{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7921029,"sourceType":"competition"}],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport polars as pl\n\nimport os\n#for dirname, _, filenames in os.walk('/kaggle/input'):\n#    for filename in filenames:\n#        print(os.path.join(dirname, filename))\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-04-29T00:28:26.34942Z","iopub.execute_input":"2024-04-29T00:28:26.349795Z","iopub.status.idle":"2024-04-29T00:28:27.694062Z","shell.execute_reply.started":"2024-04-29T00:28:26.349768Z","shell.execute_reply":"2024-04-29T00:28:27.692649Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"def file_shapes():\n    feat_defs = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/feature_definitions.csv')\n    for dirname, _, filenames in os.walk('/kaggle/input'):\n        for filename in filenames:\n            if '.parquet' in filename or 'test' in filename:\n                continue\n            else:\n                df = pd.read_csv(os.path.join(dirname, filename))\n                feat_defs[filename] = feat_defs['Variable'].isin(df.columns)\n                print(filename, df.shape)\n    feat_defs.to_csv('/kaggle/working/features.csv')","metadata":{"execution":{"iopub.status.busy":"2024-04-29T00:49:32.230412Z","iopub.execute_input":"2024-04-29T00:49:32.230799Z","iopub.status.idle":"2024-04-29T00:49:32.238757Z","shell.execute_reply.started":"2024-04-29T00:49:32.230773Z","shell.execute_reply":"2024-04-29T00:49:32.237535Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"file_shapes()","metadata":{"execution":{"iopub.status.busy":"2024-04-29T00:49:32.375395Z","iopub.execute_input":"2024-04-29T00:49:32.376205Z","iopub.status.idle":"2024-04-29T01:06:08.717904Z","shell.execute_reply.started":"2024-04-29T00:49:32.37616Z","shell.execute_reply":"2024-04-29T01:06:08.716241Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"sample_submission.csv (10, 2)\nfeature_definitions.csv (465, 2)\ntrain_credit_bureau_a_1_3.csv (2079323, 79)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (1,2,3,4,7,45,46,47,48) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_static_cb_0.csv (1500476, 53)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (27) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_applprev_1_0.csv (3887684, 41)\ntrain_person_2.csv (1643410, 11)\ntrain_base.csv (1526659, 5)\ntrain_tax_registry_a_1.csv (3275770, 5)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (20,45,46,53,57,84,143,146,167) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_static_0_0.csv (1003757, 168)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (11,12,14,30,44,54) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_credit_bureau_a_1_0.csv (4108212, 79)\ntrain_applprev_2.csv (14075487, 6)\ntrain_credit_bureau_a_2_6.csv (25511332, 19)\ntrain_credit_bureau_a_1_2.csv (3743810, 79)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (16) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_person_1.csv (2973991, 37)\ntrain_credit_bureau_a_1_1.csv (6009192, 79)\ntrain_tax_registry_c_1.csv (3343800, 5)\ntrain_credit_bureau_a_2_4.csv (27025737, 19)\ntrain_credit_bureau_a_2_9.csv (18723227, 19)\ntrain_credit_bureau_a_2_3.csv (26563901, 19)\ntrain_credit_bureau_a_2_7.csv (8055986, 19)\ntrain_credit_bureau_b_2.csv (1286755, 6)\ntrain_credit_bureau_a_2_2.csv (17893536, 19)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (20,45,46,56,57,84,143,146,167) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_static_0_1.csv (522902, 168)\ntrain_deposit_1.csv (145086, 5)\ntrain_credit_bureau_a_2_10.csv (4386062, 19)\ntrain_tax_registry_b_1.csv (1107933, 5)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (27) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_applprev_1_1.csv (2638295, 41)\ntrain_credit_bureau_a_2_1.csv (7861809, 19)\ntrain_credit_bureau_a_2_8.csv (13927071, 19)\ntrain_credit_bureau_a_2_5.csv (33053760, 19)\n","output_type":"stream"},{"name":"stderr","text":"/tmp/ipykernel_33/1065883851.py:8: DtypeWarning: Columns (32) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(os.path.join(dirname, filename))\n","output_type":"stream"},{"name":"stdout","text":"train_credit_bureau_b_1.csv (85791, 45)\ntrain_credit_bureau_a_2_0.csv (5296031, 19)\ntrain_other_1.csv (51109, 7)\ntrain_debitcard_1.csv (157302, 6)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Get basic stats on cases and target variable","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_base.csv')\ndf.describe()","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:48.026293Z","iopub.execute_input":"2024-04-28T19:52:48.026669Z","iopub.status.idle":"2024-04-28T19:52:49.09119Z","shell.execute_reply.started":"2024-04-28T19:52:48.026625Z","shell.execute_reply":"2024-04-28T19:52:49.090132Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"            case_id         MONTH      WEEK_NUM        target\ncount  1.526659e+06  1.526659e+06  1.526659e+06  1.526659e+06\nmean   1.286077e+06  2.019363e+05  4.076904e+01  3.143728e-02\nstd    7.189466e+05  4.473597e+01  2.379798e+01  1.744964e-01\nmin    0.000000e+00  2.019010e+05  0.000000e+00  0.000000e+00\n25%    7.661975e+05  2.019060e+05  2.300000e+01  0.000000e+00\n50%    1.357358e+06  2.019100e+05  4.000000e+01  0.000000e+00\n75%    1.739022e+06  2.020010e+05  5.500000e+01  0.000000e+00\nmax    2.703454e+06  2.020100e+05  9.100000e+01  1.000000e+00","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>case_id</th>\n      <th>MONTH</th>\n      <th>WEEK_NUM</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.526659e+06</td>\n      <td>1.526659e+06</td>\n      <td>1.526659e+06</td>\n      <td>1.526659e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.286077e+06</td>\n      <td>2.019363e+05</td>\n      <td>4.076904e+01</td>\n      <td>3.143728e-02</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>7.189466e+05</td>\n      <td>4.473597e+01</td>\n      <td>2.379798e+01</td>\n      <td>1.744964e-01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000e+00</td>\n      <td>2.019010e+05</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7.661975e+05</td>\n      <td>2.019060e+05</td>\n      <td>2.300000e+01</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.357358e+06</td>\n      <td>2.019100e+05</td>\n      <td>4.000000e+01</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.739022e+06</td>\n      <td>2.020010e+05</td>\n      <td>5.500000e+01</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2.703454e+06</td>\n      <td>2.020100e+05</td>\n      <td>9.100000e+01</td>\n      <td>1.000000e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df['case_id'].nunique()","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.092557Z","iopub.execute_input":"2024-04-28T19:52:49.092972Z","iopub.status.idle":"2024-04-28T19:52:49.154747Z","shell.execute_reply.started":"2024-04-28T19:52:49.092937Z","shell.execute_reply":"2024-04-28T19:52:49.153703Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"1526659"},"metadata":{}}]},{"cell_type":"code","source":"df['target'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.156472Z","iopub.execute_input":"2024-04-28T19:52:49.157381Z","iopub.status.idle":"2024-04-28T19:52:49.185349Z","shell.execute_reply.started":"2024-04-28T19:52:49.157343Z","shell.execute_reply":"2024-04-28T19:52:49.184076Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"target\n0    1478665\n1      47994\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"47994/(47994+1478665)*100","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.18672Z","iopub.execute_input":"2024-04-28T19:52:49.187048Z","iopub.status.idle":"2024-04-28T19:52:49.196962Z","shell.execute_reply.started":"2024-04-28T19:52:49.187013Z","shell.execute_reply":"2024-04-28T19:52:49.195897Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"3.1437275776712417"},"metadata":{}}]},{"cell_type":"code","source":"def applprev():\n    df = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_base.csv')\n    df_prev10 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_applprev_1_0.csv')\n    df_prev11 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_applprev_1_1.csv')\n    #df_prev2 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_applprev_2.csv')\n    df_prev = df.merge(df_prev10, how='left', on='case_id')\n    df_prev = df_prev.merge(df_prev11, how='left', on='case_id')\n    #df_prev = df_prev.merge(df_prev2, how='left', on='case_id')\n    \n    return df_prev","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.198498Z","iopub.execute_input":"2024-04-28T19:52:49.198961Z","iopub.status.idle":"2024-04-28T19:52:49.20714Z","shell.execute_reply.started":"2024-04-28T19:52:49.198924Z","shell.execute_reply":"2024-04-28T19:52:49.206164Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"def credit_bureau():\n    return df_cred","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.208488Z","iopub.execute_input":"2024-04-28T19:52:49.208835Z","iopub.status.idle":"2024-04-28T19:52:49.220748Z","shell.execute_reply.started":"2024-04-28T19:52:49.208809Z","shell.execute_reply":"2024-04-28T19:52:49.219601Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"def tax_registry():\n    return df_tax","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.222267Z","iopub.execute_input":"2024-04-28T19:52:49.222804Z","iopub.status.idle":"2024-04-28T19:52:49.230632Z","shell.execute_reply.started":"2024-04-28T19:52:49.222755Z","shell.execute_reply":"2024-04-28T19:52:49.229601Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"def static():\n    df = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_base.csv')\n    df_stat00 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_0_0.csv')\n    df_stat01 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_0_1.csv')\n    df_statcb = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_cb_0.csv')\n    df_static = df.merge(df_stat00, how='left', on='case_id')\n    df_static = df_static.merge(df_stat01, how='left', on='case_id')\n    df_static = df_static.merge(df_statcb, how='left', on='case_id')\n    return df_static","metadata":{"execution":{"iopub.status.busy":"2024-04-29T02:17:36.063255Z","iopub.execute_input":"2024-04-29T02:17:36.063731Z","iopub.status.idle":"2024-04-29T02:17:36.384588Z","shell.execute_reply.started":"2024-04-29T02:17:36.063693Z","shell.execute_reply":"2024-04-29T02:17:36.383289Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"df_prev = applprev()\ndf_prev.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:52:49.243656Z","iopub.execute_input":"2024-04-28T19:52:49.243986Z","iopub.status.idle":"2024-04-28T19:53:56.124723Z","shell.execute_reply.started":"2024-04-28T19:52:49.243959Z","shell.execute_reply":"2024-04-28T19:53:56.123473Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/1751577658.py:3: DtypeWarning: Columns (27) have mixed types. Specify dtype option on import or set low_memory=False.\n  df_prev10 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_applprev_1_0.csv')\n/tmp/ipykernel_33/1751577658.py:4: DtypeWarning: Columns (27) have mixed types. Specify dtype option on import or set low_memory=False.\n  df_prev11 = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_applprev_1_1.csv')\n","output_type":"stream"},{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"(6831116, 85)"},"metadata":{}}]},{"cell_type":"code","source":"df_prev.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:53:56.126553Z","iopub.execute_input":"2024-04-28T19:53:56.127021Z","iopub.status.idle":"2024-04-28T19:53:56.152235Z","shell.execute_reply.started":"2024-04-28T19:53:56.126981Z","shell.execute_reply":"2024-04-28T19:53:56.151003Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"   case_id date_decision   MONTH  WEEK_NUM  target  actualdpd_943P_x  \\\n0        0    2019-01-03  201901         0       0               NaN   \n1        1    2019-01-03  201901         0       0               NaN   \n2        2    2019-01-04  201901         0       0               0.0   \n3        2    2019-01-04  201901         0       0               0.0   \n4        3    2019-01-03  201901         0       0               0.0   \n\n   annuity_853A_x approvaldate_319D_x  byoccupationinc_3656910L_x  \\\n0             NaN                 NaN                         NaN   \n1             NaN                 NaN                         NaN   \n2           640.2                 NaN                         NaN   \n3          1682.4                 NaN                         NaN   \n4          6140.0                 NaN                         NaN   \n\n  cancelreason_3545846M_x  ...  num_group1_y outstandingdebt_522A_y  \\\n0                     NaN  ...           NaN                    NaN   \n1                     NaN  ...           NaN                    NaN   \n2                a55475b1  ...           NaN                    NaN   \n3                a55475b1  ...           NaN                    NaN   \n4             P94_109_143  ...           NaN                    NaN   \n\n   pmtnum_8L_y  postype_4733339M_y  profession_152M_y  rejectreason_755M_y  \\\n0          NaN                 NaN                NaN                  NaN   \n1          NaN                 NaN                NaN                  NaN   \n2          NaN                 NaN                NaN                  NaN   \n3          NaN                 NaN                NaN                  NaN   \n4          NaN                 NaN                NaN                  NaN   \n\n  rejectreasonclient_4145042M_y  revolvingaccount_394A_y  status_219L_y  \\\n0                           NaN                      NaN            NaN   \n1                           NaN                      NaN            NaN   \n2                           NaN                      NaN            NaN   \n3                           NaN                      NaN            NaN   \n4                           NaN                      NaN            NaN   \n\n  tenor_203L_y  \n0          NaN  \n1          NaN  \n2          NaN  \n3          NaN  \n4          NaN  \n\n[5 rows x 85 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>case_id</th>\n      <th>date_decision</th>\n      <th>MONTH</th>\n      <th>WEEK_NUM</th>\n      <th>target</th>\n      <th>actualdpd_943P_x</th>\n      <th>annuity_853A_x</th>\n      <th>approvaldate_319D_x</th>\n      <th>byoccupationinc_3656910L_x</th>\n      <th>cancelreason_3545846M_x</th>\n      <th>...</th>\n      <th>num_group1_y</th>\n      <th>outstandingdebt_522A_y</th>\n      <th>pmtnum_8L_y</th>\n      <th>postype_4733339M_y</th>\n      <th>profession_152M_y</th>\n      <th>rejectreason_755M_y</th>\n      <th>rejectreasonclient_4145042M_y</th>\n      <th>revolvingaccount_394A_y</th>\n      <th>status_219L_y</th>\n      <th>tenor_203L_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</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>1</th>\n      <td>1</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</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>2</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>640.2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a55475b1</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>3</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1682.4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a55475b1</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>4</th>\n      <td>3</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>6140.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>P94_109_143</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  </tbody>\n</table>\n<p>5 rows × 85 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"def correlations(df):\n    print(df.describe())\n    cor = df.select_dtypes(include=np.number).corr()\n    print(cor)\n    sns.heatmap(cor, vmin=-1, vmax=1, cmap='RdBu')","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:53:56.154122Z","iopub.execute_input":"2024-04-28T19:53:56.15447Z","iopub.status.idle":"2024-04-28T19:53:56.163711Z","shell.execute_reply.started":"2024-04-28T19:53:56.15444Z","shell.execute_reply":"2024-04-28T19:53:56.162696Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"df_prev.drop_duplicates()","metadata":{"execution":{"iopub.status.busy":"2024-04-28T19:53:56.165038Z","iopub.execute_input":"2024-04-28T19:53:56.165341Z","iopub.status.idle":"2024-04-28T19:55:09.213291Z","shell.execute_reply.started":"2024-04-28T19:53:56.165316Z","shell.execute_reply":"2024-04-28T19:55:09.21209Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"         case_id date_decision   MONTH  WEEK_NUM  target  actualdpd_943P_x  \\\n0              0    2019-01-03  201901         0       0               NaN   \n1              1    2019-01-03  201901         0       0               NaN   \n2              2    2019-01-04  201901         0       0               0.0   \n3              2    2019-01-04  201901         0       0               0.0   \n4              3    2019-01-03  201901         0       0               0.0   \n...          ...           ...     ...       ...     ...               ...   \n6831111  2703453    2020-10-05  202010        91       0               NaN   \n6831112  2703453    2020-10-05  202010        91       0               NaN   \n6831113  2703453    2020-10-05  202010        91       0               NaN   \n6831114  2703454    2020-10-05  202010        91       0               NaN   \n6831115  2703454    2020-10-05  202010        91       0               NaN   \n\n         annuity_853A_x approvaldate_319D_x  byoccupationinc_3656910L_x  \\\n0                   NaN                 NaN                         NaN   \n1                   NaN                 NaN                         NaN   \n2                 640.2                 NaN                         NaN   \n3                1682.4                 NaN                         NaN   \n4                6140.0                 NaN                         NaN   \n...                 ...                 ...                         ...   \n6831111             NaN                 NaN                         NaN   \n6831112             NaN                 NaN                         NaN   \n6831113             NaN                 NaN                         NaN   \n6831114             NaN                 NaN                         NaN   \n6831115             NaN                 NaN                         NaN   \n\n        cancelreason_3545846M_x  ...  num_group1_y outstandingdebt_522A_y  \\\n0                           NaN  ...           NaN                    NaN   \n1                           NaN  ...           NaN                    NaN   \n2                      a55475b1  ...           NaN                    NaN   \n3                      a55475b1  ...           NaN                    NaN   \n4                   P94_109_143  ...           NaN                    NaN   \n...                         ...  ...           ...                    ...   \n6831111                     NaN  ...           1.0                46806.6   \n6831112                     NaN  ...           5.0                    0.0   \n6831113                     NaN  ...           2.0                    0.0   \n6831114                     NaN  ...           0.0                 5919.2   \n6831115                     NaN  ...           1.0                    0.0   \n\n         pmtnum_8L_y  postype_4733339M_y  profession_152M_y  \\\n0                NaN                 NaN                NaN   \n1                NaN                 NaN                NaN   \n2                NaN                 NaN                NaN   \n3                NaN                 NaN                NaN   \n4                NaN                 NaN                NaN   \n...              ...                 ...                ...   \n6831111         30.0          P46_145_78           a55475b1   \n6831112         48.0        P177_117_192           a55475b1   \n6831113         30.0        P177_117_192           a55475b1   \n6831114          6.0        P177_117_192           a55475b1   \n6831115         12.0          P46_145_78           a55475b1   \n\n         rejectreason_755M_y rejectreasonclient_4145042M_y  \\\n0                        NaN                           NaN   \n1                        NaN                           NaN   \n2                        NaN                           NaN   \n3                        NaN                           NaN   \n4                        NaN                           NaN   \n...                      ...                           ...   \n6831111             a55475b1                      a55475b1   \n6831112             a55475b1                      a55475b1   \n6831113             a55475b1                      a55475b1   \n6831114             a55475b1                      a55475b1   \n6831115             a55475b1                      a55475b1   \n\n         revolvingaccount_394A_y  status_219L_y tenor_203L_y  \n0                            NaN            NaN          NaN  \n1                            NaN            NaN          NaN  \n2                            NaN            NaN          NaN  \n3                            NaN            NaN          NaN  \n4                            NaN            NaN          NaN  \n...                          ...            ...          ...  \n6831111                      NaN              A         30.0  \n6831112                      NaN              K         48.0  \n6831113                      NaN              K         30.0  \n6831114                      NaN              A          6.0  \n6831115                      NaN              K         12.0  \n\n[6831116 rows x 85 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>case_id</th>\n      <th>date_decision</th>\n      <th>MONTH</th>\n      <th>WEEK_NUM</th>\n      <th>target</th>\n      <th>actualdpd_943P_x</th>\n      <th>annuity_853A_x</th>\n      <th>approvaldate_319D_x</th>\n      <th>byoccupationinc_3656910L_x</th>\n      <th>cancelreason_3545846M_x</th>\n      <th>...</th>\n      <th>num_group1_y</th>\n      <th>outstandingdebt_522A_y</th>\n      <th>pmtnum_8L_y</th>\n      <th>postype_4733339M_y</th>\n      <th>profession_152M_y</th>\n      <th>rejectreason_755M_y</th>\n      <th>rejectreasonclient_4145042M_y</th>\n      <th>revolvingaccount_394A_y</th>\n      <th>status_219L_y</th>\n      <th>tenor_203L_y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</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>1</th>\n      <td>1</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</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>2</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>640.2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a55475b1</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>3</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1682.4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>a55475b1</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>4</th>\n      <td>3</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>6140.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>P94_109_143</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>...</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>6831111</th>\n      <td>2703453</td>\n      <td>2020-10-05</td>\n      <td>202010</td>\n      <td>91</td>\n      <td>0</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>1.0</td>\n      <td>46806.6</td>\n      <td>30.0</td>\n      <td>P46_145_78</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>NaN</td>\n      <td>A</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>6831112</th>\n      <td>2703453</td>\n      <td>2020-10-05</td>\n      <td>202010</td>\n      <td>91</td>\n      <td>0</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>5.0</td>\n      <td>0.0</td>\n      <td>48.0</td>\n      <td>P177_117_192</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <th>6831113</th>\n      <td>2703453</td>\n      <td>2020-10-05</td>\n      <td>202010</td>\n      <td>91</td>\n      <td>0</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>2.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>P177_117_192</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>30.0</td>\n    </tr>\n    <tr>\n      <th>6831114</th>\n      <td>2703454</td>\n      <td>2020-10-05</td>\n      <td>202010</td>\n      <td>91</td>\n      <td>0</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>0.0</td>\n      <td>5919.2</td>\n      <td>6.0</td>\n      <td>P177_117_192</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>NaN</td>\n      <td>A</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>6831115</th>\n      <td>2703454</td>\n      <td>2020-10-05</td>\n      <td>202010</td>\n      <td>91</td>\n      <td>0</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>1.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>P46_145_78</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>a55475b1</td>\n      <td>NaN</td>\n      <td>K</td>\n      <td>12.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>6831116 rows × 85 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"correlations(df_prev)","metadata":{"execution":{"iopub.status.busy":"2024-04-28T20:05:39.070381Z","iopub.execute_input":"2024-04-28T20:05:39.071385Z","iopub.status.idle":"2024-04-28T20:06:15.541824Z","shell.execute_reply.started":"2024-04-28T20:05:39.071347Z","shell.execute_reply":"2024-04-28T20:06:15.540641Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"            case_id         MONTH      WEEK_NUM        target  \\\ncount  6.831116e+06  6.831116e+06  6.831116e+06  6.831116e+06   \nmean   1.400316e+06  2.019420e+05  4.370393e+01  3.671069e-02   \nstd    7.852523e+05  4.685194e+01  2.456066e+01  1.880506e-01   \nmin    0.000000e+00  2.019010e+05  0.000000e+00  0.000000e+00   \n25%    8.236768e+05  2.019060e+05  2.500000e+01  0.000000e+00   \n50%    1.526194e+06  2.019110e+05  4.300000e+01  0.000000e+00   \n75%    1.840877e+06  2.020020e+05  6.000000e+01  0.000000e+00   \nmax    2.703454e+06  2.020100e+05  9.100000e+01  1.000000e+00   \n\n       actualdpd_943P_x  annuity_853A_x  byoccupationinc_3656910L_x  \\\ncount      3.885450e+06    3.731833e+06               991660.000000   \nmean       1.056300e-02    3.413166e+03                19796.484030   \nstd        3.754277e+00    2.828269e+03                30687.652507   \nmin        0.000000e+00    0.000000e+00                    0.000000   \n25%        0.000000e+00    1.710600e+03                    1.000000   \n50%        0.000000e+00    2.761200e+03                 5000.000000   \n75%        0.000000e+00    4.396400e+03                30000.000000   \nmax        3.676000e+03    1.051302e+05               200000.000000   \n\n       childnum_21L_x  credacc_actualbalance_314A_x  credacc_credlmt_575A_x  \\\ncount    1.933791e+06                  1.681780e+05            3.768614e+06   \nmean     8.434702e-01                  2.026958e+04            3.254677e+03   \nstd      1.209425e+00                  2.600278e+04            1.406135e+04   \nmin      0.000000e+00                 -1.140860e+05            0.000000e+00   \n25%      0.000000e+00                  3.030500e+00            0.000000e+00   \n50%      0.000000e+00                  1.258500e+04            0.000000e+00   \n75%      1.000000e+00                  3.044600e+04            0.000000e+00   \nmax      2.000000e+01                  2.540730e+06            4.000000e+05   \n\n       ...  credamount_590A_y  currdebt_94A_y  downpmt_134A_y  \\\ncount  ...       2.559426e+06    1.662160e+06    2.559426e+06   \nmean   ...       4.298544e+04    5.301262e+03    3.889885e+02   \nstd    ...       4.579684e+04    2.046368e+04    2.614635e+03   \nmin    ...       0.000000e+00    0.000000e+00    0.000000e+00   \n25%    ...       1.423600e+04    0.000000e+00    0.000000e+00   \n50%    ...       2.997800e+04    0.000000e+00    0.000000e+00   \n75%    ...       5.908770e+04    0.000000e+00    0.000000e+00   \nmax    ...       1.000000e+06    4.829808e+05    4.204000e+05   \n\n       mainoccupationinc_437A_y  maxdpdtolerance_577P_y  num_group1_y  \\\ncount              2.572924e+06            1.359969e+06  2.638295e+06   \nmean               4.304612e+04            1.678875e+01  4.613016e+00   \nstd                3.255007e+04            1.581358e+02  4.485515e+00   \nmin                0.000000e+00            0.000000e+00  0.000000e+00   \n25%                2.000000e+04            0.000000e+00  1.000000e+00   \n50%                3.700000e+04            0.000000e+00  3.000000e+00   \n75%                5.800000e+04            1.000000e+00  7.000000e+00   \nmax                1.996000e+05            4.362000e+03  1.900000e+01   \n\n       outstandingdebt_522A_y   pmtnum_8L_y  revolvingaccount_394A_y  \\\ncount            1.657949e+06  2.399308e+06             1.400990e+05   \nmean             7.097728e+03  1.685803e+01             7.619334e+08   \nstd              3.087146e+04  1.125159e+01             4.755881e+07   \nmin              0.000000e+00  3.000000e+00             5.403423e+08   \n25%              0.000000e+00  9.000000e+00             7.601635e+08   \n50%              0.000000e+00  1.200000e+01             7.803115e+08   \n75%              0.000000e+00  2.400000e+01             7.807900e+08   \nmax              1.029393e+06  6.300000e+01             8.006087e+08   \n\n       tenor_203L_y  \ncount  2.399308e+06  \nmean   1.685803e+01  \nstd    1.125159e+01  \nmin    3.000000e+00  \n25%    9.000000e+00  \n50%    1.200000e+01  \n75%    2.400000e+01  \nmax    6.300000e+01  \n\n[8 rows x 42 columns]\n                               case_id     MONTH  WEEK_NUM    target  \\\ncase_id                       1.000000  0.062370  0.087666  0.000266   \nMONTH                         0.062370  1.000000  0.851209  0.006539   \nWEEK_NUM                      0.087666  0.851209  1.000000  0.002543   \ntarget                        0.000266  0.006539  0.002543  1.000000   \nactualdpd_943P_x             -0.003190  0.002437  0.002433  0.001133   \nannuity_853A_x               -0.010537  0.002387  0.002693 -0.006370   \nbyoccupationinc_3656910L_x    0.020198 -0.006578 -0.006485 -0.010113   \nchildnum_21L_x               -0.008305  0.012387  0.012213  0.003385   \ncredacc_actualbalance_314A_x  0.056838  0.006028  0.005195 -0.043551   \ncredacc_credlmt_575A_x       -0.009786  0.029164  0.028933 -0.010402   \ncredacc_maxhisbal_375A_x      0.033381  0.034519  0.034690 -0.012770   \ncredacc_minhisbal_90A_x       0.078057 -0.001032 -0.000614 -0.036378   \ncredacc_transactions_402L_x  -0.041875  0.053850  0.053889  0.011493   \ncredamount_590A_x            -0.054646  0.023239  0.023803  0.011169   \ncurrdebt_94A_x               -0.060212 -0.000646 -0.000357  0.011387   \ndownpmt_134A_x                0.006033 -0.000606 -0.000772 -0.007316   \nmainoccupationinc_437A_x     -0.056769  0.039063  0.038983  0.008370   \nmaxdpdtolerance_577P_x       -0.005461  0.016827  0.016626  0.040227   \nnum_group1_x                 -0.040310  0.018703  0.019042  0.012255   \noutstandingdebt_522A_x       -0.063428 -0.002384 -0.002067  0.011305   \npmtnum_8L_x                  -0.071364  0.009090  0.009920  0.029310   \nrevolvingaccount_394A_x      -0.075161  0.137361  0.137743  0.023434   \ntenor_203L_x                 -0.071364  0.009090  0.009920  0.029310   \nactualdpd_943P_y             -0.005204  0.000197 -0.003383  0.001310   \nannuity_853A_y               -0.021483  0.012704  0.013645 -0.001631   \nbyoccupationinc_3656910L_y    0.002889  0.016956  0.021160 -0.008859   \nchildnum_21L_y                0.018196 -0.014612 -0.020332  0.000624   \ncredacc_actualbalance_314A_y  0.095623 -0.036003 -0.046798 -0.043192   \ncredacc_credlmt_575A_y        0.009743 -0.011636 -0.013237 -0.007491   \ncredacc_maxhisbal_375A_y      0.008713  0.011567  0.022981 -0.009484   \ncredacc_minhisbal_90A_y       0.017510  0.028686  0.056574 -0.027490   \ncredacc_transactions_402L_y  -0.029489 -0.021695 -0.041585  0.006632   \ncredamount_590A_y            -0.079059  0.024205  0.026162  0.018519   \ncurrdebt_94A_y               -0.061083 -0.002913 -0.016082  0.023838   \ndownpmt_134A_y                0.024761 -0.012853 -0.016747 -0.003592   \nmainoccupationinc_437A_y     -0.045371  0.006367  0.001275  0.009506   \nmaxdpdtolerance_577P_y        0.008043  0.004668  0.007222  0.039794   \nnum_group1_y                 -0.093294  0.046083  0.047771  0.016968   \noutstandingdebt_522A_y       -0.065045 -0.000323 -0.011331  0.023038   \npmtnum_8L_y                  -0.124076  0.032847  0.033127  0.035621   \nrevolvingaccount_394A_y      -0.087804  0.082548  0.127853  0.021336   \ntenor_203L_y                 -0.124076  0.032847  0.033127  0.035621   \n\n                              actualdpd_943P_x  annuity_853A_x  \\\ncase_id                              -0.003190       -0.010537   \nMONTH                                 0.002437        0.002387   \nWEEK_NUM                              0.002433        0.002693   \ntarget                                0.001133       -0.006370   \nactualdpd_943P_x                      1.000000        0.000323   \nannuity_853A_x                        0.000323        1.000000   \nbyoccupationinc_3656910L_x            0.002554        0.063675   \nchildnum_21L_x                       -0.000761        0.007073   \ncredacc_actualbalance_314A_x         -0.003557        0.009326   \ncredacc_credlmt_575A_x               -0.000373       -0.181351   \ncredacc_maxhisbal_375A_x             -0.005010       -0.003084   \ncredacc_minhisbal_90A_x              -0.006688       -0.021557   \ncredacc_transactions_402L_x          -0.001509       -0.001453   \ncredamount_590A_x                     0.001219        0.701323   \ncurrdebt_94A_x                        0.006357        0.269338   \ndownpmt_134A_x                        0.000120        0.057829   \nmainoccupationinc_437A_x             -0.000404        0.297682   \nmaxdpdtolerance_577P_x                0.037797       -0.015430   \nnum_group1_x                         -0.001970       -0.085503   \noutstandingdebt_522A_x                0.006650        0.263019   \npmtnum_8L_x                           0.002204        0.157816   \nrevolvingaccount_394A_x               0.007680       -0.009936   \ntenor_203L_x                          0.002204        0.157816   \nactualdpd_943P_y                           NaN             NaN   \nannuity_853A_y                             NaN             NaN   \nbyoccupationinc_3656910L_y                 NaN             NaN   \nchildnum_21L_y                             NaN             NaN   \ncredacc_actualbalance_314A_y               NaN             NaN   \ncredacc_credlmt_575A_y                     NaN             NaN   \ncredacc_maxhisbal_375A_y                   NaN             NaN   \ncredacc_minhisbal_90A_y                    NaN             NaN   \ncredacc_transactions_402L_y                NaN             NaN   \ncredamount_590A_y                          NaN             NaN   \ncurrdebt_94A_y                             NaN             NaN   \ndownpmt_134A_y                             NaN             NaN   \nmainoccupationinc_437A_y                   NaN             NaN   \nmaxdpdtolerance_577P_y                     NaN             NaN   \nnum_group1_y                               NaN             NaN   \noutstandingdebt_522A_y                     NaN             NaN   \npmtnum_8L_y                                NaN             NaN   \nrevolvingaccount_394A_y                    NaN             NaN   \ntenor_203L_y                               NaN             NaN   \n\n                              byoccupationinc_3656910L_x  childnum_21L_x  \\\ncase_id                                         0.020198       -0.008305   \nMONTH                                          -0.006578        0.012387   \nWEEK_NUM                                       -0.006485        0.012213   \ntarget                                         -0.010113        0.003385   \nactualdpd_943P_x                                0.002554       -0.000761   \nannuity_853A_x                                  0.063675        0.007073   \nbyoccupationinc_3656910L_x                      1.000000       -0.050947   \nchildnum_21L_x                                 -0.050947        1.000000   \ncredacc_actualbalance_314A_x                   -0.020145        0.042901   \ncredacc_credlmt_575A_x                         -0.027611        0.043340   \ncredacc_maxhisbal_375A_x                        0.001060       -0.008574   \ncredacc_minhisbal_90A_x                        -0.005727       -0.008155   \ncredacc_transactions_402L_x                    -0.006948       -0.014944   \ncredamount_590A_x                               0.063471       -0.009344   \ncurrdebt_94A_x                                 -0.005468        0.075587   \ndownpmt_134A_x                                  0.025121        0.003836   \nmainoccupationinc_437A_x                       -0.241919        0.121207   \nmaxdpdtolerance_577P_x                          0.019792       -0.012436   \nnum_group1_x                                    0.094027       -0.095435   \noutstandingdebt_522A_x                         -0.005177        0.072160   \npmtnum_8L_x                                     0.017519       -0.045833   \nrevolvingaccount_394A_x                        -0.151750        0.211184   \ntenor_203L_x                                    0.017519       -0.045833   \nactualdpd_943P_y                                     NaN             NaN   \nannuity_853A_y                                       NaN             NaN   \nbyoccupationinc_3656910L_y                           NaN             NaN   \nchildnum_21L_y                                       NaN             NaN   \ncredacc_actualbalance_314A_y                         NaN             NaN   \ncredacc_credlmt_575A_y                               NaN             NaN   \ncredacc_maxhisbal_375A_y                             NaN             NaN   \ncredacc_minhisbal_90A_y                              NaN             NaN   \ncredacc_transactions_402L_y                          NaN             NaN   \ncredamount_590A_y                                    NaN             NaN   \ncurrdebt_94A_y                                       NaN             NaN   \ndownpmt_134A_y                                       NaN             NaN   \nmainoccupationinc_437A_y                             NaN             NaN   \nmaxdpdtolerance_577P_y                               NaN             NaN   \nnum_group1_y                                         NaN             NaN   \noutstandingdebt_522A_y                               NaN             NaN   \npmtnum_8L_y                                          NaN             NaN   \nrevolvingaccount_394A_y                              NaN             NaN   \ntenor_203L_y                                         NaN             NaN   \n\n                              credacc_actualbalance_314A_x  \\\ncase_id                                           0.056838   \nMONTH                                             0.006028   \nWEEK_NUM                                          0.005195   \ntarget                                           -0.043551   \nactualdpd_943P_x                                 -0.003557   \nannuity_853A_x                                    0.009326   \nbyoccupationinc_3656910L_x                       -0.020145   \nchildnum_21L_x                                    0.042901   \ncredacc_actualbalance_314A_x                      1.000000   \ncredacc_credlmt_575A_x                            0.751365   \ncredacc_maxhisbal_375A_x                          0.094750   \ncredacc_minhisbal_90A_x                          -0.003685   \ncredacc_transactions_402L_x                      -0.021964   \ncredamount_590A_x                                 0.092880   \ncurrdebt_94A_x                                   -0.021943   \ndownpmt_134A_x                                    0.052284   \nmainoccupationinc_437A_x                          0.077354   \nmaxdpdtolerance_577P_x                           -0.013534   \nnum_group1_x                                     -0.118343   \noutstandingdebt_522A_x                           -0.025476   \npmtnum_8L_x                                      -0.076918   \nrevolvingaccount_394A_x                           0.200081   \ntenor_203L_x                                     -0.076918   \nactualdpd_943P_y                                       NaN   \nannuity_853A_y                                         NaN   \nbyoccupationinc_3656910L_y                             NaN   \nchildnum_21L_y                                         NaN   \ncredacc_actualbalance_314A_y                           NaN   \ncredacc_credlmt_575A_y                                 NaN   \ncredacc_maxhisbal_375A_y                               NaN   \ncredacc_minhisbal_90A_y                                NaN   \ncredacc_transactions_402L_y                            NaN   \ncredamount_590A_y                                      NaN   \ncurrdebt_94A_y                                         NaN   \ndownpmt_134A_y                                         NaN   \nmainoccupationinc_437A_y                               NaN   \nmaxdpdtolerance_577P_y                                 NaN   \nnum_group1_y                                           NaN   \noutstandingdebt_522A_y                                 NaN   \npmtnum_8L_y                                            NaN   \nrevolvingaccount_394A_y                                NaN   \ntenor_203L_y                                           NaN   \n\n                              credacc_credlmt_575A_x  ...  credamount_590A_y  \\\ncase_id                                    -0.009786  ...      -7.905947e-02   \nMONTH                                       0.029164  ...       2.420500e-02   \nWEEK_NUM                                    0.028933  ...       2.616223e-02   \ntarget                                     -0.010402  ...       1.851897e-02   \nactualdpd_943P_x                           -0.000373  ...                NaN   \nannuity_853A_x                             -0.181351  ...                NaN   \nbyoccupationinc_3656910L_x                 -0.027611  ...                NaN   \nchildnum_21L_x                              0.043340  ...                NaN   \ncredacc_actualbalance_314A_x                0.751365  ...                NaN   \ncredacc_credlmt_575A_x                      1.000000  ...                NaN   \ncredacc_maxhisbal_375A_x                   -0.213972  ...                NaN   \ncredacc_minhisbal_90A_x                    -0.473809  ...                NaN   \ncredacc_transactions_402L_x                 0.053026  ...                NaN   \ncredamount_590A_x                           0.115866  ...                NaN   \ncurrdebt_94A_x                              0.048219  ...                NaN   \ndownpmt_134A_x                              0.005054  ...                NaN   \nmainoccupationinc_437A_x                    0.107458  ...                NaN   \nmaxdpdtolerance_577P_x                     -0.012961  ...                NaN   \nnum_group1_x                               -0.099513  ...                NaN   \noutstandingdebt_522A_x                      0.022650  ...                NaN   \npmtnum_8L_x                                 0.010057  ...                NaN   \nrevolvingaccount_394A_x                     0.061823  ...                NaN   \ntenor_203L_x                                0.010057  ...                NaN   \nactualdpd_943P_y                                 NaN  ...       1.642321e-03   \nannuity_853A_y                                   NaN  ...       7.028003e-01   \nbyoccupationinc_3656910L_y                       NaN  ...       5.724313e-02   \nchildnum_21L_y                                   NaN  ...      -3.208311e-03   \ncredacc_actualbalance_314A_y                     NaN  ...       5.184615e-02   \ncredacc_credlmt_575A_y                           NaN  ...       1.294999e-01   \ncredacc_maxhisbal_375A_y                         NaN  ...      -1.559820e-02   \ncredacc_minhisbal_90A_y                          NaN  ...      -6.026424e-02   \ncredacc_transactions_402L_y                      NaN  ...       1.815283e-02   \ncredamount_590A_y                                NaN  ...       1.000000e+00   \ncurrdebt_94A_y                                   NaN  ...       5.408716e-01   \ndownpmt_134A_y                                   NaN  ...       7.114129e-04   \nmainoccupationinc_437A_y                         NaN  ...       3.136834e-01   \nmaxdpdtolerance_577P_y                           NaN  ...      -1.426984e-07   \nnum_group1_y                                     NaN  ...      -8.614219e-02   \noutstandingdebt_522A_y                           NaN  ...       5.225097e-01   \npmtnum_8L_y                                      NaN  ...       6.448415e-01   \nrevolvingaccount_394A_y                          NaN  ...      -3.942433e-02   \ntenor_203L_y                                     NaN  ...       6.448415e-01   \n\n                              currdebt_94A_y  downpmt_134A_y  \\\ncase_id                            -0.061083        0.024761   \nMONTH                              -0.002913       -0.012853   \nWEEK_NUM                           -0.016082       -0.016747   \ntarget                              0.023838       -0.003592   \nactualdpd_943P_x                         NaN             NaN   \nannuity_853A_x                           NaN             NaN   \nbyoccupationinc_3656910L_x               NaN             NaN   \nchildnum_21L_x                           NaN             NaN   \ncredacc_actualbalance_314A_x             NaN             NaN   \ncredacc_credlmt_575A_x                   NaN             NaN   \ncredacc_maxhisbal_375A_x                 NaN             NaN   \ncredacc_minhisbal_90A_x                  NaN             NaN   \ncredacc_transactions_402L_x              NaN             NaN   \ncredamount_590A_x                        NaN             NaN   \ncurrdebt_94A_x                           NaN             NaN   \ndownpmt_134A_x                           NaN             NaN   \nmainoccupationinc_437A_x                 NaN             NaN   \nmaxdpdtolerance_577P_x                   NaN             NaN   \nnum_group1_x                             NaN             NaN   \noutstandingdebt_522A_x                   NaN             NaN   \npmtnum_8L_x                              NaN             NaN   \nrevolvingaccount_394A_x                  NaN             NaN   \ntenor_203L_x                             NaN             NaN   \nactualdpd_943P_y                    0.009150       -0.000440   \nannuity_853A_y                      0.278328        0.047456   \nbyoccupationinc_3656910L_y          0.002783        0.019851   \nchildnum_21L_y                      0.038943        0.003395   \ncredacc_actualbalance_314A_y       -0.016270        0.053690   \ncredacc_credlmt_575A_y              0.074837        0.006071   \ncredacc_maxhisbal_375A_y           -0.050694       -0.010724   \ncredacc_minhisbal_90A_y            -0.111089       -0.015601   \ncredacc_transactions_402L_y         0.014559       -0.004904   \ncredamount_590A_y                   0.540872        0.000711   \ncurrdebt_94A_y                      1.000000        0.001034   \ndownpmt_134A_y                      0.001034        1.000000   \nmainoccupationinc_437A_y            0.256585        0.015232   \nmaxdpdtolerance_577P_y             -0.026954       -0.004663   \nnum_group1_y                       -0.198240       -0.015285   \noutstandingdebt_522A_y              0.976354       -0.001902   \npmtnum_8L_y                         0.360621       -0.064840   \nrevolvingaccount_394A_y             0.119278       -0.002460   \ntenor_203L_y                        0.360621       -0.064840   \n\n                              mainoccupationinc_437A_y  \\\ncase_id                                      -0.045371   \nMONTH                                         0.006367   \nWEEK_NUM                                      0.001275   \ntarget                                        0.009506   \nactualdpd_943P_x                                   NaN   \nannuity_853A_x                                     NaN   \nbyoccupationinc_3656910L_x                         NaN   \nchildnum_21L_x                                     NaN   \ncredacc_actualbalance_314A_x                       NaN   \ncredacc_credlmt_575A_x                             NaN   \ncredacc_maxhisbal_375A_x                           NaN   \ncredacc_minhisbal_90A_x                            NaN   \ncredacc_transactions_402L_x                        NaN   \ncredamount_590A_x                                  NaN   \ncurrdebt_94A_x                                     NaN   \ndownpmt_134A_x                                     NaN   \nmainoccupationinc_437A_x                           NaN   \nmaxdpdtolerance_577P_x                             NaN   \nnum_group1_x                                       NaN   \noutstandingdebt_522A_x                             NaN   \npmtnum_8L_x                                        NaN   \nrevolvingaccount_394A_x                            NaN   \ntenor_203L_x                                       NaN   \nactualdpd_943P_y                             -0.000782   \nannuity_853A_y                                0.282948   \nbyoccupationinc_3656910L_y                   -0.235953   \nchildnum_21L_y                                0.134467   \ncredacc_actualbalance_314A_y                  0.050040   \ncredacc_credlmt_575A_y                        0.108366   \ncredacc_maxhisbal_375A_y                     -0.005785   \ncredacc_minhisbal_90A_y                      -0.040015   \ncredacc_transactions_402L_y                   0.033982   \ncredamount_590A_y                             0.313683   \ncurrdebt_94A_y                                0.256585   \ndownpmt_134A_y                                0.015232   \nmainoccupationinc_437A_y                      1.000000   \nmaxdpdtolerance_577P_y                       -0.075507   \nnum_group1_y                                 -0.210187   \noutstandingdebt_522A_y                        0.231686   \npmtnum_8L_y                                   0.170612   \nrevolvingaccount_394A_y                       0.385466   \ntenor_203L_y                                  0.170612   \n\n                              maxdpdtolerance_577P_y  num_group1_y  \\\ncase_id                                 8.043191e-03     -0.093294   \nMONTH                                   4.668124e-03      0.046083   \nWEEK_NUM                                7.222200e-03      0.047771   \ntarget                                  3.979416e-02      0.016968   \nactualdpd_943P_x                                 NaN           NaN   \nannuity_853A_x                                   NaN           NaN   \nbyoccupationinc_3656910L_x                       NaN           NaN   \nchildnum_21L_x                                   NaN           NaN   \ncredacc_actualbalance_314A_x                     NaN           NaN   \ncredacc_credlmt_575A_x                           NaN           NaN   \ncredacc_maxhisbal_375A_x                         NaN           NaN   \ncredacc_minhisbal_90A_x                          NaN           NaN   \ncredacc_transactions_402L_x                      NaN           NaN   \ncredamount_590A_x                                NaN           NaN   \ncurrdebt_94A_x                                   NaN           NaN   \ndownpmt_134A_x                                   NaN           NaN   \nmainoccupationinc_437A_x                         NaN           NaN   \nmaxdpdtolerance_577P_x                           NaN           NaN   \nnum_group1_x                                     NaN           NaN   \noutstandingdebt_522A_x                           NaN           NaN   \npmtnum_8L_x                                      NaN           NaN   \nrevolvingaccount_394A_x                          NaN           NaN   \ntenor_203L_x                                     NaN           NaN   \nactualdpd_943P_y                        4.979925e-02     -0.002627   \nannuity_853A_y                         -1.434729e-02     -0.066074   \nbyoccupationinc_3656910L_y              2.845100e-02      0.076922   \nchildnum_21L_y                         -1.290486e-02     -0.089642   \ncredacc_actualbalance_314A_y           -1.364049e-02     -0.125929   \ncredacc_credlmt_575A_y                 -1.454116e-02     -0.090777   \ncredacc_maxhisbal_375A_y               -4.987986e-03      0.028274   \ncredacc_minhisbal_90A_y                -9.539521e-03      0.037640   \ncredacc_transactions_402L_y            -5.454745e-03      0.008563   \ncredamount_590A_y                      -1.426984e-07     -0.086142   \ncurrdebt_94A_y                         -2.695373e-02     -0.198240   \ndownpmt_134A_y                         -4.663004e-03     -0.015285   \nmainoccupationinc_437A_y               -7.550694e-02     -0.210187   \nmaxdpdtolerance_577P_y                  1.000000e+00     -0.012720   \nnum_group1_y                           -1.271996e-02      1.000000   \noutstandingdebt_522A_y                 -2.384947e-02     -0.175103   \npmtnum_8L_y                             5.283640e-02      0.004798   \nrevolvingaccount_394A_y                -1.459706e-01     -0.380113   \ntenor_203L_y                            5.283640e-02      0.004798   \n\n                              outstandingdebt_522A_y  pmtnum_8L_y  \\\ncase_id                                    -0.065045    -0.124076   \nMONTH                                      -0.000323     0.032847   \nWEEK_NUM                                   -0.011331     0.033127   \ntarget                                      0.023038     0.035621   \nactualdpd_943P_x                                 NaN          NaN   \nannuity_853A_x                                   NaN          NaN   \nbyoccupationinc_3656910L_x                       NaN          NaN   \nchildnum_21L_x                                   NaN          NaN   \ncredacc_actualbalance_314A_x                     NaN          NaN   \ncredacc_credlmt_575A_x                           NaN          NaN   \ncredacc_maxhisbal_375A_x                         NaN          NaN   \ncredacc_minhisbal_90A_x                          NaN          NaN   \ncredacc_transactions_402L_x                      NaN          NaN   \ncredamount_590A_x                                NaN          NaN   \ncurrdebt_94A_x                                   NaN          NaN   \ndownpmt_134A_x                                   NaN          NaN   \nmainoccupationinc_437A_x                         NaN          NaN   \nmaxdpdtolerance_577P_x                           NaN          NaN   \nnum_group1_x                                     NaN          NaN   \noutstandingdebt_522A_x                           NaN          NaN   \npmtnum_8L_x                                      NaN          NaN   \nrevolvingaccount_394A_x                          NaN          NaN   \ntenor_203L_x                                     NaN          NaN   \nactualdpd_943P_y                            0.008913     0.003315   \nannuity_853A_y                              0.273367     0.192565   \nbyoccupationinc_3656910L_y                  0.003659     0.014215   \nchildnum_21L_y                              0.037856    -0.041096   \ncredacc_actualbalance_314A_y               -0.023832    -0.076057   \ncredacc_credlmt_575A_y                      0.041047     0.024204   \ncredacc_maxhisbal_375A_y                   -0.028168    -0.003015   \ncredacc_minhisbal_90A_y                    -0.070546    -0.011385   \ncredacc_transactions_402L_y                 0.009330     0.003005   \ncredamount_590A_y                           0.522510     0.644841   \ncurrdebt_94A_y                              0.976354     0.360621   \ndownpmt_134A_y                             -0.001902    -0.064840   \nmainoccupationinc_437A_y                    0.231686     0.170612   \nmaxdpdtolerance_577P_y                     -0.023849     0.052836   \nnum_group1_y                               -0.175103     0.004798   \noutstandingdebt_522A_y                      1.000000     0.371622   \npmtnum_8L_y                                 0.371622     1.000000   \nrevolvingaccount_394A_y                     0.118533     0.299392   \ntenor_203L_y                                0.371622     1.000000   \n\n                              revolvingaccount_394A_y  tenor_203L_y  \ncase_id                                     -0.087804     -0.124076  \nMONTH                                        0.082548      0.032847  \nWEEK_NUM                                     0.127853      0.033127  \ntarget                                       0.021336      0.035621  \nactualdpd_943P_x                                  NaN           NaN  \nannuity_853A_x                                    NaN           NaN  \nbyoccupationinc_3656910L_x                        NaN           NaN  \nchildnum_21L_x                                    NaN           NaN  \ncredacc_actualbalance_314A_x                      NaN           NaN  \ncredacc_credlmt_575A_x                            NaN           NaN  \ncredacc_maxhisbal_375A_x                          NaN           NaN  \ncredacc_minhisbal_90A_x                           NaN           NaN  \ncredacc_transactions_402L_x                       NaN           NaN  \ncredamount_590A_x                                 NaN           NaN  \ncurrdebt_94A_x                                    NaN           NaN  \ndownpmt_134A_x                                    NaN           NaN  \nmainoccupationinc_437A_x                          NaN           NaN  \nmaxdpdtolerance_577P_x                            NaN           NaN  \nnum_group1_x                                      NaN           NaN  \noutstandingdebt_522A_x                            NaN           NaN  \npmtnum_8L_x                                       NaN           NaN  \nrevolvingaccount_394A_x                           NaN           NaN  \ntenor_203L_x                                      NaN           NaN  \nactualdpd_943P_y                             0.001336      0.003315  \nannuity_853A_y                               0.010109      0.192565  \nbyoccupationinc_3656910L_y                  -0.135449      0.014215  \nchildnum_21L_y                               0.234011     -0.041096  \ncredacc_actualbalance_314A_y                 0.066007     -0.076057  \ncredacc_credlmt_575A_y                      -0.041379      0.024204  \ncredacc_maxhisbal_375A_y                    -0.011539     -0.003015  \ncredacc_minhisbal_90A_y                     -0.107537     -0.011385  \ncredacc_transactions_402L_y                  0.057433      0.003005  \ncredamount_590A_y                           -0.039424      0.644841  \ncurrdebt_94A_y                               0.119278      0.360621  \ndownpmt_134A_y                              -0.002460     -0.064840  \nmainoccupationinc_437A_y                     0.385466      0.170612  \nmaxdpdtolerance_577P_y                      -0.145971      0.052836  \nnum_group1_y                                -0.380113      0.004798  \noutstandingdebt_522A_y                       0.118533      0.371622  \npmtnum_8L_y                                  0.299392      1.000000  \nrevolvingaccount_394A_y                      1.000000      0.299392  \ntenor_203L_y                                 0.299392      1.000000  \n\n[42 rows x 42 columns]\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 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"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2024-04-29T01:38:11.028706Z","iopub.execute_input":"2024-04-29T01:38:11.029151Z","iopub.status.idle":"2024-04-29T01:38:12.662083Z","shell.execute_reply.started":"2024-04-29T01:38:11.029118Z","shell.execute_reply":"2024-04-29T01:38:12.661155Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"static = static()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-29T01:37:03.027714Z","iopub.execute_input":"2024-04-29T01:37:03.029363Z","iopub.status.idle":"2024-04-29T01:37:19.016388Z","shell.execute_reply.started":"2024-04-29T01:37:03.029303Z","shell.execute_reply":"2024-04-29T01:37:19.015245Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/1754661275.py:1: DtypeWarning: Columns (1,2,3,4,7,45,46,47,48) have mixed types. Specify dtype option on import or set low_memory=False.\n  static = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_static_cb_0.csv')\n","output_type":"stream"},{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"            case_id  contractssum_5085716L  days120_123L  days180_256L  \\\ncount  1.500476e+06           1.573290e+05  1.385691e+06  1.385691e+06   \nmean   1.284032e+06           6.416044e+05  1.607715e+00  2.388656e+00   \nstd    7.160881e+05           9.803273e+05  2.083003e+00  2.891115e+00   \nmin    3.570000e+02           0.000000e+00  0.000000e+00  0.000000e+00   \n25%    7.685088e+05           7.853195e+04  0.000000e+00  0.000000e+00   \n50%    1.361878e+06           3.072824e+05  1.000000e+00  2.000000e+00   \n75%    1.737010e+06           8.021141e+05  2.000000e+00  3.000000e+00   \nmax    2.703454e+06           3.129676e+07  1.090000e+02  1.100000e+02   \n\n        days30_165L  days360_512L   days90_310L  firstquarter_103L  \\\ncount  1.385691e+06  1.385691e+06  1.385691e+06       1.385691e+06   \nmean   5.177078e-01  4.777066e+00  1.211420e+00       2.860590e+00   \nstd    8.992377e-01  5.168856e+00  1.655931e+00       3.610966e+00   \nmin    0.000000e+00  0.000000e+00  0.000000e+00       0.000000e+00   \n25%    0.000000e+00  1.000000e+00  0.000000e+00       0.000000e+00   \n50%    0.000000e+00  3.000000e+00  1.000000e+00       2.000000e+00   \n75%    1.000000e+00  6.500000e+00  2.000000e+00       4.000000e+00   \nmax    2.200000e+01  1.150000e+02  4.100000e+01       7.600000e+01   \n\n       for3years_128L  for3years_504L  ...  pmtaverage_4527227A  \\\ncount    36514.000000    36514.000000  ...        114978.000000   \nmean         0.000082        4.382346  ...         10033.556094   \nstd          0.009064        5.815514  ...          5455.843604   \nmin          0.000000        0.000000  ...             4.200000   \n25%          0.000000        0.000000  ...          7192.000000   \n50%          0.000000        2.000000  ...          7553.000000   \n75%          0.000000        6.000000  ...         13464.400000   \nmax          1.000000       57.000000  ...        205848.610000   \n\n       pmtaverage_4955615A  pmtcount_4527229L  pmtcount_4955617L  \\\ncount         71845.000000      114978.000000       71845.000000   \nmean          17651.732489           6.598027          13.061118   \nstd            6871.642301           2.188992           1.855216   \nmin               4.400000           1.000000           1.000000   \n25%           13664.601000           6.000000          12.000000   \n50%           15765.200000           6.000000          14.000000   \n75%           21840.000000           6.000000          14.000000   \nmax           99085.400000          15.000000          16.000000   \n\n       pmtcount_693L  pmtscount_423L    pmtssum_45A  riskassesment_940T  \\\ncount  146406.000000   572638.000000  572638.000000        53560.000000   \nmean        5.714991        5.839291   13199.935970            0.225968   \nstd         1.758117        4.148264   18117.218312            0.976170   \nmin         0.000000        0.000000       0.000000           -3.670423   \n25%         6.000000        3.000000    3156.400100           -0.227985   \n50%         6.000000        6.000000    8391.900000            0.371834   \n75%         6.000000        7.000000   16992.000000            0.971653   \nmax        66.000000      121.000000  476843.400000            2.119132   \n\n       secondquarter_766L  thirdquarter_1082L  \ncount        1.385691e+06        1.385691e+06  \nmean         2.688482e+00        2.918342e+00  \nstd          3.324546e+00        3.423862e+00  \nmin          0.000000e+00        0.000000e+00  \n25%          0.000000e+00        0.000000e+00  \n50%          2.000000e+00        2.000000e+00  \n75%          4.000000e+00        4.000000e+00  \nmax          1.090000e+02        6.200000e+01  \n\n[8 rows x 37 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>case_id</th>\n      <th>contractssum_5085716L</th>\n      <th>days120_123L</th>\n      <th>days180_256L</th>\n      <th>days30_165L</th>\n      <th>days360_512L</th>\n      <th>days90_310L</th>\n      <th>firstquarter_103L</th>\n      <th>for3years_128L</th>\n      <th>for3years_504L</th>\n      <th>...</th>\n      <th>pmtaverage_4527227A</th>\n      <th>pmtaverage_4955615A</th>\n      <th>pmtcount_4527229L</th>\n      <th>pmtcount_4955617L</th>\n      <th>pmtcount_693L</th>\n      <th>pmtscount_423L</th>\n      <th>pmtssum_45A</th>\n      <th>riskassesment_940T</th>\n      <th>secondquarter_766L</th>\n      <th>thirdquarter_1082L</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.500476e+06</td>\n      <td>1.573290e+05</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n      <td>36514.000000</td>\n      <td>36514.000000</td>\n      <td>...</td>\n      <td>114978.000000</td>\n      <td>71845.000000</td>\n      <td>114978.000000</td>\n      <td>71845.000000</td>\n      <td>146406.000000</td>\n      <td>572638.000000</td>\n      <td>572638.000000</td>\n      <td>53560.000000</td>\n      <td>1.385691e+06</td>\n      <td>1.385691e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.284032e+06</td>\n      <td>6.416044e+05</td>\n      <td>1.607715e+00</td>\n      <td>2.388656e+00</td>\n      <td>5.177078e-01</td>\n      <td>4.777066e+00</td>\n      <td>1.211420e+00</td>\n      <td>2.860590e+00</td>\n      <td>0.000082</td>\n      <td>4.382346</td>\n      <td>...</td>\n      <td>10033.556094</td>\n      <td>17651.732489</td>\n      <td>6.598027</td>\n      <td>13.061118</td>\n      <td>5.714991</td>\n      <td>5.839291</td>\n      <td>13199.935970</td>\n      <td>0.225968</td>\n      <td>2.688482e+00</td>\n      <td>2.918342e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>7.160881e+05</td>\n      <td>9.803273e+05</td>\n      <td>2.083003e+00</td>\n      <td>2.891115e+00</td>\n      <td>8.992377e-01</td>\n      <td>5.168856e+00</td>\n      <td>1.655931e+00</td>\n      <td>3.610966e+00</td>\n      <td>0.009064</td>\n      <td>5.815514</td>\n      <td>...</td>\n      <td>5455.843604</td>\n      <td>6871.642301</td>\n      <td>2.188992</td>\n      <td>1.855216</td>\n      <td>1.758117</td>\n      <td>4.148264</td>\n      <td>18117.218312</td>\n      <td>0.976170</td>\n      <td>3.324546e+00</td>\n      <td>3.423862e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>3.570000e+02</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>4.200000</td>\n      <td>4.400000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-3.670423</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>7.685088e+05</td>\n      <td>7.853195e+04</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>7192.000000</td>\n      <td>13664.601000</td>\n      <td>6.000000</td>\n      <td>12.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>3156.400100</td>\n      <td>-0.227985</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.361878e+06</td>\n      <td>3.072824e+05</td>\n      <td>1.000000e+00</td>\n      <td>2.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>3.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>2.000000e+00</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>...</td>\n      <td>7553.000000</td>\n      <td>15765.200000</td>\n      <td>6.000000</td>\n      <td>14.000000</td>\n      <td>6.000000</td>\n      <td>6.000000</td>\n      <td>8391.900000</td>\n      <td>0.371834</td>\n      <td>2.000000e+00</td>\n      <td>2.000000e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.737010e+06</td>\n      <td>8.021141e+05</td>\n      <td>2.000000e+00</td>\n      <td>3.000000e+00</td>\n      <td>1.000000e+00</td>\n      <td>6.500000e+00</td>\n      <td>2.000000e+00</td>\n      <td>4.000000e+00</td>\n      <td>0.000000</td>\n      <td>6.000000</td>\n      <td>...</td>\n      <td>13464.400000</td>\n      <td>21840.000000</td>\n      <td>6.000000</td>\n      <td>14.000000</td>\n      <td>6.000000</td>\n      <td>7.000000</td>\n      <td>16992.000000</td>\n      <td>0.971653</td>\n      <td>4.000000e+00</td>\n      <td>4.000000e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2.703454e+06</td>\n      <td>3.129676e+07</td>\n      <td>1.090000e+02</td>\n      <td>1.100000e+02</td>\n      <td>2.200000e+01</td>\n      <td>1.150000e+02</td>\n      <td>4.100000e+01</td>\n      <td>7.600000e+01</td>\n      <td>1.000000</td>\n      <td>57.000000</td>\n      <td>...</td>\n      <td>205848.610000</td>\n      <td>99085.400000</td>\n      <td>15.000000</td>\n      <td>16.000000</td>\n      <td>66.000000</td>\n      <td>121.000000</td>\n      <td>476843.400000</td>\n      <td>2.119132</td>\n      <td>1.090000e+02</td>\n      <td>6.200000e+01</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 37 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}