Visualizations/plotly.ipynb

{"cells":[{"cell_type":"markdown","id":"7RJcJB2PttDB","metadata":{"id":"7RJcJB2PttDB"},"source":["<img src=\"https://github.com/Center-for-Health-Data-Science/PythonTsunami/blob/oct_2022_3days/figures/HeaDS_logo_large_withTitle.png?raw=1\" width=\"300\">\n","\n","<img src=\"https://github.com/Center-for-Health-Data-Science/PythonTsunami/blob/oct_2022_3days/figures/tsunami_logo.PNG?raw=1\" width=\"600\">\n","\n"]},{"cell_type":"markdown","id":"0trqMAy7ttDF","metadata":{"id":"0trqMAy7ttDF"},"source":["# Plotly\n","\n","## Python Open Source Graphing Library\n","\n","Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.\n","\n","![gallery](https://miro.medium.com/max/1458/1*qKpV3vkPZYoffsvFSEuw8A.png)\n","\n","\n","Plotly has an easy-to-use interface to it called [Plotly express](https://plotly.com/python/plotly-express/). This library makes plotting with Plotly very easy. Plotly express works nicely with Pandas dataframes as input, we just need to specify which columns need to be plotted."]},{"cell_type":"markdown","id":"54_5Ge8ottDF","metadata":{"id":"54_5Ge8ottDF"},"source":["# Import modules"]},{"cell_type":"code","execution_count":1,"id":"o2PRR09NttDG","metadata":{"executionInfo":{"elapsed":1051,"status":"ok","timestamp":1710766411222,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-60},"id":"o2PRR09NttDG"},"outputs":[],"source":["import pandas as pd\n","import plotly.express as px"]},{"cell_type":"markdown","id":"8trm6kFTttDH","metadata":{"id":"8trm6kFTttDH"},"source":["# Introduction\n","Let's start exploring the Plotly Database. The regular syntax for *any* Plotly.Express chart is\n","\n","`px.chart_type(data, arguments)`\n","\n","\n","Let's try a simple line chart: `px.line(data, arguments)`.\n","We first need to create some data."]},{"cell_type":"code","execution_count":2,"id":"cFmbCkKTttDI","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cFmbCkKTttDI","outputId":"adbd619f-ebe3-4553-b853-5cb603a64274"},"outputs":[{"name":"stdout","output_type":"stream","text":["[1996, 2000, 2004, 2008, 2012, 2016]\n","[1, 4, 5, 9, 1, 2]\n"]}],"source":["year = list(range(1996,2020,4))\n","medals = [1,4,5,9,1,2]\n","\n","print(year)\n","print(medals)"]},{"cell_type":"code","execution_count":3,"id":"fNORw4WHttDL","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"fNORw4WHttDL","outputId":"1da56c96-8dbf-445f-a330-fd6b30d471ec"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Year</th>\n","      <th>Medals</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1996</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2000</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2004</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2008</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2012</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Year  Medals\n","0  1996       1\n","1  2000       4\n","2  2004       5\n","3  2008       9\n","4  2012       1"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# We create our dataframe\n","\n","df = pd.DataFrame({\"Year\" : year, \"Medals\" : medals})\n","df.head()"]},{"cell_type":"markdown","id":"b0g_5qvhnPjl","metadata":{"id":"b0g_5qvhnPjl"},"source":["Now we insert this data into our general formula:\n","\n","`px.chart_type(data, arguments)`"]},{"cell_type":"code","execution_count":4,"id":"TxR6fwkXttDL","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"TxR6fwkXttDL","outputId":"8a156a68-6fe4-4cec-aa69-a0b68b22c28a"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"Year=%{x}<br>Medals=%{y}<extra></extra>","legendgroup":"","line":{"color":"#636efa","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines","name":"","orientation":"v","showlegend":false,"type":"scatter","x":[1996,2000,2004,2008,2012,2016],"xaxis":"x","y":[1,4,5,9,1,2],"yaxis":"y"}],"layout":{"legend":{"tracegroupgap":0},"margin":{"t":60},"template":{"data":{"bar":[{"error_x":{"color":"#2a3f5f"},"error_y":{"color":"#2a3f5f"},"marker":{"line":{"color":"#E5ECF6","width":0.5},"pattern":{"fillmode":"overlay","size":10,"solidity":0.2}},"type":"bar"}],"barpolar":[{"marker":{"line":{"color":"#E5ECF6","width":0.5},"pattern":{"fillmode":"overlay","size":10,"solidity":0.2}},"type":"barpolar"}],"carpet":[{"aaxis":{"endlinecolor":"#2a3f5f","gridcolor":"white","linecolor":"white","minorgridcolor":"white","startlinecolor":"#2a3f5f"},"baxis":{"endlinecolor":"#2a3f5f","gridcolor":"white","linecolor":"white","minorgridcolor":"white","startlinecolor":"#2a3f5f"},"type":"carpet"}],"choropleth":[{"colorbar":{"outlinewidth":0,"ticks":""},"type":"choropleth"}],"contour":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"contour"}],"contourcarpet":[{"colorbar":{"outlinewidth":0,"ticks":""},"type":"contourcarpet"}],"heatmap":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"heatmap"}],"heatmapgl":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"heatmapgl"}],"histogram":[{"marker":{"pattern":{"fillmode":"overlay","size":10,"solidity":0.2}},"type":"histogram"}],"histogram2d":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"histogram2d"}],"histogram2dcontour":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"histogram2dcontour"}],"mesh3d":[{"colorbar":{"outlinewidth":0,"ticks":""},"type":"mesh3d"}],"parcoords":[{"line":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"parcoords"}],"pie":[{"automargin":true,"type":"pie"}],"scatter":[{"fillpattern":{"fillmode":"overlay","size":10,"solidity":0.2},"type":"scatter"}],"scatter3d":[{"line":{"colorbar":{"outlinewidth":0,"ticks":""}},"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scatter3d"}],"scattercarpet":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scattercarpet"}],"scattergeo":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scattergeo"}],"scattergl":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scattergl"}],"scattermapbox":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scattermapbox"}],"scatterpolar":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scatterpolar"}],"scatterpolargl":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scatterpolargl"}],"scatterternary":[{"marker":{"colorbar":{"outlinewidth":0,"ticks":""}},"type":"scatterternary"}],"surface":[{"colorbar":{"outlinewidth":0,"ticks":""},"colorscale":[[0,"#0d0887"],[0.1111111111111111,"#46039f"],[0.2222222222222222,"#7201a8"],[0.3333333333333333,"#9c179e"],[0.4444444444444444,"#bd3786"],[0.5555555555555556,"#d8576b"],[0.6666666666666666,"#ed7953"],[0.7777777777777778,"#fb9f3a"],[0.8888888888888888,"#fdca26"],[1,"#f0f921"]],"type":"surface"}],"table":[{"cells":{"fill":{"color":"#EBF0F8"},"line":{"color":"white"}},"header":{"fill":{"color":"#C8D4E3"},"line":{"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= df, x = \"Year\" , y = \"Medals\")"]},{"cell_type":"markdown","id":"pfB1DP16ttDM","metadata":{"id":"pfB1DP16ttDM"},"source":["## Shaping your dataframe to fit to plotly\n","\n","If our dataframe is in wide format, we may need to change the shape to long format. This is because in plotly we always need to have our variables of interest as columns!\n","\n","For example, lets make a wide dataframe:\n"]},{"cell_type":"code","execution_count":6,"id":"NgxUbTVPttDM","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"executionInfo":{"elapsed":415,"status":"ok","timestamp":1710766418599,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-60},"id":"NgxUbTVPttDM","outputId":"b05ac4a3-0be8-41c2-9d35-b3031b360bea"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Year</th>\n","      <th>Canada</th>\n","      <th>USA</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2004</td>\n","      <td>4</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2008</td>\n","      <td>3</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2012</td>\n","      <td>5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2016</td>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Year  Canada  USA\n","0  2004       4    5\n","1  2008       3    9\n","2  2012       5    1\n","3  2016       3    2"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.DataFrame({'Year': {0: '2004', 1: '2008', 2: '2012', 3: '2016'},\n","                   'Canada': {0: 4, 1: 3, 2: 5, 3: 3},\n","                   'USA': {0: 5, 1: 9, 2: 1, 3: 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Lets see what kind of dataset this is:\n"]},{"cell_type":"code","execution_count":11,"id":"e6k2DPs43nI_","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":447,"status":"ok","timestamp":1710770276623,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-60},"id":"e6k2DPs43nI_","outputId":"1fd876e6-f998-47d5-d760-048826e86546"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","      <th>iso_alpha</th>\n","      <th>iso_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1952</td>\n","      <td>28.801</td>\n","      <td>8425333</td>\n","      <td>779.445314</td>\n","      <td>AFG</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1957</td>\n","      <td>30.332</td>\n","      <td>9240934</td>\n","      <td>820.853030</td>\n","      <td>AFG</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1962</td>\n","      <td>31.997</td>\n","      <td>10267083</td>\n","      <td>853.100710</td>\n","      <td>AFG</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1967</td>\n","      <td>34.020</td>\n","      <td>11537966</td>\n","      <td>836.197138</td>\n","      <td>AFG</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Afghanistan</td>\n","      <td>Asia</td>\n","      <td>1972</td>\n","      <td>36.088</td>\n","      <td>13079460</td>\n","      <td>739.981106</td>\n","      <td>AFG</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1699</th>\n","      <td>Zimbabwe</td>\n","      <td>Africa</td>\n","      <td>1987</td>\n","      <td>62.351</td>\n","      <td>9216418</td>\n","      <td>706.157306</td>\n","      <td>ZWE</td>\n","      <td>716</td>\n","    </tr>\n","    <tr>\n","      <th>1700</th>\n","      <td>Zimbabwe</td>\n","      <td>Africa</td>\n","      <td>1992</td>\n","      <td>60.377</td>\n","      <td>10704340</td>\n","      <td>693.420786</td>\n","      <td>ZWE</td>\n","      <td>716</td>\n","    </tr>\n","    <tr>\n","      <th>1701</th>\n","      <td>Zimbabwe</td>\n","      <td>Africa</td>\n","      <td>1997</td>\n","      <td>46.809</td>\n","      <td>11404948</td>\n","      <td>792.449960</td>\n","      <td>ZWE</td>\n","      <td>716</td>\n","    </tr>\n","    <tr>\n","      <th>1702</th>\n","      <td>Zimbabwe</td>\n","      <td>Africa</td>\n","      <td>2002</td>\n","      <td>39.989</td>\n","      <td>11926563</td>\n","      <td>672.038623</td>\n","      <td>ZWE</td>\n","      <td>716</td>\n","    </tr>\n","    <tr>\n","      <th>1703</th>\n","      <td>Zimbabwe</td>\n","      <td>Africa</td>\n","      <td>2007</td>\n","      <td>43.487</td>\n","      <td>12311143</td>\n","      <td>469.709298</td>\n","      <td>ZWE</td>\n","      <td>716</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1704 rows × 8 columns</p>\n","</div>"],"text/plain":["          country continent  year  lifeExp       pop   gdpPercap iso_alpha  \\\n","0     Afghanistan      Asia  1952   28.801   8425333  779.445314       AFG   \n","1     Afghanistan      Asia  1957   30.332   9240934  820.853030       AFG   \n","2     Afghanistan      Asia  1962   31.997  10267083  853.100710       AFG   \n","3     Afghanistan      Asia  1967   34.020  11537966  836.197138       AFG   \n","4     Afghanistan      Asia  1972   36.088  13079460  739.981106       AFG   \n","...           ...       ...   ...      ...       ...         ...       ...   \n","1699     Zimbabwe    Africa  1987   62.351   9216418  706.157306       ZWE   \n","1700     Zimbabwe    Africa  1992   60.377  10704340  693.420786       ZWE   \n","1701     Zimbabwe    Africa  1997   46.809  11404948  792.449960       ZWE   \n","1702     Zimbabwe    Africa  2002   39.989  11926563  672.038623       ZWE   \n","1703     Zimbabwe    Africa  2007   43.487  12311143  469.709298       ZWE   \n","\n","      iso_num  \n","0           4  \n","1           4  \n","2           4  \n","3           4  \n","4           4  \n","...       ...  \n","1699      716  \n","1700      716  \n","1701      716  \n","1702      716  \n","1703      716  \n","\n","[1704 rows x 8 columns]"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["gapminder_data = px.data.gapminder()\n","gapminder_data"]},{"cell_type":"markdown","id":"1HHQxqip3ulv","metadata":{"id":"1HHQxqip3ulv"},"source":["For now we would like to only use countries from Oceania. Can you help me to subset the dataframe?"]},{"cell_type":"code","execution_count":null,"id":"f1N6IXHP4YwM","metadata":{"id":"f1N6IXHP4YwM"},"outputs":[],"source":["#Let's subset the data"]},{"cell_type":"code","execution_count":39,"id":"Q-qHX_J44Bza","metadata":{"cellView":"form","id":"Q-qHX_J44Bza"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","      <th>iso_alpha</th>\n","      <th>iso_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1103</th>\n","      <td>New Zealand</td>\n","      <td>Oceania</td>\n","      <td>2007</td>\n","      <td>80.204</td>\n","      <td>4115771</td>\n","      <td>25185.00911</td>\n","      <td>NZL</td>\n","      <td>554</td>\n","    </tr>\n","    <tr>\n","      <th>1101</th>\n","      <td>New Zealand</td>\n","      <td>Oceania</td>\n","      <td>1997</td>\n","      <td>77.550</td>\n","      <td>3676187</td>\n","      <td>21050.41377</td>\n","      <td>NZL</td>\n","      <td>554</td>\n","    </tr>\n","    <tr>\n","      <th>61</th>\n","      <td>Australia</td>\n","      <td>Oceania</td>\n","      <td>1957</td>\n","      <td>70.330</td>\n","      <td>9712569</td>\n","      <td>10949.64959</td>\n","      <td>AUS</td>\n","      <td>36</td>\n","    </tr>\n","    <tr>\n","      <th>1097</th>\n","      <td>New Zealand</td>\n","      <td>Oceania</td>\n","      <td>1977</td>\n","      <td>72.220</td>\n","      <td>3164900</td>\n","      <td>16233.71770</td>\n","      <td>NZL</td>\n","      <td>554</td>\n","    </tr>\n","    <tr>\n","      <th>70</th>\n","      <td>Australia</td>\n","      <td>Oceania</td>\n","      <td>2002</td>\n","      <td>80.370</td>\n","      <td>19546792</td>\n","      <td>30687.75473</td>\n","      <td>AUS</td>\n","      <td>36</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          country continent  year  lifeExp       pop    gdpPercap iso_alpha  \\\n","1103  New Zealand   Oceania  2007   80.204   4115771  25185.00911       NZL   \n","1101  New Zealand   Oceania  1997   77.550   3676187  21050.41377       NZL   \n","61      Australia   Oceania  1957   70.330   9712569  10949.64959       AUS   \n","1097  New Zealand   Oceania  1977   72.220   3164900  16233.71770       NZL   \n","70      Australia   Oceania  2002   80.370  19546792  30687.75473       AUS   \n","\n","      iso_num  \n","1103      554  \n","1101      554  \n","61         36  \n","1097      554  \n","70         36  "]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["#@title Solution\n","df = gapminder_data.loc[gapminder_data['continent'] == 'Oceania']\n","df.sample(5)"]},{"cell_type":"markdown","id":"afMBTun5ttDQ","metadata":{"id":"afMBTun5ttDQ"},"source":["## `Color` argument\n","\n","As shown above, we can change the color of the lines based on a dataframe colunm by using the argument `color`. In this example, we plot the life expectancy column VS the year column and the line are colored by the content of the country column. This also gives us separate lines for the separate countries."]},{"cell_type":"code","execution_count":20,"id":"eNzK3fp_ttDQ","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"eNzK3fp_ttDQ","outputId":"85b89bcf-f20a-4444-c547-631a38c114cd"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"country=Australia<br>year=%{x}<br>lifeExp=%{y}<extra></extra>","legendgroup":"Australia","line":{"color":"#636efa","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines","name":"Australia","orientation":"v","showlegend":true,"type":"scatter","x":[1952,1957,1962,1967,1972,1977,1982,1987,1992,1997,2002,2007],"xaxis":"x","y":[69.12,70.33,70.93,71.1,71.93,73.49,74.74,76.32,77.56,78.83,80.37,81.235],"yaxis":"y"},{"hovertemplate":"country=New Zealand<br>year=%{x}<br>lifeExp=%{y}<extra></extra>","legendgroup":"New 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We can separate the data from the different countries by color using the argument `color`\n","# Separating the px.line call into several lines like this is purely aesthetic. It does not influence the flow of the execution.\n","fig = px.line(df,\n","              x=\"year\",\n","              y=\"lifeExp\",\n","              color='country')\n","fig.show()"]},{"cell_type":"markdown","id":"rN6wgpiFttDR","metadata":{"id":"rN6wgpiFttDR"},"source":["## `Color_discrete_map` argument\n","\n","We can also decide the color palette to use with `color_discrete_map`. In this case, we need to specify for each level of the variable to color by, here `country`, what color should be used:"]},{"cell_type":"code","execution_count":21,"id":"v9THpFNIttDR","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"v9THpFNIttDR","outputId":"a762ff80-7ab6-42be-f281-f1835840435f"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"country=Australia<br>year=%{x}<br>lifeExp=%{y}<extra></extra>","legendgroup":"Australia","line":{"color":"Black","dash":"solid"},"marker":{"symbol":"circle"},"mode":"lines","name":"Australia","orientation":"v","showlegend":true,"type":"scatter","x":[1952,1957,1962,1967,1972,1977,1982,1987,1992,1997,2002,2007],"xaxis":"x","y":[69.12,70.33,70.93,71.1,71.93,73.49,74.74,76.32,77.56,78.83,80.37,81.235],"yaxis":"y"},{"hovertemplate":"country=New Zealand<br>year=%{x}<br>lifeExp=%{y}<extra></extra>","legendgroup":"New 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Ghana.\n","\n"]},{"cell_type":"code","execution_count":33,"id":"zvCtfSxTttDS","metadata":{"id":"zvCtfSxTttDS"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","      <th>iso_alpha</th>\n","      <th>iso_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>408</th>\n","      <td>Denmark</td>\n","      <td>Europe</td>\n","      <td>1952</td>\n","      <td>70.78</td>\n","      <td>4334000</td>\n","      <td>9692.385245</td>\n","      <td>DNK</td>\n","      <td>208</td>\n","    </tr>\n","    <tr>\n","      <th>409</th>\n","      <td>Denmark</td>\n","      <td>Europe</td>\n","      <td>1957</td>\n","      <td>71.81</td>\n","      <td>4487831</td>\n","      <td>11099.659350</td>\n","      <td>DNK</td>\n","      <td>208</td>\n","    </tr>\n","    <tr>\n","      <th>410</th>\n","      <td>Denmark</td>\n","      <td>Europe</td>\n","      <td>1962</td>\n","      <td>72.35</td>\n","      <td>4646899</td>\n","      <td>13583.313510</td>\n","      <td>DNK</td>\n","      <td>208</td>\n","    </tr>\n","    <tr>\n","      <th>411</th>\n","      <td>Denmark</td>\n","      <td>Europe</td>\n","      <td>1967</td>\n","      <td>72.96</td>\n","      <td>4838800</td>\n","      <td>15937.211230</td>\n","      <td>DNK</td>\n","      <td>208</td>\n","    </tr>\n","    <tr>\n","      <th>412</th>\n","      <td>Denmark</td>\n","      <td>Europe</td>\n","      <td>1972</td>\n","      <td>73.47</td>\n","      <td>4991596</td>\n","      <td>18866.207210</td>\n","      <td>DNK</td>\n","      <td>208</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     country continent  year  lifeExp      pop     gdpPercap iso_alpha  \\\n","408  Denmark    Europe  1952    70.78  4334000   9692.385245       DNK   \n","409  Denmark    Europe  1957    71.81  4487831  11099.659350       DNK   \n","410  Denmark    Europe  1962    72.35  4646899  13583.313510       DNK   \n","411  Denmark    Europe  1967    72.96  4838800  15937.211230       DNK   \n","412  Denmark    Europe  1972    73.47  4991596  18866.207210       DNK   \n","\n","     iso_num  \n","408      208  \n","409      208  \n","410      208  \n","411      208  \n","412      208  "]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["#creating the dataframe and verify that it has the data you want\n","#we're using `isin` instead of writing gapminder_data['country'] == 'Denmark' | gapminder_data['country'] == 'Ghana' ect\n","df = gapminder_data.loc[gapminder_data['country'].isin(['Denmark', 'Ghana', 'Romania'])]\n","df.head()\n"]},{"cell_type":"code","execution_count":null,"id":"c9wH6HAoADWl","metadata":{"id":"c9wH6HAoADWl"},"outputs":[],"source":["#now make the line plot"]},{"cell_type":"markdown","id":"8SMeF0UJttDT","metadata":{"id":"8SMeF0UJttDT"},"source":["2) Color each line by the country. What do you observe when comparing to the previous plot?"]},{"cell_type":"code","execution_count":null,"id":"oJrunaRPttDT","metadata":{"id":"oJrunaRPttDT"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"uzqxpb5HttDT","metadata":{"id":"uzqxpb5HttDT"},"source":["3) Give your plot a title."]},{"cell_type":"code","execution_count":null,"id":"eWUhNWZCttDT","metadata":{"id":"eWUhNWZCttDT"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"d8lsQ3AmttDT","metadata":{"id":"d8lsQ3AmttDT"},"source":["## Quiz 1\n","\n","What would you do if instead of a line chart you wanted to show the data in a scatter plot?"]},{"cell_type":"markdown","id":"1JZa7LBEttDT","metadata":{"id":"1JZa7LBEttDT"},"source":["# Scatter plots\n","\n","Scatter plots are coordinate plots that use x and y coordinates to show the relationship between two variables. However, the values of the variables do not necessarily need to be linked or ordered like in a line plot.\n","\n","Plotting a scatter plot is very much like plotting a line plot, but we use the `px.scatter()` function. Many of the arguments shown previously for the line plots work here as well, for example, the color argument:"]},{"cell_type":"code","execution_count":44,"id":"m9Hr8Eo9ttDT","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"m9Hr8Eo9ttDT","outputId":"626c1e34-ffa1-47e5-cae6-27cb2a54c1cc"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"country=Albania<br>lifeExp=%{x}<br>gdpPercap=%{y}<extra></extra>","legendgroup":"Albania","marker":{"color":"#636efa","symbol":"circle"},"mode":"markers","name":"Albania","orientation":"v","showlegend":true,"type":"scatter","x":[55.23,59.28,64.82,66.22,67.69,68.93,70.42,72,71.581,72.95,75.65100000000002,76.423],"xaxis":"x","y":[1601.056136,1942.284244,2312.888958,2760.196931,3313.422188,3533.003910000001,3630.880722,3738.932735,2497.437901,3193.054604,4604.211737,5937.029525999998],"yaxis":"y"},{"hovertemplate":"country=Austria<br>lifeExp=%{x}<br>gdpPercap=%{y}<extra></extra>","legendgroup":"Austria","marker":{"color":"#EF553B","symbol":"circle"},"mode":"markers","name":"Austria","orientation":"v","showlegend":true,"type":"scatter","x":[66.8,67.48,69.54,70.14,70.63,72.17,73.18,74.94,76.04,77.51,78.98,79.829],"xaxis":"x","y":[6137.076492,8842.59803,10750.72111,12834.6024,16661.6256,19749.4223,21597.08362,23687.82607,27042.01868,29095.920660000003,32417.60769,36126.4927],"yaxis":"y"},{"hovertemplate":"country=Belgium<br>lifeExp=%{x}<br>gdpPercap=%{y}<extra></extra>","legendgroup":"Belgium","marker":{"color":"#00cc96","symbol":"circle"},"mode":"markers","name":"Belgium","orientation":"v","showlegend":true,"type":"scatter","x":[68,69.24,70.25,70.94,71.44,72.8,73.93,75.35,76.46,77.53,78.32,79.441],"xaxis":"x","y":[8343.105126999999,9714.960623,10991.20676,13149.04119,16672.14356,19117.97448,20979.84589,22525.56308,25575.57069,27561.19663,30485.88375,33692.60508],"yaxis":"y"},{"hovertemplate":"country=Bosnia 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= px.scatter(df,\n","                 x=\"lifeExp\",\n","                 y=\"gdpPercap\",\n","                 color=\"country\",\n","                 size='pop') # Using population as the size for the plot\n","\n","fig.show()"]},{"cell_type":"markdown","id":"yr8LsjjkttDU","metadata":{"id":"yr8LsjjkttDU"},"source":["## Trendline argument\n","We can easily add trendlines to our scatter plot using the argument `trendline`. By default you will use the Ordinary Least Squares trendline (linear regression).\n","\n","We quickly see the relationship between GDP and life expectancy is not linear for Europe in general."]},{"cell_type":"code","execution_count":45,"id":"vXpxv5hlttDU","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"vXpxv5hlttDU","outputId":"e6ebe9df-7855-4d99-9d85-379e852fe96d"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"lifeExp=%{x}<br>gdpPercap=%{y}<extra></extra>","legendgroup":"","marker":{"color":"#636efa","symbol":"circle"},"mode":"markers","name":"","orientation":"v","showlegend":false,"type":"scatter","x":[55.23,59.28,64.82,66.22,67.69,68.93,70.42,72,71.581,72.95,75.65100000000002,76.423,66.8,67.48,69.54,70.14,70.63,72.17,73.18,74.94,76.04,77.51,78.98,79.829,68,69.24,70.25,70.94,71.44,72.8,73.93,75.35,76.46,77.53,78.32,79.441,53.82,58.45,61.93,64.79,67.45,69.86,70.69,71.14,72.178,73.244,74.09,74.852,59.6,66.61,69.51,70.42,70.9,70.81,71.08,71.34,71.19,70.32,72.14,73.005,61.21,64.77,67.13,68.5,69.61,70.64,70.46,71.52,72.527,73.68,74.876,75.748,66.87,69.03,69.9,70.38,70.29,70.71,70.96,71.58,72.4,74.01,75.51,76.486,70.78,71.81,72.35,72.96,73.47,74.69,74.63,74.8,75.33,76.11,77.18,78.332,66.55,67.49,68.75,69.83,70.87,72.52,74.55,74.83,75.7,77.13,78.37,79.313,67.41,68.93,70.51,71.55,72.38,73.83,74.89,76.34,77.46,78.64,79.59,80.657,67.5,69.1,70.3,70.8,71,72.5,73.8,74.847,76.07,77.34,78.67,79.406,65.86,67.86,69.51,71,72.34,73.68,75.24,76.67,77.03,77.869,78.256,79.483,64.03,66.41,67.96,69.5,69.76,69.95,69.39,69.58,69.17,71.04,72.59,73.33800000000002,72.49,73.47,73.68,73.73,74.46,76.11,76.99,77.23,78.77,78.95,80.5,81.757,66.91,68.9,70.29,71.08,71.28,72.03,73.1,74.36,75.467,76.122,77.783,78.885,65.94,67.81,69.24,71.06,72.19,73.48,74.98,76.42,77.44,78.82,80.24,80.546,59.164,61.448,63.728,67.178,70.63600000000002,73.066,74.101,74.865,75.435,75.445,73.98100000000002,74.543,72.13,72.99,73.23,73.82,73.75,75.24,76.05,76.83,77.42,78.03,78.53,79.762,72.67,73.44,73.47,74.08,74.34,75.37,75.97,75.89,77.32,78.32,79.05,80.196,61.31,65.77,67.64,69.61,70.85,70.67,71.32,70.98,70.99,72.75,74.67,75.563,59.82,61.51,64.39,66.6,69.26,70.41,72.77,74.06,74.86,75.97,77.29,78.098,61.05,64.1,66.8,66.8,69.21,69.46,69.66,69.53,69.36,69.72,71.322,72.476,57.996,61.685,64.531,66.914,68.7,70.3,70.16199999999999,71.218,71.65899999999998,72.232,73.21300000000002,74.002,64.36,67.45,70.33,70.98,70.35,70.45,70.8,71.08,71.38,72.71,73.8,74.663,65.57,67.85,69.15,69.18,69.82,70.97,71.063,72.25,73.64,75.13,76.66,77.926,64.94,66.66,69.69,71.44,73.06,74.39,76.3,76.9,77.57,78.77,79.78,80.941,71.86,72.49,73.37,74.16,74.72,75.44,76.42,77.19,78.16,79.39,80.04,80.884,69.62,70.56,71.32,72.77,73.78,75.39,76.21,77.41,78.03,79.37,80.62,81.70100000000002,43.585,48.07899999999999,52.098,54.33600000000001,57.005,59.507,61.036,63.108,66.146,68.835,70.845,71.777,69.18,70.42,70.76,71.36,72.01,72.76,74.04,75.007,76.42,77.218,78.471,79.425],"xaxis":"x","y":[1601.056136,1942.284244,2312.888958,2760.196931,3313.422188,3533.003910000001,3630.880722,3738.932735,2497.437901,3193.054604,4604.211737,5937.029525999998,6137.076492,8842.59803,10750.72111,12834.6024,16661.6256,19749.4223,21597.08362,23687.82607,27042.01868,29095.920660000003,32417.60769,36126.4927,8343.105126999999,9714.960623,10991.20676,13149.04119,16672.14356,19117.97448,20979.84589,22525.56308,25575.57069,27561.19663,30485.88375,33692.60508,973.5331948,1353.989176,1709.683679,2172.3524230000007,2860.16975,3528.481305,4126.613157,4314.114757,2546.781445,4766.355904,6018.975239,7446.298803,2444.286648,3008.670727,4254.337839,5577.0028,6597.494398,7612.240438,8224.191647,8239.854824,6302.623438000001,5970.38876,7696.777725,10680.79282,3119.23652,4338.231617,5477.890018,6960.297861,9164.090127,11305.38517,13221.82184,13822.58394,8447.794873,9875.604515,11628.38895,14619.222719999998,6876.14025,8256.343918,10136.86713,11399.44489,13108.4536,14800.16062,15377.22855,16310.4434,14297.02122,16048.51424,17596.210219999994,22833.30851,9692.385245,11099.65935,13583.31351,15937.21123,18866.20721,20422.9015,21688.04048,25116.17581,26406.73985,29804.34567,32166.50006,35278.41874,6424.519071,7545.415386,9371.842561,10921.63626,14358.8759,15605.42283,18533.15761,21141.01223,20647.16499,23723.9502,28204.59057,33207.0844,7029.809327,8662.834898000001,10560.48553,12999.91766,16107.19171,18292.63514,20293.89746,22066.44214,24703.79615,25889.78487,28926.03234,30470.0167,7144.114393000002,10187.82665,12902.46291,14745.62561,18016.18027,20512.92123,22031.53274,24639.18566,26505.30317,27788.88416,30035.80198,32170.37442,3530.690067,4916.299889,6017.190732999999,8513.097016,12724.82957,14195.52428,15268.42089,16120.52839,17541.49634,18747.69814,22514.2548,27538.41188,5263.673816,6040.180011,7550.359877,9326.64467,10168.65611,11674.83737,12545.99066,12986.47998,10535.62855,11712.7768,14843.93556,18008.94444,7267.688428,9244.001412,10350.15906,13319.89568,15798.06362,19654.96247,23269.6075,26923.20628,25144.39201,28061.099660000003,31163.20196,36180.78919,5210.280328,5599.077872,6631.597314,7655.568963,9530.772896,1115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= px.scatter(df,\n","                 x=\"lifeExp\",\n","                 y=\"gdpPercap\",\n","                 trendline = \"ols\") # fitting a trendline with ordinary least squares\n","\n","fig.show()"]},{"cell_type":"markdown","id":"J7Z3LHI9ttDU","metadata":{"id":"J7Z3LHI9ttDU"},"source":["If you have separated the countries using the `color` argument, you will get one trendline per country.\n","\n","*This will look quite ugly since there are many countries. Some of them actually look like the relationship could be linear.*"]},{"cell_type":"code","execution_count":46,"id":"T309aGb3ttDU","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"id":"T309aGb3ttDU","outputId":"8a28fd98-aee1-4751-a63d-2ea0739f77f4"},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"hovertemplate":"country=Albania<br>lifeExp=%{x}<br>gdpPercap=%{y}<extra></extra>","legendgroup":"Albania","marker":{"color":"#636efa","symbol":"circle"},"mode":"markers","name":"Albania","orientation":"v","showlegend":true,"type":"scatter","x":[55.23,59.28,64.82,66.22,67.69,68.93,70.42,72,71.581,72.95,75.65100000000002,76.423],"xaxis":"x","y":[1601.056136,1942.284244,2312.888958,2760.196931,3313.422188,3533.003910000001,3630.880722,3738.932735,2497.437901,3193.054604,4604.211737,5937.029525999998],"yaxis":"y"},{"hovertemplate":"<b>OLS trendline</b><br>gdpPercap = 157.86 * lifeExp + 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= px.scatter(df,\n","                 x=\"lifeExp\",\n","                 y=\"gdpPercap\",\n","                 color=\"country\",\n","                 trendline = \"lowess\",\n","                 trendline_scope=\"overall\")\n","\n","fig.show()"]},{"cell_type":"markdown","id":"6UU5RT3pttDV","metadata":{"id":"6UU5RT3pttDV"},"source":["## Exercise 2: Scatter plots and trendlines (10 mins)\n","1) Using the data from 'Africa', create a scatter plot of GDP versus population. You will first have to subset the data like we did in above for Europe.\n","\n","Use different symbols and colors for the different countries."]},{"cell_type":"code","execution_count":null,"id":"yyMHwKNuttDV","metadata":{"id":"yyMHwKNuttDV"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"fdNT2n5UttDV","metadata":{"id":"fdNT2n5UttDV"},"source":["2) Make two separate plots that model the correlation between GDP and population for each country, once using an OLS fit and once a LOWESS fit. Which fit do you think looks more convincing?"]},{"cell_type":"code","execution_count":null,"id":"0Gyw5dOdDD8I","metadata":{"id":"0Gyw5dOdDD8I"},"outputs":[],"source":["#ols"]},{"cell_type":"code","execution_count":null,"id":"uxveJjt4DDwO","metadata":{"id":"uxveJjt4DDwO"},"outputs":[],"source":["#lowess"]},{"cell_type":"markdown","id":"09YyPOW9ttDV","metadata":{"id":"09YyPOW9ttDV"},"source":["# Bar Charts\n","\n","With `px.bar()`, each row of the DataFrame is represented as a rectangular mark. Bar plots are very useful to show quantitative information across qualitative features such as years, countries or other categorical data.\n","\n","`px.bar()` shares a lot of arguments with line and scatter plots."]},{"cell_type":"code","execution_count":71,"id":"zl19hVUYttDV","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":542},"executionInfo":{"elapsed":327,"status":"ok","timestamp":1710767590499,"user":{"displayName":"Henrike 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= px.bar(df, x='year', y='pop', color='country', text_auto='.2s') #this format will show only two digits\n","fig.show()"]},{"cell_type":"markdown","id":"buJqAy9XttDZ","metadata":{"id":"buJqAy9XttDZ"},"source":["## Exercise 3: Bar charts (10 mins)"]},{"cell_type":"markdown","id":"XhMJs2hxttDa","metadata":{"id":"XhMJs2hxttDa"},"source":["1) Use the gapminder data for Oceania and show the GDP for each year in a bar plot.\n"]},{"cell_type":"code","execution_count":null,"id":"aX-elNGSyTm2","metadata":{"id":"aX-elNGSyTm2"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"ojO-tcocRQWF","metadata":{"id":"ojO-tcocRQWF"},"source":["2) Now separate the bars into countries and put them next to each other instead of stacked on top of each other."]},{"cell_type":"code","execution_count":null,"id":"ezyYRbUZyUmu","metadata":{"id":"ezyYRbUZyUmu"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"9Hep5AQvx6sR","metadata":{"id":"9Hep5AQvx6sR"},"source":["3) Now, add the GDP as text with two digits."]},{"cell_type":"code","execution_count":null,"id":"SMotgp-2ygkk","metadata":{"id":"SMotgp-2ygkk"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"lf7Cy5RattDY","metadata":{"id":"lf7Cy5RattDY"},"source":["# Histograms\n","\n","In statistics, a histogram is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in Plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...) which can be used to visualize data on categorical and date axes as well as linear axes.\n","\n","Compared to `px.bar()`, `px.histogram()` can work with only the `x` argument, which can be a continuous or categorical variable.\n","\n","Let use the tips dataset which reports on tips given by customers and some information about the customer. It looks like this:\n"]},{"cell_type":"code","execution_count":73,"id":"rTwJUHPk3pUS","metadata":{"id":"rTwJUHPk3pUS"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>total_bill</th>\n","      <th>tip</th>\n","      <th>sex</th>\n","      <th>smoker</th>\n","      <th>day</th>\n","      <th>time</th>\n","      <th>size</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>16.99</td>\n","      <td>1.01</td>\n","      <td>Female</td>\n","      <td>No</td>\n","      <td>Sun</td>\n","      <td>Dinner</td>\n","      <td>2</td>\n","    </tr>\n","    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paying:"]},{"cell_type":"code","execution_count":45,"id":"X6alW5ZI3wGI","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1710770657573,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-60},"id":"X6alW5ZI3wGI","outputId":"44eb3bb3-ec25-4df0-826b-510727aa557c"},"outputs":[{"data":{"text/plain":["sex\n","Male      157\n","Female     87\n","dtype: int64"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["tips_df.value_counts('sex')"]},{"cell_type":"markdown","id":"OlfrBARF44iu","metadata":{"id":"OlfrBARF44iu"},"source":["So just comparing counts doesn't give us the full picture. what if we want to know if men are more likely to have a higher total bill? 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= px.histogram(tips_df,\n","                   x=\"total_bill\",\n","                   color=\"sex\",\n","                   barmode='group',\n","                   histnorm='probability')\n","fig.show()"]},{"cell_type":"markdown","id":"mJAzfNrp6puD","metadata":{"id":"mJAzfNrp6puD"},"source":["What happens here is that we say if all men in the dataset are 100%, then how many have a total bill of 8 - 10 dollars? Now we can compare between the two groups."]},{"cell_type":"markdown","id":"5Y4FJCSG1Qa9","metadata":{"id":"5Y4FJCSG1Qa9"},"source":["## Exercise 4: Histogram (5 mins)"]},{"cell_type":"markdown","id":"5kT1bkReRjCi","metadata":{"id":"5kT1bkReRjCi"},"source":["Make a histogram of the tip value from the tips dataframe. Then split it up by whether the customer is a smoker or not, and put the bars next to each other. Change from looking at counts to looking at the probability density (like above). You can do this step by step or all at once.\n","\n","What do you observe? 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= px.violin(tips_df, y=\"total_bill\", x=\"smoker\", color=\"sex\", points=\"all\", box=True)\n","fig.update_traces(meanline_visible=True)\n","fig.show()"]},{"cell_type":"markdown","id":"HDjjV__IXKS2","metadata":{"id":"HDjjV__IXKS2"},"source":["## Exercise 5: Boxplots and violin plots (10 mins)\n","\n","Have a look at the following dataframe. What information does it contain?"]},{"cell_type":"code","execution_count":null,"id":"WBGIjjZrXUc9","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":313,"status":"ok","timestamp":1695727266753,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-120},"id":"WBGIjjZrXUc9","outputId":"c41e85b6-0be5-4fb9-ec7a-e1b5da76d894"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-2d5da46c-df5d-48a1-84d7-c8236c4f0d88\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>country</th>\n","      <th>continent</th>\n","      <th>year</th>\n","      <th>lifeExp</th>\n","      <th>pop</th>\n","      <th>gdpPercap</th>\n","      <th>iso_alpha</th>\n","      <th>iso_num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>19</th>\n","      <td>Albania</td>\n","      <td>Europe</td>\n","      <td>1987</td>\n","      <td>72.000</td>\n","      <td>3075321</td>\n","      <td>3738.932735</td>\n","      <td>ALB</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>Albania</td>\n","      <td>Europe</td>\n","      <td>2007</td>\n","      <td>76.423</td>\n","      <td>3600523</td>\n","      <td>5937.029526</td>\n","      <td>ALB</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>79</th>\n","      <td>Austria</td>\n","      <td>Europe</td>\n","      <td>1987</td>\n","      <td>74.940</td>\n","      <td>7578903</td>\n","      <td>23687.826070</td>\n","      <td>AUT</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>83</th>\n","      <td>Austria</td>\n","      <td>Europe</td>\n","      <td>2007</td>\n","      <td>79.829</td>\n","      <td>8199783</td>\n","      <td>36126.492700</td>\n","      <td>AUT</td>\n","      <td>40</td>\n","    </tr>\n","    <tr>\n","      <th>115</th>\n","      <td>Belgium</td>\n","      <td>Europe</td>\n","      <td>1987</td>\n","      <td>75.350</td>\n","      <td>9870200</td>\n","      <td>22525.563080</td>\n","      <td>BEL</td>\n","      <td>56</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d5da46c-df5d-48a1-84d7-c8236c4f0d88')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-2d5da46c-df5d-48a1-84d7-c8236c4f0d88 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Are the mean and the median close? What's the difference between them?"]},{"cell_type":"code","execution_count":null,"id":"71824276","metadata":{},"outputs":[],"source":[]},{"cell_type":"markdown","id":"MRIHdoe3ttDc","metadata":{"id":"MRIHdoe3ttDc"},"source":["# Heatmaps\n","\n","The `px.imshow()` function can be used to display heatmaps (as well as full-color images, as its name suggests). It accepts both array-like objects like lists of lists, as well as pandas.DataFrame objects. Heatmaps are particularly useful to display correlations between the variables of the data"]},{"cell_type":"markdown","id":"MMsTtO7OUvNu","metadata":{"id":"MMsTtO7OUvNu"},"source":["We can use `corr` to see how much the variables in the tip data set are correlated with each other. Correlation can only be calculated on numerical columns."]},{"cell_type":"code","execution_count":62,"id":"ryg2v3OkUn4s","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1710774773719,"user":{"displayName":"Henrike Zschach","userId":"10873369072901591129"},"user_tz":-60},"id":"ryg2v3OkUn4s","outputId":"696e2a62-1aad-4abd-e930-b9f299a262ea"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"corr_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"total_bill\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21310904884233575,\n        \"min\": 0.5983151309049014,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.6757341092113648,\n          0.5983151309049014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25843188743740914,\n        \"min\": 0.48929877523035714,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6757341092113648,\n          1.0,\n          0.48929877523035714\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2689643845213583,\n        \"min\": 0.48929877523035714,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5983151309049014,\n          0.48929877523035714,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"corr_df"},"text/html":["\n","  <div id=\"df-90273485-72a9-49c2-b7de-19e6bc6293f2\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>total_bill</th>\n","      <th>tip</th>\n","      <th>size</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>total_bill</th>\n","      <td>1.000000</td>\n","      <td>0.675734</td>\n","      <td>0.598315</td>\n","    </tr>\n","    <tr>\n","      <th>tip</th>\n","      <td>0.675734</td>\n","      <td>1.000000</td>\n","      <td>0.489299</td>\n","    </tr>\n","    <tr>\n","      <th>size</th>\n","      <td>0.598315</td>\n","      <td>0.489299</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90273485-72a9-49c2-b7de-19e6bc6293f2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-90273485-72a9-49c2-b7de-19e6bc6293f2 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Plot the result in a heatmap. What do you observe? Are the correlations as you expected?\n","\n","Change the color scheme to something you find pleasing or useful and add the correlation values to the squares."]},{"cell_type":"code","execution_count":null,"id":"VVWYYkLwZgB2","metadata":{"id":"VVWYYkLwZgB2"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"omkczL_tamyy","metadata":{"id":"omkczL_tamyy"},"source":["Now, do the same Africa. What do you observe? Are you surprised?"]},{"cell_type":"code","execution_count":null,"id":"c4_6lD2YaKzd","metadata":{"id":"c4_6lD2YaKzd"},"outputs":[],"source":[]},{"cell_type":"markdown","id":"05d2768c","metadata":{},"source":["## Facet plots\n","\n","Another cool thing we can do in many types of plots is to split the chart into rows or columns depending on a variable. For example, we can look at the relationship between total bill and tip, split up by day of the week. \n"]},{"cell_type":"code","execution_count":103,"id":"6d2e8264","metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>total_bill</th>\n","      <th>tip</th>\n","      <th>sex</th>\n","      <th>smoker</th>\n","      <th>day</th>\n","      <th>time</th>\n","      <th>size</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>36</th>\n","      <td>16.31</td>\n","      <td>2.00</td>\n","      <td>Male</td>\n","      <td>No</td>\n","      <td>Sat</td>\n","      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smoker  day    time  size\n","36        16.31  2.00  Male     No  Sat  Dinner     3\n","174       16.82  4.00  Male    Yes  Sun  Dinner     2\n","60        20.29  3.21  Male    Yes  Sat  Dinner     2\n","210       30.06  2.00  Male    Yes  Sat  Dinner     3\n","172        7.25  5.15  Male    Yes  Sun  Dinner     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These plots can be easily added by just using the attributes: `marginal_x` and 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