trainity-data-analyst-python-master-class-8.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Working with pandas","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"## Data Manipulation using Pandas","metadata":{}},{"cell_type":"code","source":"import pandas as pd","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:38:04.370363Z","iopub.execute_input":"2024-06-14T06:38:04.370759Z","iopub.status.idle":"2024-06-14T06:38:04.823630Z","shell.execute_reply.started":"2024-06-14T06:38:04.370726Z","shell.execute_reply":"2024-06-14T06:38:04.822412Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"!kaggle datasets download -d mahejabeenabdul/insurance-dataset-regression-analysis","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:41:27.552663Z","iopub.execute_input":"2024-06-14T06:41:27.553245Z","iopub.status.idle":"2024-06-14T06:41:29.776251Z","shell.execute_reply.started":"2024-06-14T06:41:27.553213Z","shell.execute_reply":"2024-06-14T06:41:29.774747Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Dataset URL: https://www.kaggle.com/datasets/mahejabeenabdul/insurance-dataset-regression-analysis\nLicense(s): unknown\nDownloading insurance-dataset-regression-analysis.zip to /kaggle/working\n  0%|                                               | 0.00/13.4k [00:00<?, ?B/s]\n100%|██████████████████████████████████████| 13.4k/13.4k [00:00<00:00, 21.0MB/s]\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/working/insurance-dataset-regression-analysis.zip')","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:42:00.301826Z","iopub.execute_input":"2024-06-14T06:42:00.302902Z","iopub.status.idle":"2024-06-14T06:42:00.326647Z","shell.execute_reply.started":"2024-06-14T06:42:00.302839Z","shell.execute_reply":"2024-06-14T06:42:00.325242Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:42:05.433910Z","iopub.execute_input":"2024-06-14T06:42:05.434333Z","iopub.status.idle":"2024-06-14T06:42:05.459410Z","shell.execute_reply.started":"2024-06-14T06:42:05.434298Z","shell.execute_reply":"2024-06-14T06:42:05.458244Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses\n0   19  female  27.9         0    yes  southwest  16884.92\n1   18    male  33.8         1     no  southeast   1725.55\n2   28    male  33.0         3     no  southeast   4449.46\n3   33    male  22.7         0     no  northwest  21984.47\n4   32    male  28.9         0     no  northwest   3866.86","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:42:25.773871Z","iopub.execute_input":"2024-06-14T06:42:25.774291Z","iopub.status.idle":"2024-06-14T06:42:25.781924Z","shell.execute_reply.started":"2024-06-14T06:42:25.774257Z","shell.execute_reply":"2024-06-14T06:42:25.780636Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"(1338, 7)"},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:42:37.337915Z","iopub.execute_input":"2024-06-14T06:42:37.338369Z","iopub.status.idle":"2024-06-14T06:42:37.368123Z","shell.execute_reply.started":"2024-06-14T06:42:37.338334Z","shell.execute_reply":"2024-06-14T06:42:37.366718Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"               age          bmi     children      expenses\ncount  1338.000000  1338.000000  1338.000000   1338.000000\nmean     39.207025    30.665471     1.094918  13270.422414\nstd      14.049960     6.098382     1.205493  12110.011240\nmin      18.000000    16.000000     0.000000   1121.870000\n25%      27.000000    26.300000     0.000000   4740.287500\n50%      39.000000    30.400000     1.000000   9382.030000\n75%      51.000000    34.700000     2.000000  16639.915000\nmax      64.000000    53.100000     5.000000  63770.430000","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>age</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>expenses</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n      <td>1338.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>39.207025</td>\n      <td>30.665471</td>\n      <td>1.094918</td>\n      <td>13270.422414</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>14.049960</td>\n      <td>6.098382</td>\n      <td>1.205493</td>\n      <td>12110.011240</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>18.000000</td>\n      <td>16.000000</td>\n      <td>0.000000</td>\n      <td>1121.870000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>27.000000</td>\n      <td>26.300000</td>\n      <td>0.000000</td>\n      <td>4740.287500</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>39.000000</td>\n      <td>30.400000</td>\n      <td>1.000000</td>\n      <td>9382.030000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>51.000000</td>\n      <td>34.700000</td>\n      <td>2.000000</td>\n      <td>16639.915000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>64.000000</td>\n      <td>53.100000</td>\n      <td>5.000000</td>\n      <td>63770.430000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:42:45.539590Z","iopub.execute_input":"2024-06-14T06:42:45.540005Z","iopub.status.idle":"2024-06-14T06:42:45.562393Z","shell.execute_reply.started":"2024-06-14T06:42:45.539973Z","shell.execute_reply":"2024-06-14T06:42:45.561090Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1338 entries, 0 to 1337\nData columns (total 7 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   age       1338 non-null   int64  \n 1   sex       1338 non-null   object \n 2   bmi       1338 non-null   float64\n 3   children  1338 non-null   int64  \n 4   smoker    1338 non-null   object \n 5   region    1338 non-null   object \n 6   expenses  1338 non-null   float64\ndtypes: float64(2), int64(2), object(3)\nmemory usage: 73.3+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df_1 = df.copy()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:43:21.760019Z","iopub.execute_input":"2024-06-14T06:43:21.760441Z","iopub.status.idle":"2024-06-14T06:43:21.766054Z","shell.execute_reply.started":"2024-06-14T06:43:21.760407Z","shell.execute_reply":"2024-06-14T06:43:21.764829Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"df_1['family_count'] = df_1['children'] + 1","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:45:00.890359Z","iopub.execute_input":"2024-06-14T06:45:00.890889Z","iopub.status.idle":"2024-06-14T06:45:00.899039Z","shell.execute_reply.started":"2024-06-14T06:45:00.890829Z","shell.execute_reply":"2024-06-14T06:45:00.897684Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"df_1.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:45:12.060355Z","iopub.execute_input":"2024-06-14T06:45:12.060756Z","iopub.status.idle":"2024-06-14T06:45:12.080033Z","shell.execute_reply.started":"2024-06-14T06:45:12.060721Z","shell.execute_reply":"2024-06-14T06:45:12.078573Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count\n0   19  female  27.9         0    yes  southwest  16884.92             1\n1   18    male  33.8         1     no  southeast   1725.55             2\n2   28    male  33.0         3     no  southeast   4449.46             4\n3   33    male  22.7         0     no  northwest  21984.47             1\n4   32    male  28.9         0     no  northwest   3866.86             1","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1['Avg_expense_per_head'] = df_1['expenses'] / df_1['family_count']","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:48:13.164567Z","iopub.execute_input":"2024-06-14T06:48:13.165013Z","iopub.status.idle":"2024-06-14T06:48:13.171698Z","shell.execute_reply.started":"2024-06-14T06:48:13.164979Z","shell.execute_reply":"2024-06-14T06:48:13.170460Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"df_1.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:48:25.256418Z","iopub.execute_input":"2024-06-14T06:48:25.256797Z","iopub.status.idle":"2024-06-14T06:48:25.278097Z","shell.execute_reply.started":"2024-06-14T06:48:25.256767Z","shell.execute_reply":"2024-06-14T06:48:25.276922Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count  \\\n0   19  female  27.9         0    yes  southwest  16884.92             1   \n1   18    male  33.8         1     no  southeast   1725.55             2   \n2   28    male  33.0         3     no  southeast   4449.46             4   \n3   33    male  22.7         0     no  northwest  21984.47             1   \n4   32    male  28.9         0     no  northwest   3866.86             1   \n5   31  female  25.7         0     no  southeast   3756.62             1   \n6   46  female  33.4         1     no  southeast   8240.59             2   \n7   37  female  27.7         3     no  northwest   7281.51             4   \n8   37    male  29.8         2     no  northeast   6406.41             3   \n9   60  female  25.8         0     no  northwest  28923.14             1   \n\n   Avg_expense_per_head  \n0            16884.9200  \n1              862.7750  \n2             1112.3650  \n3            21984.4700  \n4             3866.8600  \n5             3756.6200  \n6             4120.2950  \n7             1820.3775  \n8             2135.4700  \n9            28923.1400  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.9200</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.7750</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.3650</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.4700</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.8600</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>31</td>\n      <td>female</td>\n      <td>25.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>3756.62</td>\n      <td>1</td>\n      <td>3756.6200</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>46</td>\n      <td>female</td>\n      <td>33.4</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>8240.59</td>\n      <td>2</td>\n      <td>4120.2950</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>37</td>\n      <td>female</td>\n      <td>27.7</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>7281.51</td>\n      <td>4</td>\n      <td>1820.3775</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>37</td>\n      <td>male</td>\n      <td>29.8</td>\n      <td>2</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>6406.41</td>\n      <td>3</td>\n      <td>2135.4700</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>60</td>\n      <td>female</td>\n      <td>25.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>28923.14</td>\n      <td>1</td>\n      <td>28923.1400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Drop columns\ndf_2 = df_1.drop('children',axis=1) ## drops column named 'children' from the dataset","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:52:48.415711Z","iopub.execute_input":"2024-06-14T06:52:48.416150Z","iopub.status.idle":"2024-06-14T06:52:48.423457Z","shell.execute_reply.started":"2024-06-14T06:52:48.416115Z","shell.execute_reply":"2024-06-14T06:52:48.422188Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"df_1.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:52:56.295961Z","iopub.execute_input":"2024-06-14T06:52:56.296358Z","iopub.status.idle":"2024-06-14T06:52:56.313690Z","shell.execute_reply.started":"2024-06-14T06:52:56.296326Z","shell.execute_reply":"2024-06-14T06:52:56.312478Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count  \\\n0   19  female  27.9         0    yes  southwest  16884.92             1   \n1   18    male  33.8         1     no  southeast   1725.55             2   \n2   28    male  33.0         3     no  southeast   4449.46             4   \n3   33    male  22.7         0     no  northwest  21984.47             1   \n4   32    male  28.9         0     no  northwest   3866.86             1   \n\n   Avg_expense_per_head  \n0             16884.920  \n1               862.775  \n2              1112.365  \n3             21984.470  \n4              3866.860  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.775</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.470</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.860</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:53:03.044359Z","iopub.execute_input":"2024-06-14T06:53:03.044786Z","iopub.status.idle":"2024-06-14T06:53:03.065399Z","shell.execute_reply.started":"2024-06-14T06:53:03.044748Z","shell.execute_reply":"2024-06-14T06:53:03.063699Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi smoker     region  expenses  family_count  \\\n0   19  female  27.9    yes  southwest  16884.92             1   \n1   18    male  33.8     no  southeast   1725.55             2   \n2   28    male  33.0     no  southeast   4449.46             4   \n3   33    male  22.7     no  northwest  21984.47             1   \n4   32    male  28.9     no  northwest   3866.86             1   \n\n   Avg_expense_per_head  \n0             16884.920  \n1               862.775  \n2              1112.365  \n3             21984.470  \n4              3866.860  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.775</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.470</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.860</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_2.drop('bmi', axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:53:47.242288Z","iopub.execute_input":"2024-06-14T06:53:47.242709Z","iopub.status.idle":"2024-06-14T06:53:47.249162Z","shell.execute_reply.started":"2024-06-14T06:53:47.242674Z","shell.execute_reply":"2024-06-14T06:53:47.247974Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"df_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:53:53.682950Z","iopub.execute_input":"2024-06-14T06:53:53.683326Z","iopub.status.idle":"2024-06-14T06:53:53.698700Z","shell.execute_reply.started":"2024-06-14T06:53:53.683297Z","shell.execute_reply":"2024-06-14T06:53:53.697243Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"   age     sex smoker     region  expenses  family_count  Avg_expense_per_head\n0   19  female    yes  southwest  16884.92             1             16884.920\n1   18    male     no  southeast   1725.55             2               862.775\n2   28    male     no  southeast   4449.46             4              1112.365\n3   33    male     no  northwest  21984.47             1             21984.470\n4   32    male     no  northwest   3866.86             1              3866.860","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>age</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.775</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.365</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.470</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.860</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Filtering and Sorting","metadata":{}},{"cell_type":"code","source":"df_1.sort_values(by='age')","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:56:36.818866Z","iopub.execute_input":"2024-06-14T06:56:36.819235Z","iopub.status.idle":"2024-06-14T06:56:36.840472Z","shell.execute_reply.started":"2024-06-14T06:56:36.819205Z","shell.execute_reply":"2024-06-14T06:56:36.839405Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n1248   18  female  39.8         0     no  southeast   1633.96             1   \n482    18  female  31.4         0     no  southeast   1622.19             1   \n492    18  female  25.1         0     no  northeast   2196.47             1   \n525    18  female  33.9         0     no  southeast  11482.63             1   \n529    18    male  25.5         0     no  northeast   1708.00             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n398    64    male  25.6         2     no  southwest  14988.43             3   \n335    64    male  34.5         0     no  southwest  13822.80             1   \n378    64  female  30.1         3     no  northwest  16455.71             4   \n1265   64    male  23.8         0    yes  southeast  26926.51             1   \n635    64    male  38.2         0     no  northeast  14410.93             1   \n\n      Avg_expense_per_head  \n1248           1633.960000  \n482            1622.190000  \n492            2196.470000  \n525           11482.630000  \n529            1708.000000  \n...                    ...  \n398            4996.143333  \n335           13822.800000  \n378            4113.927500  \n1265          26926.510000  \n635           14410.930000  \n\n[1338 rows x 9 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1248</th>\n      <td>18</td>\n      <td>female</td>\n      <td>39.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1633.96</td>\n      <td>1</td>\n      <td>1633.960000</td>\n    </tr>\n    <tr>\n      <th>482</th>\n      <td>18</td>\n      <td>female</td>\n      <td>31.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1622.19</td>\n      <td>1</td>\n      <td>1622.190000</td>\n    </tr>\n    <tr>\n      <th>492</th>\n      <td>18</td>\n      <td>female</td>\n      <td>25.1</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>2196.47</td>\n      <td>1</td>\n      <td>2196.470000</td>\n    </tr>\n    <tr>\n      <th>525</th>\n      <td>18</td>\n      <td>female</td>\n      <td>33.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>11482.63</td>\n      <td>1</td>\n      <td>11482.630000</td>\n    </tr>\n    <tr>\n      <th>529</th>\n      <td>18</td>\n      <td>male</td>\n      <td>25.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>1708.00</td>\n      <td>1</td>\n      <td>1708.000000</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>398</th>\n      <td>64</td>\n      <td>male</td>\n      <td>25.6</td>\n      <td>2</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>14988.43</td>\n      <td>3</td>\n      <td>4996.143333</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>64</td>\n      <td>male</td>\n      <td>34.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>13822.80</td>\n      <td>1</td>\n      <td>13822.800000</td>\n    </tr>\n    <tr>\n      <th>378</th>\n      <td>64</td>\n      <td>female</td>\n      <td>30.1</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>16455.71</td>\n      <td>4</td>\n      <td>4113.927500</td>\n    </tr>\n    <tr>\n      <th>1265</th>\n      <td>64</td>\n      <td>male</td>\n      <td>23.8</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southeast</td>\n      <td>26926.51</td>\n      <td>1</td>\n      <td>26926.510000</td>\n    </tr>\n    <tr>\n      <th>635</th>\n      <td>64</td>\n      <td>male</td>\n      <td>38.2</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>14410.93</td>\n      <td>1</td>\n      <td>14410.930000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1338 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.sort_values(by=['age', 'bmi', 'family_count'], ascending=[False,False,True])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T06:58:48.533157Z","iopub.execute_input":"2024-06-14T06:58:48.533562Z","iopub.status.idle":"2024-06-14T06:58:48.557027Z","shell.execute_reply.started":"2024-06-14T06:58:48.533528Z","shell.execute_reply":"2024-06-14T06:58:48.555750Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n534    64    male  40.5         0     no  southeast  13831.12             1   \n768    64  female  39.7         0     no  southwest  14319.03             1   \n199    64  female  39.3         0     no  northeast  14901.52             1   \n418    64    male  39.2         1     no  southeast  14418.28             2   \n603    64  female  39.1         3     no  southeast  16085.13             4   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1033   18    male  21.6         0    yes  northeast  13747.87             1   \n1212   18    male  21.5         0     no  northeast   1702.46             1   \n359    18  female  20.8         0     no  southeast   1607.51             1   \n250    18    male  17.3         2    yes  northeast  12829.46             3   \n172    18    male  16.0         0     no  northeast   1694.80             1   \n\n      Avg_expense_per_head  \n534           13831.120000  \n768           14319.030000  \n199           14901.520000  \n418            7209.140000  \n603            4021.282500  \n...                    ...  \n1033          13747.870000  \n1212           1702.460000  \n359            1607.510000  \n250            4276.486667  \n172            1694.800000  \n\n[1338 rows x 9 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>534</th>\n      <td>64</td>\n      <td>male</td>\n      <td>40.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>13831.12</td>\n      <td>1</td>\n      <td>13831.120000</td>\n    </tr>\n    <tr>\n      <th>768</th>\n      <td>64</td>\n      <td>female</td>\n      <td>39.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>14319.03</td>\n      <td>1</td>\n      <td>14319.030000</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>64</td>\n      <td>female</td>\n      <td>39.3</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>14901.52</td>\n      <td>1</td>\n      <td>14901.520000</td>\n    </tr>\n    <tr>\n      <th>418</th>\n      <td>64</td>\n      <td>male</td>\n      <td>39.2</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>14418.28</td>\n      <td>2</td>\n      <td>7209.140000</td>\n    </tr>\n    <tr>\n      <th>603</th>\n      <td>64</td>\n      <td>female</td>\n      <td>39.1</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>16085.13</td>\n      <td>4</td>\n      <td>4021.282500</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>1033</th>\n      <td>18</td>\n      <td>male</td>\n      <td>21.6</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northeast</td>\n      <td>13747.87</td>\n      <td>1</td>\n      <td>13747.870000</td>\n    </tr>\n    <tr>\n      <th>1212</th>\n      <td>18</td>\n      <td>male</td>\n      <td>21.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>1702.46</td>\n      <td>1</td>\n      <td>1702.460000</td>\n    </tr>\n    <tr>\n      <th>359</th>\n      <td>18</td>\n      <td>female</td>\n      <td>20.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1607.51</td>\n      <td>1</td>\n      <td>1607.510000</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>18</td>\n      <td>male</td>\n      <td>17.3</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>northeast</td>\n      <td>12829.46</td>\n      <td>3</td>\n      <td>4276.486667</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>18</td>\n      <td>male</td>\n      <td>16.0</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>1694.80</td>\n      <td>1</td>\n      <td>1694.800000</td>\n    </tr>\n  </tbody>\n</table>\n<p>1338 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.sort_index()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:00:31.971402Z","iopub.execute_input":"2024-06-14T07:00:31.971799Z","iopub.status.idle":"2024-06-14T07:00:31.993081Z","shell.execute_reply.started":"2024-06-14T07:00:31.971757Z","shell.execute_reply":"2024-06-14T07:00:31.991825Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n0      19  female  27.9         0    yes  southwest  16884.92             1   \n1      18    male  33.8         1     no  southeast   1725.55             2   \n2      28    male  33.0         3     no  southeast   4449.46             4   \n3      33    male  22.7         0     no  northwest  21984.47             1   \n4      32    male  28.9         0     no  northwest   3866.86             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1333   50    male  31.0         3     no  northwest  10600.55             4   \n1334   18  female  31.9         0     no  northeast   2205.98             1   \n1335   18  female  36.9         0     no  southeast   1629.83             1   \n1336   21  female  25.8         0     no  southwest   2007.95             1   \n1337   61  female  29.1         0    yes  northwest  29141.36             1   \n\n      Avg_expense_per_head  \n0               16884.9200  \n1                 862.7750  \n2                1112.3650  \n3               21984.4700  \n4                3866.8600  \n...                    ...  \n1333             2650.1375  \n1334             2205.9800  \n1335             1629.8300  \n1336             2007.9500  \n1337            29141.3600  \n\n[1338 rows x 9 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.9200</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.7750</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.3650</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.4700</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.8600</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>1333</th>\n      <td>50</td>\n      <td>male</td>\n      <td>31.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>10600.55</td>\n      <td>4</td>\n      <td>2650.1375</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>18</td>\n      <td>female</td>\n      <td>31.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>2205.98</td>\n      <td>1</td>\n      <td>2205.9800</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>18</td>\n      <td>female</td>\n      <td>36.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1629.83</td>\n      <td>1</td>\n      <td>1629.8300</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>21</td>\n      <td>female</td>\n      <td>25.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>2007.95</td>\n      <td>1</td>\n      <td>2007.9500</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>61</td>\n      <td>female</td>\n      <td>29.1</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>29141.36</td>\n      <td>1</td>\n      <td>29141.3600</td>\n    </tr>\n  </tbody>\n</table>\n<p>1338 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Grouping and Aggregation ","metadata":{}},{"cell_type":"code","source":"df_1['age'].min(), df_1['age'].max()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:01:46.628673Z","iopub.execute_input":"2024-06-14T07:01:46.629139Z","iopub.status.idle":"2024-06-14T07:01:46.638258Z","shell.execute_reply.started":"2024-06-14T07:01:46.629089Z","shell.execute_reply":"2024-06-14T07:01:46.636926Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"(18, 64)"},"metadata":{}}]},{"cell_type":"code","source":"df_1['bmi'].min(), df_1['bmi'].max()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:02:12.686771Z","iopub.execute_input":"2024-06-14T07:02:12.687743Z","iopub.status.idle":"2024-06-14T07:02:12.695399Z","shell.execute_reply.started":"2024-06-14T07:02:12.687707Z","shell.execute_reply":"2024-06-14T07:02:12.694180Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"(16.0, 53.1)"},"metadata":{}}]},{"cell_type":"code","source":"df_1['Avg_expense_per_head'].min(), df_1['Avg_expense_per_head'].max()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:02:43.101755Z","iopub.execute_input":"2024-06-14T07:02:43.102545Z","iopub.status.idle":"2024-06-14T07:02:43.110334Z","shell.execute_reply.started":"2024-06-14T07:02:43.102510Z","shell.execute_reply":"2024-06-14T07:02:43.109161Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"(768.0, 63770.43)"},"metadata":{}}]},{"cell_type":"code","source":"type(df_1.groupby('age'))","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:04:24.304202Z","iopub.execute_input":"2024-06-14T07:04:24.304613Z","iopub.status.idle":"2024-06-14T07:04:24.312068Z","shell.execute_reply.started":"2024-06-14T07:04:24.304577Z","shell.execute_reply":"2024-06-14T07:04:24.310921Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"pandas.core.groupby.generic.DataFrameGroupBy"},"metadata":{}}]},{"cell_type":"code","source":"# Avg expense per head by age\ndf_1.groupby('age')['Avg_expense_per_head'].mean()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:05:39.749669Z","iopub.execute_input":"2024-06-14T07:05:39.750373Z","iopub.status.idle":"2024-06-14T07:05:39.761072Z","shell.execute_reply.started":"2024-06-14T07:05:39.750334Z","shell.execute_reply":"2024-06-14T07:05:39.759926Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"age\n18     5844.538594\n19     8318.573725\n20     6715.886092\n21     2721.650696\n22     6206.272262\n23     7159.838393\n24     9612.090244\n25     5525.646417\n26     3107.508494\n27     8868.835536\n28     5352.318899\n29     6020.556877\n30     6419.847130\n31     4805.285031\n32     4671.187923\n33     8531.368487\n34     6374.848686\n35     4687.920160\n36     6548.692847\n37    10310.551667\n38     3702.388400\n39     4840.713860\n40     5215.031901\n41     4485.472963\n42     7784.285556\n43     9674.311667\n44     9119.037407\n45     8952.840259\n46     7166.901236\n47     9235.692718\n48     8602.939138\n49     6699.034030\n50     8678.248667\n51     8618.830632\n52     9172.137701\n53     9324.043244\n54    10141.672024\n55    11437.414808\n56    10936.468987\n57    12748.351603\n58    11750.175933\n59    10932.092333\n60    19448.705761\n61    15297.283014\n62    14677.168804\n63    16390.902138\n64    16187.065076\nName: Avg_expense_per_head, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"# Avg expense per head by age\ndf_1.groupby('age')['bmi'].mean()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:05:56.178925Z","iopub.execute_input":"2024-06-14T07:05:56.179337Z","iopub.status.idle":"2024-06-14T07:05:56.189715Z","shell.execute_reply.started":"2024-06-14T07:05:56.179298Z","shell.execute_reply":"2024-06-14T07:05:56.188677Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"age\n18    31.333333\n19    28.598529\n20    30.627586\n21    28.189286\n22    31.092857\n23    31.460714\n24    29.142857\n25    29.689286\n26    29.435714\n27    29.342857\n28    29.482143\n29    29.374074\n30    30.555556\n31    29.922222\n32    31.588462\n33    31.165385\n34    30.273077\n35    31.392000\n36    29.368000\n37    31.216000\n38    29.004000\n39    29.908000\n40    30.144444\n41    31.518519\n42    30.337037\n43    30.207407\n44    30.848148\n45    29.782759\n46    31.341379\n47    30.655172\n48    31.927586\n49    30.314286\n50    31.134483\n51    31.731034\n52    32.941379\n53    30.371429\n54    31.232143\n55    31.950000\n56    31.600000\n57    30.838462\n58    32.728000\n59    30.576000\n60    30.330435\n61    32.552174\n62    32.347826\n63    31.930435\n64    32.986364\nName: bmi, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('age')['Avg_expense_per_head'].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:07:11.141595Z","iopub.execute_input":"2024-06-14T07:07:11.142048Z","iopub.status.idle":"2024-06-14T07:07:11.165775Z","shell.execute_reply.started":"2024-06-14T07:07:11.142015Z","shell.execute_reply":"2024-06-14T07:07:11.164628Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"             mean  count           std\nage                                   \n18    5844.538594     69   9520.074546\n19    8318.573725     68  11855.634963\n20    6715.886092     29   8516.566620\n21    2721.650696     28   3251.111162\n22    6206.272262     28   9623.579993\n23    7159.838393     28   8245.868070\n24    9612.090244     28  12412.526039\n25    5525.646417     28   7361.834999\n26    3107.508494     28   3451.487700\n27    8868.835536     28  11408.063733\n28    5352.318899     28   8075.055315\n29    6020.556877     27   7627.704122\n30    6419.847130     27   9333.992576\n31    4805.285031     27   6341.799011\n32    4671.187923     26   4730.147506\n33    8531.368487     26  12806.837158\n34    6374.848686     26   7772.206694\n35    4687.920160     25   5108.445761\n36    6548.692847     25   7727.272091\n37   10310.551667     25  11165.589091\n38    3702.388400     25   3128.799963\n39    4840.713860     25   4602.002146\n40    5215.031901     27   4484.639465\n41    4485.472963     27   3678.319323\n42    7784.285556     27   6666.420964\n43    9674.311667     27  10627.248962\n44    9119.037407     27  11587.396117\n45    8952.840259     29  12613.279597\n46    7166.901236     29   8606.386529\n47    9235.692718     29   8937.738997\n48    8602.939138     29   8171.131650\n49    6699.034030     28   7010.528521\n50    8678.248667     29   8247.582887\n51    8618.830632     29   7716.046610\n52    9172.137701     29   6541.607488\n53    9324.043244     28   8622.285998\n54   10141.672024     28  11797.427259\n55   11437.414808     26  10648.675978\n56   10936.468987     26   7991.299170\n57   12748.351603     26   9166.957694\n58   11750.175933     25   3461.107131\n59   10932.092333     25   6931.859337\n60   19448.705761     23  14884.940349\n61   15297.283014     23   9240.890108\n62   14677.168804     23   8820.455938\n63   16390.902138     23  11767.151903\n64   16187.065076     22   9903.503368","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>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18</th>\n      <td>5844.538594</td>\n      <td>69</td>\n      <td>9520.074546</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>8318.573725</td>\n      <td>68</td>\n      <td>11855.634963</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>6715.886092</td>\n      <td>29</td>\n      <td>8516.566620</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>2721.650696</td>\n      <td>28</td>\n      <td>3251.111162</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>6206.272262</td>\n      <td>28</td>\n      <td>9623.579993</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>7159.838393</td>\n      <td>28</td>\n      <td>8245.868070</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>9612.090244</td>\n      <td>28</td>\n      <td>12412.526039</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>5525.646417</td>\n      <td>28</td>\n      <td>7361.834999</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>3107.508494</td>\n      <td>28</td>\n      <td>3451.487700</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>8868.835536</td>\n      <td>28</td>\n      <td>11408.063733</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>5352.318899</td>\n      <td>28</td>\n      <td>8075.055315</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>6020.556877</td>\n      <td>27</td>\n      <td>7627.704122</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>6419.847130</td>\n      <td>27</td>\n      <td>9333.992576</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>4805.285031</td>\n      <td>27</td>\n      <td>6341.799011</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>4671.187923</td>\n      <td>26</td>\n      <td>4730.147506</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>8531.368487</td>\n      <td>26</td>\n      <td>12806.837158</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>6374.848686</td>\n      <td>26</td>\n      <td>7772.206694</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>4687.920160</td>\n      <td>25</td>\n      <td>5108.445761</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>6548.692847</td>\n      <td>25</td>\n      <td>7727.272091</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>10310.551667</td>\n      <td>25</td>\n      <td>11165.589091</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>3702.388400</td>\n      <td>25</td>\n      <td>3128.799963</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>4840.713860</td>\n      <td>25</td>\n      <td>4602.002146</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>5215.031901</td>\n      <td>27</td>\n      <td>4484.639465</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>4485.472963</td>\n      <td>27</td>\n      <td>3678.319323</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>7784.285556</td>\n      <td>27</td>\n      <td>6666.420964</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>9674.311667</td>\n      <td>27</td>\n      <td>10627.248962</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>9119.037407</td>\n      <td>27</td>\n      <td>11587.396117</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>8952.840259</td>\n      <td>29</td>\n      <td>12613.279597</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>7166.901236</td>\n      <td>29</td>\n      <td>8606.386529</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>9235.692718</td>\n      <td>29</td>\n      <td>8937.738997</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>8602.939138</td>\n      <td>29</td>\n      <td>8171.131650</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>6699.034030</td>\n      <td>28</td>\n      <td>7010.528521</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>8678.248667</td>\n      <td>29</td>\n      <td>8247.582887</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>8618.830632</td>\n      <td>29</td>\n      <td>7716.046610</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>9172.137701</td>\n      <td>29</td>\n      <td>6541.607488</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>9324.043244</td>\n      <td>28</td>\n      <td>8622.285998</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>10141.672024</td>\n      <td>28</td>\n      <td>11797.427259</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>11437.414808</td>\n      <td>26</td>\n      <td>10648.675978</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>10936.468987</td>\n      <td>26</td>\n      <td>7991.299170</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>12748.351603</td>\n      <td>26</td>\n      <td>9166.957694</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>11750.175933</td>\n      <td>25</td>\n      <td>3461.107131</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>10932.092333</td>\n      <td>25</td>\n      <td>6931.859337</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>19448.705761</td>\n      <td>23</td>\n      <td>14884.940349</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>15297.283014</td>\n      <td>23</td>\n      <td>9240.890108</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>14677.168804</td>\n      <td>23</td>\n      <td>8820.455938</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>16390.902138</td>\n      <td>23</td>\n      <td>11767.151903</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>16187.065076</td>\n      <td>22</td>\n      <td>9903.503368</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('age')['bmi'].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:08:07.335485Z","iopub.execute_input":"2024-06-14T07:08:07.335837Z","iopub.status.idle":"2024-06-14T07:08:07.356508Z","shell.execute_reply.started":"2024-06-14T07:08:07.335809Z","shell.execute_reply":"2024-06-14T07:08:07.355237Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"          mean  count       std\nage                            \n18   31.333333     69  6.965011\n19   28.598529     68  5.860887\n20   30.627586     29  4.634259\n21   28.189286     28  6.335400\n22   31.092857     28  6.964616\n23   31.460714     28  7.313475\n24   29.142857     28  4.894020\n25   29.689286     28  6.120902\n26   29.435714     28  6.879234\n27   29.342857     28  6.480537\n28   29.482143     28  5.407406\n29   29.374074     27  5.328486\n30   30.555556     27  6.605611\n31   29.922222     27  5.000256\n32   31.588462     26  6.564043\n33   31.165385     26  7.203274\n34   30.273077     26  6.085495\n35   31.392000     25  6.040552\n36   29.368000     25  4.670182\n37   31.216000     25  6.813631\n38   29.004000     25  6.867889\n39   29.908000     25  6.804283\n40   30.144444     27  5.763435\n41   31.518519     27  4.378710\n42   30.337037     27  5.374809\n43   30.207407     27  5.952306\n44   30.848148     27  6.213841\n45   29.782759     29  5.586020\n46   31.341379     29  7.041637\n47   30.655172     29  6.695285\n48   31.927586     29  4.726060\n49   30.314286     28  5.660239\n50   31.134483     29  5.935744\n51   31.731034     29  6.878855\n52   32.941379     29  7.176873\n53   30.371429     28  5.910120\n54   31.232143     28  6.555012\n55   31.950000     26  4.423686\n56   31.600000     26  5.796206\n57   30.838462     26  6.973841\n58   32.728000     25  6.026367\n59   30.576000     25  4.754847\n60   30.330435     23  5.767734\n61   32.552174     23  6.135209\n62   32.347826     23  5.028905\n63   31.930435     23  5.996774\n64   32.986364     22  5.765356","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>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18</th>\n      <td>31.333333</td>\n      <td>69</td>\n      <td>6.965011</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>28.598529</td>\n      <td>68</td>\n      <td>5.860887</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>30.627586</td>\n      <td>29</td>\n      <td>4.634259</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>28.189286</td>\n      <td>28</td>\n      <td>6.335400</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>31.092857</td>\n      <td>28</td>\n      <td>6.964616</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>31.460714</td>\n      <td>28</td>\n      <td>7.313475</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>29.142857</td>\n      <td>28</td>\n      <td>4.894020</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>29.689286</td>\n      <td>28</td>\n      <td>6.120902</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>29.435714</td>\n      <td>28</td>\n      <td>6.879234</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>29.342857</td>\n      <td>28</td>\n      <td>6.480537</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29.482143</td>\n      <td>28</td>\n      <td>5.407406</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>29.374074</td>\n      <td>27</td>\n      <td>5.328486</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>30.555556</td>\n      <td>27</td>\n      <td>6.605611</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>29.922222</td>\n      <td>27</td>\n      <td>5.000256</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>31.588462</td>\n      <td>26</td>\n      <td>6.564043</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>31.165385</td>\n      <td>26</td>\n      <td>7.203274</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>30.273077</td>\n      <td>26</td>\n      <td>6.085495</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>31.392000</td>\n      <td>25</td>\n      <td>6.040552</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>29.368000</td>\n      <td>25</td>\n      <td>4.670182</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>31.216000</td>\n      <td>25</td>\n      <td>6.813631</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>29.004000</td>\n      <td>25</td>\n      <td>6.867889</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>29.908000</td>\n      <td>25</td>\n      <td>6.804283</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>30.144444</td>\n      <td>27</td>\n      <td>5.763435</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>31.518519</td>\n      <td>27</td>\n      <td>4.378710</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>30.337037</td>\n      <td>27</td>\n      <td>5.374809</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>30.207407</td>\n      <td>27</td>\n      <td>5.952306</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>30.848148</td>\n      <td>27</td>\n      <td>6.213841</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>29.782759</td>\n      <td>29</td>\n      <td>5.586020</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>31.341379</td>\n      <td>29</td>\n      <td>7.041637</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>30.655172</td>\n      <td>29</td>\n      <td>6.695285</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>31.927586</td>\n      <td>29</td>\n      <td>4.726060</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>30.314286</td>\n      <td>28</td>\n      <td>5.660239</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>31.134483</td>\n      <td>29</td>\n      <td>5.935744</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>31.731034</td>\n      <td>29</td>\n      <td>6.878855</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>32.941379</td>\n      <td>29</td>\n      <td>7.176873</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>30.371429</td>\n      <td>28</td>\n      <td>5.910120</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>31.232143</td>\n      <td>28</td>\n      <td>6.555012</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>31.950000</td>\n      <td>26</td>\n      <td>4.423686</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>31.600000</td>\n      <td>26</td>\n      <td>5.796206</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>30.838462</td>\n      <td>26</td>\n      <td>6.973841</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>32.728000</td>\n      <td>25</td>\n      <td>6.026367</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>30.576000</td>\n      <td>25</td>\n      <td>4.754847</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>30.330435</td>\n      <td>23</td>\n      <td>5.767734</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>32.552174</td>\n      <td>23</td>\n      <td>6.135209</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>32.347826</td>\n      <td>23</td>\n      <td>5.028905</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>31.930435</td>\n      <td>23</td>\n      <td>5.996774</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>32.986364</td>\n      <td>22</td>\n      <td>5.765356</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('sex')['bmi'].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:08:23.016927Z","iopub.execute_input":"2024-06-14T07:08:23.017368Z","iopub.status.idle":"2024-06-14T07:08:23.031692Z","shell.execute_reply.started":"2024-06-14T07:08:23.017333Z","shell.execute_reply":"2024-06-14T07:08:23.030544Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"             mean  count       std\nsex                               \nfemale  30.379758    662  6.046508\nmale    30.945266    676  6.140346","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>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>female</th>\n      <td>30.379758</td>\n      <td>662</td>\n      <td>6.046508</td>\n    </tr>\n    <tr>\n      <th>male</th>\n      <td>30.945266</td>\n      <td>676</td>\n      <td>6.140346</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('sex')['Avg_expense_per_head'].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:08:43.439124Z","iopub.execute_input":"2024-06-14T07:08:43.439498Z","iopub.status.idle":"2024-06-14T07:08:43.455254Z","shell.execute_reply.started":"2024-06-14T07:08:43.439470Z","shell.execute_reply":"2024-06-14T07:08:43.454141Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"               mean  count          std\nsex                                    \nfemale  7953.502939    662  8994.598358\nmale    8579.132756    676  9775.412578","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>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>sex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>female</th>\n      <td>7953.502939</td>\n      <td>662</td>\n      <td>8994.598358</td>\n    </tr>\n    <tr>\n      <th>male</th>\n      <td>8579.132756</td>\n      <td>676</td>\n      <td>9775.412578</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('smoker')[['bmi','Avg_expense_per_head']].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:09:44.659739Z","iopub.execute_input":"2024-06-14T07:09:44.660183Z","iopub.status.idle":"2024-06-14T07:09:44.685483Z","shell.execute_reply.started":"2024-06-14T07:09:44.660149Z","shell.execute_reply":"2024-06-14T07:09:44.684386Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"              bmi                 Avg_expense_per_head                    \n             mean count       std                 mean count           std\nsmoker                                                                    \nno      30.653195  1064  6.042763          5198.523843  1064   4774.593999\nyes     30.713139   274  6.320840         20195.194597   274  12820.754257","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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">bmi</th>\n      <th colspan=\"3\" halign=\"left\">Avg_expense_per_head</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>smoker</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>no</th>\n      <td>30.653195</td>\n      <td>1064</td>\n      <td>6.042763</td>\n      <td>5198.523843</td>\n      <td>1064</td>\n      <td>4774.593999</td>\n    </tr>\n    <tr>\n      <th>yes</th>\n      <td>30.713139</td>\n      <td>274</td>\n      <td>6.320840</td>\n      <td>20195.194597</td>\n      <td>274</td>\n      <td>12820.754257</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('region')[['bmi','Avg_expense_per_head']].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:10:05.330606Z","iopub.execute_input":"2024-06-14T07:10:05.331030Z","iopub.status.idle":"2024-06-14T07:10:05.355658Z","shell.execute_reply.started":"2024-06-14T07:10:05.330997Z","shell.execute_reply":"2024-06-14T07:10:05.354531Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"                 bmi                 Avg_expense_per_head                    \n                mean count       std                 mean count           std\nregion                                                                       \nnortheast  29.176235   324  5.936847          8434.432126   324   8500.557168\nnorthwest  29.201846   325  5.136140          7351.393573   325   8072.089854\nsoutheast  33.359341   364  6.478891          9419.815416   364  11367.188348\nsouthwest  30.596615   325  5.691836          7735.202944   325   8940.314654","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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">bmi</th>\n      <th colspan=\"3\" halign=\"left\">Avg_expense_per_head</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>northeast</th>\n      <td>29.176235</td>\n      <td>324</td>\n      <td>5.936847</td>\n      <td>8434.432126</td>\n      <td>324</td>\n      <td>8500.557168</td>\n    </tr>\n    <tr>\n      <th>northwest</th>\n      <td>29.201846</td>\n      <td>325</td>\n      <td>5.136140</td>\n      <td>7351.393573</td>\n      <td>325</td>\n      <td>8072.089854</td>\n    </tr>\n    <tr>\n      <th>southeast</th>\n      <td>33.359341</td>\n      <td>364</td>\n      <td>6.478891</td>\n      <td>9419.815416</td>\n      <td>364</td>\n      <td>11367.188348</td>\n    </tr>\n    <tr>\n      <th>southwest</th>\n      <td>30.596615</td>\n      <td>325</td>\n      <td>5.691836</td>\n      <td>7735.202944</td>\n      <td>325</td>\n      <td>8940.314654</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('region')[['bmi','Avg_expense_per_head','age']].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:10:25.835829Z","iopub.execute_input":"2024-06-14T07:10:25.836251Z","iopub.status.idle":"2024-06-14T07:10:25.865250Z","shell.execute_reply.started":"2024-06-14T07:10:25.836217Z","shell.execute_reply":"2024-06-14T07:10:25.863907Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"                 bmi                 Avg_expense_per_head                      \\\n                mean count       std                 mean count           std   \nregion                                                                          \nnortheast  29.176235   324  5.936847          8434.432126   324   8500.557168   \nnorthwest  29.201846   325  5.136140          7351.393573   325   8072.089854   \nsoutheast  33.359341   364  6.478891          9419.815416   364  11367.188348   \nsouthwest  30.596615   325  5.691836          7735.202944   325   8940.314654   \n\n                 age                   \n                mean count        std  \nregion                                 \nnortheast  39.268519   324  14.069007  \nnorthwest  39.196923   325  14.051646  \nsoutheast  38.939560   364  14.164585  \nsouthwest  39.455385   325  13.959886  ","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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">bmi</th>\n      <th colspan=\"3\" halign=\"left\">Avg_expense_per_head</th>\n      <th colspan=\"3\" halign=\"left\">age</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>region</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>northeast</th>\n      <td>29.176235</td>\n      <td>324</td>\n      <td>5.936847</td>\n      <td>8434.432126</td>\n      <td>324</td>\n      <td>8500.557168</td>\n      <td>39.268519</td>\n      <td>324</td>\n      <td>14.069007</td>\n    </tr>\n    <tr>\n      <th>northwest</th>\n      <td>29.201846</td>\n      <td>325</td>\n      <td>5.136140</td>\n      <td>7351.393573</td>\n      <td>325</td>\n      <td>8072.089854</td>\n      <td>39.196923</td>\n      <td>325</td>\n      <td>14.051646</td>\n    </tr>\n    <tr>\n      <th>southeast</th>\n      <td>33.359341</td>\n      <td>364</td>\n      <td>6.478891</td>\n      <td>9419.815416</td>\n      <td>364</td>\n      <td>11367.188348</td>\n      <td>38.939560</td>\n      <td>364</td>\n      <td>14.164585</td>\n    </tr>\n    <tr>\n      <th>southwest</th>\n      <td>30.596615</td>\n      <td>325</td>\n      <td>5.691836</td>\n      <td>7735.202944</td>\n      <td>325</td>\n      <td>8940.314654</td>\n      <td>39.455385</td>\n      <td>325</td>\n      <td>13.959886</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.groupby('age')[['bmi','Avg_expense_per_head']].agg(['mean','count','std'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:10:42.386018Z","iopub.execute_input":"2024-06-14T07:10:42.386452Z","iopub.status.idle":"2024-06-14T07:10:42.421010Z","shell.execute_reply.started":"2024-06-14T07:10:42.386418Z","shell.execute_reply":"2024-06-14T07:10:42.419651Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"           bmi                 Avg_expense_per_head                    \n          mean count       std                 mean count           std\nage                                                                    \n18   31.333333    69  6.965011          5844.538594    69   9520.074546\n19   28.598529    68  5.860887          8318.573725    68  11855.634963\n20   30.627586    29  4.634259          6715.886092    29   8516.566620\n21   28.189286    28  6.335400          2721.650696    28   3251.111162\n22   31.092857    28  6.964616          6206.272262    28   9623.579993\n23   31.460714    28  7.313475          7159.838393    28   8245.868070\n24   29.142857    28  4.894020          9612.090244    28  12412.526039\n25   29.689286    28  6.120902          5525.646417    28   7361.834999\n26   29.435714    28  6.879234          3107.508494    28   3451.487700\n27   29.342857    28  6.480537          8868.835536    28  11408.063733\n28   29.482143    28  5.407406          5352.318899    28   8075.055315\n29   29.374074    27  5.328486          6020.556877    27   7627.704122\n30   30.555556    27  6.605611          6419.847130    27   9333.992576\n31   29.922222    27  5.000256          4805.285031    27   6341.799011\n32   31.588462    26  6.564043          4671.187923    26   4730.147506\n33   31.165385    26  7.203274          8531.368487    26  12806.837158\n34   30.273077    26  6.085495          6374.848686    26   7772.206694\n35   31.392000    25  6.040552          4687.920160    25   5108.445761\n36   29.368000    25  4.670182          6548.692847    25   7727.272091\n37   31.216000    25  6.813631         10310.551667    25  11165.589091\n38   29.004000    25  6.867889          3702.388400    25   3128.799963\n39   29.908000    25  6.804283          4840.713860    25   4602.002146\n40   30.144444    27  5.763435          5215.031901    27   4484.639465\n41   31.518519    27  4.378710          4485.472963    27   3678.319323\n42   30.337037    27  5.374809          7784.285556    27   6666.420964\n43   30.207407    27  5.952306          9674.311667    27  10627.248962\n44   30.848148    27  6.213841          9119.037407    27  11587.396117\n45   29.782759    29  5.586020          8952.840259    29  12613.279597\n46   31.341379    29  7.041637          7166.901236    29   8606.386529\n47   30.655172    29  6.695285          9235.692718    29   8937.738997\n48   31.927586    29  4.726060          8602.939138    29   8171.131650\n49   30.314286    28  5.660239          6699.034030    28   7010.528521\n50   31.134483    29  5.935744          8678.248667    29   8247.582887\n51   31.731034    29  6.878855          8618.830632    29   7716.046610\n52   32.941379    29  7.176873          9172.137701    29   6541.607488\n53   30.371429    28  5.910120          9324.043244    28   8622.285998\n54   31.232143    28  6.555012         10141.672024    28  11797.427259\n55   31.950000    26  4.423686         11437.414808    26  10648.675978\n56   31.600000    26  5.796206         10936.468987    26   7991.299170\n57   30.838462    26  6.973841         12748.351603    26   9166.957694\n58   32.728000    25  6.026367         11750.175933    25   3461.107131\n59   30.576000    25  4.754847         10932.092333    25   6931.859337\n60   30.330435    23  5.767734         19448.705761    23  14884.940349\n61   32.552174    23  6.135209         15297.283014    23   9240.890108\n62   32.347826    23  5.028905         14677.168804    23   8820.455938\n63   31.930435    23  5.996774         16390.902138    23  11767.151903\n64   32.986364    22  5.765356         16187.065076    22   9903.503368","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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">bmi</th>\n      <th colspan=\"3\" halign=\"left\">Avg_expense_per_head</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n      <th>mean</th>\n      <th>count</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>age</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>18</th>\n      <td>31.333333</td>\n      <td>69</td>\n      <td>6.965011</td>\n      <td>5844.538594</td>\n      <td>69</td>\n      <td>9520.074546</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>28.598529</td>\n      <td>68</td>\n      <td>5.860887</td>\n      <td>8318.573725</td>\n      <td>68</td>\n      <td>11855.634963</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>30.627586</td>\n      <td>29</td>\n      <td>4.634259</td>\n      <td>6715.886092</td>\n      <td>29</td>\n      <td>8516.566620</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>28.189286</td>\n      <td>28</td>\n      <td>6.335400</td>\n      <td>2721.650696</td>\n      <td>28</td>\n      <td>3251.111162</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>31.092857</td>\n      <td>28</td>\n      <td>6.964616</td>\n      <td>6206.272262</td>\n      <td>28</td>\n      <td>9623.579993</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>31.460714</td>\n      <td>28</td>\n      <td>7.313475</td>\n      <td>7159.838393</td>\n      <td>28</td>\n      <td>8245.868070</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>29.142857</td>\n      <td>28</td>\n      <td>4.894020</td>\n      <td>9612.090244</td>\n      <td>28</td>\n      <td>12412.526039</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>29.689286</td>\n      <td>28</td>\n      <td>6.120902</td>\n      <td>5525.646417</td>\n      <td>28</td>\n      <td>7361.834999</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>29.435714</td>\n      <td>28</td>\n      <td>6.879234</td>\n      <td>3107.508494</td>\n      <td>28</td>\n      <td>3451.487700</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>29.342857</td>\n      <td>28</td>\n      <td>6.480537</td>\n      <td>8868.835536</td>\n      <td>28</td>\n      <td>11408.063733</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29.482143</td>\n      <td>28</td>\n      <td>5.407406</td>\n      <td>5352.318899</td>\n      <td>28</td>\n      <td>8075.055315</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>29.374074</td>\n      <td>27</td>\n      <td>5.328486</td>\n      <td>6020.556877</td>\n      <td>27</td>\n      <td>7627.704122</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>30.555556</td>\n      <td>27</td>\n      <td>6.605611</td>\n      <td>6419.847130</td>\n      <td>27</td>\n      <td>9333.992576</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>29.922222</td>\n      <td>27</td>\n      <td>5.000256</td>\n      <td>4805.285031</td>\n      <td>27</td>\n      <td>6341.799011</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>31.588462</td>\n      <td>26</td>\n      <td>6.564043</td>\n      <td>4671.187923</td>\n      <td>26</td>\n      <td>4730.147506</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>31.165385</td>\n      <td>26</td>\n      <td>7.203274</td>\n      <td>8531.368487</td>\n      <td>26</td>\n      <td>12806.837158</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>30.273077</td>\n      <td>26</td>\n      <td>6.085495</td>\n      <td>6374.848686</td>\n      <td>26</td>\n      <td>7772.206694</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>31.392000</td>\n      <td>25</td>\n      <td>6.040552</td>\n      <td>4687.920160</td>\n      <td>25</td>\n      <td>5108.445761</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>29.368000</td>\n      <td>25</td>\n      <td>4.670182</td>\n      <td>6548.692847</td>\n      <td>25</td>\n      <td>7727.272091</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>31.216000</td>\n      <td>25</td>\n      <td>6.813631</td>\n      <td>10310.551667</td>\n      <td>25</td>\n      <td>11165.589091</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>29.004000</td>\n      <td>25</td>\n      <td>6.867889</td>\n      <td>3702.388400</td>\n      <td>25</td>\n      <td>3128.799963</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>29.908000</td>\n      <td>25</td>\n      <td>6.804283</td>\n      <td>4840.713860</td>\n      <td>25</td>\n      <td>4602.002146</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>30.144444</td>\n      <td>27</td>\n      <td>5.763435</td>\n      <td>5215.031901</td>\n      <td>27</td>\n      <td>4484.639465</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>31.518519</td>\n      <td>27</td>\n      <td>4.378710</td>\n      <td>4485.472963</td>\n      <td>27</td>\n      <td>3678.319323</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>30.337037</td>\n      <td>27</td>\n      <td>5.374809</td>\n      <td>7784.285556</td>\n      <td>27</td>\n      <td>6666.420964</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>30.207407</td>\n      <td>27</td>\n      <td>5.952306</td>\n      <td>9674.311667</td>\n      <td>27</td>\n      <td>10627.248962</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>30.848148</td>\n      <td>27</td>\n      <td>6.213841</td>\n      <td>9119.037407</td>\n      <td>27</td>\n      <td>11587.396117</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>29.782759</td>\n      <td>29</td>\n      <td>5.586020</td>\n      <td>8952.840259</td>\n      <td>29</td>\n      <td>12613.279597</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>31.341379</td>\n      <td>29</td>\n      <td>7.041637</td>\n      <td>7166.901236</td>\n      <td>29</td>\n      <td>8606.386529</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>30.655172</td>\n      <td>29</td>\n      <td>6.695285</td>\n      <td>9235.692718</td>\n      <td>29</td>\n      <td>8937.738997</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>31.927586</td>\n      <td>29</td>\n      <td>4.726060</td>\n      <td>8602.939138</td>\n      <td>29</td>\n      <td>8171.131650</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>30.314286</td>\n      <td>28</td>\n      <td>5.660239</td>\n      <td>6699.034030</td>\n      <td>28</td>\n      <td>7010.528521</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>31.134483</td>\n      <td>29</td>\n      <td>5.935744</td>\n      <td>8678.248667</td>\n      <td>29</td>\n      <td>8247.582887</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>31.731034</td>\n      <td>29</td>\n      <td>6.878855</td>\n      <td>8618.830632</td>\n      <td>29</td>\n      <td>7716.046610</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>32.941379</td>\n      <td>29</td>\n      <td>7.176873</td>\n      <td>9172.137701</td>\n      <td>29</td>\n      <td>6541.607488</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>30.371429</td>\n      <td>28</td>\n      <td>5.910120</td>\n      <td>9324.043244</td>\n      <td>28</td>\n      <td>8622.285998</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>31.232143</td>\n      <td>28</td>\n      <td>6.555012</td>\n      <td>10141.672024</td>\n      <td>28</td>\n      <td>11797.427259</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>31.950000</td>\n      <td>26</td>\n      <td>4.423686</td>\n      <td>11437.414808</td>\n      <td>26</td>\n      <td>10648.675978</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>31.600000</td>\n      <td>26</td>\n      <td>5.796206</td>\n      <td>10936.468987</td>\n      <td>26</td>\n      <td>7991.299170</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>30.838462</td>\n      <td>26</td>\n      <td>6.973841</td>\n      <td>12748.351603</td>\n      <td>26</td>\n      <td>9166.957694</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>32.728000</td>\n      <td>25</td>\n      <td>6.026367</td>\n      <td>11750.175933</td>\n      <td>25</td>\n      <td>3461.107131</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>30.576000</td>\n      <td>25</td>\n      <td>4.754847</td>\n      <td>10932.092333</td>\n      <td>25</td>\n      <td>6931.859337</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>30.330435</td>\n      <td>23</td>\n      <td>5.767734</td>\n      <td>19448.705761</td>\n      <td>23</td>\n      <td>14884.940349</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>32.552174</td>\n      <td>23</td>\n      <td>6.135209</td>\n      <td>15297.283014</td>\n      <td>23</td>\n      <td>9240.890108</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>32.347826</td>\n      <td>23</td>\n      <td>5.028905</td>\n      <td>14677.168804</td>\n      <td>23</td>\n      <td>8820.455938</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>31.930435</td>\n      <td>23</td>\n      <td>5.996774</td>\n      <td>16390.902138</td>\n      <td>23</td>\n      <td>11767.151903</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>32.986364</td>\n      <td>22</td>\n      <td>5.765356</td>\n      <td>16187.065076</td>\n      <td>22</td>\n      <td>9903.503368</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Applying Functions to data","metadata":{}},{"cell_type":"code","source":"df_1.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:11:33.855710Z","iopub.execute_input":"2024-06-14T07:11:33.856128Z","iopub.status.idle":"2024-06-14T07:11:33.875312Z","shell.execute_reply.started":"2024-06-14T07:11:33.856095Z","shell.execute_reply":"2024-06-14T07:11:33.874050Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count  \\\n0   19  female  27.9         0    yes  southwest  16884.92             1   \n1   18    male  33.8         1     no  southeast   1725.55             2   \n2   28    male  33.0         3     no  southeast   4449.46             4   \n3   33    male  22.7         0     no  northwest  21984.47             1   \n4   32    male  28.9         0     no  northwest   3866.86             1   \n5   31  female  25.7         0     no  southeast   3756.62             1   \n6   46  female  33.4         1     no  southeast   8240.59             2   \n7   37  female  27.7         3     no  northwest   7281.51             4   \n8   37    male  29.8         2     no  northeast   6406.41             3   \n9   60  female  25.8         0     no  northwest  28923.14             1   \n\n   Avg_expense_per_head  \n0            16884.9200  \n1              862.7750  \n2             1112.3650  \n3            21984.4700  \n4             3866.8600  \n5             3756.6200  \n6             4120.2950  \n7             1820.3775  \n8             2135.4700  \n9            28923.1400  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.9200</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.7750</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.3650</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.4700</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.8600</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>31</td>\n      <td>female</td>\n      <td>25.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>3756.62</td>\n      <td>1</td>\n      <td>3756.6200</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>46</td>\n      <td>female</td>\n      <td>33.4</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>8240.59</td>\n      <td>2</td>\n      <td>4120.2950</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>37</td>\n      <td>female</td>\n      <td>27.7</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>7281.51</td>\n      <td>4</td>\n      <td>1820.3775</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>37</td>\n      <td>male</td>\n      <td>29.8</td>\n      <td>2</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>6406.41</td>\n      <td>3</td>\n      <td>2135.4700</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>60</td>\n      <td>female</td>\n      <td>25.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>28923.14</td>\n      <td>1</td>\n      <td>28923.1400</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Student age <= 23\n# Adult age <= 50 and age > 23\n# Old age > 50\n\ndef compute_age_group(age):\n    if age <= 23:\n        return 'Student'\n    elif (age > 23) and (age < 50):\n        return 'Adult'\n    else:\n        return 'Aged'    ","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:16:38.574414Z","iopub.execute_input":"2024-06-14T07:16:38.575342Z","iopub.status.idle":"2024-06-14T07:16:38.581315Z","shell.execute_reply.started":"2024-06-14T07:16:38.575305Z","shell.execute_reply":"2024-06-14T07:16:38.579892Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"df_1['Category'] = df_1['age'].apply(compute_age_group)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:17:22.612093Z","iopub.execute_input":"2024-06-14T07:17:22.612502Z","iopub.status.idle":"2024-06-14T07:17:22.619831Z","shell.execute_reply.started":"2024-06-14T07:17:22.612471Z","shell.execute_reply":"2024-06-14T07:17:22.618659Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"df_1.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:17:31.288117Z","iopub.execute_input":"2024-06-14T07:17:31.288522Z","iopub.status.idle":"2024-06-14T07:17:31.306049Z","shell.execute_reply.started":"2024-06-14T07:17:31.288490Z","shell.execute_reply":"2024-06-14T07:17:31.304941Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count  \\\n0   19  female  27.9         0    yes  southwest  16884.92             1   \n1   18    male  33.8         1     no  southeast   1725.55             2   \n2   28    male  33.0         3     no  southeast   4449.46             4   \n3   33    male  22.7         0     no  northwest  21984.47             1   \n4   32    male  28.9         0     no  northwest   3866.86             1   \n\n   Avg_expense_per_head Category  \n0             16884.920  Student  \n1               862.775  Student  \n2              1112.365    Adult  \n3             21984.470    Adult  \n4              3866.860    Adult  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.775</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.365</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.470</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.860</td>\n      <td>Adult</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1['age'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:17:57.760510Z","iopub.execute_input":"2024-06-14T07:17:57.760926Z","iopub.status.idle":"2024-06-14T07:17:57.771552Z","shell.execute_reply.started":"2024-06-14T07:17:57.760890Z","shell.execute_reply":"2024-06-14T07:17:57.770465Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"age\n18    69\n19    68\n50    29\n51    29\n47    29\n46    29\n45    29\n20    29\n48    29\n52    29\n22    28\n49    28\n54    28\n53    28\n21    28\n26    28\n24    28\n25    28\n28    28\n27    28\n23    28\n43    27\n29    27\n30    27\n41    27\n42    27\n44    27\n31    27\n40    27\n32    26\n33    26\n56    26\n34    26\n55    26\n57    26\n37    25\n59    25\n58    25\n36    25\n38    25\n35    25\n39    25\n61    23\n60    23\n63    23\n62    23\n64    22\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['sex'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:05.327087Z","iopub.execute_input":"2024-06-14T07:18:05.327486Z","iopub.status.idle":"2024-06-14T07:18:05.337785Z","shell.execute_reply.started":"2024-06-14T07:18:05.327454Z","shell.execute_reply":"2024-06-14T07:18:05.336429Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"sex\nmale      676\nfemale    662\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['Category'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:12.443927Z","iopub.execute_input":"2024-06-14T07:18:12.444297Z","iopub.status.idle":"2024-06-14T07:18:12.454783Z","shell.execute_reply.started":"2024-06-14T07:18:12.444269Z","shell.execute_reply":"2024-06-14T07:18:12.453667Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"Category\nAdult      703\nAged       385\nStudent    250\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['smoker'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:23.141598Z","iopub.execute_input":"2024-06-14T07:18:23.142110Z","iopub.status.idle":"2024-06-14T07:18:23.151197Z","shell.execute_reply.started":"2024-06-14T07:18:23.142076Z","shell.execute_reply":"2024-06-14T07:18:23.150064Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"smoker\nno     1064\nyes     274\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['region'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:37.239529Z","iopub.execute_input":"2024-06-14T07:18:37.239962Z","iopub.status.idle":"2024-06-14T07:18:37.249726Z","shell.execute_reply.started":"2024-06-14T07:18:37.239928Z","shell.execute_reply":"2024-06-14T07:18:37.248455Z"},"trusted":true},"execution_count":62,"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"region\nsoutheast    364\nsouthwest    325\nnorthwest    325\nnortheast    324\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['family_count'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:45.779676Z","iopub.execute_input":"2024-06-14T07:18:45.780120Z","iopub.status.idle":"2024-06-14T07:18:45.790499Z","shell.execute_reply.started":"2024-06-14T07:18:45.780088Z","shell.execute_reply":"2024-06-14T07:18:45.789392Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"family_count\n1    574\n2    324\n3    240\n4    157\n5     25\n6     18\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_1['children'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:18:53.172605Z","iopub.execute_input":"2024-06-14T07:18:53.173034Z","iopub.status.idle":"2024-06-14T07:18:53.183806Z","shell.execute_reply.started":"2024-06-14T07:18:53.173001Z","shell.execute_reply":"2024-06-14T07:18:53.182514Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"children\n0    574\n1    324\n2    240\n3    157\n4     25\n5     18\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data Cleaning and Preparation ","metadata":{}},{"cell_type":"markdown","source":"### Handling duplicate values","metadata":{}},{"cell_type":"code","source":"df_1.drop_duplicates(subset=['age','sex','bmi'])","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:21:07.984964Z","iopub.execute_input":"2024-06-14T07:21:07.985371Z","iopub.status.idle":"2024-06-14T07:21:08.010386Z","shell.execute_reply.started":"2024-06-14T07:21:07.985339Z","shell.execute_reply":"2024-06-14T07:21:08.009339Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n0      19  female  27.9         0    yes  southwest  16884.92             1   \n1      18    male  33.8         1     no  southeast   1725.55             2   \n2      28    male  33.0         3     no  southeast   4449.46             4   \n3      33    male  22.7         0     no  northwest  21984.47             1   \n4      32    male  28.9         0     no  northwest   3866.86             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1332   52  female  44.7         3     no  southwest  11411.69             4   \n1333   50    male  31.0         3     no  northwest  10600.55             4   \n1334   18  female  31.9         0     no  northeast   2205.98             1   \n1336   21  female  25.8         0     no  southwest   2007.95             1   \n1337   61  female  29.1         0    yes  northwest  29141.36             1   \n\n      Avg_expense_per_head Category  \n0               16884.9200  Student  \n1                 862.7750  Student  \n2                1112.3650    Adult  \n3               21984.4700    Adult  \n4                3866.8600    Adult  \n...                    ...      ...  \n1332             2852.9225     Aged  \n1333             2650.1375     Aged  \n1334             2205.9800  Student  \n1336             2007.9500  Student  \n1337            29141.3600     Aged  \n\n[1293 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.9200</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.7750</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.3650</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.4700</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.8600</td>\n      <td>Adult</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1332</th>\n      <td>52</td>\n      <td>female</td>\n      <td>44.7</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>11411.69</td>\n      <td>4</td>\n      <td>2852.9225</td>\n      <td>Aged</td>\n    </tr>\n    <tr>\n      <th>1333</th>\n      <td>50</td>\n      <td>male</td>\n      <td>31.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>10600.55</td>\n      <td>4</td>\n      <td>2650.1375</td>\n      <td>Aged</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>18</td>\n      <td>female</td>\n      <td>31.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>2205.98</td>\n      <td>1</td>\n      <td>2205.9800</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>21</td>\n      <td>female</td>\n      <td>25.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>2007.95</td>\n      <td>1</td>\n      <td>2007.9500</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>61</td>\n      <td>female</td>\n      <td>29.1</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>29141.36</td>\n      <td>1</td>\n      <td>29141.3600</td>\n      <td>Aged</td>\n    </tr>\n  </tbody>\n</table>\n<p>1293 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:21:15.264736Z","iopub.execute_input":"2024-06-14T07:21:15.265146Z","iopub.status.idle":"2024-06-14T07:21:15.279935Z","shell.execute_reply.started":"2024-06-14T07:21:15.265111Z","shell.execute_reply":"2024-06-14T07:21:15.278551Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1338 entries, 0 to 1337\nData columns (total 10 columns):\n #   Column                Non-Null Count  Dtype  \n---  ------                --------------  -----  \n 0   age                   1338 non-null   int64  \n 1   sex                   1338 non-null   object \n 2   bmi                   1338 non-null   float64\n 3   children              1338 non-null   int64  \n 4   smoker                1338 non-null   object \n 5   region                1338 non-null   object \n 6   expenses              1338 non-null   float64\n 7   family_count          1338 non-null   int64  \n 8   Avg_expense_per_head  1338 non-null   float64\n 9   Category              1338 non-null   object \ndtypes: float64(3), int64(3), object(4)\nmemory usage: 104.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df_1.drop_duplicates()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:21:28.922079Z","iopub.execute_input":"2024-06-14T07:21:28.922463Z","iopub.status.idle":"2024-06-14T07:21:28.947095Z","shell.execute_reply.started":"2024-06-14T07:21:28.922433Z","shell.execute_reply":"2024-06-14T07:21:28.945907Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n0      19  female  27.9         0    yes  southwest  16884.92             1   \n1      18    male  33.8         1     no  southeast   1725.55             2   \n2      28    male  33.0         3     no  southeast   4449.46             4   \n3      33    male  22.7         0     no  northwest  21984.47             1   \n4      32    male  28.9         0     no  northwest   3866.86             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1333   50    male  31.0         3     no  northwest  10600.55             4   \n1334   18  female  31.9         0     no  northeast   2205.98             1   \n1335   18  female  36.9         0     no  southeast   1629.83             1   \n1336   21  female  25.8         0     no  southwest   2007.95             1   \n1337   61  female  29.1         0    yes  northwest  29141.36             1   \n\n      Avg_expense_per_head Category  \n0               16884.9200  Student  \n1                 862.7750  Student  \n2                1112.3650    Adult  \n3               21984.4700    Adult  \n4                3866.8600    Adult  \n...                    ...      ...  \n1333             2650.1375     Aged  \n1334             2205.9800  Student  \n1335             1629.8300  Student  \n1336             2007.9500  Student  \n1337            29141.3600     Aged  \n\n[1337 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.9200</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.7750</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.3650</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.4700</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.8600</td>\n      <td>Adult</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1333</th>\n      <td>50</td>\n      <td>male</td>\n      <td>31.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>10600.55</td>\n      <td>4</td>\n      <td>2650.1375</td>\n      <td>Aged</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>18</td>\n      <td>female</td>\n      <td>31.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northeast</td>\n      <td>2205.98</td>\n      <td>1</td>\n      <td>2205.9800</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>18</td>\n      <td>female</td>\n      <td>36.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1629.83</td>\n      <td>1</td>\n      <td>1629.8300</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>21</td>\n      <td>female</td>\n      <td>25.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>2007.95</td>\n      <td>1</td>\n      <td>2007.9500</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>61</td>\n      <td>female</td>\n      <td>29.1</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>29141.36</td>\n      <td>1</td>\n      <td>29141.3600</td>\n      <td>Aged</td>\n    </tr>\n  </tbody>\n</table>\n<p>1337 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Handling Null Values / Missing Values","metadata":{}},{"cell_type":"code","source":"df_1.isna()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:23:31.452644Z","iopub.execute_input":"2024-06-14T07:23:31.453058Z","iopub.status.idle":"2024-06-14T07:23:31.474359Z","shell.execute_reply.started":"2024-06-14T07:23:31.453024Z","shell.execute_reply":"2024-06-14T07:23:31.473167Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"        age    sex    bmi  children  smoker  region  expenses  family_count  \\\n0     False  False  False     False   False   False     False         False   \n1     False  False  False     False   False   False     False         False   \n2     False  False  False     False   False   False     False         False   \n3     False  False  False     False   False   False     False         False   \n4     False  False  False     False   False   False     False         False   \n...     ...    ...    ...       ...     ...     ...       ...           ...   \n1333  False  False  False     False   False   False     False         False   \n1334  False  False  False     False   False   False     False         False   \n1335  False  False  False     False   False   False     False         False   \n1336  False  False  False     False   False   False     False         False   \n1337  False  False  False     False   False   False     False         False   \n\n      Avg_expense_per_head  Category  \n0                    False     False  \n1                    False     False  \n2                    False     False  \n3                    False     False  \n4                    False     False  \n...                    ...       ...  \n1333                 False     False  \n1334                 False     False  \n1335                 False     False  \n1336                 False     False  \n1337                 False     False  \n\n[1338 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1333</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>1338 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1.isnull()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:23:40.157979Z","iopub.execute_input":"2024-06-14T07:23:40.158378Z","iopub.status.idle":"2024-06-14T07:23:40.179367Z","shell.execute_reply.started":"2024-06-14T07:23:40.158338Z","shell.execute_reply":"2024-06-14T07:23:40.178135Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"        age    sex    bmi  children  smoker  region  expenses  family_count  \\\n0     False  False  False     False   False   False     False         False   \n1     False  False  False     False   False   False     False         False   \n2     False  False  False     False   False   False     False         False   \n3     False  False  False     False   False   False     False         False   \n4     False  False  False     False   False   False     False         False   \n...     ...    ...    ...       ...     ...     ...       ...           ...   \n1333  False  False  False     False   False   False     False         False   \n1334  False  False  False     False   False   False     False         False   \n1335  False  False  False     False   False   False     False         False   \n1336  False  False  False     False   False   False     False         False   \n1337  False  False  False     False   False   False     False         False   \n\n      Avg_expense_per_head  Category  \n0                    False     False  \n1                    False     False  \n2                    False     False  \n3                    False     False  \n4                    False     False  \n...                    ...       ...  \n1333                 False     False  \n1334                 False     False  \n1335                 False     False  \n1336                 False     False  \n1337                 False     False  \n\n[1338 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1333</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>1338 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_null_bool = df_1.isnull()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:24:39.193892Z","iopub.execute_input":"2024-06-14T07:24:39.194270Z","iopub.status.idle":"2024-06-14T07:24:39.201066Z","shell.execute_reply.started":"2024-06-14T07:24:39.194241Z","shell.execute_reply":"2024-06-14T07:24:39.199702Z"},"trusted":true},"execution_count":70,"outputs":[]},{"cell_type":"code","source":"df_null_bool.sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:24:46.965981Z","iopub.execute_input":"2024-06-14T07:24:46.966387Z","iopub.status.idle":"2024-06-14T07:24:46.975793Z","shell.execute_reply.started":"2024-06-14T07:24:46.966354Z","shell.execute_reply":"2024-06-14T07:24:46.974600Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"age                     0\nsex                     0\nbmi                     0\nchildren                0\nsmoker                  0\nregion                  0\nexpenses                0\nfamily_count            0\nAvg_expense_per_head    0\nCategory                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_null_bool.sum(axis=1)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:25:33.510936Z","iopub.execute_input":"2024-06-14T07:25:33.511371Z","iopub.status.idle":"2024-06-14T07:25:33.522834Z","shell.execute_reply.started":"2024-06-14T07:25:33.511336Z","shell.execute_reply":"2024-06-14T07:25:33.521704Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"0       0\n1       0\n2       0\n3       0\n4       0\n       ..\n1333    0\n1334    0\n1335    0\n1336    0\n1337    0\nLength: 1338, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_null_bool.sum(axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:25:41.746454Z","iopub.execute_input":"2024-06-14T07:25:41.747111Z","iopub.status.idle":"2024-06-14T07:25:41.756028Z","shell.execute_reply.started":"2024-06-14T07:25:41.747076Z","shell.execute_reply":"2024-06-14T07:25:41.754811Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"age                     0\nsex                     0\nbmi                     0\nchildren                0\nsmoker                  0\nregion                  0\nexpenses                0\nfamily_count            0\nAvg_expense_per_head    0\nCategory                0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### Value based indexing","metadata":{}},{"cell_type":"code","source":"df_1.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:28:05.357783Z","iopub.execute_input":"2024-06-14T07:28:05.358305Z","iopub.status.idle":"2024-06-14T07:28:05.379400Z","shell.execute_reply.started":"2024-06-14T07:28:05.358271Z","shell.execute_reply":"2024-06-14T07:28:05.378142Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"   age     sex   bmi  children smoker     region  expenses  family_count  \\\n0   19  female  27.9         0    yes  southwest  16884.92             1   \n1   18    male  33.8         1     no  southeast   1725.55             2   \n2   28    male  33.0         3     no  southeast   4449.46             4   \n3   33    male  22.7         0     no  northwest  21984.47             1   \n4   32    male  28.9         0     no  northwest   3866.86             1   \n\n   Avg_expense_per_head Category  \n0             16884.920  Student  \n1               862.775  Student  \n2              1112.365    Adult  \n3             21984.470    Adult  \n4              3866.860    Adult  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>18</td>\n      <td>male</td>\n      <td>33.8</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>1725.55</td>\n      <td>2</td>\n      <td>862.775</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>28</td>\n      <td>male</td>\n      <td>33.0</td>\n      <td>3</td>\n      <td>no</td>\n      <td>southeast</td>\n      <td>4449.46</td>\n      <td>4</td>\n      <td>1112.365</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>33</td>\n      <td>male</td>\n      <td>22.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>21984.47</td>\n      <td>1</td>\n      <td>21984.470</td>\n      <td>Adult</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>32</td>\n      <td>male</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>3866.86</td>\n      <td>1</td>\n      <td>3866.860</td>\n      <td>Adult</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Series Boolean\ndf_1['age'] == 19","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:29:39.374163Z","iopub.execute_input":"2024-06-14T07:29:39.374838Z","iopub.status.idle":"2024-06-14T07:29:39.383674Z","shell.execute_reply.started":"2024-06-14T07:29:39.374805Z","shell.execute_reply":"2024-06-14T07:29:39.382579Z"},"trusted":true},"execution_count":78,"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"0        True\n1       False\n2       False\n3       False\n4       False\n        ...  \n1333    False\n1334    False\n1335    False\n1336    False\n1337    False\nName: age, Length: 1338, dtype: bool"},"metadata":{}}]},{"cell_type":"code","source":"df_1.loc[df_1['age'] == 19, :]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:30:26.588307Z","iopub.execute_input":"2024-06-14T07:30:26.588687Z","iopub.status.idle":"2024-06-14T07:30:26.613161Z","shell.execute_reply.started":"2024-06-14T07:30:26.588656Z","shell.execute_reply":"2024-06-14T07:30:26.612024Z"},"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n0      19  female  27.9         0    yes  southwest  16884.92             1   \n15     19    male  24.6         1     no  southwest   1837.24             2   \n32     19  female  28.6         5     no  southwest   4687.80             6   \n35     19    male  20.4         0     no  northwest   1625.43             1   \n65     19  female  28.9         0     no  southwest   1743.21             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1251   19    male  19.8         0     no  southwest   1241.57             1   \n1291   19    male  34.9         0    yes  southwest  34828.65             1   \n1299   19  female  25.7         1     no  northwest   2710.83             2   \n1313   19  female  34.7         2    yes  southwest  36397.58             3   \n1316   19  female  20.6         0     no  southwest   1731.68             1   \n\n      Avg_expense_per_head Category  \n0             16884.920000  Student  \n15              918.620000  Student  \n32              781.300000  Student  \n35             1625.430000  Student  \n65             1743.210000  Student  \n...                    ...      ...  \n1251           1241.570000  Student  \n1291          34828.650000  Student  \n1299           1355.415000  Student  \n1313          12132.526667  Student  \n1316           1731.680000  Student  \n\n[68 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>19</td>\n      <td>male</td>\n      <td>24.6</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1837.24</td>\n      <td>2</td>\n      <td>918.620000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>19</td>\n      <td>female</td>\n      <td>28.6</td>\n      <td>5</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>4687.80</td>\n      <td>6</td>\n      <td>781.300000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1625.43</td>\n      <td>1</td>\n      <td>1625.430000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>19</td>\n      <td>female</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1743.21</td>\n      <td>1</td>\n      <td>1743.210000</td>\n      <td>Student</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1251</th>\n      <td>19</td>\n      <td>male</td>\n      <td>19.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1241.57</td>\n      <td>1</td>\n      <td>1241.570000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1291</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>34828.65</td>\n      <td>1</td>\n      <td>34828.650000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1299</th>\n      <td>19</td>\n      <td>female</td>\n      <td>25.7</td>\n      <td>1</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>2710.83</td>\n      <td>2</td>\n      <td>1355.415000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1313</th>\n      <td>19</td>\n      <td>female</td>\n      <td>34.7</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>36397.58</td>\n      <td>3</td>\n      <td>12132.526667</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1316</th>\n      <td>19</td>\n      <td>female</td>\n      <td>20.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1731.68</td>\n      <td>1</td>\n      <td>1731.680000</td>\n      <td>Student</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1[df_1['age'] == 19]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:28:30.698086Z","iopub.execute_input":"2024-06-14T07:28:30.698485Z","iopub.status.idle":"2024-06-14T07:28:30.721570Z","shell.execute_reply.started":"2024-06-14T07:28:30.698453Z","shell.execute_reply":"2024-06-14T07:28:30.720363Z"},"trusted":true},"execution_count":75,"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"      age     sex   bmi  children smoker     region  expenses  family_count  \\\n0      19  female  27.9         0    yes  southwest  16884.92             1   \n15     19    male  24.6         1     no  southwest   1837.24             2   \n32     19  female  28.6         5     no  southwest   4687.80             6   \n35     19    male  20.4         0     no  northwest   1625.43             1   \n65     19  female  28.9         0     no  southwest   1743.21             1   \n...   ...     ...   ...       ...    ...        ...       ...           ...   \n1251   19    male  19.8         0     no  southwest   1241.57             1   \n1291   19    male  34.9         0    yes  southwest  34828.65             1   \n1299   19  female  25.7         1     no  northwest   2710.83             2   \n1313   19  female  34.7         2    yes  southwest  36397.58             3   \n1316   19  female  20.6         0     no  southwest   1731.68             1   \n\n      Avg_expense_per_head Category  \n0             16884.920000  Student  \n15              918.620000  Student  \n32              781.300000  Student  \n35             1625.430000  Student  \n65             1743.210000  Student  \n...                    ...      ...  \n1251           1241.570000  Student  \n1291          34828.650000  Student  \n1299           1355.415000  Student  \n1313          12132.526667  Student  \n1316           1731.680000  Student  \n\n[68 rows x 10 columns]","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>19</td>\n      <td>female</td>\n      <td>27.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16884.92</td>\n      <td>1</td>\n      <td>16884.920000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>19</td>\n      <td>male</td>\n      <td>24.6</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1837.24</td>\n      <td>2</td>\n      <td>918.620000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>19</td>\n      <td>female</td>\n      <td>28.6</td>\n      <td>5</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>4687.80</td>\n      <td>6</td>\n      <td>781.300000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1625.43</td>\n      <td>1</td>\n      <td>1625.430000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>19</td>\n      <td>female</td>\n      <td>28.9</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1743.21</td>\n      <td>1</td>\n      <td>1743.210000</td>\n      <td>Student</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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1251</th>\n      <td>19</td>\n      <td>male</td>\n      <td>19.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1241.57</td>\n      <td>1</td>\n      <td>1241.570000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1291</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>34828.65</td>\n      <td>1</td>\n      <td>34828.650000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1299</th>\n      <td>19</td>\n      <td>female</td>\n      <td>25.7</td>\n      <td>1</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>2710.83</td>\n      <td>2</td>\n      <td>1355.415000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1313</th>\n      <td>19</td>\n      <td>female</td>\n      <td>34.7</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>36397.58</td>\n      <td>3</td>\n      <td>12132.526667</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1316</th>\n      <td>19</td>\n      <td>female</td>\n      <td>20.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1731.68</td>\n      <td>1</td>\n      <td>1731.680000</td>\n      <td>Student</td>\n    </tr>\n  </tbody>\n</table>\n<p>68 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_1[(df_1['age'] == 19) & (df_1['sex'] == 'male')]","metadata":{"execution":{"iopub.status.busy":"2024-06-14T07:29:11.808781Z","iopub.execute_input":"2024-06-14T07:29:11.809211Z","iopub.status.idle":"2024-06-14T07:29:11.843021Z","shell.execute_reply.started":"2024-06-14T07:29:11.809179Z","shell.execute_reply":"2024-06-14T07:29:11.841924Z"},"trusted":true},"execution_count":77,"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"      age   sex   bmi  children smoker     region  expenses  family_count  \\\n15     19  male  24.6         1     no  southwest   1837.24             2   \n35     19  male  20.4         0     no  northwest   1625.43             1   \n133    19  male  25.6         0     no  northwest   1632.56             1   \n136    19  male  34.1         0     no  southwest   1261.44             1   \n149    19  male  28.4         1     no  southwest   1842.52             2   \n195    19  male  30.6         0     no  northwest   1639.56             1   \n223    19  male  34.8         0    yes  southwest  34779.62             1   \n238    19  male  29.1         0    yes  northwest  17352.68             1   \n248    19  male  20.9         1     no  southwest   1832.09             2   \n259    19  male  31.9         0    yes  northwest  33750.29             1   \n263    19  male  37.0         0    yes  northwest  36219.41             1   \n276    19  male  20.6         2     no  northwest   2803.70             3   \n296    19  male  27.7         0    yes  southwest  16297.85             1   \n349    19  male  27.8         0     no  northwest   1635.73             1   \n385    19  male  34.4         0     no  southwest   1261.86             1   \n410    19  male  17.5         0     no  northwest   1621.34             1   \n430    19  male  33.1         0     no  southwest  23082.96             1   \n464    19  male  25.2         0     no  northwest   1632.04             1   \n487    19  male  28.7         0     no  southwest   1253.94             1   \n503    19  male  30.3         0    yes  southeast  32548.34             1   \n513    19  male  30.4         0     no  southwest   1256.30             1   \n559    19  male  35.5         0     no  northwest   1646.43             1   \n581    19  male  30.6         0     no  northwest   1639.56             1   \n584    19  male  20.7         0     no  southwest   1242.82             1   \n681    19  male  20.3         0     no  southwest   1242.26             1   \n723    19  male  35.4         0     no  southwest   1263.25             1   \n747    19  male  21.8         0     no  northwest   1627.28             1   \n791    19  male  27.6         0     no  southwest   1252.41             1   \n897    19  male  25.6         1     no  northwest   2221.56             2   \n943    19  male  22.6         0     no  northwest   1628.47             1   \n1026   19  male  26.0         1    yes  northwest  16450.89             2   \n1039   19  male  27.3         2     no  northwest  22493.66             3   \n1156   19  male  44.9         0    yes  southeast  39722.75             1   \n1251   19  male  19.8         0     no  southwest   1241.57             1   \n1291   19  male  34.9         0    yes  southwest  34828.65             1   \n\n      Avg_expense_per_head Category  \n15              918.620000  Student  \n35             1625.430000  Student  \n133            1632.560000  Student  \n136            1261.440000  Student  \n149             921.260000  Student  \n195            1639.560000  Student  \n223           34779.620000  Student  \n238           17352.680000  Student  \n248             916.045000  Student  \n259           33750.290000  Student  \n263           36219.410000  Student  \n276             934.566667  Student  \n296           16297.850000  Student  \n349            1635.730000  Student  \n385            1261.860000  Student  \n410            1621.340000  Student  \n430           23082.960000  Student  \n464            1632.040000  Student  \n487            1253.940000  Student  \n503           32548.340000  Student  \n513            1256.300000  Student  \n559            1646.430000  Student  \n581            1639.560000  Student  \n584            1242.820000  Student  \n681            1242.260000  Student  \n723            1263.250000  Student  \n747            1627.280000  Student  \n791            1252.410000  Student  \n897            1110.780000  Student  \n943            1628.470000  Student  \n1026           8225.445000  Student  \n1039           7497.886667  Student  \n1156          39722.750000  Student  \n1251           1241.570000  Student  \n1291          34828.650000  Student  ","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>age</th>\n      <th>sex</th>\n      <th>bmi</th>\n      <th>children</th>\n      <th>smoker</th>\n      <th>region</th>\n      <th>expenses</th>\n      <th>family_count</th>\n      <th>Avg_expense_per_head</th>\n      <th>Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15</th>\n      <td>19</td>\n      <td>male</td>\n      <td>24.6</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1837.24</td>\n      <td>2</td>\n      <td>918.620000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1625.43</td>\n      <td>1</td>\n      <td>1625.430000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>133</th>\n      <td>19</td>\n      <td>male</td>\n      <td>25.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1632.56</td>\n      <td>1</td>\n      <td>1632.560000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>136</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.1</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1261.44</td>\n      <td>1</td>\n      <td>1261.440000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>19</td>\n      <td>male</td>\n      <td>28.4</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1842.52</td>\n      <td>2</td>\n      <td>921.260000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>195</th>\n      <td>19</td>\n      <td>male</td>\n      <td>30.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1639.56</td>\n      <td>1</td>\n      <td>1639.560000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>223</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.8</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>34779.62</td>\n      <td>1</td>\n      <td>34779.620000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>238</th>\n      <td>19</td>\n      <td>male</td>\n      <td>29.1</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>17352.68</td>\n      <td>1</td>\n      <td>17352.680000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.9</td>\n      <td>1</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1832.09</td>\n      <td>2</td>\n      <td>916.045000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>259</th>\n      <td>19</td>\n      <td>male</td>\n      <td>31.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>33750.29</td>\n      <td>1</td>\n      <td>33750.290000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>263</th>\n      <td>19</td>\n      <td>male</td>\n      <td>37.0</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>36219.41</td>\n      <td>1</td>\n      <td>36219.410000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>276</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.6</td>\n      <td>2</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>2803.70</td>\n      <td>3</td>\n      <td>934.566667</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>296</th>\n      <td>19</td>\n      <td>male</td>\n      <td>27.7</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>16297.85</td>\n      <td>1</td>\n      <td>16297.850000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>349</th>\n      <td>19</td>\n      <td>male</td>\n      <td>27.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1635.73</td>\n      <td>1</td>\n      <td>1635.730000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>385</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1261.86</td>\n      <td>1</td>\n      <td>1261.860000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>410</th>\n      <td>19</td>\n      <td>male</td>\n      <td>17.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1621.34</td>\n      <td>1</td>\n      <td>1621.340000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>430</th>\n      <td>19</td>\n      <td>male</td>\n      <td>33.1</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>23082.96</td>\n      <td>1</td>\n      <td>23082.960000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>464</th>\n      <td>19</td>\n      <td>male</td>\n      <td>25.2</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1632.04</td>\n      <td>1</td>\n      <td>1632.040000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>487</th>\n      <td>19</td>\n      <td>male</td>\n      <td>28.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1253.94</td>\n      <td>1</td>\n      <td>1253.940000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <td>19</td>\n      <td>male</td>\n      <td>30.3</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southeast</td>\n      <td>32548.34</td>\n      <td>1</td>\n      <td>32548.340000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>513</th>\n      <td>19</td>\n      <td>male</td>\n      <td>30.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1256.30</td>\n      <td>1</td>\n      <td>1256.300000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>559</th>\n      <td>19</td>\n      <td>male</td>\n      <td>35.5</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1646.43</td>\n      <td>1</td>\n      <td>1646.430000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>581</th>\n      <td>19</td>\n      <td>male</td>\n      <td>30.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1639.56</td>\n      <td>1</td>\n      <td>1639.560000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>584</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.7</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1242.82</td>\n      <td>1</td>\n      <td>1242.820000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>681</th>\n      <td>19</td>\n      <td>male</td>\n      <td>20.3</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1242.26</td>\n      <td>1</td>\n      <td>1242.260000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>723</th>\n      <td>19</td>\n      <td>male</td>\n      <td>35.4</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1263.25</td>\n      <td>1</td>\n      <td>1263.250000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>747</th>\n      <td>19</td>\n      <td>male</td>\n      <td>21.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1627.28</td>\n      <td>1</td>\n      <td>1627.280000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>791</th>\n      <td>19</td>\n      <td>male</td>\n      <td>27.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1252.41</td>\n      <td>1</td>\n      <td>1252.410000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>897</th>\n      <td>19</td>\n      <td>male</td>\n      <td>25.6</td>\n      <td>1</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>2221.56</td>\n      <td>2</td>\n      <td>1110.780000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>943</th>\n      <td>19</td>\n      <td>male</td>\n      <td>22.6</td>\n      <td>0</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>1628.47</td>\n      <td>1</td>\n      <td>1628.470000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1026</th>\n      <td>19</td>\n      <td>male</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>yes</td>\n      <td>northwest</td>\n      <td>16450.89</td>\n      <td>2</td>\n      <td>8225.445000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1039</th>\n      <td>19</td>\n      <td>male</td>\n      <td>27.3</td>\n      <td>2</td>\n      <td>no</td>\n      <td>northwest</td>\n      <td>22493.66</td>\n      <td>3</td>\n      <td>7497.886667</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1156</th>\n      <td>19</td>\n      <td>male</td>\n      <td>44.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southeast</td>\n      <td>39722.75</td>\n      <td>1</td>\n      <td>39722.750000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1251</th>\n      <td>19</td>\n      <td>male</td>\n      <td>19.8</td>\n      <td>0</td>\n      <td>no</td>\n      <td>southwest</td>\n      <td>1241.57</td>\n      <td>1</td>\n      <td>1241.570000</td>\n      <td>Student</td>\n    </tr>\n    <tr>\n      <th>1291</th>\n      <td>19</td>\n      <td>male</td>\n      <td>34.9</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>southwest</td>\n      <td>34828.65</td>\n      <td>1</td>\n      <td>34828.650000</td>\n      <td>Student</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}