{"cells":[{"cell_type":"markdown","source":["<img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open in Google Colaboratory\"></a><p><a href=\"https://githubtocolab.com/vmcf-konfmi/MB100T01/blob/main/advanced_image_analysis_mb100t01/01_pandas_statistics/03_Pandas_EDA.ipynb\">"],"metadata":{"id":"8B0bvhGssIL5"}},{"cell_type":"markdown","metadata":{"id":"Vkf1B-vMwpVB"},"source":["# Exploratory Data Analysis"]},{"cell_type":"markdown","metadata":{"id":"fpPtZgqIvuXz"},"source":["Inspiration and some of the parts came from: Python Data Science [GitHub repository](https://github.com/jakevdp/PythonDataScienceHandbook/tree/master), [MIT License](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/LICENSE-CODE) and [Introduction to Pandas](https://colab.research.google.com/notebooks/mlcc/intro_to_pandas.ipynb) by Google, [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)\n","\n","If running this from Google Colab, uncomment the cell below and run it. Otherwise, just skip it."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5saSBc40voZF"},"outputs":[],"source":["#!pip install seaborn\n","#!pip install watermark"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KoPnqdBKsHas"},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","from scipy import stats"]},{"cell_type":"markdown","metadata":{"id":"ZkUd2sa-yP5e"},"source":["## Learning Objectives:\n","\n"," * descriptive statistics/EDA\n"," * corr matrix\n","\n","For this notebook, we will use the california housing dataframes."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":359,"status":"ok","timestamp":1692082289947,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"av6RYOraVG1V","outputId":"6d1929a3-fec1-4e28-c968-b5d78c224fe9"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-114.31</td>\n","      <td>34.19</td>\n","      <td>15.0</td>\n","      <td>5612.0</td>\n","      <td>1283.0</td>\n","      <td>1015.0</td>\n","      <td>472.0</td>\n","      <td>1.4936</td>\n","      <td>66900.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-114.47</td>\n","      <td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1901.0</td>\n","      <td>1129.0</td>\n","      <td>463.0</td>\n","      <td>1.8200</td>\n","      <td>80100.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-114.56</td>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>174.0</td>\n","      <td>333.0</td>\n","      <td>117.0</td>\n","      <td>1.6509</td>\n","      <td>85700.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-114.57</td>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>337.0</td>\n","      <td>515.0</td>\n","      <td>226.0</td>\n","      <td>3.1917</td>\n","      <td>73400.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-114.57</td>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>326.0</td>\n","      <td>624.0</td>\n","      <td>262.0</td>\n","      <td>1.9250</td>\n","      <td>65500.0</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","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16995</th>\n","      <td>-124.26</td>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>394.0</td>\n","      <td>907.0</td>\n","      <td>369.0</td>\n","      <td>2.3571</td>\n","      <td>111400.0</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>-124.27</td>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>528.0</td>\n","      <td>1194.0</td>\n","      <td>465.0</td>\n","      <td>2.5179</td>\n","      <td>79000.0</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>-124.30</td>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>531.0</td>\n","      <td>1244.0</td>\n","      <td>456.0</td>\n","      <td>3.0313</td>\n","      <td>103600.0</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>-124.30</td>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>552.0</td>\n","      <td>1298.0</td>\n","      <td>478.0</td>\n","      <td>1.9797</td>\n","      <td>85800.0</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>-124.35</td>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>300.0</td>\n","      <td>806.0</td>\n","      <td>270.0</td>\n","      <td>3.0147</td>\n","      <td>94600.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 9 columns</p>\n","</div>"],"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","0        -114.31     34.19                15.0       5612.0          1283.0   \n","1        -114.47     34.40                19.0       7650.0          1901.0   \n","2        -114.56     33.69                17.0        720.0           174.0   \n","3        -114.57     33.64                14.0       1501.0           337.0   \n","4        -114.57     33.57                20.0       1454.0           326.0   \n","...          ...       ...                 ...          ...             ...   \n","16995    -124.26     40.58                52.0       2217.0           394.0   \n","16996    -124.27     40.69                36.0       2349.0           528.0   \n","16997    -124.30     41.84                17.0       2677.0           531.0   \n","16998    -124.30     41.80                19.0       2672.0           552.0   \n","16999    -124.35     40.54                52.0       1820.0           300.0   \n","\n","       population  households  median_income  median_house_value  \n","0          1015.0       472.0         1.4936             66900.0  \n","1          1129.0       463.0         1.8200             80100.0  \n","2           333.0       117.0         1.6509             85700.0  \n","3           515.0       226.0         3.1917             73400.0  \n","4           624.0       262.0         1.9250             65500.0  \n","...           ...         ...            ...                 ...  \n","16995       907.0       369.0         2.3571            111400.0  \n","16996      1194.0       465.0         2.5179             79000.0  \n","16997      1244.0       456.0         3.0313            103600.0  \n","16998      1298.0       478.0         1.9797             85800.0  \n","16999       806.0       270.0         3.0147             94600.0  \n","\n","[17000 rows x 9 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n","california_housing_dataframe"]},{"cell_type":"markdown","metadata":{"id":"dRGUa2xD_-kt"},"source":["## Exploring Data"]},{"cell_type":"markdown","metadata":{"id":"5tP90yYg8vP7"},"source":["As shown above, after loading a large `DataFrame`, it may be a bit hard to have a good overview of what is inside it just by looking at a few rows. Thus, the `DataFrame.describe` method is useful to show interesting statistics about a `DataFrame`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":411,"status":"ok","timestamp":1692082316437,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"pEn_CnT28vQJ","outputId":"e68ecb73-8a09-46fe-b456-7be9ed403b7c"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","      <td>17000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-119.562108</td>\n","      <td>35.625225</td>\n","      <td>28.589353</td>\n","      <td>2643.664412</td>\n","      <td>539.410824</td>\n","      <td>1429.573941</td>\n","      <td>501.221941</td>\n","      <td>3.883578</td>\n","      <td>207300.912353</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.005166</td>\n","      <td>2.137340</td>\n","      <td>12.586937</td>\n","      <td>2179.947071</td>\n","      <td>421.499452</td>\n","      <td>1147.852959</td>\n","      <td>384.520841</td>\n","      <td>1.908157</td>\n","      <td>115983.764387</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-124.350000</td>\n","      <td>32.540000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","      <td>0.499900</td>\n","      <td>14999.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-121.790000</td>\n","      <td>33.930000</td>\n","      <td>18.000000</td>\n","      <td>1462.000000</td>\n","      <td>297.000000</td>\n","      <td>790.000000</td>\n","      <td>282.000000</td>\n","      <td>2.566375</td>\n","      <td>119400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-118.490000</td>\n","      <td>34.250000</td>\n","      <td>29.000000</td>\n","      <td>2127.000000</td>\n","      <td>434.000000</td>\n","      <td>1167.000000</td>\n","      <td>409.000000</td>\n","      <td>3.544600</td>\n","      <td>180400.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>-118.000000</td>\n","      <td>37.720000</td>\n","      <td>37.000000</td>\n","      <td>3151.250000</td>\n","      <td>648.250000</td>\n","      <td>1721.000000</td>\n","      <td>605.250000</td>\n","      <td>4.767000</td>\n","      <td>265000.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>-114.310000</td>\n","      <td>41.950000</td>\n","      <td>52.000000</td>\n","      <td>37937.000000</td>\n","      <td>6445.000000</td>\n","      <td>35682.000000</td>\n","      <td>6082.000000</td>\n","      <td>15.000100</td>\n","      <td>500001.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          longitude      latitude  housing_median_age   total_rooms  \\\n","count  17000.000000  17000.000000        17000.000000  17000.000000   \n","mean    -119.562108     35.625225           28.589353   2643.664412   \n","std        2.005166      2.137340           12.586937   2179.947071   \n","min     -124.350000     32.540000            1.000000      2.000000   \n","25%     -121.790000     33.930000           18.000000   1462.000000   \n","50%     -118.490000     34.250000           29.000000   2127.000000   \n","75%     -118.000000     37.720000           37.000000   3151.250000   \n","max     -114.310000     41.950000           52.000000  37937.000000   \n","\n","       total_bedrooms    population    households  median_income  \\\n","count    17000.000000  17000.000000  17000.000000   17000.000000   \n","mean       539.410824   1429.573941    501.221941       3.883578   \n","std        421.499452   1147.852959    384.520841       1.908157   \n","min          1.000000      3.000000      1.000000       0.499900   \n","25%        297.000000    790.000000    282.000000       2.566375   \n","50%        434.000000   1167.000000    409.000000       3.544600   \n","75%        648.250000   1721.000000    605.250000       4.767000   \n","max       6445.000000  35682.000000   6082.000000      15.000100   \n","\n","       median_house_value  \n","count        17000.000000  \n","mean        207300.912353  \n","std         115983.764387  \n","min          14999.000000  \n","25%         119400.000000  \n","50%         180400.000000  \n","75%         265000.000000  \n","max         500001.000000  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe.describe()"]},{"cell_type":"markdown","metadata":{"id":"pFuzC-Gh8vQK"},"source":["Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`. You can give it a number of rows to display."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":501,"status":"ok","timestamp":1692082318640,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"s3ND3bgOkB5k","outputId":"0487c669-1316-4f15-8bf7-7a6c30840053"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-114.31</td>\n","      <td>34.19</td>\n","      <td>15.0</td>\n","      <td>5612.0</td>\n","      <td>1283.0</td>\n","      <td>1015.0</td>\n","      <td>472.0</td>\n","      <td>1.4936</td>\n","      <td>66900.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-114.47</td>\n","      <td>34.40</td>\n","      <td>19.0</td>\n","      <td>7650.0</td>\n","      <td>1901.0</td>\n","      <td>1129.0</td>\n","      <td>463.0</td>\n","      <td>1.8200</td>\n","      <td>80100.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-114.56</td>\n","      <td>33.69</td>\n","      <td>17.0</td>\n","      <td>720.0</td>\n","      <td>174.0</td>\n","      <td>333.0</td>\n","      <td>117.0</td>\n","      <td>1.6509</td>\n","      <td>85700.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>-114.57</td>\n","      <td>33.64</td>\n","      <td>14.0</td>\n","      <td>1501.0</td>\n","      <td>337.0</td>\n","      <td>515.0</td>\n","      <td>226.0</td>\n","      <td>3.1917</td>\n","      <td>73400.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-114.57</td>\n","      <td>33.57</td>\n","      <td>20.0</td>\n","      <td>1454.0</td>\n","      <td>326.0</td>\n","      <td>624.0</td>\n","      <td>262.0</td>\n","      <td>1.9250</td>\n","      <td>65500.0</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>-114.58</td>\n","      <td>33.63</td>\n","      <td>29.0</td>\n","      <td>1387.0</td>\n","      <td>236.0</td>\n","      <td>671.0</td>\n","      <td>239.0</td>\n","      <td>3.3438</td>\n","      <td>74000.0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>-114.58</td>\n","      <td>33.61</td>\n","      <td>25.0</td>\n","      <td>2907.0</td>\n","      <td>680.0</td>\n","      <td>1841.0</td>\n","      <td>633.0</td>\n","      <td>2.6768</td>\n","      <td>82400.0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>-114.59</td>\n","      <td>34.83</td>\n","      <td>41.0</td>\n","      <td>812.0</td>\n","      <td>168.0</td>\n","      <td>375.0</td>\n","      <td>158.0</td>\n","      <td>1.7083</td>\n","      <td>48500.0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>-114.59</td>\n","      <td>33.61</td>\n","      <td>34.0</td>\n","      <td>4789.0</td>\n","      <td>1175.0</td>\n","      <td>3134.0</td>\n","      <td>1056.0</td>\n","      <td>2.1782</td>\n","      <td>58400.0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>-114.60</td>\n","      <td>34.83</td>\n","      <td>46.0</td>\n","      <td>1497.0</td>\n","      <td>309.0</td>\n","      <td>787.0</td>\n","      <td>271.0</td>\n","      <td>2.1908</td>\n","      <td>48100.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","0    -114.31     34.19                15.0       5612.0          1283.0   \n","1    -114.47     34.40                19.0       7650.0          1901.0   \n","2    -114.56     33.69                17.0        720.0           174.0   \n","3    -114.57     33.64                14.0       1501.0           337.0   \n","4    -114.57     33.57                20.0       1454.0           326.0   \n","5    -114.58     33.63                29.0       1387.0           236.0   \n","6    -114.58     33.61                25.0       2907.0           680.0   \n","7    -114.59     34.83                41.0        812.0           168.0   \n","8    -114.59     33.61                34.0       4789.0          1175.0   \n","9    -114.60     34.83                46.0       1497.0           309.0   \n","\n","   population  households  median_income  median_house_value  \n","0      1015.0       472.0         1.4936             66900.0  \n","1      1129.0       463.0         1.8200             80100.0  \n","2       333.0       117.0         1.6509             85700.0  \n","3       515.0       226.0         3.1917             73400.0  \n","4       624.0       262.0         1.9250             65500.0  \n","5       671.0       239.0         3.3438             74000.0  \n","6      1841.0       633.0         2.6768             82400.0  \n","7       375.0       158.0         1.7083             48500.0  \n","8      3134.0      1056.0         2.1782             58400.0  \n","9       787.0       271.0         2.1908             48100.0  "]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe.head(10)"]},{"cell_type":"markdown","metadata":{"id":"2O6_QUmu9Ncp"},"source":["Or `DataFrame.tail`, which displays the last few records of a `DataFrame`:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":558,"status":"ok","timestamp":1692082320554,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"YWzM1PxE9Nc0","outputId":"7a26815d-5b4a-4a25-881c-7e3d1ae381ee"},"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>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>16995</th>\n","      <td>-124.26</td>\n","      <td>40.58</td>\n","      <td>52.0</td>\n","      <td>2217.0</td>\n","      <td>394.0</td>\n","      <td>907.0</td>\n","      <td>369.0</td>\n","      <td>2.3571</td>\n","      <td>111400.0</td>\n","    </tr>\n","    <tr>\n","      <th>16996</th>\n","      <td>-124.27</td>\n","      <td>40.69</td>\n","      <td>36.0</td>\n","      <td>2349.0</td>\n","      <td>528.0</td>\n","      <td>1194.0</td>\n","      <td>465.0</td>\n","      <td>2.5179</td>\n","      <td>79000.0</td>\n","    </tr>\n","    <tr>\n","      <th>16997</th>\n","      <td>-124.30</td>\n","      <td>41.84</td>\n","      <td>17.0</td>\n","      <td>2677.0</td>\n","      <td>531.0</td>\n","      <td>1244.0</td>\n","      <td>456.0</td>\n","      <td>3.0313</td>\n","      <td>103600.0</td>\n","    </tr>\n","    <tr>\n","      <th>16998</th>\n","      <td>-124.30</td>\n","      <td>41.80</td>\n","      <td>19.0</td>\n","      <td>2672.0</td>\n","      <td>552.0</td>\n","      <td>1298.0</td>\n","      <td>478.0</td>\n","      <td>1.9797</td>\n","      <td>85800.0</td>\n","    </tr>\n","    <tr>\n","      <th>16999</th>\n","      <td>-124.35</td>\n","      <td>40.54</td>\n","      <td>52.0</td>\n","      <td>1820.0</td>\n","      <td>300.0</td>\n","      <td>806.0</td>\n","      <td>270.0</td>\n","      <td>3.0147</td>\n","      <td>94600.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","16995    -124.26     40.58                52.0       2217.0           394.0   \n","16996    -124.27     40.69                36.0       2349.0           528.0   \n","16997    -124.30     41.84                17.0       2677.0           531.0   \n","16998    -124.30     41.80                19.0       2672.0           552.0   \n","16999    -124.35     40.54                52.0       1820.0           300.0   \n","\n","       population  households  median_income  median_house_value  \n","16995       907.0       369.0         2.3571            111400.0  \n","16996      1194.0       465.0         2.5179             79000.0  \n","16997      1244.0       456.0         3.0313            103600.0  \n","16998      1298.0       478.0         1.9797             85800.0  \n","16999       806.0       270.0         3.0147             94600.0  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["california_housing_dataframe.tail()"]},{"cell_type":"markdown","metadata":{"id":"ALnk--Ap37Ny"},"source":["## Correletaion Matrix\n","\n","Consider the table of measurements below."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lWtYeuES-Jtq","outputId":"afdceba3-5d99-42d3-efd3-ef0b3b2c6bd3"},"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>area</th>\n","      <th>mean_intensity</th>\n","      <th>minor_axis_length</th>\n","      <th>major_axis_length</th>\n","      <th>eccentricity</th>\n","      <th>extent</th>\n","      <th>feret_diameter_max</th>\n","      <th>equivalent_diameter_area</th>\n","      <th>bbox-0</th>\n","      <th>bbox-1</th>\n","      <th>bbox-2</th>\n","      <th>bbox-3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>422</td>\n","      <td>192.379147</td>\n","      <td>16.488550</td>\n","      <td>34.566789</td>\n","      <td>0.878900</td>\n","      <td>0.586111</td>\n","      <td>35.227830</td>\n","      <td>23.179885</td>\n","      <td>0</td>\n","      <td>11</td>\n","      <td>30</td>\n","      <td>35</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>182</td>\n","      <td>180.131868</td>\n","      <td>11.736074</td>\n","      <td>20.802697</td>\n","      <td>0.825665</td>\n","      <td>0.787879</td>\n","      <td>21.377558</td>\n","      <td>15.222667</td>\n","      <td>0</td>\n","      <td>53</td>\n","      <td>11</td>\n","      <td>74</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>661</td>\n","      <td>205.216339</td>\n","      <td>28.409502</td>\n","      <td>30.208433</td>\n","      <td>0.339934</td>\n","      <td>0.874339</td>\n","      <td>32.756679</td>\n","      <td>29.010538</td>\n","      <td>0</td>\n","      <td>95</td>\n","      <td>28</td>\n","      <td>122</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>437</td>\n","      <td>216.585812</td>\n","      <td>23.143996</td>\n","      <td>24.606130</td>\n","      <td>0.339576</td>\n","      <td>0.826087</td>\n","      <td>26.925824</td>\n","      <td>23.588253</td>\n","      <td>0</td>\n","      <td>144</td>\n","      <td>23</td>\n","      <td>167</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>476</td>\n","      <td>212.302521</td>\n","      <td>19.852882</td>\n","      <td>31.075106</td>\n","      <td>0.769317</td>\n","      <td>0.863884</td>\n","      <td>31.384710</td>\n","      <td>24.618327</td>\n","      <td>0</td>\n","      <td>237</td>\n","      <td>29</td>\n","      <td>256</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   area  mean_intensity  minor_axis_length  major_axis_length  eccentricity  \\\n","0   422      192.379147          16.488550          34.566789      0.878900   \n","1   182      180.131868          11.736074          20.802697      0.825665   \n","2   661      205.216339          28.409502          30.208433      0.339934   \n","3   437      216.585812          23.143996          24.606130      0.339576   \n","4   476      212.302521          19.852882          31.075106      0.769317   \n","\n","     extent  feret_diameter_max  equivalent_diameter_area  bbox-0  bbox-1  \\\n","0  0.586111           35.227830                 23.179885       0      11   \n","1  0.787879           21.377558                 15.222667       0      53   \n","2  0.874339           32.756679                 29.010538       0      95   \n","3  0.826087           26.925824                 23.588253       0     144   \n","4  0.863884           31.384710                 24.618327       0     237   \n","\n","   bbox-2  bbox-3  \n","0      30      35  \n","1      11      74  \n","2      28     122  \n","3      23     167  \n","4      29     256  "]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["blobs_statistics = pd.read_csv('../../data/blobs_statistics.csv', index_col=0)\n","blobs_statistics.head()"]},{"cell_type":"markdown","metadata":{"id":"kjZrpPdcsHaw"},"source":["After measuring many features / properties, it is often common that some of them are strongly correlated and may not bring much new information. In pandas, we can calculate correlation among columns like this."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1692083624021,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"SKuQJYgB-CvX","outputId":"62a97fbc-5989-4591-9dff-a1360930dbe7"},"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>area</th>\n","      <th>mean_intensity</th>\n","      <th>minor_axis_length</th>\n","      <th>major_axis_length</th>\n","      <th>eccentricity</th>\n","      <th>extent</th>\n","      <th>feret_diameter_max</th>\n","      <th>equivalent_diameter_area</th>\n","      <th>bbox-0</th>\n","      <th>bbox-1</th>\n","      <th>bbox-2</th>\n","      <th>bbox-3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>area</th>\n","      <td>1.000000</td>\n","      <td>0.548612</td>\n","      <td>0.890649</td>\n","      <td>0.895282</td>\n","      <td>-0.192147</td>\n","      <td>-0.267454</td>\n","      <td>0.916652</td>\n","      <td>0.975964</td>\n","      <td>-0.066508</td>\n","      <td>-0.081937</td>\n","      <td>0.034083</td>\n","      <td>-0.003961</td>\n","    </tr>\n","    <tr>\n","      <th>mean_intensity</th>\n","      <td>0.548612</td>\n","      <td>1.000000</td>\n","      <td>0.657131</td>\n","      <td>0.440678</td>\n","      <td>-0.362592</td>\n","      <td>-0.011555</td>\n","      <td>0.487183</td>\n","      <td>0.611103</td>\n","      <td>0.015188</td>\n","      <td>0.217484</td>\n","      <td>0.069184</td>\n","      <td>0.266504</td>\n","    </tr>\n","    <tr>\n","      <th>minor_axis_length</th>\n","      <td>0.890649</td>\n","      <td>0.657131</td>\n","      <td>1.000000</td>\n","      <td>0.664507</td>\n","      <td>-0.566486</td>\n","      <td>-0.037872</td>\n","      <td>0.716706</td>\n","      <td>0.937795</td>\n","      <td>-0.163017</td>\n","      <td>-0.056785</td>\n","      <td>-0.077817</td>\n","      <td>0.015790</td>\n","    </tr>\n","    <tr>\n","      <th>major_axis_length</th>\n","      <td>0.895282</td>\n","      <td>0.440678</td>\n","      <td>0.664507</td>\n","      <td>1.000000</td>\n","      <td>0.168454</td>\n","      <td>-0.551362</td>\n","      <td>0.995196</td>\n","      <td>0.880909</td>\n","      <td>-0.010743</td>\n","      <td>-0.128821</td>\n","      <td>0.093556</td>\n","      <td>-0.057776</td>\n","    </tr>\n","    <tr>\n","      <th>eccentricity</th>\n","      <td>-0.192147</td>\n","      <td>-0.362592</td>\n","      <td>-0.566486</td>\n","      <td>0.168454</td>\n","      <td>1.000000</td>\n","      <td>-0.432629</td>\n","      <td>0.103529</td>\n","      <td>-0.272402</td>\n","      <td>0.257938</td>\n","      <td>-0.060467</td>\n","      <td>0.253671</td>\n","      <td>-0.076793</td>\n","    </tr>\n","    <tr>\n","      <th>extent</th>\n","      <td>-0.267454</td>\n","      <td>-0.011555</td>\n","      <td>-0.037872</td>\n","      <td>-0.551362</td>\n","      <td>-0.432629</td>\n","      <td>1.000000</td>\n","      <td>-0.517428</td>\n","      <td>-0.278453</td>\n","      <td>-0.076688</td>\n","      <td>0.048511</td>\n","      <td>-0.128149</td>\n","      <td>0.019310</td>\n","    </tr>\n","    <tr>\n","      <th>feret_diameter_max</th>\n","      <td>0.916652</td>\n","      <td>0.487183</td>\n","      <td>0.716706</td>\n","      <td>0.995196</td>\n","      <td>0.103529</td>\n","      <td>-0.517428</td>\n","      <td>1.000000</td>\n","      <td>0.911211</td>\n","      <td>-0.025173</td>\n","      <td>-0.122607</td>\n","      <td>0.080054</td>\n","      <td>-0.049283</td>\n","    </tr>\n","    <tr>\n","      <th>equivalent_diameter_area</th>\n","      <td>0.975964</td>\n","      <td>0.611103</td>\n","      <td>0.937795</td>\n","      <td>0.880909</td>\n","      <td>-0.272402</td>\n","      <td>-0.278453</td>\n","      <td>0.911211</td>\n","      <td>1.000000</td>\n","      <td>-0.107059</td>\n","      <td>-0.096706</td>\n","      <td>-0.004660</td>\n","      <td>-0.018489</td>\n","    </tr>\n","    <tr>\n","      <th>bbox-0</th>\n","      <td>-0.066508</td>\n","      <td>0.015188</td>\n","      <td>-0.163017</td>\n","      <td>-0.010743</td>\n","      <td>0.257938</td>\n","      <td>-0.076688</td>\n","      <td>-0.025173</td>\n","      <td>-0.107059</td>\n","      <td>1.000000</td>\n","      <td>0.050957</td>\n","      <td>0.993418</td>\n","      <td>0.053563</td>\n","    </tr>\n","    <tr>\n","      <th>bbox-1</th>\n","      <td>-0.081937</td>\n","      <td>0.217484</td>\n","      <td>-0.056785</td>\n","      <td>-0.128821</td>\n","      <td>-0.060467</td>\n","      <td>0.048511</td>\n","      <td>-0.122607</td>\n","      <td>-0.096706</td>\n","      <td>0.050957</td>\n","      <td>1.000000</td>\n","      <td>0.032728</td>\n","      <td>0.996062</td>\n","    </tr>\n","    <tr>\n","      <th>bbox-2</th>\n","      <td>0.034083</td>\n","      <td>0.069184</td>\n","      <td>-0.077817</td>\n","      <td>0.093556</td>\n","      <td>0.253671</td>\n","      <td>-0.128149</td>\n","      <td>0.080054</td>\n","      <td>-0.004660</td>\n","      <td>0.993418</td>\n","      <td>0.032728</td>\n","      <td>1.000000</td>\n","      <td>0.041855</td>\n","    </tr>\n","    <tr>\n","      <th>bbox-3</th>\n","      <td>-0.003961</td>\n","      <td>0.266504</td>\n","      <td>0.015790</td>\n","      <td>-0.057776</td>\n","      <td>-0.076793</td>\n","      <td>0.019310</td>\n","      <td>-0.049283</td>\n","      <td>-0.018489</td>\n","      <td>0.053563</td>\n","      <td>0.996062</td>\n","      <td>0.041855</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              area  mean_intensity  minor_axis_length  \\\n","area                      1.000000        0.548612           0.890649   \n","mean_intensity            0.548612        1.000000           0.657131   \n","minor_axis_length         0.890649        0.657131           1.000000   \n","major_axis_length         0.895282        0.440678           0.664507   \n","eccentricity             -0.192147       -0.362592          -0.566486   \n","extent                   -0.267454       -0.011555          -0.037872   \n","feret_diameter_max        0.916652        0.487183           0.716706   \n","equivalent_diameter_area  0.975964        0.611103           0.937795   \n","bbox-0                   -0.066508        0.015188          -0.163017   \n","bbox-1                   -0.081937        0.217484          -0.056785   \n","bbox-2                    0.034083        0.069184          -0.077817   \n","bbox-3                   -0.003961        0.266504           0.015790   \n","\n","                          major_axis_length  eccentricity    extent  \\\n","area                               0.895282     -0.192147 -0.267454   \n","mean_intensity                     0.440678     -0.362592 -0.011555   \n","minor_axis_length                  0.664507     -0.566486 -0.037872   \n","major_axis_length                  1.000000      0.168454 -0.551362   \n","eccentricity                       0.168454      1.000000 -0.432629   \n","extent                            -0.551362     -0.432629  1.000000   \n","feret_diameter_max                 0.995196      0.103529 -0.517428   \n","equivalent_diameter_area           0.880909     -0.272402 -0.278453   \n","bbox-0                            -0.010743      0.257938 -0.076688   \n","bbox-1                            -0.128821     -0.060467  0.048511   \n","bbox-2                             0.093556      0.253671 -0.128149   \n","bbox-3                            -0.057776     -0.076793  0.019310   \n","\n","                          feret_diameter_max  equivalent_diameter_area  \\\n","area                                0.916652                  0.975964   \n","mean_intensity                      0.487183                  0.611103   \n","minor_axis_length                   0.716706                  0.937795   \n","major_axis_length                   0.995196                  0.880909   \n","eccentricity                        0.103529                 -0.272402   \n","extent                             -0.517428                 -0.278453   \n","feret_diameter_max                  1.000000                  0.911211   \n","equivalent_diameter_area            0.911211                  1.000000   \n","bbox-0                             -0.025173                 -0.107059   \n","bbox-1                             -0.122607                 -0.096706   \n","bbox-2                              0.080054                 -0.004660   \n","bbox-3                             -0.049283                 -0.018489   \n","\n","                            bbox-0    bbox-1    bbox-2    bbox-3  \n","area                     -0.066508 -0.081937  0.034083 -0.003961  \n","mean_intensity            0.015188  0.217484  0.069184  0.266504  \n","minor_axis_length        -0.163017 -0.056785 -0.077817  0.015790  \n","major_axis_length        -0.010743 -0.128821  0.093556 -0.057776  \n","eccentricity              0.257938 -0.060467  0.253671 -0.076793  \n","extent                   -0.076688  0.048511 -0.128149  0.019310  \n","feret_diameter_max       -0.025173 -0.122607  0.080054 -0.049283  \n","equivalent_diameter_area -0.107059 -0.096706 -0.004660 -0.018489  \n","bbox-0                    1.000000  0.050957  0.993418  0.053563  \n","bbox-1                    0.050957  1.000000  0.032728  0.996062  \n","bbox-2                    0.993418  0.032728  1.000000  0.041855  \n","bbox-3                    0.053563  0.996062  0.041855  1.000000  "]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["blobs_statistics.corr()\n"]},{"cell_type":"markdown","metadata":{"id":"23_l5V_DBGrp"},"source":["It can be hard to read in numeric format. I wonder if there is beter way how to look at the data?\n","\n","Below we take a quick shortcut to seaborn to show how the correlation can be displayed as a heatmap."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":620},"executionInfo":{"elapsed":991,"status":"ok","timestamp":1692083686368,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"A0KoCKpwBG9A","outputId":"aa6d5813-d204-496c-adf7-ebf9f97d3395"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":25,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# calculate the correlation matrix on the numeric columns\n","corr = blobs_statistics.select_dtypes('number').corr()\n","\n","# plot the heatmap\n","sns.heatmap(corr, cmap=\"Blues\", annot=False)"]},{"cell_type":"markdown","metadata":{"id":"fH1zusN7GKCx"},"source":["**Watermark**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":312,"status":"ok","timestamp":1692083689688,"user":{"displayName":"Martin Schätz","userId":"14609383414092679868"},"user_tz":-120},"id":"iH1jL0baGMJW","outputId":"b40c2332-1e58-487f-d385-12bb80706639"},"outputs":[{"name":"stdout","output_type":"stream","text":["Last updated: 2023-08-24T14:26:10.347260+02:00\n","\n","Python implementation: CPython\n","Python version       : 3.9.17\n","IPython version      : 8.14.0\n","\n","Compiler    : MSC v.1929 64 bit (AMD64)\n","OS          : Windows\n","Release     : 10\n","Machine     : AMD64\n","Processor   : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel\n","CPU cores   : 16\n","Architecture: 64bit\n","\n","watermark   : 2.4.3\n","numpy       : 1.23.5\n","pandas      : 2.0.3\n","seaborn     : 0.12.2\n","pivottablejs: 0.9.0\n","\n"]}],"source":["from watermark import watermark\n","watermark(iversions=True, globals_=globals())\n","print(watermark())\n","print(watermark(packages=\"watermark,numpy,pandas,seaborn,pivottablejs\"))"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.9.17"}},"nbformat":4,"nbformat_minor":0}