x", + "y": [ + "NCR", + "NCR", + "NCR", + "NCR", + "NCR", + "NCR" + ], + "yaxis": "y" + }, + { + "hovertemplate": "City=%{y}
year=%{x}
mean=%{marker.size}", + "legendgroup": "Bangalore", + "marker": { + "color": "#EF553B", + "size": [ + 6202400.654545454, + 46065863.35403727, + 21502811.5942029, + 17621756.761363637, + 49996734.48275862, + 109179430 + ], + "sizemode": "area", + "sizeref": 275000, + "symbol": "circle" + }, + "mode": "markers", + "name": "Bangalore", + "orientation": "h", + "showlegend": true, + "type": "scatter", + "x": [ + 2016, + 2017, + 2015, + 2018, + 2019, + 2020 + ], + "xaxis": "x", + "y": [ + "Bangalore", + "Bangalore", + "Bangalore", + "Bangalore", + "Bangalore", + "Bangalore" + ], + "yaxis": "y" + }, + { + "hovertemplate": "City=%{y}
year=%{x}
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u/ZE4P1vZVswH/puEvv7xYf6anVCCTnfNmhG3fd+uxJ/xPV/77dGuU4Ax5KTjhuJeubT28eZnPxLz3uHHIeuZR8V32trHjPkZeL6Y9//XiWuG/dMfiT3iOcuQP90lQdjTfntwfbS7oXhLi4mPJ3HNe3+4+pwljdZT+8Rvd7TfbhVhOgoZ04MxLHLncOnBnxmMbdWYjsdu/PtJ1z8Y06Ouqz/WfhiPiun4CwV9+z/n+r1CGzhtcZ5+aPWBqK/zsxXjl7df/pUEdlv7uFHzduOH0nn9xaHtpf/vqNPRPj7MXy/oSVSrrlkz1qwh9+1K/JnItrTfHt12vEj7GPLSD5fF++rztdvKiMcg5/GfSWixfcySkdMW+75/nrhmjHvOsmZt4fFp0+iHyxJcPJf4fz7ycUhhW1mzjR24T0LL1H67VYTpKEVMDx7qvW377qE91eu9Z3rS9a/HnulRlw2i+1CPJE3DltYLv1Q+zNt8/SUO80Zp2L44n/9hzWHexlP7xTbK8Z4iXF8rhiueF05/mPe//FBcP9J+ezA/l1qedI2W0mHe5u9eELvFGjNLJn1CgPJh3vf/d3E7+m8Pro/VliedCR/DuO6HeT/7OId5zwiFj+l+oPYvi8fozl37UsX0zl37ht4XvXPXvomxPXj9Wd4zHQ/n+M/0L+sHuO4JlWTD9sU++bFyTLcvnOfkLyiLTVdaSw1xTp4Q89F7xN77YzHmHpZ2rVGaj47A9bdrt8V4fO9UMd2ae1g6JXnCgutnu96U5oP/MzGm7ZMnSnNGXVwf7ZYtrRfmpo7p1rOPc3LDTWTF8MRtu7nFtPnyc2K3yrH9wHQUMqbjh0DH4zh+CHaamBYZPtR61AnJxl1/lrN5j9oLHT+bd5lOQLbYdMWpG9J89GeJMd167imxa4b28WJxbJkd8buuRI4tVtPiTO8zrtHqiP3W61PFtDX/Jke/zKDVlifdam1iTJuv/UacBsb8CGgAACAASURBVI9Bs2bD8sX57NTUMe38/rg0bdaWzWTX6YoRe/66UTFtn/pIjJK8kAfTUbiYzps9+w9OPMFZ3uieUKq2q3VpPvQvY2O69fSj4nCGXRxh0/bFC5e1jwOLoedPPtR7Ykzf8yPxOMR7Zq3VLOnWm9J85N7hmL53t9jH3xGnykfvzapu15Pm/f89e0z/7MfiuoT0ZrNldRNfwF2vmHZNW5ZKcmQmTMfMxXR8r/DgXukioHtCpdFpGGJ//IG0nnlM7MfuFevxn0vr+afE/sPvxWKPNI6RmMZBm0Z75FtHVGLa+fwP0uCj1WbaassTp1oXt+1KePGseM2muB1PzKalfWyoz4bti/PZycwxbX1yQhpWOfYqoro10xPn2282PqYf+1mpdijBdMxcTBcd3RMqrU07ELtpS9jpSNB1xTZsqVvleCUO9UhMY9zmQk2cz0c/8R0X084Xn0uz2tI+diyGFcOVMFrmkH9ctdWwxP70k9Qx7Zw8IS12CGxa201TWs/9YkNjun3+rDTM8jwXhukgpguG7gmV1Y4XidUJtY8Diy8xjaM0DVvs05+K8fA9E2PaOLBXnK+/FKPV0T5mLJZBuMzJxnBIY7Em9h9+L81/iZ3lfUxM2x+9L61qU/u4ceOstjzpxM63sJ4x3Xr6UWk3y/VCL0wHMV0wdE+orBLTqCoxjeOsW750TFs6166K+cohaR2ak+4vH5TWoV+K+eqL0rl5UzqmI/USveKP+UlM4yibhiOuH4n1zhvSeurBNTFtPPmA2G8fFi/oSZO3jcyEZtUQ+71jGxLTrmXLUqNcL/bCdBDTBUP3hMoqMY2qEtOYZN3ypWV2xDEskU5bHMOSVqsrNSIaJ0hM4yQNJ5CO5YhrdyR0HAlMS9x2Vzp2R1pmV/v4MF+duiH2m6+sa0x3b96URqN8L8jAdBDTBUP3hMoqMY2qEtOYxjKvi5ivxDSq6riRtD0+CWDWta/fFOvYq+sS092bC1JbKOcnCMB0ENMFQ/eEyioxjaoS05jGMq+LmK/ENKpqd0NxXGIaXbFqTXE+Pj72o9SSYrp1aE461ZrUauXbI90XpoOYLhi6J1RWiWlUlZjGNJZ5XcR8JaZRVWIaB202bHHbrljzR8V49GdKMd16+lHpXLsqTt2QSqPcbxOA6SCmC4buCZVVYhpVJaYxjWVeFzFfiWlUlZjGUbYalnQtR7r1pljvHBbr6Ctiv/SsdJ47INYrh8R654i0v/hcPC8Uu1qXWkv/mNdDmA5iumDonlBZJaZRVWIa01jmdRHzlZhGVYlpnGS15YlheWKaHelYbem1HbFajphmVxqWLxVjc50ME6aDmC4YuidUVolpVJWYxjSWeV3EfCWmUVViGlWtm56E0eZ+zgLTQUwXDN0TKqvENKpKTGMay7wuYr4S06gqMY2qEtOQBDFdMHRPqKwS06gqMY1pLPO6iPlKTKOqxDSqSkxDEsR0wdA9obJKTKOqxDSmsczrIuYrMY2qEtOoKjENSRDTBUP3hMoqMY2qEtOYxjKvi5ivxDSqSkyjqsQ0JEFMFwzdEyqrxDSqSkxjGsu8LmK+EtOoKjGNqhLTkAQxXTB0T6isEtOoKjGNaSzzuoj5SkyjqsQ0qkpMQxLEdMHQPaGySkyjqsQ0prHM6yLmKzGNqhLTqCoxDUkQ0wVD94TKKjGNqhLTmMYyr4uYr8Q0qkpMo6rENCRBTBcM3RMqq8Q0qkpMYxrLvC5ivhLTqCoxjaoS05AEMV0wdE+orBLTqCoxjWks87qI+UpMo6rENKpKTEMSxHTB0D2hskpMo6rENKaxzOsi5isxjaoS06gqMQ1JENMFQ/eEyioxjaoS05jGMq+LmK/ENKpKTKOqxDQkQUwXDN0TKqvENKpKTGMay7wuYj4utTxp2L5EvVtid0OpmZ72MWExbVi+VBxXWr4vrSCQiuPyAgxOlJiGJIjpgqF7QmWVmEZViWlMY5nXRdxYlwxPKu2u1FxPnq9ekCer38iLtYvycWtJrrbbUrH0jxGLYc30xPQCOda8If/l6in5p6sfyz9e+1j+y9WTcqRxTSwv4EUYHCkxDUkQ0+vAtu275ej8qUy/e3T+lGzZumP1a90TKqvENKpKTGMay7wu4sZZMVy52nFk99VP5HvfviHf+/YN2Xb+iPzb7w7L9759Q77/7RF5fum8XLEd7WNFvV412vKN3ZK/PP/O6rbyJ+cOy5+cO7z69Z+fe1u+sJtytcn2gsMS05DEpovpbdt3D8Vpny1bd8i27bs37G/OckxXDE9cPxKnS0xjsoYTiE9MY4ILVkeani9LoSv1ricLtv4xYTFcanlS6XZXQ2hUTPc9VLsgN52O9jGjHuumL+cca822Eo/pvl/bhtTZQ40DEtOQxKaM6Z279sncoSOrl80dOiI7d+0rZEzH0T2hUmt25aJjy9edplzoWnKt3eZQKRzrTacjThDKedeSG+22NCzeq4bDVpquLHa6cu+Nz2X7+Xn5s4tH5e8uvCePLp6Raw57jdCVpW5X/npgL+OkmP7et2/I282brDUzquUF8ufn3lKO6X//3Ztie+wYwDsS05DEpozp02cuDO3t3bJ1hxydPzUU0/EAjn9/y9Ydsmf/QdmydYds2bpD5g4dGfr69JkLQ9c1d+jI6vfi3+/vfe47+HfiY9U9odK4YHXlRKuyckjducPyJ2dXHpg+t+oENa7xRqcjOy8el3/33WHZdv6I/LtvD8unZl1umOw1wjsudVzZfeXk6pPbP7t4dPXf/3L9c1los73Msg3Ll1fqV9ZE0KSY3n5+Xq62eSFm1mxYvrxWvzpyWxkX09/79g15vnpBmnagffxYDOumJxExDRPYtDG9Z/9BmTt0ZNWsMd3/Xj+oRUT27D8oO3ftG7quwd+NH7rdv57Bn+9fVtaYrrY8OW02Vx98BmP6e9++IVfbbe1jxOK40HblHy5/JN/79o3VmO5vK985piwZ+seI+q1bvrwee/I7GNPf+/YNmTcWOAxzhl1yPPnx1VOpYvp7374hZ+wW68yMebPdHXpPvWpM/8Plj2Sh3dU+ftRvxXDlRrctf2zX5fImPv8CTMemjelKzRjaC5wlpvt7l/vXVakZStcV//04/cPORdbGtN0NS2HHi+QDa3H1AWnb+SOy7fyR1a8vdC1pu/rHicXQj3ry788dlT85d1i+f/6w/OmFO9vKh9aidP1I+xhRv27Qk7nqd6vbxp+cOyx/dvHo0Ncv1M+zvYzQausfQy630w/kP1/9aGib6PunF47I98+vvfxPzh2Wb9sGj0mzZhjID65+OHJ7iD9nGfQ/XH5fnLAA40ftLnmu/J/n35I/u3BEft24IK7f0z6mjRCmY9PGtIis7p0W0R/T/ROj9R0X0203KoVe0JNjrevy/XOH5fvnDsuf3n5g6n/9dceQrqd/nFgMu1G0um1sO39k5Unv7a8PG9fE9Xvax4gFMIjksaWvV7eN79+O6cGv56rfiRewvcR1CjCGPDSDQP7TlQ+HtonVx6ELw49Dg37ZbkqXdWamtIJAdl45PnJ72HZ+/Lbyt5feEzMMtI8f9bvod+V/v/Cm/OmFI/JE5WvxgmXtY9oIYTo2dUwPojOm+++p7jNpz7TuQz1UrbY8OWnWxh7mfdGxpWJwKCauWGm78vcXPxh5mPcpqy7VFtsKrrzH8Ujz+uTDvFsLUrfYXmbVG05bfnjl96kP8/7UrLHOzJg32125e8y2Mukw7x9c+lCu2ZybAV25YrTlrNOSD52KXNnEJ8CE6ZjZmN65a9/Qe5nj73ueJqb37D849rr6f7vsMb3YdOWG3ZHXG1fXxPSHrYosWLzfCO+4ZHhyqW3L/3H2raGYftu4IVdN3l+Pd6y7nvzthfdGxvR/vHRclrr6x4j6bNjpT0D25+feksUOcTRrNu2152BQielD1QtiOJyADFc0bF+iHicgg/HMbEz3A3nwbN3TxPS4s3X3fz5+iPdmiOnF5kpQ/8Gqy1vmDTluLcqXVpOQxpEutTy52enIla4jf2jX5LxjyZKjf1xYLCuGJwudjvzj5Y/l3357+zDv7w7Lj6+elOudtlQ4idTM6wSh/G9n31SO6aPN69K0+WisWbTrR/L9EdvEpJh2g572cWNx5KOxIIlNF9NlR/eEymLN9MULetLxIs6Wiom22oH40bIsccglTvBauyNu0JMvuk3p+pHcdHiRDlesm76cc0ylmP7ZjT9KpaN/zKjHBcuV940F5Zh+11iQRZO1Bu9ITEMSxHTB0D2hstrxIrE6ofZxYPFt2r544eZ+YML1s8zrIm6cV5qOfGE15T9cfH9sTD+8+LVc2sQfZ4NqXm+15URrKTGm540FOdNsaR8vFktiGpIgpguG7gmVVWIaVSWmMY1lXhdxY622PKm5nnxlG/LAzS/l3oXP5aHFr+TF2iX5xmnJTZs9jLjigtUV14/k2aVz8vcXP5C7Lr4rf33xXfl/Ln4gT1W+lY4fySJvUcMREtOQBDFdMHRPqKwS06gqMY1pLPO6iPlYMz1pOYHYtz/OqGn7ssSnSeAIDduXG05n5XOkw1BuOB1pOQHnYsCxEtOQBDFdMHRPqKwS06gqMY1pLPO6iPkahMvS4ERjqKDdDcVxI+3jwOJLTEMSxHTB0D2hskpMo6rENKaxzOsi5isxjaoS06gqMQ1JENMFQ/eEyioxjaoS05jGMq+LmK/ENKpKTKOqxDQkQUwXDN0TKqvENKpKTGMay7wuYr4S06gqMY2qEtOQBDFdMHRPqKwS06gqMY1pLPO6iPlKTKOqxDSqSkxDEsR0wdA9obJKTKOqxDSmsczrIuYrMY2qEtOoKjENSRDTBUP3hMoqMY2qEtOYxjKvi5ivxDSqSkyjqsQ0JEFMFwzdEyqrxDSqSkxjGsu8LmK+EtOoKjGNqhLTkAQxXTB0T6isEtOoKjGNaSzzuoj5SkyjqsQ0qkpMQxLEdMHQPaGySkyjqsQ0prHM6yLmKzGNqhLTqCoxDUkQ0wVD94TKKjGNqhLTmMYyr4uYr8Q0qkpMo6rENCRBTBcM3RMqq8Q0qkpMYxrLvC5ivhLTqCoxjaoS05AEMV0wdE+orBLTqCoxjWks87qI+UpMo6rENKpKTEMSxHTB0D2hskpMo6rENKaxzOsi5isxjaoS06gqMQ1JENMFQ/eEyioxjaoS05jGMq+LmK/ENKpKTKOqxDQkQUwXDN0TKqvENKpKTGMay7wuYr4S06gqMY2qEtOQBDFdMHRPqKwS06gqMY1pLPO6iPlKTKOqxDSqSkxDEsR0wdA9obJKTKOqxDSmsczrIuYrMY2qEtOoKjENSUyM6S1bd8jcoSN5jQWkvE8aiWlUlZjGNJZ5XcR8JaZRVWIaVSWmIYmJMX10/pRs2bpj1T37D+Y1rplF94TKKjGNqhLTmMYyr4uYr8Q0qkpMo6rENCSR6jBvwnrj0T2hskpMo6rENKaxzOsi5mO15UnT9iXq3RLHDaVuedrHhMWWmEZViWlIQjmmKzVjKKYH3blr30aOcabQPaGySkyjqsQ0prHM6yJurBXDFcuKxDQiufBxKOfnI7n4cSgLZyMxmz1ptALtY8RiSkyjqsQ0JDExpvfsP6i0N3rL1h0bMbaZRPeEyioxjaoS06hiteWJ4QQiItK0A6m22NuId6w0XTFqPfn0xUDmH/Bl/gFfTjwUyvsP3vn6/IeR1Gs8LuFaiWlUlZiGJDgBWYz4Cwhbtu6Q02cu5Pb3dU+orBLTqCoxjUk2W6EsXY7kyuehnH2zJ1f/GEr1aiRNgzUGV15osc3eajSPi+n5B3y5+EkkBtsNxiSmUVViGpLgo7Fi7Nl/cGgP/OkzF3INat0TKqvENKracgIJolvax4HFtF4L5cpn4WoMffRItPrv619FUlvi0N1Z17Z78vGcrxTT8w/4svBdJA2LE5PhHR03krZHTGOyxDQkkbhnelREHp0/Jdu2796wQekkHtMiIjt37VvdQ79t+245On9q9Xvx+6L//cE925WaMXR9c4eOjP2+7gmVxVrTF6+7LF1nWWomh2LieGvVQMxaT4wbt8Q0IqkYbC94x7oRyM1voqEQGozp+Qd8WbwQcYKpGbZueXIjto0kxfTvn/al1SSccMVmIxCv2xO3c4ujFjBRYhqSyBTT/b21m5H1iOnB+2bP/oNDJ2ibO3Rk6Ofjv697QqVeZJqBVC5FcnIulJO/DKV2LeK9jThSwwjl8qcrT3Y/fCiUDx7zpe3wBBfvaFuRnHzWnxjTn74YiGX2tI8V9dg0Avni1bUhPSmm5x/wpXYjkiUem2beZjOUcx+G8sH+UI7vD+Xbd0OCGidKTEMSmWI6HoSbiXhM9/cy9/ceq+6ZnvT9+H06eD87blgq3e6yfPKvoZx4OJQPHwrljy+G0m0vax8XFk/f78mJR1e2kxMPR3J8fyjVcz3pBj3tY8NiGITLcvz2k9y+Hz0SDX194tFQAv+W9rEWTbsbaR9DHnY7y3L6pXDNdnJ8fygfPRzJ8YdGf8+sLkvHn437CMcb+Lfk+P6Vx6EPb28rbmdZ2gUYGxbTrhdJb3lzP+bAdKyJ6f5e5yTzPClXnow6AdngYdjTxnTS/el0o1LZdXqxmI6k6yxrHxcWz8Bflo8OhHJ8IKbrl3rS9Xrax4bFMIyW18RQPKY/OhBK4LPGxLW6ofYx5GG3vSxfvByljmm7viwdV//4Ua9heCemTzy8sm343i3t48Li2vEiWV7e3NsITEemPdObmVGHeQ+yHjE96T7VfahHWqsNX6pXIvnshVA+ez6U+gKHeeNom61Arn955zDv3z/ji2VymDfe0TIVDvP+dcD7X2dYoxXKF6+PPpR70mHe9YVIljhHw8zbNFZOcNg/zPv8R6E0GxzmjePlMG9IgrN5x0iK6Z279g19f9v23alies/+g2sOkd+z/+Dq3m/dEyrrQuN2l6XTXiakcaKNRiCO2RO7ektazUgqBRgTFse65cvSxcknIKvf6EmdMzPPrHXTkxtn0p2A7JNnfTEavACDKzaNUMJoWcLwFiGNiRLTkAQxHSMppis1Y+jw7Pj7x5NiWmTt2bzLfAKyvnw0FqpqOL74m/yBCbNbrQRy48zoj8ZaPNuTyk1Ceta1rEg+eUY9phcvhbwAg0PyOdOoKjENSYyM6S1bd6z5eKdRwvqje0JllZhGVZu2L164uR+YcDobzUBq1yNZPNuTi+/3ZOFsJLUboTRMPmMaXakYK2d+V4npK59H0qjz2ITDEtOoKjENSbBnumDonlBZJaZRVWIaVVxqedKwfBERaVi+LBn6x4TFsWK4YhqRfPFqMDKmP3jUl8ufRlJd4gUYXCsxjaoS05AEMV0wdE+orBLTqCoxjWks87qIG2vFcMW0Q2m3e3L5s1AunYjk2h9DWbocSqsRSYNDu3GMxDSqSkxDEiNjun+I9+B7f1W+B9Oje0JllZhGVYlpTGOZ10XMxyVj5SiGqHdLrG4oNZOIxskS06gqMQ1JjIzp+Bmr4+zZf1B27tq3YYOaZXRPqKwS06gqMY1pLPO6iPkahMvSsAlpTJaYRlWJaUhi7AnIJn0W8ukzFzgB2Qahe0JllZhGVYlpTGOZ10XMV2IaVSWmUVViGpIgpguG7gmVVWIaVSWmMY1lXhcxX4lpVJWYRlWJaUhiZExv2747Mabjn50M64PuCZVVYhpVJaYxjWVeFzFfiWlUlZhGVYlpSGJkTM8dOjLxPdFJ76mG7OieUFklplFVYhrTWOZ1EfOVmEZViWlUlZiGJMZ+NNa27btH7n3etn03h3hvILonVFaJaVSVmMY0lnldxHwlplFVYhpVJaYhiYmfMz136Ihs2bpjSPZIbyy6J1RWiWlUlZjGNJZ5XcR8JaZRVWIaVSWmIYmJMQ35o3tCZZWYRlWJaUxjmddFzFdiGlUlplFVYhqSIKYLhu4JlVViGlUlpjGNZV4XMV+JaVSVmEZViWlIgpguGLonVFaJaVSVmMY0lnldxHwlplFVYhpVJaYhCWK6YOieUFklplFVYhrTWOZ1EfOVmEZViWlUlZiGJIjpgqF7QmWVmEZViWlMY5nXRcxXYhpVJaZRVWIakiCmC4buCZVVYhpVJaYxjWVeFzFfiWlUlZhGVYlpSIKYLhi6J1RWiWlUlZjGNJZ5XcR8JaZRVWIaVSWmIQliumDonlBZJaZRVWIa01jmdRHzlZhGVYlpVJWYhiSI6YKhe0JllZhGVYlpTGOZ10XMV2IaVSWmUVViGpIgpguG7gmVVWIaVSWmMY1lXhcxX4lpVJWYRlWJaUiCmC4YuidUVolpVJWYxjSWeV3EfCWmUVViGlUlpiEJYrpg6J5QWSWmUVViGtNY5nUR85WYRlWJaVSVmIYkiOmCoXtCZZWYRlWJaUxjmddFzFdiGlUlplFVYhqSIKYLhu4JlVViGlUlpjGNZV4XMV+JaUyy2vJWHoOCnnjhsjRtX6otT/u4sLgS05AEMV0wdE+orBLTqCoxjWks87qI+UpM4yRtuyPdxqJ4Z49L+M1bEn1zTLyzx6VbWxDHbmsfHxZTYhqS2FQxfXT+lGzbvntD/8a27bvl6Pypdbu++Jh1T6isEtOoKjGNaSzzuoj5SkzjON2uK97nvxXv2M/FO/ZzCd/eK9E7e1e/9j59WVyf5zC4VmIaktAa09u275a5Q0eGLps7dER27tqX6fqIaX0S06gqMY0qVgxXDCcQERHDCbSPB4svMY2jdF1PvA+euBPOo2L62M/Fe+dB8TzWGhyWmIYkiOmUENOjJaZRVWIak6w1bHE7HfEufyby9WviXv5cPD+UJUP/2LC4htGyNB1iGu9o2x3xvjo2HM3jYvrYz8X74nWxna72cWNxJKYhiULH9NyhI2vieOeufUO/s3PXPtmydceq8Z+fO3Rk6PuVmrH6vS1bd8jR+VND3xv8etT19cc8+DOnz1xY/X7S7yeNWfeEyqob9KTjcWZMnGyl6Uq37UjP70rN5KQvuNaq6Ylrt1af3Mr8/Xee6HbaBDWOtGNbstxaEM9uSrPV0T4e1G/FcMV17LXBPCmmj/1cXLMpFdYZvC0xDUkUOqZFZChWKzVDtmzdsfq9PfsPDv1sfC9vPMbj3x8Vu3v2H1wzxsHLtm3fveY642Oa9PtJY9Y9obJoG4ZEN76QYPE7aTWa2seDxbXj2BJ89pL0Tj0nbu0qQY1rdGxHvI+eHh3Tnzwvju1oHyMWy267Ld4Hj8vyu/dL8NbPxVs8J4bJCaVm3Ybli3vty/QxfflTaVgc4YArEtOQhPaYHtxD23cwNvfsP7gao4P/FpGRe4UHw3Tb9t1D34//Tvz3RxGP+1GHeU+6nkkvDowac9ePSqUX9CRa/FaW5++X5Xfvl55xU7ygp31cWDz9sCe9a59J79375db8/dL7eE5Ct619XFgsI68rvfkHpPfOXum9s1dk/v7Vf/c+eFgir6N9jEW07eofgw69sCe9+mXpvbN3ZV15937pffIriXxX+9hQr37Yk97Fj++sHwMuv3v7OcuI7/XOfyB+yPMYXNELerK8fEv7ODZSmA7tMZ20Z3pwb/SovdSDh22P2vM8yqSYjkd+2pge9/sqYzY7Yal03EiC+rWVB6V375fArIrV1T8uLJ5tL5Lg5tcSvbNXlufvl/D3vxKv09Y+LiyWYRBK+P4jEr69V8K3V2K6/+/wxC8k8DztYyyirbb+MejQdiMJmzckfHslpqN39kp46gXxu6wts27H70l49Y931o8Be7efs4z6XnjpE+l4kfbxYzF0uqH0lm9pH8dGCtNR+JgWWXmPcd9BkvbyJu15HvX9+JjS7plO+v2kMes+1COLzWZLIqsmodWQWtPWPh4srlazKcGVT6V37j1xLYP3peEabbsj3qlfjz7M+7PfimVx+C4O65gt8b5+W5bfe1CC40+I21iQaou3kMy6ddOT7uKF1Id5d6+fkTqHeeNtOcwbkihFTPfflxyP2J279g0d9r1z176hMN2z/+DI90T39wyPiun4ZfGIj8d0/G8k/X7SmHVPqKx2/UjsDmfzxmTtbihh7xYhjWPt2pZ4J345HNOfvCBdx9I+NiymltWWZa8tbtuRKudiwNt2rJZ4x9ZG88SYNg3t48biSExDEqWI6fiJxwYZPJx6VDzHz7ydtOc6fjbuUTE96WzdSb+fNGbdEyqrfDQWqspHY2GSFcOTTrstrmWILH4lrmVIx2GPNE6Wz5nGuIbZEe/cR+oxffa4tCw+GgvvSExDElpjWpU9+w+uie7Niu4JlVViGlUlplHVmumJiHDILipJTOMobdMW75v3kmP6q7fEMU3t48ViSUxDEoWP6VEn7drM6J5QWSWmUVViGtNY5nUR85WYxnE6dlvcq1+KN//I2ph+92Fxr5yWNh+7hyMkpiGJwsf0rKF7QmWVmEZViWlMY5nXRcxXYhon2bR96diWdJeuSti8LlH9snSXrkjXMsUwO9rHh8WUmIYkiOmCoXtCZZWYRlWJaUxjmddFzFdiGlWsmZ50vEi6fo+3kGCixDQkQUwXDN0TKqvENKpKTGMay7wuYr4S06iq3Q3FcSPt48DiS0xDEsR0wdA9obJKTKOqxDSmsczrIuYrMY2qEtOoKjENSRDTBUP3hMoqMY2qEtOYxjKvi5ivxDSqSkyjqsQ0JEFMFwzdEyqrxDSqSkxjGsu8LmK+EtOoKjGNqhLTkAQxXTB0T6isEtOoKjGNaSzzuoj5SkyjqsQ0qkpMQxLEdMHQPaGySkyjqsQ0prHM6yLmKzGNqhLTqCoxDUkQ0wVD94TKKjGNqhLTmMYyr4uYr8Q0qkpMo6rENCRBTBcM3RMqq8Q0qkpMYxrLvC5ivhLTqCoxjaoS05AEMV0wdE+orBLTqCoxjWks87qI+UpMo6rENKpKTEMSxHTB0D2hskpMo6rENKaxzOsi5isxjaoS06gqMTQsYQAAGUpJREFUMQ1JENMFQ/eEyioxjaoS05jGMq+LmK/ENKpKTKOqxDQkQUwXDN0TKqvENKpKTGMay7wuYr4S06gqMY2qEtOQBDFdMHRPqKwS06gqMY1pLPO6iPlKTKOqxDSqSkxDEsR0wdA9obJKTKOqxDSmsczrIuYrMY2qEtOoKjENSRDTBUP3hMoqMY2qEtOYxjKvi5ivxDSqSkyjqsQ0JEFMFwzdEyqrxDSqSkxjGsu8LmK+EtOoKjGNqhLTkAQxXTB0T6isEtOoKjGNaSzzuoj5SkyjqsQ0qkpMQxLEdMHQPaGySkyjqsQ0prHM6yLmKzGNqhLTqCoxDUkQ0wVD94TKKjGNqhLTmMYyr4uYr8Q0qkpMo6rENCRBTBcM3RMqq8Q0qkpMYxrLvC5ivhLTqCoxjaoS05AEMV0wdE+orBLTqCoxjWks87qI+UpMo6rENKpKTEMSxHTB0D2hskpMo6rENKaxzOsi5isxjaoS06gqMQ1JzERMb9u+W47On9I9jJEcnT8lW7buWP1a94RKa9UOxHAjaUfLEkTLUncjWbID7ePC4kpMYxrLuC5ivi45oZheT256y+KHPal0I6lbRDWOl5hGVYlpSGLTxPS27btly9YdQw5+j5hefxfaoTxf8+U/X+nIXZe78leXuvKPV7tyrOnLVYegxtES05jGsq2LmJ8Vw5W235Of33Bl5+WObL/Slb+62JF/utqVL+xQLpo8DuFoiWlUsW750uxGYka3pNUOpGJ42se0EcJ0lD6mKzVDtmzdIXOHjgxdPnfoyOplRY7pOLonlKpLnVAeWnDlLy605S8utFdjuv/1izVPKhz2jTFrpieGt/LAVOUFF0zQcAIxeyIG2wqOsBv05O8utlcfd7Zf6cpfXuysfv2hGcoCR0phzLrpieX3xA6WpW5tzjjC6b1k+fKB4cuBiif/Y9GTf11ypeNHsrQJgxqmo/QxvXPXPtmz/+DEn+nH9OBe60rNGPqZuUNHxn4/6feTvh//3rbtu1e/d/rMhdLtmW7avrxY91efsIyK6b+40Jb3DF8aHGqHt73e8uRSO5T7Fl3555uePFv15RqRhGNcaAfyy6ov9yx58nTVkxtt9iLhHZecQH520x16zInH9F9caMu3TijV1uZ78ovZvGF68rUTySNLnjy05MsfrEAucwQDxqzagbxvrDzP/cuLK0e99NeUdtCTSgHGuJ7CdJQ+prds3SGnz1yY+DP9Q8D77Nl/UHbu2rf69dyhI0OBe3T+1NDXSb+f9P147G/bvnv1sjLGdKUTyp4bbmJMP7nkygKxhE1XlgxPzjnh6gPTXbcfmJ6r+VJlzxHGbQfyP66vbCN/e21lrdl705Vqm6NdcEXb78n/damdGNNHm740OSkZNl2ptjz5ylp5HPrry13568sra8xnVig1kxdc8I7Vbri6hsRjev+CK/X25nreAtNR6pjuH+Id38scJ36Y96hYjgf5YKSr/P6k78eZO3RkNbbjMe2HvcJbD3vyf1/tyvYrd/ybqysOXvbPN10xw2Xt40X9htGyfNSOBrYXV7Zf6cp/W/DECPSPD4ulES7L39zeVv722sq28vdXu9JkPRlrx4u0jyEvg6gnV/3e0OPN4Loy6KNVT4yI7QZXHod+1wrXPGc52Agk6rGN4IpBtCznvN7YtWUzPreF6Sh1TIuo75meFLvxE5f1Xc+Yjp8gbVxMNyy/+HqR/NO1lT0Afe+6svIq7+Bl9y24UnMj/eNF7ZrtQM62Q/nLix35q0ud1T1I+xZdqXfZRnBYw+vJf7q9jfztNVf+8mJH/ulaV5oe28o46wUYQ5564fLQ401/79FfXRq+7Nd1XwzWGLRWHoc+sVYeh+663JW7bj9necsIxO6E2seHxbBp+2L4vdU15K8udeRvrnY39XNbmI7Sx3Sa90z3GRXTk4J82pjetn330AnSJu2Z1n2oh4oLTiD7FpIP8/7Vkic1DvPG29a6ody/4A4d5n2t2+PwOlxjxfbllBkMHeb9pb3yZFf32LAgdiL5f690Eg/zPmGGnGQKV625kfzkWnf1MO9/uNKRpsf5GHDYm51QfnC5M/Iw79/WvE331hGYjtLHdD9G42fzPjp/auzZvOOxu2f/wTV7kvfsP7h6+Pi0MR2P9Z279pU6pqstT5bcKDGmvaCnfaxYLBtuTy51I/m03ZML7VCubsKzYuI6aQVyuR3KabcnF51QbhLSOGDD8uWkGU6M6buvdqXOxx9hzEonlEV/Wa55PT51BEe6ZLhysR3If7zcGYrpl+u+XN2E53mB6Sh9TPeJH6Kd9jDs+Nm80+55nvT9+Nm8yx7Ti01Xblq+fGmHI2P67y625YwTyvUWT35xrWY7kCC6tSk/XgLX1yXDExHZtJ/tidN5xQ7kraY/Mqb/v6sdueiErDM40rYXScfjBX8cb8VwZbETSsXtyeednlTcaNOeBBOmY9PE9GZB94RK44Lly2I3knkjkCervjxd8+W4GYrpRXLdJKRxtE175f2OuseB5bBs6yLm64Lty2kzkNcavuxf8uVgzZf3WoHU3M35ebC4PtrdUByOWkAFm5YvUbS8qdcTmA5iumDonlCZFhrbFyNYFuP2yYF0jweLLTGNaSzruoj5WW15YjiB1P2e1Lq8tx6TJaZR1brpSRht7ucsMB3EdMHQPaGy2vEisXjvESpITGMay7wuYr4G4bI0NtmJgXBjJKZRVWIakiCmC4buCZVVYhpVJaYxjWVeFzFfiWlUlZhGVYlpSIKYLhi6J1RWiWlUlZjGNJZ5XcR8JaZRVWIaVSWmIQliumDonlBZJaZRVWIa01jmdRHzlZhGVYlpVJWYhiSI6YKhe0JllZhGVYlpTGOZ10XMV2IaVSWmUVViGpIgpguG7gmVVWIaVSWmMY1lXhcxX4lpVJWYRlWJaUiCmC4YuidUVolpVJWYxjSWeV3EfCWmUVViGlUlpiEJYrpg6J5QWSWmUVViGtNY5nUR85WYRlWJaVSVmIYkiOmCoXtCZZWYRlWJaUxjmddFzFdiGlUlplFVYhqSIKYLhu4JlVViGlUlpjGNZV4XMV+JaVSVmEZViWlIgpguGLonVFaJaVSVmMY0lnldxHwlplFVYhpVJaYhCWK6YOieUFklplFVYhrTWOZ1EfOVmEZViWlUlZiGJIjpgqF7QmWVmEZViWlMY5nXRcxXYhpVJaZRVWIakiCmC4buCZVVYhpVJaYxjWVeFzFfiWlUlZhGVYlpSIKYLhi6J1RWiWlUlZjGNJZ5XcR8JaZRVWIaVSWmIQliumDonlBZJaZRVWIa01jmdRHzlZjGcS61PGmZnjiNtnQtV0I3kNANpGu54jQ70jJdqZqe9nFi8SSmIQliumDonlBZJaZRVWIa01jmdRHzlZjGuPWWJ+2KKV6zLd6pc+L9r4/Ee+59Cf/1mERPHRPvufdWLvvknHgNRzoVSxot/ePG4khMQxLEdMHQPaGySkyjqsQ0prHM6yLmKzGNfSuGK+1WV7yrdfGefke8B18dMnz4NYkefn3N5d7c2+JeXJKOuXIdum8H6peYhiSI6YKhe0JllZhGVYlpTGOZ10XMV2IaF5uu1ExPPC8S79DxtbGcFNN9n/9AvG4otRaHfs+6xDQkQUwXDN0TKqvENKpKTGMay7wuYr4S07hU74jbcMZHsmpM39atO7JU72q/XahPYhqSIKYLhu4JlVViGlUlpjGNZV4XMV+J6dm22vLENbuJgZwmpr0HXxW34bCHeoYlpiEJYrpg6J5QWSWmUVViGtNY5nUR85WYnm27Rke8h9QCOU1Mew++Km6LvdOzKjENSRDTBUP3hMoqMY2qEtOYxjKvi5ivxPTsapueeO98qRzHaWPaO/KZOBZ7p2dRYhqSIKYLhu4JlVViGlUlpjGNZV4XMV+J6dl0qeWJZ7nqYZwlph98Vdxmm8+inkGJaUiCmE7B0flTsmXrjg39G7onVFaJaVSVmMY0lnldxHwlpmdTy3LFO/r5hse09+pJsU39txfzlZiGJGY6prds3bFqpWYk/jwxPd6uH4ndjbSPA4svMY1pLPO6iPlKTM+mXjcQ74k3Nz6mH3lDPI/nObMmMQ1JzHRMi4icPnNBOabzQPeESuuS4Uq74UhkdSVse9JutrWPCYstMY1pLOO6iHokpmfPmumLe62eLoqzxvSDr4p7YUnqFtvYLElMQxLEdCym+3uf+27bvnvNz/bZtn23zB06MvTzp89cGPr+0flTq18fnT81dH1btu4Y+nuVmqF9QqVxqeWJW7PFe+JNiR55XcKHXxPvV++Ky2FQOMGm7Yu/yR+YcP0UIaYx2WazKz03kI7lSqUA48Gc/t/tQLwz13OLae+zS2I4gfbbjflJTEMSxHQspvfsPzj0/W3bd69eNiqmB+O4H9aD30+K6cGvRcr1pNExOuIdOi7eg6/eiekHXxXv9T+IxcdI4BjdVld6bV+WDE7kgsmWbV3E/G02u+KeXZDlp9+W4DcnpMMRUjOj6QTifXw2v5h+/2sxbR67ZkliGpIgphMO8547dER27to39LN94rEsIkN7p1VienBPtohIGC2XxmXHleXHD8vyo6/LrUffkFuPviHLj74uy8+/J72Op318WDyj3rLcOn1Jbh2cl2Xb1T4eLLa9ji/y3Q3peYH2sRRd9/9v716a2rrvOA6/H94Ea14DC3a8A9izZ5gmXTLtlGaYbjodO8XdxG0mmWntTBJ3Ek/a1E0vmcbEGMRdFwP2rwtXVJIF5/yFdC7S88x8FjYIK5Ojg75ISK+uSr8OZXV13o7XH/w+3vz0w3j9/ofx5rsf4/LqTenXSwX8v29fxOvfff72vkdCffdZUvrtZ3HVcj6apS6vXsebN9N9PuFujOkhY3phcbXvqdtFjumXx53a1D7rROeXf4jOxr24eu9+XP7kfnQ27kXn3uNonrRLv36qXgcnr+KicRavv9+Pk4Oz0q+Pqt3+SSei1Yn9ClwXVbezk3Z0Hnwer9//8O0zpH5oROP0VenXS5Pv9OxVdD7++u19j4Qu37sfV+/dT75c56M/x9mZY2uWOjx9FZdXb0q/HpOMuzGmB8Z09/egu4p+ZLrsp3qkdNBoRvu7H995mnfrxXHseS9G3VDjtBOvLt+Ufj1Uj+p2XlQ5nb48jdff70f7+WG82PM071mpcfoqWl9+V9zTvB99G4d+Z3qm8jRvssz8mM4auMsr67nH9NrGVt/XWl5Z7/sd7MHfsa77mH5+0IqDvdNofvs8Lj/9Ji4efRvNHxrxcu+89Oul6ubVvJVSHc+LKqeLy9fRODN0Zqn94040v/p3ca/m/cV3XjF+xjKmyTKzY3p5ZX3oK3APvpp31pi+6ZW/IyJ29xp9H9/c3pm6Mf384O1bU3QurqLZvowXhx6R1u0Z00qprudFFZ+3xprN2p3L4h6Zbr7yavEzljFNlpkd0+Mw7Gned1X2DWrUztuXcXx+Ufr1UPUzppVSnc+LKjZjejY7O25F+9d/nPyY/tUncX7sAYNZy5gmizF9B8b0/zOmlTdjWinV+byoYjOmZ7P94060Et9repQx3fryH7Hv2XczlzFNFmO6Ysq+QY2aMa28GdNKqc7nRRWbMT27NY9b0f7g48mN6Z9/FE3vLz2TGdNkMaYrpuwb1KgZ08qbMa2U6nxeVLEZ07Pb3lE7mn/5z8TGdOurf8VL71IykxnTZDGmK6bsG9SoGdPKmzGtlOp8XlSxGdOz3dH+ebS/yPc2WSljuvWnv8bRy9PS//tUTsY0WYzpiin7BjVqxrTyZkwrpTqfF1VsxrSOfjjKNahzj+lHf4vj50el/3epvIxpshjTFVP2DWrUjGnlzZhWSnU+L6rYjGk9P2jFyf5ZtL6+/b2nc43pJ/+M04Pz0v97VG7GNFmM6Yop+wY1asa08mZMK6U6nxdVbMa0ujX2z6P18iTa9z5LH9O/eRStvZM43DekZUyTzZiumLJvUKNmTCtvxrRSqvN5UcVmTKu3HxutODtsRuvvL6L96TfR/sXDm8f0zz6K9iffROvZbpwdNuOFt8DS/zKmyWJMV0zZN6hRM6aVN2NaKdX5vKhiM6Y1rL2jdhyddKJ52o7mwXm0GudxsXsYF7uH0WqcRfOoGa2zThwdd2LPK3ZrIGOaLMZ0xZR9gxo1Y1p5M6aVUp3Piyo2Y1pZ7TZa8eKwHeftyzhvX8aLw3bsNsq/XqpuxjRZjOmKKfsGNWrGtPJmTCulOp8XVWzGtPJ20ryI09Zl6ddD1c+YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxXTFl36BGzZhW3oxppVTn86KKzZhW3oxp5c2YJosxDQAAAImMaQAAAEhkTAMAAEAiYxoAAAASGdP0WdvYirn5peuWV9bf+ZzllfVbPx4R8eDh41hYXH3n7ze3d/q+fjfqZ9LHSlfvv/Hg4eOxXX+KM8ljZXevMfSc4rxSX0WcWxYWVx0rU6DoY+Wmy1N9dz1WxnWsMX2MafoMfjNZWFyNze2d6z+vbWz1nSCWV9ZjbWPr+s9Pnj67PpHcNKadYKbDpI+V7kgyoOtv0sfKoM3tnb7LUy+TPl4GP3/w61Efkz5WBr/ewuKqc0tN3fVYuevlmV7GNLcaHL8Li6vx5Omz6z8/efps6Deg2x6ZdqdlOo37WFnb2Or7RsX0GPexMmhufil29xrjubKUbtzHy8Liat8P6fIeV1TfOI+V7tDO+jvqadRjZVyXZ3oY09xqeWX9etB0HynsvZM67O8i8j/N24lmeoz7WOkeH73Hi4E0HcZ9rPTyqPT0mdT3oe7X7P361Ns4j5Vhw/mmy1M/ox4r47o808OY5kbdOxxd477TG/H2ZOSR6vob97HS/dzen/IO/hvU06TPK+68TJdJHC/dz+/9YR31N4ljpfeHLrddnnq5y7EyjsszXYxphnrw8HGuE8Ndx7SnTNXfJI6Vmz53cGBTL5M+r6xtbHlUeopM6njxg7rpM6lj5aYXOKS+xnGs3OXyTB9jmnfcdsdi3L/baEzX2ySPlWHD2Ziur0mfV9xxmS6TOl7c6Z0+Rd5n8Wsk9XbXY2UcxxrTx5imT9bTrvO+WuFtL/wy+GffmOpp0sfK2sZW399vbu/4xlRTkz5Wul/DuWQ6TPp4mZtf6vt855b6KuLc0tX94b8futTTXY+VcR1rTB9jmmu3vV9r70/bbnsfvd63mejW+/tGvZcdvENDfRRxrET0v6+jO7v1VMSxMuxpd9RTUeeW3o85t9RTkecWT++ut7seK+M41phexjQAAAAkMqYBAAAgkTENAAAAiYxpAAAASGRMAwAAQCJjGgAAABIZ0wAAAJDImAYAAIBExjQAAAAkMqYBAAAgkTENAAAAiYxpAAAASGRMAwAAQCJjGgAAABIZ0wAAAJDImAYAAIBExjQAAAAkMqYBAAAgkTENAAAAiYxpAAAASGRMA0BFbG7vxNz80jt/Pze/FJvbO9d/XtvYirn5pet29xo3fmzw6y2vrMfaxlbf5/VeHgDIx5gGgAqZm1+KBw8fX//5wcPHfYN4bWMrllfWb/147zheXlnv+/zllfV3/g0AIJ0xDQAVMjiWu48kR0Ts7jWGPpK8sLh64zh+8PBxLCyuDv16AMDojGkAqJAnT5/F3PxSPHn67Ho8P3n6rO9jw+od0wuLqzc+1duYBoDxMKYBoGIWFldjbWMrNrd3+h5V7o7p28zNL/WN5cGngRvTADAexjQAVEz3hcgWFlf7Xnhs8JHqQcPGtjENAJNhTANAxXRH87Dfj+6+Cnevze2doU8LjwhP8waACTGmAaCCbhu9t739VfdR7d7fpTamAWD8jGkAqKDbns4NAJTPmAaAihl8OysAoHqMaQComMEXHgMAqseYBgAAgETGNAAAACQypgEAACCRMQ0AAACJjGkAAABIZEwDAABAImMaAAAAEhnTAAAAkMiYBgAAgETGNAAAACQypgEAACCRMQ0AAACJjGkAAABIZEwDAABAImMaAAAAEhnTAAAAkMiYBgAAgETGNAAAACQypgEAACCRMQ0AAACJjGkAAABIZEwDAABAImMaAAAAEhnTAAAAkMiYBgAAgETGNAAAACQypgEAACCRMQ0AAACJjGkAAABIZEwDAABAImMaAAAAEhnTAAAAkMiYBgAAgETGNAAAACQypgEAACCRMQ0AAACJ/gs0YnoJJRKr5wAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter(temp_df, \n", + " x='year', \n", + " y='City', \n", + " color='City', \n", + " size='mean',\n", + " title='Mean funding value by location over time')\n", + "fig.update_layout(showlegend=False)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Task6: Industry Vertical\n", + "\n", + "Let us now have a look at the industry verticals and the number of funding deals for each vertical.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Can we get an overview of the Industry verticals and the number of funding deals?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "819" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.IndustryVertical.nunique()" + ] + }, + { + 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IndustryVerticalyearsizemean
115Consumer Internet20165396.291066e+06
116Consumer Internet20173091.272341e+07
776Technology20172237.064185e+06
775Technology20161905.687608e+06
176E-Commerce20161639.614323e+06
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" + ], + "text/plain": [ + " IndustryVertical year size mean\n", + "115 Consumer Internet 2016 539 6.291066e+06\n", + "116 Consumer Internet 2017 309 1.272341e+07\n", + "776 Technology 2017 223 7.064185e+06\n", + "775 Technology 2016 190 5.687608e+06\n", + "176 E-Commerce 2016 163 9.614323e+06" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_df=(df.groupby(['IndustryVertical','year'])\n", + " .CleanedAmount.agg(['size','mean'])\n", + " .reset_index()\n", + " .sort_values('size',ascending=False)\n", + ")\n", + "temp_df = temp_df.loc[temp_df.IndustryVertical.isin(top10_industries.index)]\n", + "temp_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "IndustryVertical=%{y}
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter(temp_df, \n", + " x='year', \n", + " y='IndustryVertical', \n", + " color='IndustryVertical', \n", + " size='mean',\n", + " title='Mean funding value by industry over time')\n", + "fig.update_layout(showlegend=False)\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Insight:\n", + "E-commerce is by far the most popular investment industry, Education, Finance and Healthcare are picking up." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Do it yourself\n", + "\n", + "* Can we get information about the investors?\n", + "* Can we get information about the subvertical?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}