{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","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":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-11-04T08:06:12.867783Z","iopub.execute_input":"2022-11-04T08:06:12.868164Z","iopub.status.idle":"2022-11-04T08:06:12.878342Z","shell.execute_reply.started":"2022-11-04T08:06:12.868137Z","shell.execute_reply":"2022-11-04T08:06:12.877448Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"/kaggle/input/amex-default-prediction/sample_submission.csv\n/kaggle/input/amex-default-prediction/train_data.csv\n/kaggle/input/amex-default-prediction/test_data.csv\n/kaggle/input/amex-default-prediction/train_labels.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n#Imorting oyplot interface using matplotlib\nimport seaborn as sns\n#importing seaborn library for visulization\n","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:14.348563Z","iopub.execute_input":"2022-11-04T08:06:14.350202Z","iopub.status.idle":"2022-11-04T08:06:14.354618Z","shell.execute_reply.started":"2022-11-04T08:06:14.350147Z","shell.execute_reply":"2022-11-04T08:06:14.353607Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"#load train_data.csv\nt_df=pd.read_csv('../input/amex-default-prediction/train_data.csv', nrows=200000)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:15.915514Z","iopub.execute_input":"2022-11-04T08:06:15.915919Z","iopub.status.idle":"2022-11-04T08:06:29.536542Z","shell.execute_reply.started":"2022-11-04T08:06:15.915887Z","shell.execute_reply":"2022-11-04T08:06:29.535315Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"#Reading first 5 rows of the data\nt_df.head(10)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:29.538196Z","iopub.execute_input":"2022-11-04T08:06:29.538653Z","iopub.status.idle":"2022-11-04T08:06:29.581658Z","shell.execute_reply.started":"2022-11-04T08:06:29.53862Z","shell.execute_reply":"2022-11-04T08:06:29.579969Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"                                         customer_ID         S_2       P_2  \\\n0  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-03-09  0.938469   \n1  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-04-07  0.936665   \n2  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-05-28  0.954180   \n3  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-06-13  0.960384   \n4  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-07-16  0.947248   \n5  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-08-04  0.945964   \n6  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-09-18  0.940705   \n7  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-10-08  0.914767   \n8  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-11-20  0.950845   \n9  0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...  2017-12-04  0.868580   \n\n       D_39       B_1       B_2       R_1       S_3      D_41       B_3  ...  \\\n0  0.001733  0.008724  1.006838  0.009228  0.124035  0.008771  0.004709  ...   \n1  0.005775  0.004923  1.000653  0.006151  0.126750  0.000798  0.002714  ...   \n2  0.091505  0.021655  1.009672  0.006815  0.123977  0.007598  0.009423  ...   \n3  0.002455  0.013683  1.002700  0.001373  0.117169  0.000685  0.005531  ...   \n4  0.002483  0.015193  1.000727  0.007605  0.117325  0.004653  0.009312  ...   \n5  0.001746  0.007863  1.005006  0.004220  0.110946  0.009857  0.009866  ...   \n6  0.002183  0.018859  1.008024  0.004509  0.103329  0.006603  0.000783  ...   \n7  0.003029  0.014324  1.000242  0.000263  0.108115  0.009527  0.007836  ...   \n8  0.009896  0.016888  1.003995  0.001789  0.102792  0.002519  0.009817  ...   \n9  0.001082  0.001930  1.007504  0.001772  0.100470  0.004626  0.006073  ...   \n\n   D_136  D_137  D_138     D_139     D_140     D_141  D_142     D_143  \\\n0    NaN    NaN    NaN  0.002427  0.003706  0.003818    NaN  0.000569   \n1    NaN    NaN    NaN  0.003954  0.003167  0.005032    NaN  0.009576   \n2    NaN    NaN    NaN  0.003269  0.007329  0.000427    NaN  0.003429   \n3    NaN    NaN    NaN  0.006117  0.004516  0.003200    NaN  0.008419   \n4    NaN    NaN    NaN  0.003671  0.004946  0.008889    NaN  0.001670   \n5    NaN    NaN    NaN  0.001924  0.008598  0.004529    NaN  0.000674   \n6    NaN    NaN    NaN  0.001336  0.004361  0.009387    NaN  0.007727   \n7    NaN    NaN    NaN  0.002397  0.008452  0.005553    NaN  0.001831   \n8    NaN    NaN    NaN  0.009742  0.003968  0.007945    NaN  0.008722   \n9    NaN    NaN    NaN  0.003611  0.009607  0.007266    NaN  0.008763   \n\n      D_144     D_145  \n0  0.000610  0.002674  \n1  0.005492  0.009217  \n2  0.006986  0.002603  \n3  0.006527  0.009600  \n4  0.008126  0.009827  \n5  0.002223  0.002884  \n6  0.007661  0.002225  \n7  0.009616  0.007385  \n8  0.004369  0.000995  \n9  0.004753  0.009068  \n\n[10 rows x 190 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>customer_ID</th>\n      <th>S_2</th>\n      <th>P_2</th>\n      <th>D_39</th>\n      <th>B_1</th>\n      <th>B_2</th>\n      <th>R_1</th>\n      <th>S_3</th>\n      <th>D_41</th>\n      <th>B_3</th>\n      <th>...</th>\n      <th>D_136</th>\n      <th>D_137</th>\n      <th>D_138</th>\n      <th>D_139</th>\n      <th>D_140</th>\n      <th>D_141</th>\n      <th>D_142</th>\n      <th>D_143</th>\n      <th>D_144</th>\n      <th>D_145</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-03-09</td>\n      <td>0.938469</td>\n      <td>0.001733</td>\n      <td>0.008724</td>\n      <td>1.006838</td>\n      <td>0.009228</td>\n      <td>0.124035</td>\n      <td>0.008771</td>\n      <td>0.004709</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.002427</td>\n      <td>0.003706</td>\n      <td>0.003818</td>\n      <td>NaN</td>\n      <td>0.000569</td>\n      <td>0.000610</td>\n      <td>0.002674</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-04-07</td>\n      <td>0.936665</td>\n      <td>0.005775</td>\n      <td>0.004923</td>\n      <td>1.000653</td>\n      <td>0.006151</td>\n      <td>0.126750</td>\n      <td>0.000798</td>\n      <td>0.002714</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003954</td>\n      <td>0.003167</td>\n      <td>0.005032</td>\n      <td>NaN</td>\n      <td>0.009576</td>\n      <td>0.005492</td>\n      <td>0.009217</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-05-28</td>\n      <td>0.954180</td>\n      <td>0.091505</td>\n      <td>0.021655</td>\n      <td>1.009672</td>\n      <td>0.006815</td>\n      <td>0.123977</td>\n      <td>0.007598</td>\n      <td>0.009423</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003269</td>\n      <td>0.007329</td>\n      <td>0.000427</td>\n      <td>NaN</td>\n      <td>0.003429</td>\n      <td>0.006986</td>\n      <td>0.002603</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-06-13</td>\n      <td>0.960384</td>\n      <td>0.002455</td>\n      <td>0.013683</td>\n      <td>1.002700</td>\n      <td>0.001373</td>\n      <td>0.117169</td>\n      <td>0.000685</td>\n      <td>0.005531</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006117</td>\n      <td>0.004516</td>\n      <td>0.003200</td>\n      <td>NaN</td>\n      <td>0.008419</td>\n      <td>0.006527</td>\n      <td>0.009600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-07-16</td>\n      <td>0.947248</td>\n      <td>0.002483</td>\n      <td>0.015193</td>\n      <td>1.000727</td>\n      <td>0.007605</td>\n      <td>0.117325</td>\n      <td>0.004653</td>\n      <td>0.009312</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003671</td>\n      <td>0.004946</td>\n      <td>0.008889</td>\n      <td>NaN</td>\n      <td>0.001670</td>\n      <td>0.008126</td>\n      <td>0.009827</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-08-04</td>\n      <td>0.945964</td>\n      <td>0.001746</td>\n      <td>0.007863</td>\n      <td>1.005006</td>\n      <td>0.004220</td>\n      <td>0.110946</td>\n      <td>0.009857</td>\n      <td>0.009866</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001924</td>\n      <td>0.008598</td>\n      <td>0.004529</td>\n      <td>NaN</td>\n      <td>0.000674</td>\n      <td>0.002223</td>\n      <td>0.002884</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-09-18</td>\n      <td>0.940705</td>\n      <td>0.002183</td>\n      <td>0.018859</td>\n      <td>1.008024</td>\n      <td>0.004509</td>\n      <td>0.103329</td>\n      <td>0.006603</td>\n      <td>0.000783</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001336</td>\n      <td>0.004361</td>\n      <td>0.009387</td>\n      <td>NaN</td>\n      <td>0.007727</td>\n      <td>0.007661</td>\n      <td>0.002225</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-10-08</td>\n      <td>0.914767</td>\n      <td>0.003029</td>\n      <td>0.014324</td>\n      <td>1.000242</td>\n      <td>0.000263</td>\n      <td>0.108115</td>\n      <td>0.009527</td>\n      <td>0.007836</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.002397</td>\n      <td>0.008452</td>\n      <td>0.005553</td>\n      <td>NaN</td>\n      <td>0.001831</td>\n      <td>0.009616</td>\n      <td>0.007385</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-11-20</td>\n      <td>0.950845</td>\n      <td>0.009896</td>\n      <td>0.016888</td>\n      <td>1.003995</td>\n      <td>0.001789</td>\n      <td>0.102792</td>\n      <td>0.002519</td>\n      <td>0.009817</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.009742</td>\n      <td>0.003968</td>\n      <td>0.007945</td>\n      <td>NaN</td>\n      <td>0.008722</td>\n      <td>0.004369</td>\n      <td>0.000995</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0000099d6bd597052cdcda90ffabf56573fe9d7c79be5f...</td>\n      <td>2017-12-04</td>\n      <td>0.868580</td>\n      <td>0.001082</td>\n      <td>0.001930</td>\n      <td>1.007504</td>\n      <td>0.001772</td>\n      <td>0.100470</td>\n      <td>0.004626</td>\n      <td>0.006073</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003611</td>\n      <td>0.009607</td>\n      <td>0.007266</td>\n      <td>NaN</td>\n      <td>0.008763</td>\n      <td>0.004753</td>\n      <td>0.009068</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 190 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Reading the column of the data\nt_df.columns","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:32.699487Z","iopub.execute_input":"2022-11-04T08:06:32.700387Z","iopub.status.idle":"2022-11-04T08:06:32.709111Z","shell.execute_reply.started":"2022-11-04T08:06:32.700356Z","shell.execute_reply":"2022-11-04T08:06:32.707845Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"Index(['customer_ID', 'S_2', 'P_2', 'D_39', 'B_1', 'B_2', 'R_1', 'S_3', 'D_41',\n       'B_3',\n       ...\n       'D_136', 'D_137', 'D_138', 'D_139', 'D_140', 'D_141', 'D_142', 'D_143',\n       'D_144', 'D_145'],\n      dtype='object', length=190)"},"metadata":{}}]},{"cell_type":"code","source":"#Reading last 5 rows of data\nt_df.tail(10)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:33.276771Z","iopub.execute_input":"2022-11-04T08:06:33.27729Z","iopub.status.idle":"2022-11-04T08:06:33.307951Z","shell.execute_reply.started":"2022-11-04T08:06:33.277255Z","shell.execute_reply":"2022-11-04T08:06:33.306703Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"                                              customer_ID         S_2  \\\n199990  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-03-15   \n199991  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-04-15   \n199992  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-05-07   \n199993  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-06-13   \n199994  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-07-15   \n199995  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-08-28   \n199996  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-09-15   \n199997  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-10-10   \n199998  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-11-16   \n199999  09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...  2017-12-15   \n\n             P_2      D_39       B_1       B_2       R_1       S_3      D_41  \\\n199990  0.592624  0.003602  0.009956  1.001670  0.006492  0.090117  0.007572   \n199991  0.597678  0.008949  0.009070  1.007012  0.005554  0.093682  0.007493   \n199992  0.607084  0.002390  0.008172  1.000738  0.008643  0.097931  0.000080   \n199993  0.606220  0.004091  0.013853  0.814175  0.004399  0.101770  0.008711   \n199994  0.608304  0.002245  0.015774  0.812785  0.500715  0.111703  0.008880   \n199995  0.539011  0.002428  0.006173  1.006607  0.007721  0.155389  0.002317   \n199996  0.545751  0.062144  0.014536  0.816225  0.009416  0.145824  0.007354   \n199997  0.579735  0.009152  0.010878  0.815024  0.005641  0.149669  0.001961   \n199998  0.577626  0.090158  0.013903  1.008488  0.007109  0.153834  0.004685   \n199999  0.655403  0.002060  0.022364  1.007560  0.006147  0.151334  0.009964   \n\n             B_3  ...  D_136  D_137  D_138     D_139     D_140     D_141  \\\n199990  0.004509  ...    NaN    NaN    NaN  0.007190  0.004185  0.007595   \n199991  0.008712  ...    NaN    NaN    NaN  0.006179  0.003925  0.008045   \n199992  0.007975  ...    NaN    NaN    NaN  0.003224  0.000141  0.003189   \n199993  0.015920  ...    NaN    NaN    NaN  0.006636  0.006153  0.001421   \n199994  0.011113  ...    NaN    NaN    NaN  0.000580  0.008281  0.004493   \n199995  0.014583  ...    NaN    NaN    NaN  0.001474  0.005978  0.003926   \n199996  0.011436  ...    NaN    NaN    NaN  0.003072  0.008800  0.000814   \n199997  0.008332  ...    NaN    NaN    NaN  0.006708  0.007236  0.007473   \n199998  0.010484  ...    NaN    NaN    NaN  0.003593  0.005189  0.005273   \n199999  0.003983  ...    NaN    NaN    NaN  0.003830  0.007001  0.005250   \n\n        D_142     D_143     D_144     D_145  \n199990    NaN  0.002265  0.003770  0.009151  \n199991    NaN  0.009616  0.001725  0.005290  \n199992    NaN  0.007965  0.001097  0.002762  \n199993    NaN  0.001979  0.008299  0.001916  \n199994    NaN  0.003352  0.003254  0.007920  \n199995    NaN  0.007297  0.006689  0.009181  \n199996    NaN  0.003730  0.005946  0.006949  \n199997    NaN  0.004778  0.000594  0.001202  \n199998    NaN  0.007717  0.008678  0.005638  \n199999    NaN  0.008395  0.000767  0.006576  \n\n[10 rows x 190 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>customer_ID</th>\n      <th>S_2</th>\n      <th>P_2</th>\n      <th>D_39</th>\n      <th>B_1</th>\n      <th>B_2</th>\n      <th>R_1</th>\n      <th>S_3</th>\n      <th>D_41</th>\n      <th>B_3</th>\n      <th>...</th>\n      <th>D_136</th>\n      <th>D_137</th>\n      <th>D_138</th>\n      <th>D_139</th>\n      <th>D_140</th>\n      <th>D_141</th>\n      <th>D_142</th>\n      <th>D_143</th>\n      <th>D_144</th>\n      <th>D_145</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>199990</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-03-15</td>\n      <td>0.592624</td>\n      <td>0.003602</td>\n      <td>0.009956</td>\n      <td>1.001670</td>\n      <td>0.006492</td>\n      <td>0.090117</td>\n      <td>0.007572</td>\n      <td>0.004509</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.007190</td>\n      <td>0.004185</td>\n      <td>0.007595</td>\n      <td>NaN</td>\n      <td>0.002265</td>\n      <td>0.003770</td>\n      <td>0.009151</td>\n    </tr>\n    <tr>\n      <th>199991</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-04-15</td>\n      <td>0.597678</td>\n      <td>0.008949</td>\n      <td>0.009070</td>\n      <td>1.007012</td>\n      <td>0.005554</td>\n      <td>0.093682</td>\n      <td>0.007493</td>\n      <td>0.008712</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006179</td>\n      <td>0.003925</td>\n      <td>0.008045</td>\n      <td>NaN</td>\n      <td>0.009616</td>\n      <td>0.001725</td>\n      <td>0.005290</td>\n    </tr>\n    <tr>\n      <th>199992</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-05-07</td>\n      <td>0.607084</td>\n      <td>0.002390</td>\n      <td>0.008172</td>\n      <td>1.000738</td>\n      <td>0.008643</td>\n      <td>0.097931</td>\n      <td>0.000080</td>\n      <td>0.007975</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003224</td>\n      <td>0.000141</td>\n      <td>0.003189</td>\n      <td>NaN</td>\n      <td>0.007965</td>\n      <td>0.001097</td>\n      <td>0.002762</td>\n    </tr>\n    <tr>\n      <th>199993</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-06-13</td>\n      <td>0.606220</td>\n      <td>0.004091</td>\n      <td>0.013853</td>\n      <td>0.814175</td>\n      <td>0.004399</td>\n      <td>0.101770</td>\n      <td>0.008711</td>\n      <td>0.015920</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006636</td>\n      <td>0.006153</td>\n      <td>0.001421</td>\n      <td>NaN</td>\n      <td>0.001979</td>\n      <td>0.008299</td>\n      <td>0.001916</td>\n    </tr>\n    <tr>\n      <th>199994</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-07-15</td>\n      <td>0.608304</td>\n      <td>0.002245</td>\n      <td>0.015774</td>\n      <td>0.812785</td>\n      <td>0.500715</td>\n      <td>0.111703</td>\n      <td>0.008880</td>\n      <td>0.011113</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000580</td>\n      <td>0.008281</td>\n      <td>0.004493</td>\n      <td>NaN</td>\n      <td>0.003352</td>\n      <td>0.003254</td>\n      <td>0.007920</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-08-28</td>\n      <td>0.539011</td>\n      <td>0.002428</td>\n      <td>0.006173</td>\n      <td>1.006607</td>\n      <td>0.007721</td>\n      <td>0.155389</td>\n      <td>0.002317</td>\n      <td>0.014583</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001474</td>\n      <td>0.005978</td>\n      <td>0.003926</td>\n      <td>NaN</td>\n      <td>0.007297</td>\n      <td>0.006689</td>\n      <td>0.009181</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-09-15</td>\n      <td>0.545751</td>\n      <td>0.062144</td>\n      <td>0.014536</td>\n      <td>0.816225</td>\n      <td>0.009416</td>\n      <td>0.145824</td>\n      <td>0.007354</td>\n      <td>0.011436</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003072</td>\n      <td>0.008800</td>\n      <td>0.000814</td>\n      <td>NaN</td>\n      <td>0.003730</td>\n      <td>0.005946</td>\n      <td>0.006949</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-10-10</td>\n      <td>0.579735</td>\n      <td>0.009152</td>\n      <td>0.010878</td>\n      <td>0.815024</td>\n      <td>0.005641</td>\n      <td>0.149669</td>\n      <td>0.001961</td>\n      <td>0.008332</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006708</td>\n      <td>0.007236</td>\n      <td>0.007473</td>\n      <td>NaN</td>\n      <td>0.004778</td>\n      <td>0.000594</td>\n      <td>0.001202</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-11-16</td>\n      <td>0.577626</td>\n      <td>0.090158</td>\n      <td>0.013903</td>\n      <td>1.008488</td>\n      <td>0.007109</td>\n      <td>0.153834</td>\n      <td>0.004685</td>\n      <td>0.010484</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003593</td>\n      <td>0.005189</td>\n      <td>0.005273</td>\n      <td>NaN</td>\n      <td>0.007717</td>\n      <td>0.008678</td>\n      <td>0.005638</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>09572fafe01b8bbb560809da84c7d1c8d9e79eb7287bc5...</td>\n      <td>2017-12-15</td>\n      <td>0.655403</td>\n      <td>0.002060</td>\n      <td>0.022364</td>\n      <td>1.007560</td>\n      <td>0.006147</td>\n      <td>0.151334</td>\n      <td>0.009964</td>\n      <td>0.003983</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003830</td>\n      <td>0.007001</td>\n      <td>0.005250</td>\n      <td>NaN</td>\n      <td>0.008395</td>\n      <td>0.000767</td>\n      <td>0.006576</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 190 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"t_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:34.124954Z","iopub.execute_input":"2022-11-04T08:06:34.125285Z","iopub.status.idle":"2022-11-04T08:06:34.133698Z","shell.execute_reply.started":"2022-11-04T08:06:34.125258Z","shell.execute_reply":"2022-11-04T08:06:34.132968Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"customer_ID     object\nS_2             object\nP_2            float64\nD_39           float64\nB_1            float64\n                ...   \nD_141          float64\nD_142          float64\nD_143          float64\nD_144          float64\nD_145          float64\nLength: 190, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"#shape of the dataframe\nt_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:34.182828Z","iopub.execute_input":"2022-11-04T08:06:34.183674Z","iopub.status.idle":"2022-11-04T08:06:34.189454Z","shell.execute_reply.started":"2022-11-04T08:06:34.183645Z","shell.execute_reply":"2022-11-04T08:06:34.188631Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"(200000, 190)"},"metadata":{}}]},{"cell_type":"code","source":"#Summary of the dataframe\nt_df.info()","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:34.338831Z","iopub.execute_input":"2022-11-04T08:06:34.339509Z","iopub.status.idle":"2022-11-04T08:06:34.369681Z","shell.execute_reply.started":"2022-11-04T08:06:34.33948Z","shell.execute_reply":"2022-11-04T08:06:34.368738Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 200000 entries, 0 to 199999\nColumns: 190 entries, customer_ID to D_145\ndtypes: float64(185), int64(1), object(4)\nmemory usage: 289.9+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"#extract columns names for easy access\nd_columns=[col for col in t_df.columns if 'D_' in col]\nprint (\"total number of colums starting with D_:\",len(d_columns))\nprint(d_columns)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:34.638509Z","iopub.execute_input":"2022-11-04T08:06:34.639406Z","iopub.status.idle":"2022-11-04T08:06:34.6453Z","shell.execute_reply.started":"2022-11-04T08:06:34.639368Z","shell.execute_reply":"2022-11-04T08:06:34.644286Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"total number of colums starting with D_: 96\n['D_39', 'D_41', 'D_42', 'D_43', 'D_44', 'D_45', 'D_46', 'D_47', 'D_48', 'D_49', 'D_50', 'D_51', 'D_52', 'D_53', 'D_54', 'D_55', 'D_56', 'D_58', 'D_59', 'D_60', 'D_61', 'D_62', 'D_63', 'D_64', 'D_65', 'D_66', 'D_68', 'D_69', 'D_70', 'D_71', 'D_72', 'D_73', 'D_74', 'D_75', 'D_76', 'D_77', 'D_78', 'D_79', 'D_80', 'D_81', 'D_82', 'D_83', 'D_84', 'D_86', 'D_87', 'D_88', 'D_89', 'D_91', 'D_92', 'D_93', 'D_94', 'D_96', 'D_102', 'D_103', 'D_104', 'D_105', 'D_106', 'D_107', 'D_108', 'D_109', 'D_110', 'D_111', 'D_112', 'D_113', 'D_114', 'D_115', 'D_116', 'D_117', 'D_118', 'D_119', 'D_120', 'D_121', 'D_122', 'D_123', 'D_124', 'D_125', 'D_126', 'D_127', 'D_128', 'D_129', 'D_130', 'D_131', 'D_132', 'D_133', 'D_134', 'D_135', 'D_136', 'D_137', 'D_138', 'D_139', 'D_140', 'D_141', 'D_142', 'D_143', 'D_144', 'D_145']\n","output_type":"stream"}]},{"cell_type":"code","source":"#data for columns starting with D_\nt_df.filter(like='D_',axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:34.925529Z","iopub.execute_input":"2022-11-04T08:06:34.925906Z","iopub.status.idle":"2022-11-04T08:06:34.999424Z","shell.execute_reply.started":"2022-11-04T08:06:34.925875Z","shell.execute_reply":"2022-11-04T08:06:34.997414Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"            D_39      D_41  D_42      D_43      D_44      D_45      D_46  \\\n0       0.001733  0.008771   NaN       NaN  0.000630  0.708906  0.358587   \n1       0.005775  0.000798   NaN       NaN  0.002526  0.712795  0.353630   \n2       0.091505  0.007598   NaN       NaN  0.007605  0.720884  0.334650   \n3       0.002455  0.000685   NaN       NaN  0.006406  0.723997  0.323271   \n4       0.002483  0.004653   NaN       NaN  0.007731  0.720619  0.231009   \n...          ...       ...   ...       ...       ...       ...       ...   \n199995  0.002428  0.002317   NaN  0.008918  0.132358  0.085868  0.497148   \n199996  0.062144  0.007354   NaN  0.015039  0.131428  0.083608  0.502207   \n199997  0.009152  0.001961   NaN  0.009934  0.128482  0.087426  0.582045   \n199998  0.090158  0.004685   NaN  0.009042  0.134761  0.089027  0.525009   \n199999  0.002060  0.009964   NaN  0.008768  0.129202  0.096172  0.467609   \n\n            D_47      D_48  D_49  ...  D_136  D_137  D_138     D_139  \\\n0       0.525351  0.255736   NaN  ...    NaN    NaN    NaN  0.002427   \n1       0.521311  0.223329   NaN  ...    NaN    NaN    NaN  0.003954   \n2       0.524568  0.189424   NaN  ...    NaN    NaN    NaN  0.003269   \n3       0.530929  0.135586   NaN  ...    NaN    NaN    NaN  0.006117   \n4       0.529305       NaN   NaN  ...    NaN    NaN    NaN  0.003671   \n...          ...       ...   ...  ...    ...    ...    ...       ...   \n199995  0.733127  0.141010   NaN  ...    NaN    NaN    NaN  0.001474   \n199996  0.731633  0.138016   NaN  ...    NaN    NaN    NaN  0.003072   \n199997  0.736988  0.122563   NaN  ...    NaN    NaN    NaN  0.006708   \n199998  0.740202  0.099794   NaN  ...    NaN    NaN    NaN  0.003593   \n199999  0.735939  0.087106   NaN  ...    NaN    NaN    NaN  0.003830   \n\n           D_140     D_141  D_142     D_143     D_144     D_145  \n0       0.003706  0.003818    NaN  0.000569  0.000610  0.002674  \n1       0.003167  0.005032    NaN  0.009576  0.005492  0.009217  \n2       0.007329  0.000427    NaN  0.003429  0.006986  0.002603  \n3       0.004516  0.003200    NaN  0.008419  0.006527  0.009600  \n4       0.004946  0.008889    NaN  0.001670  0.008126  0.009827  \n...          ...       ...    ...       ...       ...       ...  \n199995  0.005978  0.003926    NaN  0.007297  0.006689  0.009181  \n199996  0.008800  0.000814    NaN  0.003730  0.005946  0.006949  \n199997  0.007236  0.007473    NaN  0.004778  0.000594  0.001202  \n199998  0.005189  0.005273    NaN  0.007717  0.008678  0.005638  \n199999  0.007001  0.005250    NaN  0.008395  0.000767  0.006576  \n\n[200000 rows x 96 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>D_39</th>\n      <th>D_41</th>\n      <th>D_42</th>\n      <th>D_43</th>\n      <th>D_44</th>\n      <th>D_45</th>\n      <th>D_46</th>\n      <th>D_47</th>\n      <th>D_48</th>\n      <th>D_49</th>\n      <th>...</th>\n      <th>D_136</th>\n      <th>D_137</th>\n      <th>D_138</th>\n      <th>D_139</th>\n      <th>D_140</th>\n      <th>D_141</th>\n      <th>D_142</th>\n      <th>D_143</th>\n      <th>D_144</th>\n      <th>D_145</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.001733</td>\n      <td>0.008771</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000630</td>\n      <td>0.708906</td>\n      <td>0.358587</td>\n      <td>0.525351</td>\n      <td>0.255736</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.002427</td>\n      <td>0.003706</td>\n      <td>0.003818</td>\n      <td>NaN</td>\n      <td>0.000569</td>\n      <td>0.000610</td>\n      <td>0.002674</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.005775</td>\n      <td>0.000798</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.002526</td>\n      <td>0.712795</td>\n      <td>0.353630</td>\n      <td>0.521311</td>\n      <td>0.223329</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003954</td>\n      <td>0.003167</td>\n      <td>0.005032</td>\n      <td>NaN</td>\n      <td>0.009576</td>\n      <td>0.005492</td>\n      <td>0.009217</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.091505</td>\n      <td>0.007598</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.007605</td>\n      <td>0.720884</td>\n      <td>0.334650</td>\n      <td>0.524568</td>\n      <td>0.189424</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003269</td>\n      <td>0.007329</td>\n      <td>0.000427</td>\n      <td>NaN</td>\n      <td>0.003429</td>\n      <td>0.006986</td>\n      <td>0.002603</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.002455</td>\n      <td>0.000685</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006406</td>\n      <td>0.723997</td>\n      <td>0.323271</td>\n      <td>0.530929</td>\n      <td>0.135586</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006117</td>\n      <td>0.004516</td>\n      <td>0.003200</td>\n      <td>NaN</td>\n      <td>0.008419</td>\n      <td>0.006527</td>\n      <td>0.009600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.002483</td>\n      <td>0.004653</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.007731</td>\n      <td>0.720619</td>\n      <td>0.231009</td>\n      <td>0.529305</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003671</td>\n      <td>0.004946</td>\n      <td>0.008889</td>\n      <td>NaN</td>\n      <td>0.001670</td>\n      <td>0.008126</td>\n      <td>0.009827</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>0.002428</td>\n      <td>0.002317</td>\n      <td>NaN</td>\n      <td>0.008918</td>\n      <td>0.132358</td>\n      <td>0.085868</td>\n      <td>0.497148</td>\n      <td>0.733127</td>\n      <td>0.141010</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001474</td>\n      <td>0.005978</td>\n      <td>0.003926</td>\n      <td>NaN</td>\n      <td>0.007297</td>\n      <td>0.006689</td>\n      <td>0.009181</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>0.062144</td>\n      <td>0.007354</td>\n      <td>NaN</td>\n      <td>0.015039</td>\n      <td>0.131428</td>\n      <td>0.083608</td>\n      <td>0.502207</td>\n      <td>0.731633</td>\n      <td>0.138016</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003072</td>\n      <td>0.008800</td>\n      <td>0.000814</td>\n      <td>NaN</td>\n      <td>0.003730</td>\n      <td>0.005946</td>\n      <td>0.006949</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>0.009152</td>\n      <td>0.001961</td>\n      <td>NaN</td>\n      <td>0.009934</td>\n      <td>0.128482</td>\n      <td>0.087426</td>\n      <td>0.582045</td>\n      <td>0.736988</td>\n      <td>0.122563</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006708</td>\n      <td>0.007236</td>\n      <td>0.007473</td>\n      <td>NaN</td>\n      <td>0.004778</td>\n      <td>0.000594</td>\n      <td>0.001202</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>0.090158</td>\n      <td>0.004685</td>\n      <td>NaN</td>\n      <td>0.009042</td>\n      <td>0.134761</td>\n      <td>0.089027</td>\n      <td>0.525009</td>\n      <td>0.740202</td>\n      <td>0.099794</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003593</td>\n      <td>0.005189</td>\n      <td>0.005273</td>\n      <td>NaN</td>\n      <td>0.007717</td>\n      <td>0.008678</td>\n      <td>0.005638</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>0.002060</td>\n      <td>0.009964</td>\n      <td>NaN</td>\n      <td>0.008768</td>\n      <td>0.129202</td>\n      <td>0.096172</td>\n      <td>0.467609</td>\n      <td>0.735939</td>\n      <td>0.087106</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003830</td>\n      <td>0.007001</td>\n      <td>0.005250</td>\n      <td>NaN</td>\n      <td>0.008395</td>\n      <td>0.000767</td>\n      <td>0.006576</td>\n    </tr>\n  </tbody>\n</table>\n<p>200000 rows × 96 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#extract columns names for easy access\ns_columns=[col for col in t_df.columns if 'S_' in col]\nprint (\"total number of colums starting with S_:\",len(s_columns))\nprint(s_columns)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:35.143766Z","iopub.execute_input":"2022-11-04T08:06:35.144648Z","iopub.status.idle":"2022-11-04T08:06:35.151213Z","shell.execute_reply.started":"2022-11-04T08:06:35.144614Z","shell.execute_reply":"2022-11-04T08:06:35.149866Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"total number of colums starting with S_: 22\n['S_2', 'S_3', 'S_5', 'S_6', 'S_7', 'S_8', 'S_9', 'S_11', 'S_12', 'S_13', 'S_15', 'S_16', 'S_17', 'S_18', 'S_19', 'S_20', 'S_22', 'S_23', 'S_24', 'S_25', 'S_26', 'S_27']\n","output_type":"stream"}]},{"cell_type":"code","source":"#data for columns starting with D_\nt_df.filter(like='S_',axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:35.332599Z","iopub.execute_input":"2022-11-04T08:06:35.332999Z","iopub.status.idle":"2022-11-04T08:06:35.388245Z","shell.execute_reply.started":"2022-11-04T08:06:35.332972Z","shell.execute_reply":"2022-11-04T08:06:35.386814Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"               S_2       S_3       S_5       S_6       S_7       S_8  \\\n0       2017-03-09  0.124035  0.023381  0.008322  0.161345  0.922998   \n1       2017-04-07  0.126750  0.030599  0.002482  0.140951  0.919414   \n2       2017-05-28  0.123977  0.048367  0.000530  0.112229  1.001977   \n3       2017-06-13  0.117169  0.030063  0.000783  0.102838  0.704016   \n4       2017-07-16  0.117325  0.054221  0.006698  0.094311  0.917133   \n...            ...       ...       ...       ...       ...       ...   \n199995  2017-08-28  0.155389  0.012120  0.005386  0.103516  0.322559   \n199996  2017-09-15  0.145824  0.020137  0.003455  0.106415  0.328923   \n199997  2017-10-10  0.149669  0.005692  0.000648  0.109146  0.364398   \n199998  2017-11-16  0.153834  0.008997  0.002133  0.103667  0.363039   \n199999  2017-12-15  0.151334  0.010659  0.004060  0.109797  0.604411   \n\n             S_9      S_11      S_12      S_13  ...      S_17      S_18  \\\n0       0.065728  0.401619  0.272008  0.515222  ...  0.008033  0.005720   \n1       0.093935  0.406326  0.188970  0.509048  ...  0.000760  0.007584   \n2       0.084757  0.406768  0.495308  0.679257  ...  0.004056  0.005901   \n3       0.048382  0.405175  0.508670  0.515282  ...  0.006969  0.002520   \n4       0.039259  0.487460  0.216507  0.507712  ...  0.001770  0.000155   \n...          ...       ...       ...       ...  ...       ...       ...   \n199995       NaN  0.321095  0.186297  0.006264  ...  0.009756  0.008361   \n199996       NaN  0.366346  0.219759  0.000435  ...  0.006829  0.007131   \n199997       NaN  0.365339  0.192101  0.001748  ...  0.001689  0.008098   \n199998       NaN  0.408564  0.186177  0.001452  ...  0.003155  0.004967   \n199999       NaN  0.325135  0.190948  0.000474  ...  0.006113  0.004384   \n\n            S_19      S_20      S_22      S_23      S_24      S_25      S_26  \\\n0       0.002537  0.009705  0.894090  0.135561  0.911191  0.974539  0.001243   \n1       0.008427  0.009924  0.902135  0.136333  0.919876  0.975624  0.004561   \n2       0.007327  0.008446  0.939654  0.134938  0.958699  0.974067  0.011736   \n3       0.007053  0.006614  0.913205  0.140058  0.926341  0.975499  0.007571   \n4       0.007728  0.005511  0.921026  0.131620  0.933479  0.978027  0.018200   \n...          ...       ...       ...       ...       ...       ...       ...   \n199995  0.001701  0.003009  0.881482  0.136106  0.886762  0.979001  0.009607   \n199996  0.005066  0.003094  0.900866  0.139611  0.918009  0.975453  0.007869   \n199997  0.001777  0.003946  0.854839  0.133568  0.844380  0.975327  0.003136   \n199998  0.003110  0.004115  0.883965  0.137168  0.889344  0.969646  0.005028   \n199999  0.006395  0.004662  0.906108  0.137456  0.917921  0.978623  0.000831   \n\n            S_27  \n0       0.676922  \n1       0.822281  \n2       0.853498  \n3       0.844667  \n4       0.811199  \n...          ...  \n199995  0.009652  \n199996  0.009405  \n199997  0.002664  \n199998  0.006272  \n199999  0.008721  \n\n[200000 rows x 22 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>S_2</th>\n      <th>S_3</th>\n      <th>S_5</th>\n      <th>S_6</th>\n      <th>S_7</th>\n      <th>S_8</th>\n      <th>S_9</th>\n      <th>S_11</th>\n      <th>S_12</th>\n      <th>S_13</th>\n      <th>...</th>\n      <th>S_17</th>\n      <th>S_18</th>\n      <th>S_19</th>\n      <th>S_20</th>\n      <th>S_22</th>\n      <th>S_23</th>\n      <th>S_24</th>\n      <th>S_25</th>\n      <th>S_26</th>\n      <th>S_27</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2017-03-09</td>\n      <td>0.124035</td>\n      <td>0.023381</td>\n      <td>0.008322</td>\n      <td>0.161345</td>\n      <td>0.922998</td>\n      <td>0.065728</td>\n      <td>0.401619</td>\n      <td>0.272008</td>\n      <td>0.515222</td>\n      <td>...</td>\n      <td>0.008033</td>\n      <td>0.005720</td>\n      <td>0.002537</td>\n      <td>0.009705</td>\n      <td>0.894090</td>\n      <td>0.135561</td>\n      <td>0.911191</td>\n      <td>0.974539</td>\n      <td>0.001243</td>\n      <td>0.676922</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2017-04-07</td>\n      <td>0.126750</td>\n      <td>0.030599</td>\n      <td>0.002482</td>\n      <td>0.140951</td>\n      <td>0.919414</td>\n      <td>0.093935</td>\n      <td>0.406326</td>\n      <td>0.188970</td>\n      <td>0.509048</td>\n      <td>...</td>\n      <td>0.000760</td>\n      <td>0.007584</td>\n      <td>0.008427</td>\n      <td>0.009924</td>\n      <td>0.902135</td>\n      <td>0.136333</td>\n      <td>0.919876</td>\n      <td>0.975624</td>\n      <td>0.004561</td>\n      <td>0.822281</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2017-05-28</td>\n      <td>0.123977</td>\n      <td>0.048367</td>\n      <td>0.000530</td>\n      <td>0.112229</td>\n      <td>1.001977</td>\n      <td>0.084757</td>\n      <td>0.406768</td>\n      <td>0.495308</td>\n      <td>0.679257</td>\n      <td>...</td>\n      <td>0.004056</td>\n      <td>0.005901</td>\n      <td>0.007327</td>\n      <td>0.008446</td>\n      <td>0.939654</td>\n      <td>0.134938</td>\n      <td>0.958699</td>\n      <td>0.974067</td>\n      <td>0.011736</td>\n      <td>0.853498</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2017-06-13</td>\n      <td>0.117169</td>\n      <td>0.030063</td>\n      <td>0.000783</td>\n      <td>0.102838</td>\n      <td>0.704016</td>\n      <td>0.048382</td>\n      <td>0.405175</td>\n      <td>0.508670</td>\n      <td>0.515282</td>\n      <td>...</td>\n      <td>0.006969</td>\n      <td>0.002520</td>\n      <td>0.007053</td>\n      <td>0.006614</td>\n      <td>0.913205</td>\n      <td>0.140058</td>\n      <td>0.926341</td>\n      <td>0.975499</td>\n      <td>0.007571</td>\n      <td>0.844667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2017-07-16</td>\n      <td>0.117325</td>\n      <td>0.054221</td>\n      <td>0.006698</td>\n      <td>0.094311</td>\n      <td>0.917133</td>\n      <td>0.039259</td>\n      <td>0.487460</td>\n      <td>0.216507</td>\n      <td>0.507712</td>\n      <td>...</td>\n      <td>0.001770</td>\n      <td>0.000155</td>\n      <td>0.007728</td>\n      <td>0.005511</td>\n      <td>0.921026</td>\n      <td>0.131620</td>\n      <td>0.933479</td>\n      <td>0.978027</td>\n      <td>0.018200</td>\n      <td>0.811199</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>2017-08-28</td>\n      <td>0.155389</td>\n      <td>0.012120</td>\n      <td>0.005386</td>\n      <td>0.103516</td>\n      <td>0.322559</td>\n      <td>NaN</td>\n      <td>0.321095</td>\n      <td>0.186297</td>\n      <td>0.006264</td>\n      <td>...</td>\n      <td>0.009756</td>\n      <td>0.008361</td>\n      <td>0.001701</td>\n      <td>0.003009</td>\n      <td>0.881482</td>\n      <td>0.136106</td>\n      <td>0.886762</td>\n      <td>0.979001</td>\n      <td>0.009607</td>\n      <td>0.009652</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>2017-09-15</td>\n      <td>0.145824</td>\n      <td>0.020137</td>\n      <td>0.003455</td>\n      <td>0.106415</td>\n      <td>0.328923</td>\n      <td>NaN</td>\n      <td>0.366346</td>\n      <td>0.219759</td>\n      <td>0.000435</td>\n      <td>...</td>\n      <td>0.006829</td>\n      <td>0.007131</td>\n      <td>0.005066</td>\n      <td>0.003094</td>\n      <td>0.900866</td>\n      <td>0.139611</td>\n      <td>0.918009</td>\n      <td>0.975453</td>\n      <td>0.007869</td>\n      <td>0.009405</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>2017-10-10</td>\n      <td>0.149669</td>\n      <td>0.005692</td>\n      <td>0.000648</td>\n      <td>0.109146</td>\n      <td>0.364398</td>\n      <td>NaN</td>\n      <td>0.365339</td>\n      <td>0.192101</td>\n      <td>0.001748</td>\n      <td>...</td>\n      <td>0.001689</td>\n      <td>0.008098</td>\n      <td>0.001777</td>\n      <td>0.003946</td>\n      <td>0.854839</td>\n      <td>0.133568</td>\n      <td>0.844380</td>\n      <td>0.975327</td>\n      <td>0.003136</td>\n      <td>0.002664</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>2017-11-16</td>\n      <td>0.153834</td>\n      <td>0.008997</td>\n      <td>0.002133</td>\n      <td>0.103667</td>\n      <td>0.363039</td>\n      <td>NaN</td>\n      <td>0.408564</td>\n      <td>0.186177</td>\n      <td>0.001452</td>\n      <td>...</td>\n      <td>0.003155</td>\n      <td>0.004967</td>\n      <td>0.003110</td>\n      <td>0.004115</td>\n      <td>0.883965</td>\n      <td>0.137168</td>\n      <td>0.889344</td>\n      <td>0.969646</td>\n      <td>0.005028</td>\n      <td>0.006272</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>2017-12-15</td>\n      <td>0.151334</td>\n      <td>0.010659</td>\n      <td>0.004060</td>\n      <td>0.109797</td>\n      <td>0.604411</td>\n      <td>NaN</td>\n      <td>0.325135</td>\n      <td>0.190948</td>\n      <td>0.000474</td>\n      <td>...</td>\n      <td>0.006113</td>\n      <td>0.004384</td>\n      <td>0.006395</td>\n      <td>0.004662</td>\n      <td>0.906108</td>\n      <td>0.137456</td>\n      <td>0.917921</td>\n      <td>0.978623</td>\n      <td>0.000831</td>\n      <td>0.008721</td>\n    </tr>\n  </tbody>\n</table>\n<p>200000 rows × 22 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#extract columns names for easy access\np_columns=[col for col in t_df.columns if 'P_' in col]\nprint (\"total number of colums starting with P_:\",len(p_columns))\nprint(p_columns)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:35.541663Z","iopub.execute_input":"2022-11-04T08:06:35.542064Z","iopub.status.idle":"2022-11-04T08:06:35.548826Z","shell.execute_reply.started":"2022-11-04T08:06:35.542034Z","shell.execute_reply":"2022-11-04T08:06:35.547492Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"total number of colums starting with P_: 3\n['P_2', 'P_3', 'P_4']\n","output_type":"stream"}]},{"cell_type":"code","source":"#data for columns starting with D_\nt_df.filter(like='P_',axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:35.816953Z","iopub.execute_input":"2022-11-04T08:06:35.817964Z","iopub.status.idle":"2022-11-04T08:06:35.831514Z","shell.execute_reply.started":"2022-11-04T08:06:35.81776Z","shell.execute_reply":"2022-11-04T08:06:35.830374Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"             P_2       P_3       P_4\n0       0.938469  0.736463  0.007554\n1       0.936665  0.720886  0.004832\n2       0.954180  0.738044  0.006561\n3       0.960384  0.741813  0.009559\n4       0.947248  0.691986  0.008156\n...          ...       ...       ...\n199995  0.539011  0.522020  0.001166\n199996  0.545751  0.522165  0.005773\n199997  0.579735  0.646329  0.001366\n199998  0.577626  0.615034  0.009917\n199999  0.655403  0.791043  0.004918\n\n[200000 rows x 3 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>P_2</th>\n      <th>P_3</th>\n      <th>P_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.938469</td>\n      <td>0.736463</td>\n      <td>0.007554</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.936665</td>\n      <td>0.720886</td>\n      <td>0.004832</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.954180</td>\n      <td>0.738044</td>\n      <td>0.006561</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.960384</td>\n      <td>0.741813</td>\n      <td>0.009559</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.947248</td>\n      <td>0.691986</td>\n      <td>0.008156</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>0.539011</td>\n      <td>0.522020</td>\n      <td>0.001166</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>0.545751</td>\n      <td>0.522165</td>\n      <td>0.005773</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>0.579735</td>\n      <td>0.646329</td>\n      <td>0.001366</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>0.577626</td>\n      <td>0.615034</td>\n      <td>0.009917</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>0.655403</td>\n      <td>0.791043</td>\n      <td>0.004918</td>\n    </tr>\n  </tbody>\n</table>\n<p>200000 rows × 3 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#extract columns names for easy access\nb_columns=[col for col in t_df.columns if 'B_' in col]\nprint (\"total number of colums starting with B_:\",len(b_columns))\nprint(b_columns)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:36.038672Z","iopub.execute_input":"2022-11-04T08:06:36.039073Z","iopub.status.idle":"2022-11-04T08:06:36.045599Z","shell.execute_reply.started":"2022-11-04T08:06:36.039041Z","shell.execute_reply":"2022-11-04T08:06:36.044224Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"total number of colums starting with B_: 40\n['B_1', 'B_2', 'B_3', 'B_4', 'B_5', 'B_6', 'B_7', 'B_8', 'B_9', 'B_10', 'B_11', 'B_12', 'B_13', 'B_14', 'B_15', 'B_16', 'B_17', 'B_18', 'B_19', 'B_20', 'B_21', 'B_22', 'B_23', 'B_24', 'B_25', 'B_26', 'B_27', 'B_28', 'B_29', 'B_30', 'B_31', 'B_32', 'B_33', 'B_36', 'B_37', 'B_38', 'B_39', 'B_40', 'B_41', 'B_42']\n","output_type":"stream"}]},{"cell_type":"code","source":"#data for columns starting with D_\nt_df.filter(like='B_',axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:36.321564Z","iopub.execute_input":"2022-11-04T08:06:36.322168Z","iopub.status.idle":"2022-11-04T08:06:36.377039Z","shell.execute_reply.started":"2022-11-04T08:06:36.322135Z","shell.execute_reply":"2022-11-04T08:06:36.375509Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"             B_1       B_2       B_3       B_4       B_5       B_6       B_7  \\\n0       0.008724  1.006838  0.004709  0.080986  0.170600  0.063902  0.059416   \n1       0.004923  1.000653  0.002714  0.069419  0.113239  0.065261  0.057744   \n2       0.021655  1.009672  0.009423  0.068839  0.060492  0.066982  0.056647   \n3       0.013683  1.002700  0.005531  0.055630  0.166782  0.083720  0.049253   \n4       0.015193  1.000727  0.009312  0.038862  0.143630  0.075900  0.048918   \n...          ...       ...       ...       ...       ...       ...       ...   \n199995  0.006173  1.006607  0.014583  0.005547  0.026292  0.121392  0.019991   \n199996  0.014536  0.816225  0.011436  0.020602  0.012180  0.138287  0.022339   \n199997  0.010878  0.815024  0.008332  0.014219  0.012390  0.086974  0.017381   \n199998  0.013903  1.008488  0.010484  0.125175  0.004984  0.079123  0.100139   \n199999  0.022364  1.007560  0.003983  0.090217  0.010813  0.079803  0.076099   \n\n             B_8       B_9      B_10  ...  B_31      B_32      B_33      B_36  \\\n0       0.006466  0.008207  0.096219  ...     1  0.006626  1.001101  0.009968   \n1       0.001614  0.008373  0.099804  ...     1  0.001854  1.006779  0.003921   \n2       0.005126  0.009355  0.134073  ...     1  0.008686  1.001014  0.001264   \n3       0.001418  0.006782  0.134437  ...     1  0.002478  1.002775  0.002729   \n4       0.001199  0.000519  0.121518  ...     1  0.002199  1.006536  0.009998   \n...          ...       ...       ...  ...   ...       ...       ...       ...   \n199995  1.007281  0.006486  0.299724  ...     1  0.009920  1.009008  0.008315   \n199996  1.006993  0.007024  0.328421  ...     1  0.008464  1.009319  0.005408   \n199997  1.001861  0.004900  0.313672  ...     1  0.003971  1.008971  0.002997   \n199998  1.007477  0.001398  0.115795  ...     1  0.000154  1.009978  0.006518   \n199999  1.009641  0.003938  0.110191  ...     1  0.000246  1.008834  0.001792   \n\n            B_37  B_38  B_39      B_40      B_41  B_42  \n0       0.004572   2.0   NaN  0.210060  0.006805   NaN  \n1       0.004654   2.0   NaN  0.184093  0.004407   NaN  \n2       0.019176   2.0   NaN  0.154837  0.003221   NaN  \n3       0.011720   2.0   NaN  0.153939  0.007703   NaN  \n4       0.017598   2.0   NaN  0.120717  0.009823   NaN  \n...          ...   ...   ...       ...       ...   ...  \n199995  0.006312   3.0   NaN  0.019257  0.005978   NaN  \n199996  0.012990   2.0   NaN  0.012446  0.008493   NaN  \n199997  0.006903   2.0   NaN  0.015656  0.000014   NaN  \n199998  0.007405   2.0   NaN  0.100259  0.008226   NaN  \n199999  0.026603   2.0   NaN  0.082225  0.005686   NaN  \n\n[200000 rows x 40 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>B_1</th>\n      <th>B_2</th>\n      <th>B_3</th>\n      <th>B_4</th>\n      <th>B_5</th>\n      <th>B_6</th>\n      <th>B_7</th>\n      <th>B_8</th>\n      <th>B_9</th>\n      <th>B_10</th>\n      <th>...</th>\n      <th>B_31</th>\n      <th>B_32</th>\n      <th>B_33</th>\n      <th>B_36</th>\n      <th>B_37</th>\n      <th>B_38</th>\n      <th>B_39</th>\n      <th>B_40</th>\n      <th>B_41</th>\n      <th>B_42</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.008724</td>\n      <td>1.006838</td>\n      <td>0.004709</td>\n      <td>0.080986</td>\n      <td>0.170600</td>\n      <td>0.063902</td>\n      <td>0.059416</td>\n      <td>0.006466</td>\n      <td>0.008207</td>\n      <td>0.096219</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.006626</td>\n      <td>1.001101</td>\n      <td>0.009968</td>\n      <td>0.004572</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.210060</td>\n      <td>0.006805</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.004923</td>\n      <td>1.000653</td>\n      <td>0.002714</td>\n      <td>0.069419</td>\n      <td>0.113239</td>\n      <td>0.065261</td>\n      <td>0.057744</td>\n      <td>0.001614</td>\n      <td>0.008373</td>\n      <td>0.099804</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.001854</td>\n      <td>1.006779</td>\n      <td>0.003921</td>\n      <td>0.004654</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.184093</td>\n      <td>0.004407</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.021655</td>\n      <td>1.009672</td>\n      <td>0.009423</td>\n      <td>0.068839</td>\n      <td>0.060492</td>\n      <td>0.066982</td>\n      <td>0.056647</td>\n      <td>0.005126</td>\n      <td>0.009355</td>\n      <td>0.134073</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.008686</td>\n      <td>1.001014</td>\n      <td>0.001264</td>\n      <td>0.019176</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.154837</td>\n      <td>0.003221</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.013683</td>\n      <td>1.002700</td>\n      <td>0.005531</td>\n      <td>0.055630</td>\n      <td>0.166782</td>\n      <td>0.083720</td>\n      <td>0.049253</td>\n      <td>0.001418</td>\n      <td>0.006782</td>\n      <td>0.134437</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.002478</td>\n      <td>1.002775</td>\n      <td>0.002729</td>\n      <td>0.011720</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.153939</td>\n      <td>0.007703</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.015193</td>\n      <td>1.000727</td>\n      <td>0.009312</td>\n      <td>0.038862</td>\n      <td>0.143630</td>\n      <td>0.075900</td>\n      <td>0.048918</td>\n      <td>0.001199</td>\n      <td>0.000519</td>\n      <td>0.121518</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.002199</td>\n      <td>1.006536</td>\n      <td>0.009998</td>\n      <td>0.017598</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.120717</td>\n      <td>0.009823</td>\n      <td>NaN</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>0.006173</td>\n      <td>1.006607</td>\n      <td>0.014583</td>\n      <td>0.005547</td>\n      <td>0.026292</td>\n      <td>0.121392</td>\n      <td>0.019991</td>\n      <td>1.007281</td>\n      <td>0.006486</td>\n      <td>0.299724</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.009920</td>\n      <td>1.009008</td>\n      <td>0.008315</td>\n      <td>0.006312</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>0.019257</td>\n      <td>0.005978</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>0.014536</td>\n      <td>0.816225</td>\n      <td>0.011436</td>\n      <td>0.020602</td>\n      <td>0.012180</td>\n      <td>0.138287</td>\n      <td>0.022339</td>\n      <td>1.006993</td>\n      <td>0.007024</td>\n      <td>0.328421</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.008464</td>\n      <td>1.009319</td>\n      <td>0.005408</td>\n      <td>0.012990</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.012446</td>\n      <td>0.008493</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>0.010878</td>\n      <td>0.815024</td>\n      <td>0.008332</td>\n      <td>0.014219</td>\n      <td>0.012390</td>\n      <td>0.086974</td>\n      <td>0.017381</td>\n      <td>1.001861</td>\n      <td>0.004900</td>\n      <td>0.313672</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.003971</td>\n      <td>1.008971</td>\n      <td>0.002997</td>\n      <td>0.006903</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.015656</td>\n      <td>0.000014</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>0.013903</td>\n      <td>1.008488</td>\n      <td>0.010484</td>\n      <td>0.125175</td>\n      <td>0.004984</td>\n      <td>0.079123</td>\n      <td>0.100139</td>\n      <td>1.007477</td>\n      <td>0.001398</td>\n      <td>0.115795</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.000154</td>\n      <td>1.009978</td>\n      <td>0.006518</td>\n      <td>0.007405</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.100259</td>\n      <td>0.008226</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>0.022364</td>\n      <td>1.007560</td>\n      <td>0.003983</td>\n      <td>0.090217</td>\n      <td>0.010813</td>\n      <td>0.079803</td>\n      <td>0.076099</td>\n      <td>1.009641</td>\n      <td>0.003938</td>\n      <td>0.110191</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0.000246</td>\n      <td>1.008834</td>\n      <td>0.001792</td>\n      <td>0.026603</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.082225</td>\n      <td>0.005686</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>200000 rows × 40 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#extract columns names for easy access\nr_columns=[col for col in t_df.columns if 'R_' in col]\nprint (\"total number of colums starting with R_:\",len(r_columns))\nprint(r_columns)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:36.64583Z","iopub.execute_input":"2022-11-04T08:06:36.6462Z","iopub.status.idle":"2022-11-04T08:06:36.653508Z","shell.execute_reply.started":"2022-11-04T08:06:36.646174Z","shell.execute_reply":"2022-11-04T08:06:36.651987Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"total number of colums starting with R_: 28\n['R_1', 'R_2', 'R_3', 'R_4', 'R_5', 'R_6', 'R_7', 'R_8', 'R_9', 'R_10', 'R_11', 'R_12', 'R_13', 'R_14', 'R_15', 'R_16', 'R_17', 'R_18', 'R_19', 'R_20', 'R_21', 'R_22', 'R_23', 'R_24', 'R_25', 'R_26', 'R_27', 'R_28']\n","output_type":"stream"}]},{"cell_type":"code","source":"#data for columns starting with D_\nt_df.filter(like='R_',axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:37.033525Z","iopub.execute_input":"2022-11-04T08:06:37.034583Z","iopub.status.idle":"2022-11-04T08:06:37.077477Z","shell.execute_reply.started":"2022-11-04T08:06:37.034525Z","shell.execute_reply":"2022-11-04T08:06:37.076609Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"             R_1       R_2       R_3       R_4       R_5       R_6       R_7  \\\n0       0.009228  0.006204  0.001423  0.008298  0.001882  0.008363  0.007562   \n1       0.006151  0.006206  0.001984  0.005136  0.001610  0.004030  0.005304   \n2       0.006815  0.003259  0.007426  0.006961  0.006328  0.006838  0.001422   \n3       0.001373  0.009918  0.003515  0.008706  0.004980  0.008183  0.006363   \n4       0.007605  0.006667  0.001362  0.003846  0.001653  0.008605  0.004831   \n...          ...       ...       ...       ...       ...       ...       ...   \n199995  0.007721  0.001900  0.003054  0.001098  0.007419  0.000810  0.009935   \n199996  0.009416  0.009152  0.003218  0.003483  0.003115  0.008392  0.007041   \n199997  0.005641  0.002904  0.006054  0.005104  0.005332  0.002271  0.009310   \n199998  0.007109  0.001038  0.009323  0.003336  0.001104  0.002859  0.008389   \n199999  0.006147  0.002115  0.003693  0.002776  0.007512  0.009223  0.005972   \n\n             R_8  R_9      R_10  ...      R_19      R_20      R_21      R_22  \\\n0       0.001434  NaN  0.007121  ...  0.005177  0.007782  0.002450  0.007479   \n1       0.000509  NaN  0.005966  ...  0.008979  0.005987  0.002247  0.006827   \n2       0.008295  NaN  0.005447  ...  0.002016  0.007291  0.007794  0.009820   \n3       0.005153  NaN  0.001888  ...  0.003909  0.009977  0.007686  0.000458   \n4       0.007338  NaN  0.006111  ...  0.003432  0.004105  0.009656  0.003341   \n...          ...  ...       ...  ...       ...       ...       ...       ...   \n199995  0.004653  NaN  0.002228  ...  0.009139  0.005118  0.006673  0.007088   \n199996  0.008398  NaN  0.002385  ...  0.006671  0.004209  0.009013  0.008342   \n199997  0.000856  NaN  0.003273  ...  0.002794  0.001084  0.004683  0.000785   \n199998  0.000602  NaN  0.006711  ...  0.007136  0.005972  0.007200  0.002062   \n199999  0.005174  NaN  0.006757  ...  0.000627  0.004851  0.006083  0.003161   \n\n            R_23      R_24      R_25  R_26      R_27      R_28  \n0       0.006893  0.003950  0.003647   NaN  1.008949  0.001535  \n1       0.002837  0.008351  0.008850   NaN  1.003205  0.004931  \n2       0.005080  0.002471  0.009769   NaN  1.000754  0.009123  \n3       0.007320  0.008507  0.004858   NaN  1.005338  0.002409  \n4       0.000264  0.007190  0.002983   NaN  1.003175  0.004462  \n...          ...       ...       ...   ...       ...       ...  \n199995  0.008682  0.008265  0.006005   NaN  1.004729  0.003938  \n199996  0.001726  0.000757  0.001559   NaN  1.005343  0.008879  \n199997  0.005856  0.007442  0.004040   NaN  1.000900  0.002692  \n199998  0.002504  0.001526  0.007132   NaN  1.008970  0.008715  \n199999  0.008352  0.004313  0.000805   NaN  1.000833  0.007962  \n\n[200000 rows x 28 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>R_1</th>\n      <th>R_2</th>\n      <th>R_3</th>\n      <th>R_4</th>\n      <th>R_5</th>\n      <th>R_6</th>\n      <th>R_7</th>\n      <th>R_8</th>\n      <th>R_9</th>\n      <th>R_10</th>\n      <th>...</th>\n      <th>R_19</th>\n      <th>R_20</th>\n      <th>R_21</th>\n      <th>R_22</th>\n      <th>R_23</th>\n      <th>R_24</th>\n      <th>R_25</th>\n      <th>R_26</th>\n      <th>R_27</th>\n      <th>R_28</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.009228</td>\n      <td>0.006204</td>\n      <td>0.001423</td>\n      <td>0.008298</td>\n      <td>0.001882</td>\n      <td>0.008363</td>\n      <td>0.007562</td>\n      <td>0.001434</td>\n      <td>NaN</td>\n      <td>0.007121</td>\n      <td>...</td>\n      <td>0.005177</td>\n      <td>0.007782</td>\n      <td>0.002450</td>\n      <td>0.007479</td>\n      <td>0.006893</td>\n      <td>0.003950</td>\n      <td>0.003647</td>\n      <td>NaN</td>\n      <td>1.008949</td>\n      <td>0.001535</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.006151</td>\n      <td>0.006206</td>\n      <td>0.001984</td>\n      <td>0.005136</td>\n      <td>0.001610</td>\n      <td>0.004030</td>\n      <td>0.005304</td>\n      <td>0.000509</td>\n      <td>NaN</td>\n      <td>0.005966</td>\n      <td>...</td>\n      <td>0.008979</td>\n      <td>0.005987</td>\n      <td>0.002247</td>\n      <td>0.006827</td>\n      <td>0.002837</td>\n      <td>0.008351</td>\n      <td>0.008850</td>\n      <td>NaN</td>\n      <td>1.003205</td>\n      <td>0.004931</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.006815</td>\n      <td>0.003259</td>\n      <td>0.007426</td>\n      <td>0.006961</td>\n      <td>0.006328</td>\n      <td>0.006838</td>\n      <td>0.001422</td>\n      <td>0.008295</td>\n      <td>NaN</td>\n      <td>0.005447</td>\n      <td>...</td>\n      <td>0.002016</td>\n      <td>0.007291</td>\n      <td>0.007794</td>\n      <td>0.009820</td>\n      <td>0.005080</td>\n      <td>0.002471</td>\n      <td>0.009769</td>\n      <td>NaN</td>\n      <td>1.000754</td>\n      <td>0.009123</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.001373</td>\n      <td>0.009918</td>\n      <td>0.003515</td>\n      <td>0.008706</td>\n      <td>0.004980</td>\n      <td>0.008183</td>\n      <td>0.006363</td>\n      <td>0.005153</td>\n      <td>NaN</td>\n      <td>0.001888</td>\n      <td>...</td>\n      <td>0.003909</td>\n      <td>0.009977</td>\n      <td>0.007686</td>\n      <td>0.000458</td>\n      <td>0.007320</td>\n      <td>0.008507</td>\n      <td>0.004858</td>\n      <td>NaN</td>\n      <td>1.005338</td>\n      <td>0.002409</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.007605</td>\n      <td>0.006667</td>\n      <td>0.001362</td>\n      <td>0.003846</td>\n      <td>0.001653</td>\n      <td>0.008605</td>\n      <td>0.004831</td>\n      <td>0.007338</td>\n      <td>NaN</td>\n      <td>0.006111</td>\n      <td>...</td>\n      <td>0.003432</td>\n      <td>0.004105</td>\n      <td>0.009656</td>\n      <td>0.003341</td>\n      <td>0.000264</td>\n      <td>0.007190</td>\n      <td>0.002983</td>\n      <td>NaN</td>\n      <td>1.003175</td>\n      <td>0.004462</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>199995</th>\n      <td>0.007721</td>\n      <td>0.001900</td>\n      <td>0.003054</td>\n      <td>0.001098</td>\n      <td>0.007419</td>\n      <td>0.000810</td>\n      <td>0.009935</td>\n      <td>0.004653</td>\n      <td>NaN</td>\n      <td>0.002228</td>\n      <td>...</td>\n      <td>0.009139</td>\n      <td>0.005118</td>\n      <td>0.006673</td>\n      <td>0.007088</td>\n      <td>0.008682</td>\n      <td>0.008265</td>\n      <td>0.006005</td>\n      <td>NaN</td>\n      <td>1.004729</td>\n      <td>0.003938</td>\n    </tr>\n    <tr>\n      <th>199996</th>\n      <td>0.009416</td>\n      <td>0.009152</td>\n      <td>0.003218</td>\n      <td>0.003483</td>\n      <td>0.003115</td>\n      <td>0.008392</td>\n      <td>0.007041</td>\n      <td>0.008398</td>\n      <td>NaN</td>\n      <td>0.002385</td>\n      <td>...</td>\n      <td>0.006671</td>\n      <td>0.004209</td>\n      <td>0.009013</td>\n      <td>0.008342</td>\n      <td>0.001726</td>\n      <td>0.000757</td>\n      <td>0.001559</td>\n      <td>NaN</td>\n      <td>1.005343</td>\n      <td>0.008879</td>\n    </tr>\n    <tr>\n      <th>199997</th>\n      <td>0.005641</td>\n      <td>0.002904</td>\n      <td>0.006054</td>\n      <td>0.005104</td>\n      <td>0.005332</td>\n      <td>0.002271</td>\n      <td>0.009310</td>\n      <td>0.000856</td>\n      <td>NaN</td>\n      <td>0.003273</td>\n      <td>...</td>\n      <td>0.002794</td>\n      <td>0.001084</td>\n      <td>0.004683</td>\n      <td>0.000785</td>\n      <td>0.005856</td>\n      <td>0.007442</td>\n      <td>0.004040</td>\n      <td>NaN</td>\n      <td>1.000900</td>\n      <td>0.002692</td>\n    </tr>\n    <tr>\n      <th>199998</th>\n      <td>0.007109</td>\n      <td>0.001038</td>\n      <td>0.009323</td>\n      <td>0.003336</td>\n      <td>0.001104</td>\n      <td>0.002859</td>\n      <td>0.008389</td>\n      <td>0.000602</td>\n      <td>NaN</td>\n      <td>0.006711</td>\n      <td>...</td>\n      <td>0.007136</td>\n      <td>0.005972</td>\n      <td>0.007200</td>\n      <td>0.002062</td>\n      <td>0.002504</td>\n      <td>0.001526</td>\n      <td>0.007132</td>\n      <td>NaN</td>\n      <td>1.008970</td>\n      <td>0.008715</td>\n    </tr>\n    <tr>\n      <th>199999</th>\n      <td>0.006147</td>\n      <td>0.002115</td>\n      <td>0.003693</td>\n      <td>0.002776</td>\n      <td>0.007512</td>\n      <td>0.009223</td>\n      <td>0.005972</td>\n      <td>0.005174</td>\n      <td>NaN</td>\n      <td>0.006757</td>\n      <td>...</td>\n      <td>0.000627</td>\n      <td>0.004851</td>\n      <td>0.006083</td>\n      <td>0.003161</td>\n      <td>0.008352</td>\n      <td>0.004313</td>\n      <td>0.000805</td>\n      <td>NaN</td>\n      <td>1.000833</td>\n      <td>0.007962</td>\n    </tr>\n  </tbody>\n</table>\n<p>200000 rows × 28 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**D_*=Delinquency variables**","metadata":{}},{"cell_type":"code","source":"#display summary of column D\nprint(t_df[d_columns].info())","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:38.881695Z","iopub.execute_input":"2022-11-04T08:06:38.882731Z","iopub.status.idle":"2022-11-04T08:06:38.973839Z","shell.execute_reply.started":"2022-11-04T08:06:38.882693Z","shell.execute_reply":"2022-11-04T08:06:38.972854Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 200000 entries, 0 to 199999\nData columns (total 96 columns):\n #   Column  Non-Null Count   Dtype  \n---  ------  --------------   -----  \n 0   D_39    200000 non-null  float64\n 1   D_41    199928 non-null  float64\n 2   D_42    29193 non-null   float64\n 3   D_43    139942 non-null  float64\n 4   D_44    190071 non-null  float64\n 5   D_45    199928 non-null  float64\n 6   D_46    156105 non-null  float64\n 7   D_47    200000 non-null  float64\n 8   D_48    173989 non-null  float64\n 9   D_49    20462 non-null   float64\n 10  D_50    86464 non-null   float64\n 11  D_51    200000 non-null  float64\n 12  D_52    198991 non-null  float64\n 13  D_53    52597 non-null   float64\n 14  D_54    199928 non-null  float64\n 15  D_55    193476 non-null  float64\n 16  D_56    91119 non-null   float64\n 17  D_58    200000 non-null  float64\n 18  D_59    196233 non-null  float64\n 19  D_60    200000 non-null  float64\n 20  D_61    178557 non-null  float64\n 21  D_62    172567 non-null  float64\n 22  D_63    200000 non-null  object \n 23  D_64    191960 non-null  object \n 24  D_65    200000 non-null  float64\n 25  D_66    22180 non-null   float64\n 26  D_68    192128 non-null  float64\n 27  D_69    193006 non-null  float64\n 28  D_70    196680 non-null  float64\n 29  D_71    200000 non-null  float64\n 30  D_72    199202 non-null  float64\n 31  D_73    2196 non-null    float64\n 32  D_74    199326 non-null  float64\n 33  D_75    200000 non-null  float64\n 34  D_76    22344 non-null   float64\n 35  D_77    108405 non-null  float64\n 36  D_78    190071 non-null  float64\n 37  D_79    197398 non-null  float64\n 38  D_80    199326 non-null  float64\n 39  D_81    199090 non-null  float64\n 40  D_82    53319 non-null   float64\n 41  D_83    193006 non-null  float64\n 42  D_84    198991 non-null  float64\n 43  D_86    200000 non-null  float64\n 44  D_87    116 non-null     float64\n 45  D_88    264 non-null     float64\n 46  D_89    198991 non-null  float64\n 47  D_91    194413 non-null  float64\n 48  D_92    200000 non-null  float64\n 49  D_93    200000 non-null  float64\n 50  D_94    200000 non-null  float64\n 51  D_96    200000 non-null  float64\n 52  D_102   198579 non-null  float64\n 53  D_103   196497 non-null  float64\n 54  D_104   196497 non-null  float64\n 55  D_105   90933 non-null   float64\n 56  D_106   20304 non-null   float64\n 57  D_107   196497 non-null  float64\n 58  D_108   1058 non-null    float64\n 59  D_109   199929 non-null  float64\n 60  D_110   1232 non-null    float64\n 61  D_111   1232 non-null    float64\n 62  D_112   199894 non-null  float64\n 63  D_113   193655 non-null  float64\n 64  D_114   193655 non-null  float64\n 65  D_115   193655 non-null  float64\n 66  D_116   193655 non-null  float64\n 67  D_117   193655 non-null  float64\n 68  D_118   193655 non-null  float64\n 69  D_119   193655 non-null  float64\n 70  D_120   193655 non-null  float64\n 71  D_121   193655 non-null  float64\n 72  D_122   193655 non-null  float64\n 73  D_123   193655 non-null  float64\n 74  D_124   193655 non-null  float64\n 75  D_125   193655 non-null  float64\n 76  D_126   195775 non-null  float64\n 77  D_127   200000 non-null  float64\n 78  D_128   196497 non-null  float64\n 79  D_129   196497 non-null  float64\n 80  D_130   196497 non-null  float64\n 81  D_131   196497 non-null  float64\n 82  D_132   20358 non-null   float64\n 83  D_133   198519 non-null  float64\n 84  D_134   7186 non-null    float64\n 85  D_135   7186 non-null    float64\n 86  D_136   7186 non-null    float64\n 87  D_137   7186 non-null    float64\n 88  D_138   7186 non-null    float64\n 89  D_139   196497 non-null  float64\n 90  D_140   198581 non-null  float64\n 91  D_141   196497 non-null  float64\n 92  D_142   34356 non-null   float64\n 93  D_143   196497 non-null  float64\n 94  D_144   198567 non-null  float64\n 95  D_145   196497 non-null  float64\ndtypes: float64(94), object(2)\nmemory usage: 146.5+ MB\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"t_df[d_columns].describe(include='all')","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:06:39.141268Z","iopub.execute_input":"2022-11-04T08:06:39.141861Z","iopub.status.idle":"2022-11-04T08:06:39.988742Z","shell.execute_reply.started":"2022-11-04T08:06:39.141817Z","shell.execute_reply":"2022-11-04T08:06:39.987603Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"                D_39          D_41          D_42          D_43          D_44  \\\ncount   2.000000e+05  1.999280e+05  29193.000000  1.399420e+05  1.900710e+05   \nunique           NaN           NaN           NaN           NaN           NaN   \ntop              NaN           NaN           NaN           NaN           NaN   \nfreq             NaN           NaN           NaN           NaN           NaN   \nmean    1.534126e-01  6.221769e-02      0.181170  1.560354e-01  1.211628e-01   \nstd     2.723813e-01  2.081621e-01      0.216851  2.169740e-01  2.225137e-01   \nmin     3.892609e-07  5.627163e-08     -0.000219  8.705647e-07  1.031370e-08   \n25%     4.541390e-03  2.897833e-03      0.039449  4.235285e-02  3.895040e-03   \n50%     9.066380e-03  5.768750e-03      0.120749  8.828850e-02  7.754950e-03   \n75%     2.360171e-01  8.652639e-03      0.250728  1.850985e-01  1.323509e-01   \nmax     5.331360e+00  6.798167e+00      3.252056  9.089694e+00  2.880841e+00   \n\n                 D_45           D_46           D_47           D_48  \\\ncount   199928.000000  156105.000000  200000.000000  173989.000000   \nunique            NaN            NaN            NaN            NaN   \ntop               NaN            NaN            NaN            NaN   \nfreq              NaN            NaN            NaN            NaN   \nmean         0.251120       0.474857       0.405916       0.387539   \nstd          0.241662       0.172133       0.234302       0.327490   \nmin          0.000002      -3.943876      -0.026620      -0.009609   \n25%          0.053897       0.425019       0.233453       0.083570   \n50%          0.181943       0.459619       0.380551       0.296788   \n75%          0.372579       0.516514       0.554812       0.679270   \nmax          1.561621       5.096873       1.299580       2.857023   \n\n                D_49  ...        D_136         D_137        D_138  \\\ncount   20462.000000  ...  7186.000000  7.186000e+03  7186.000000   \nunique           NaN  ...          NaN           NaN          NaN   \ntop              NaN  ...          NaN           NaN          NaN   \nfreq             NaN  ...          NaN           NaN          NaN   \nmean        0.188989  ...     0.247905  1.424359e-02     0.162349   \nstd         0.195089  ...     0.209903  9.543979e-02     0.258647   \nmin         0.000018  ...     0.000001  4.129697e-08     0.000002   \n25%         0.062665  ...     0.009480  2.589397e-03     0.003541   \n50%         0.132630  ...     0.254192  5.141117e-03     0.007028   \n75%         0.254204  ...     0.258553  7.667529e-03     0.501621   \nmax         2.922477  ...     1.505785  1.009913e+00     1.509486   \n\n               D_139         D_140         D_141         D_142         D_143  \\\ncount   1.964970e+05  1.985810e+05  1.964970e+05  34356.000000  1.964970e+05   \nunique           NaN           NaN           NaN           NaN           NaN   \ntop              NaN           NaN           NaN           NaN           NaN   \nfreq             NaN           NaN           NaN           NaN           NaN   \nmean    1.798358e-01  2.622542e-02  1.653822e-01      0.392596  1.797362e-01   \nstd     3.798505e-01  1.441568e-01  3.490555e-01      0.238923  3.797464e-01   \nmin     7.139375e-08  5.277736e-08  5.642931e-08     -0.014441  1.653580e-08   \n25%     3.018427e-03  2.551375e-03  3.033245e-03      0.196174  3.034162e-03   \n50%     6.041458e-03  5.109293e-03  6.062756e-03      0.382038  6.077791e-03   \n75%     9.096570e-03  7.658175e-03  9.094362e-03      0.566102  9.088631e-03   \nmax     1.010000e+00  1.009994e+00  1.174753e+00      1.751388  1.010000e+00   \n\n               D_144         D_145  \ncount   1.985670e+05  1.964970e+05  \nunique           NaN           NaN  \ntop              NaN           NaN  \nfreq             NaN           NaN  \nmean    5.339187e-02  6.179908e-02  \nstd     1.851044e-01  1.901802e-01  \nmin     1.161969e-07  3.397747e-08  \n25%     2.752575e-03  3.027842e-03  \n50%     5.499113e-03  6.059988e-03  \n75%     8.267479e-03  9.093526e-03  \nmax     1.343284e+00  4.282032e+00  \n\n[11 rows x 96 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>D_39</th>\n      <th>D_41</th>\n      <th>D_42</th>\n      <th>D_43</th>\n      <th>D_44</th>\n      <th>D_45</th>\n      <th>D_46</th>\n      <th>D_47</th>\n      <th>D_48</th>\n      <th>D_49</th>\n      <th>...</th>\n      <th>D_136</th>\n      <th>D_137</th>\n      <th>D_138</th>\n      <th>D_139</th>\n      <th>D_140</th>\n      <th>D_141</th>\n      <th>D_142</th>\n      <th>D_143</th>\n      <th>D_144</th>\n      <th>D_145</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2.000000e+05</td>\n      <td>1.999280e+05</td>\n      <td>29193.000000</td>\n      <td>1.399420e+05</td>\n      <td>1.900710e+05</td>\n      <td>199928.000000</td>\n      <td>156105.000000</td>\n      <td>200000.000000</td>\n      <td>173989.000000</td>\n      <td>20462.000000</td>\n      <td>...</td>\n      <td>7186.000000</td>\n      <td>7.186000e+03</td>\n      <td>7186.000000</td>\n      <td>1.964970e+05</td>\n      <td>1.985810e+05</td>\n      <td>1.964970e+05</td>\n      <td>34356.000000</td>\n      <td>1.964970e+05</td>\n      <td>1.985670e+05</td>\n      <td>1.964970e+05</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.534126e-01</td>\n      <td>6.221769e-02</td>\n      <td>0.181170</td>\n      <td>1.560354e-01</td>\n      <td>1.211628e-01</td>\n      <td>0.251120</td>\n      <td>0.474857</td>\n      <td>0.405916</td>\n      <td>0.387539</td>\n      <td>0.188989</td>\n      <td>...</td>\n      <td>0.247905</td>\n      <td>1.424359e-02</td>\n      <td>0.162349</td>\n      <td>1.798358e-01</td>\n      <td>2.622542e-02</td>\n      <td>1.653822e-01</td>\n      <td>0.392596</td>\n      <td>1.797362e-01</td>\n      <td>5.339187e-02</td>\n      <td>6.179908e-02</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.723813e-01</td>\n      <td>2.081621e-01</td>\n      <td>0.216851</td>\n      <td>2.169740e-01</td>\n      <td>2.225137e-01</td>\n      <td>0.241662</td>\n      <td>0.172133</td>\n      <td>0.234302</td>\n      <td>0.327490</td>\n      <td>0.195089</td>\n      <td>...</td>\n      <td>0.209903</td>\n      <td>9.543979e-02</td>\n      <td>0.258647</td>\n      <td>3.798505e-01</td>\n      <td>1.441568e-01</td>\n      <td>3.490555e-01</td>\n      <td>0.238923</td>\n      <td>3.797464e-01</td>\n      <td>1.851044e-01</td>\n      <td>1.901802e-01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.892609e-07</td>\n      <td>5.627163e-08</td>\n      <td>-0.000219</td>\n      <td>8.705647e-07</td>\n      <td>1.031370e-08</td>\n      <td>0.000002</td>\n      <td>-3.943876</td>\n      <td>-0.026620</td>\n      <td>-0.009609</td>\n      <td>0.000018</td>\n      <td>...</td>\n      <td>0.000001</td>\n      <td>4.129697e-08</td>\n      <td>0.000002</td>\n      <td>7.139375e-08</td>\n      <td>5.277736e-08</td>\n      <td>5.642931e-08</td>\n      <td>-0.014441</td>\n      <td>1.653580e-08</td>\n      <td>1.161969e-07</td>\n      <td>3.397747e-08</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>4.541390e-03</td>\n      <td>2.897833e-03</td>\n      <td>0.039449</td>\n      <td>4.235285e-02</td>\n      <td>3.895040e-03</td>\n      <td>0.053897</td>\n      <td>0.425019</td>\n      <td>0.233453</td>\n      <td>0.083570</td>\n      <td>0.062665</td>\n      <td>...</td>\n      <td>0.009480</td>\n      <td>2.589397e-03</td>\n      <td>0.003541</td>\n      <td>3.018427e-03</td>\n      <td>2.551375e-03</td>\n      <td>3.033245e-03</td>\n      <td>0.196174</td>\n      <td>3.034162e-03</td>\n      <td>2.752575e-03</td>\n      <td>3.027842e-03</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>9.066380e-03</td>\n      <td>5.768750e-03</td>\n      <td>0.120749</td>\n      <td>8.828850e-02</td>\n      <td>7.754950e-03</td>\n      <td>0.181943</td>\n      <td>0.459619</td>\n      <td>0.380551</td>\n      <td>0.296788</td>\n      <td>0.132630</td>\n      <td>...</td>\n      <td>0.254192</td>\n      <td>5.141117e-03</td>\n      <td>0.007028</td>\n      <td>6.041458e-03</td>\n      <td>5.109293e-03</td>\n      <td>6.062756e-03</td>\n      <td>0.382038</td>\n      <td>6.077791e-03</td>\n      <td>5.499113e-03</td>\n      <td>6.059988e-03</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.360171e-01</td>\n      <td>8.652639e-03</td>\n      <td>0.250728</td>\n      <td>1.850985e-01</td>\n      <td>1.323509e-01</td>\n      <td>0.372579</td>\n      <td>0.516514</td>\n      <td>0.554812</td>\n      <td>0.679270</td>\n      <td>0.254204</td>\n      <td>...</td>\n      <td>0.258553</td>\n      <td>7.667529e-03</td>\n      <td>0.501621</td>\n      <td>9.096570e-03</td>\n      <td>7.658175e-03</td>\n      <td>9.094362e-03</td>\n      <td>0.566102</td>\n      <td>9.088631e-03</td>\n      <td>8.267479e-03</td>\n      <td>9.093526e-03</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>5.331360e+00</td>\n      <td>6.798167e+00</td>\n      <td>3.252056</td>\n      <td>9.089694e+00</td>\n      <td>2.880841e+00</td>\n      <td>1.561621</td>\n      <td>5.096873</td>\n      <td>1.299580</td>\n      <td>2.857023</td>\n      <td>2.922477</td>\n      <td>...</td>\n      <td>1.505785</td>\n      <td>1.009913e+00</td>\n      <td>1.509486</td>\n      <td>1.010000e+00</td>\n      <td>1.009994e+00</td>\n      <td>1.174753e+00</td>\n      <td>1.751388</td>\n      <td>1.010000e+00</td>\n      <td>1.343284e+00</td>\n      <td>4.282032e+00</td>\n    </tr>\n  </tbody>\n</table>\n<p>11 rows × 96 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import missingno as msno\n#missing value plot\nmsno.bar(t_df[d_columns[:50]],figsize=(25,10),sort=\"ascending\",color=\"red\")\n","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:31:36.794487Z","iopub.execute_input":"2022-11-04T08:31:36.795203Z","iopub.status.idle":"2022-11-04T08:31:44.914629Z","shell.execute_reply.started":"2022-11-04T08:31:36.795159Z","shell.execute_reply":"2022-11-04T08:31:44.913676Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x720 with 3 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"import missingno as msno\n#missing value plot\nmsno.bar(t_df[d_columns[50:]],figsize=(25,10),sort=\"ascending\",color=\"red\")\n","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:32:18.367674Z","iopub.execute_input":"2022-11-04T08:32:18.368877Z","iopub.status.idle":"2022-11-04T08:32:24.698491Z","shell.execute_reply.started":"2022-11-04T08:32:18.368768Z","shell.execute_reply":"2022-11-04T08:32:24.696954Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1800x720 with 3 Axes>","image/png":"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                               Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_27/188408267.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md_columns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'person'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m75\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mannot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Oranges'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlinecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mcorr\u001b[0;34m(self, method, min_periods)\u001b[0m\n\u001b[1;32m   9406\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9407\u001b[0m             raise ValueError(\n\u001b[0;32m-> 9408\u001b[0;31m                 \u001b[0;34m\"method must be either 'pearson', \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9409\u001b[0m                 \u001b[0;34m\"'spearman', 'kendall', or a callable, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9410\u001b[0m                 \u001b[0;34mf\"'{method}' was supplied\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: method must be either 'pearson', 'spearman', 'kendall', or a callable, 'person' was supplied"],"ename":"ValueError","evalue":"method must be either 'pearson', 'spearman', 'kendall', or a callable, 'person' was supplied","output_type":"error"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"t_df[d_columns].isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-11-04T06:05:09.014894Z","iopub.execute_input":"2022-11-04T06:05:09.015704Z","iopub.status.idle":"2022-11-04T06:05:09.135754Z","shell.execute_reply.started":"2022-11-04T06:05:09.015666Z","shell.execute_reply":"2022-11-04T06:05:09.134248Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"D_39          0\nD_41         72\nD_42     170807\nD_43      60058\nD_44       9929\n          ...  \nD_141      3503\nD_142    165644\nD_143      3503\nD_144      1433\nD_145      3503\nLength: 96, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nsns.pairplot(t_df)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:07:27.169633Z","iopub.execute_input":"2022-11-04T08:07:27.17Z","iopub.status.idle":"2022-11-04T08:27:04.788698Z","shell.execute_reply.started":"2022-11-04T08:07:27.169974Z","shell.execute_reply":"2022-11-04T08:27:04.786857Z"},"trusted":true},"execution_count":29,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_27/2161998500.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m 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           \u001b[0mhue_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2098\u001b[0;31m                     height=height, aspect=aspect, dropna=dropna, **grid_kws)\n\u001b[0m\u001b[1;32m   2099\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m     \u001b[0;31m# Add the markers here as PairGrid has figured out how many levels of the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m 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    \u001b[0;32mreturn\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/matplotlib/gridspec.py\u001b[0m in \u001b[0;36msubplots\u001b[0;34m(self, sharex, sharey, squeeze, subplot_kw)\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0msubplot_kw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"sharey\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshared_with\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msharey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m                 axarr[row, col] = figure.add_subplot(\n\u001b[0;32m--> 308\u001b[0;31m                     self[row, col], **subplot_kw)\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;31m# turn off redundant tick 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  \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_axes_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36m_add_axes_internal\u001b[0;34m(self, ax, key)\u001b[0m\n\u001b[1;32m    778\u001b[0m         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"],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"},{"name":"stdout","text":"Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f3f57bb1d40> (for post_execute):\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2059\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2060\u001b[0;31m                 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2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m                 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2060\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2062\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"},{"name":"stdout","text":"Error in callback <function flush_figures at 0x7f3f58168560> (for post_execute):\n","output_type":"stream"},{"name":"stderr","text":"\nKeyboardInterrupt\n\n","output_type":"stream"}]},{"cell_type":"code","source":"corr=t_df['d_columns'].corr()\ncorr","metadata":{"execution":{"iopub.status.busy":"2022-11-04T08:27:05.146378Z","iopub.execute_input":"2022-11-04T08:27:05.146894Z","iopub.status.idle":"2022-11-04T08:27:05.20636Z","shell.execute_reply.started":"2022-11-04T08:27:05.146851Z","shell.execute_reply":"2022-11-04T08:27:05.204583Z"},"trusted":true},"execution_count":30,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'd_columns'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_27/4163042068.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'd_columns'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcorr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'd_columns'"],"ename":"KeyError","evalue":"'d_columns'","output_type":"error"}]},{"cell_type":"code","source":"sns.heatmap(corr)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#droping columns having above 75% missing values\ni=0\nfor col in t_df.columns:\n    if(t_df[col].isnull().sum()/len(t_df[col])*100)>=75:\n        print(\"droping column\",col)\n        t_df.drop(labels=col,axis=1,inplace=True)\n        i=i+1  \n    \nprint(\"total number of columns droped in train dataframe\",i)","metadata":{"execution":{"iopub.status.busy":"2022-11-04T06:05:09.138131Z","iopub.execute_input":"2022-11-04T06:05:09.138721Z","iopub.status.idle":"2022-11-04T06:05:11.585436Z","shell.execute_reply.started":"2022-11-04T06:05:09.138675Z","shell.execute_reply":"2022-11-04T06:05:11.58394Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"droping column D_42\ndroping column D_49\ndroping column D_66\ndroping column D_73\ndroping column D_76\ndroping column R_9\ndroping column B_29\ndroping column D_87\ndroping column D_88\ndroping column D_106\ndroping column R_26\ndroping column D_108\ndroping column D_110\ndroping column D_111\ndroping column B_39\ndroping column B_42\ndroping column D_132\ndroping column D_134\ndroping column D_135\ndroping column D_136\ndroping column D_137\ndroping column D_138\ndroping column D_142\ntotal number of columns droped in train dataframe 23\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}