Card Approval part 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/babacafe/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "import math\n",
    "from sklearn import preprocessing\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import metrics    \n",
    "from sklearn import feature_selection\n",
    "label=preprocessing.LabelEncoder()\n",
    "import random as rnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize']=12,8\n",
    "\n",
    "def plot_correlation_map( df ):\n",
    "    corr = df.corr()\n",
    "    ht , ax = plt.subplots( figsize =( 16 , 12 ) )\n",
    "    cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "    ht = sns.heatmap(\n",
    "        corr, \n",
    "        cmap = cmap,\n",
    "        square=True, \n",
    "        cbar_kws={ 'shrink' : .9 }, \n",
    "        ax=ax, \n",
    "        annot = True, \n",
    "        annot_kws = { 'fontsize' : 12 })\n",
    "def describe_more( df ):\n",
    "    var = [] ; l = [] ; t = []\n",
    "    for x in df:\n",
    "        var.append( x )\n",
    "        l.append( len( pd.value_counts( df[ x ] ) ) )\n",
    "        t.append( df[ x ].dtypes )\n",
    "    #print(var,l,t)\n",
    "    levels = pd.DataFrame( { 'Variable' : var , 'Levels' : l , 'Datatype' : t } )\n",
    "    levels.sort_values( by = 'Levels' , inplace = True )\n",
    "    return levels\n",
    "\n",
    "def intervals(df):\n",
    "#     for i in ['year_tax_1','year_ops_1','year_ops_2', 'year_tax_2','year_combined_1','year_combined_2']:\n",
    "    for i in df.columns:\n",
    "#         df[i]=df[i].astype('float64')\n",
    "        if df[i].dtypes=='float64' or df[i].dtypes=='int64' :\n",
    "            print(i)\n",
    "            print(i,df[i].max(),df[i].min())\n",
    "            j=6\n",
    "            while j<7 and j>1:\n",
    "                try:\n",
    "                    df[i+'_interval']=pd.qcut(df[i],j)\n",
    "                    break\n",
    "                except:\n",
    "                    j=j-1\n",
    "            if j==1:\n",
    "                j=3\n",
    "                df[i+'_interval']=pd.cut(df[i],j)\n",
    "            print('bins=',j)\n",
    "            df[i+'count']=df[i+'_interval']\n",
    "            print(df.groupby([i+'_interval'],as_index=False).agg({i+'count':'count'}))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('puzzle_train_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "                                    ids default                   score_1  \\\n",
       "0  810e3277-619e-3154-7ba0-ebddfc5f7ea9   False  smzX0nxh5QlePvtVf6EAeg==   \n",
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       "4  e643bf65-9288-92f2-df13-eed631fe237c   False  4DLlLW62jReXaqbPaHp1vQ==   \n",
       "\n",
       "                    score_2  score_3     score_4   score_5     score_6  \\\n",
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       "\n",
       "   risk_rate  amount_borrowed   ...                        state  \\\n",
       "0       0.43         20024.31   ...     xsd3ZdsI3356I3xMxZeiqQ==   \n",
       "1       0.23         10046.51   ...     xsd3ZdsI3356I3xMxZeiqQ==   \n",
       "2       0.30         21228.25   ...     /L8vvVesB5WyAv190Hw/rQ==   \n",
       "3       0.15         23032.33   ...     GW2VZ3dN3OGHSjQ6JkfqQw==   \n",
       "4       0.08         24026.29   ...     sjJbkqJS7cXalHLBFA+EOQ==   \n",
       "\n",
       "                        zip                   channel  \\\n",
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       "\n",
       "                                            job_name  \\\n",
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       "4  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "\n",
       "                 real_state ok_since n_bankruptcies n_defaulted_loans  \\\n",
       "0  n+xK9CfX0bCn77lClTWviw==     14.0            1.0               0.0   \n",
       "1  n+xK9CfX0bCn77lClTWviw==     75.0            0.0               0.0   \n",
       "2  N5/CE7lSkAfB04hVFFwllw==      NaN            0.0               0.0   \n",
       "3  N5/CE7lSkAfB04hVFFwllw==      NaN            0.0               0.0   \n",
       "4  N5/CE7lSkAfB04hVFFwllw==     15.0            0.0               0.0   \n",
       "\n",
       "  n_accounts n_issues  \n",
       "0        9.0      9.0  \n",
       "1        3.0      NaN  \n",
       "2        5.0      NaN  \n",
       "3        5.0      NaN  \n",
       "4       10.0     10.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>score_3</td>\n",
       "      <td>88</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ok_since</td>\n",
       "      <td>104</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>last_payment</td>\n",
       "      <td>108</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>end_last_loan</td>\n",
       "      <td>110</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>zip</td>\n",
       "      <td>830</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>reason</td>\n",
       "      <td>20345</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>credit_limit</td>\n",
       "      <td>28259</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>job_name</td>\n",
       "      <td>44604</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>amount_borrowed</td>\n",
       "      <td>53348</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>income</td>\n",
       "      <td>57430</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>score_6</td>\n",
       "      <td>64592</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>score_5</td>\n",
       "      <td>64592</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>score_4</td>\n",
       "      <td>64592</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ids</td>\n",
       "      <td>64592</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable  Levels Datatype\n",
       "21             channel       1   object\n",
       "1              default       2   object\n",
       "16    facebook_profile       2   object\n",
       "15              gender       2   object\n",
       "10  borrowed_in_months       2  float64\n",
       "26   n_defaulted_loans       5  float64\n",
       "23          real_state       5   object\n",
       "2              score_1       7   object\n",
       "25      n_bankruptcies       7  float64\n",
       "14                sign      12   object\n",
       "3              score_2      35   object\n",
       "27          n_accounts      45  float64\n",
       "28            n_issues      45  float64\n",
       "19               state      50   object\n",
       "8            risk_rate      82  float64\n",
       "4              score_3      88  float64\n",
       "24            ok_since     104  float64\n",
       "17        last_payment     108   object\n",
       "18       end_last_loan     110   object\n",
       "20                 zip     830   object\n",
       "12              reason   20345   object\n",
       "11        credit_limit   28259  float64\n",
       "22            job_name   44604   object\n",
       "9      amount_borrowed   53348  float64\n",
       "13              income   57430  float64\n",
       "7              score_6   64592  float64\n",
       "6              score_5   64592  float64\n",
       "5              score_4   64592  float64\n",
       "0                  ids   64592   object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe_more(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('puzzle_test_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                    ids                   score_1  \\\n",
       "0  e4366223-7aa2-0904-7a47-66479ae46b2a  DGCQep2AE5QRkNCshIAlFQ==   \n",
       "1  c6416108-c6d7-e6be-c4b5-923dd36c8ec4  4DLlLW62jReXaqbPaHp1vQ==   \n",
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       "3  c5b96a7f-389a-28d0-242d-95db05e69da0  1Rk8w4Ucd5yR3KcqZzLdow==   \n",
       "4  1b461faa-926d-565d-b15d-0b452968ac81  8k8UDR4Yx0qasAjkGrUZLw==   \n",
       "\n",
       "                    score_2  score_3     score_4   score_5     score_6  \\\n",
       "0  osCzpM4hJrxugqWWuZmMWw==    230.0  101.906686  0.677760   77.245320   \n",
       "1  7h8PTkrlTWUPP3yuyP4rUg==    400.0   96.276632  0.278711   94.980774   \n",
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       "4  d/7Hedyz7ovK9Pn1CYN4+A==    320.0  101.303601  0.234202  107.162760   \n",
       "\n",
       "   risk_rate  amount_borrowed  borrowed_in_months   ...     \\\n",
       "0       0.22         21016.38                36.0   ...      \n",
       "1       0.13          1211.74                36.0   ...      \n",
       "2       0.25          6990.63                36.0   ...      \n",
       "3       0.46         15025.67                60.0   ...      \n",
       "4       0.29          7027.81                36.0   ...      \n",
       "\n",
       "                      state                       zip  \\\n",
       "0  GW2VZ3dN3OGHSjQ6JkfqQw==  gupRYCEwfV8MlD+HhQ9c3w==   \n",
       "1  0gQEZgRHqS5KSF3oWQrQfw==  ExyXSIu9zSfRKXY18ZnOGA==   \n",
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       "4  xsd3ZdsI3356I3xMxZeiqQ==  sfVhE81w8VwY0KuIEpmLTg==   \n",
       "\n",
       "                    channel  \\\n",
       "0  NCqL3QBx0pscDnx3ixKwXg==   \n",
       "1  NCqL3QBx0pscDnx3ixKwXg==   \n",
       "2  NCqL3QBx0pscDnx3ixKwXg==   \n",
       "3  NCqL3QBx0pscDnx3ixKwXg==   \n",
       "4  NCqL3QBx0pscDnx3ixKwXg==   \n",
       "\n",
       "                                            job_name  \\\n",
       "0  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "1  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "2  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "3  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "4  mLVIVxoGY7TUDJ1FyFoSIZi1SFcaBmO01AydRchaEiGYtU...   \n",
       "\n",
       "                 real_state ok_since n_bankruptcies n_defaulted_loans  \\\n",
       "0  N5/CE7lSkAfB04hVFFwllw==     73.0            0.0               0.0   \n",
       "1  N5/CE7lSkAfB04hVFFwllw==     39.0            0.0               0.0   \n",
       "2  N5/CE7lSkAfB04hVFFwllw==     33.0            0.0               0.0   \n",
       "3  n+xK9CfX0bCn77lClTWviw==     12.0            0.0               0.0   \n",
       "4  n+xK9CfX0bCn77lClTWviw==     31.0            0.0               0.0   \n",
       "\n",
       "  n_accounts n_issues  \n",
       "0       15.0     15.0  \n",
       "1       11.0      NaN  \n",
       "2       21.0      NaN  \n",
       "3       10.0     10.0  \n",
       "4        6.0      NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35000, 28)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>score_3</td>\n",
       "      <td>83</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ok_since</td>\n",
       "      <td>100</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>end_last_loan</td>\n",
       "      <td>105</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>last_payment</td>\n",
       "      <td>106</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>zip</td>\n",
       "      <td>821</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>reason</td>\n",
       "      <td>12013</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>credit_limit</td>\n",
       "      <td>16810</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>job_name</td>\n",
       "      <td>25703</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>amount_borrowed</td>\n",
       "      <td>31059</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>income</td>\n",
       "      <td>32616</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>score_6</td>\n",
       "      <td>35000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>score_5</td>\n",
       "      <td>35000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>score_4</td>\n",
       "      <td>35000</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ids</td>\n",
       "      <td>35000</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable  Levels Datatype\n",
       "20             channel       1   object\n",
       "15    facebook_profile       2   object\n",
       "14              gender       2   object\n",
       "9   borrowed_in_months       2  float64\n",
       "25   n_defaulted_loans       5  float64\n",
       "22          real_state       5   object\n",
       "24      n_bankruptcies       6  float64\n",
       "1              score_1       7   object\n",
       "13                sign      12   object\n",
       "2              score_2      35   object\n",
       "27            n_issues      45  float64\n",
       "26          n_accounts      46  float64\n",
       "18               state      47   object\n",
       "7            risk_rate      80  float64\n",
       "3              score_3      83  float64\n",
       "23            ok_since     100  float64\n",
       "17       end_last_loan     105   object\n",
       "16        last_payment     106   object\n",
       "19                 zip     821   object\n",
       "11              reason   12013   object\n",
       "10        credit_limit   16810  float64\n",
       "21            job_name   25703   object\n",
       "8      amount_borrowed   31059  float64\n",
       "12              income   32616  float64\n",
       "6              score_6   35000  float64\n",
       "5              score_5   35000  float64\n",
       "4              score_4   35000  float64\n",
       "0                  ids   35000   object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe_more(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_train=pd.read_csv('big_case_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>credit_line</th>\n",
       "      <th>month</th>\n",
       "      <th>spends</th>\n",
       "      <th>revolving_balance</th>\n",
       "      <th>card_request</th>\n",
       "      <th>minutes_cs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
       "      <td>1285</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
       "      <td>1285</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
       "      <td>1285</td>\n",
       "      <td>2</td>\n",
       "      <td>196.69</td>\n",
       "      <td>13.33</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
       "      <td>1285</td>\n",
       "      <td>3</td>\n",
       "      <td>151.73</td>\n",
       "      <td>106.26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>810e3277-619e-3154-7ba0-ebddfc5f7ea9</td>\n",
       "      <td>1285</td>\n",
       "      <td>4</td>\n",
       "      <td>982.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ids  credit_line  month  spends  \\\n",
       "0  810e3277-619e-3154-7ba0-ebddfc5f7ea9         1285      0    0.00   \n",
       "1  810e3277-619e-3154-7ba0-ebddfc5f7ea9         1285      1    0.00   \n",
       "2  810e3277-619e-3154-7ba0-ebddfc5f7ea9         1285      2  196.69   \n",
       "3  810e3277-619e-3154-7ba0-ebddfc5f7ea9         1285      3  151.73   \n",
       "4  810e3277-619e-3154-7ba0-ebddfc5f7ea9         1285      4  982.02   \n",
       "\n",
       "   revolving_balance  card_request  minutes_cs  \n",
       "0               0.00             1         0.0  \n",
       "1               0.00             0         0.0  \n",
       "2              13.33             0         0.0  \n",
       "3             106.26             0         0.0  \n",
       "4               0.00             0         0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Variable</th>\n",
       "      <th>Levels</th>\n",
       "      <th>Datatype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>card_request</td>\n",
       "      <td>2</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>month</td>\n",
       "      <td>36</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>minutes_cs</td>\n",
       "      <td>2151</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_line</td>\n",
       "      <td>15490</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ids</td>\n",
       "      <td>64592</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>revolving_balance</td>\n",
       "      <td>77617</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spends</td>\n",
       "      <td>311129</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Variable  Levels Datatype\n",
       "5       card_request       2    int64\n",
       "2              month      36    int64\n",
       "6         minutes_cs    2151  float64\n",
       "1        credit_line   15490    int64\n",
       "0                ids   64592   object\n",
       "4  revolving_balance   77617  float64\n",
       "3             spends  311129  float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "describe_more(big_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_train.groupby('ids',as_index=False).agg({'credit_line':'nunique'}).credit_line.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1406306, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit_line</th>\n",
       "      <th>month</th>\n",
       "      <th>spends</th>\n",
       "      <th>revolving_balance</th>\n",
       "      <th>card_request</th>\n",
       "      <th>minutes_cs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.406306e+06</td>\n",
       "      <td>1.406306e+06</td>\n",
       "      <td>1.406306e+06</td>\n",
       "      <td>1.406306e+06</td>\n",
       "      <td>1.406306e+06</td>\n",
       "      <td>1.406306e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.439334e+03</td>\n",
       "      <td>1.209055e+01</td>\n",
       "      <td>2.875423e+03</td>\n",
       "      <td>4.809796e+02</td>\n",
       "      <td>9.294990e-02</td>\n",
       "      <td>2.788997e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.561354e+03</td>\n",
       "      <td>8.509357e+00</td>\n",
       "      <td>3.527589e+03</td>\n",
       "      <td>1.432618e+03</td>\n",
       "      <td>2.903623e-01</td>\n",
       "      <td>6.637394e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.040000e+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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.946000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>3.350000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.295000e+03</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.565430e+03</td>\n",
       "      <td>1.530000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.722000e+03</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>4.122030e+03</td>\n",
       "      <td>1.429375e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.720000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.049000e+04</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>2.047574e+04</td>\n",
       "      <td>2.036944e+04</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.558000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        credit_line         month        spends  revolving_balance  \\\n",
       "count  1.406306e+06  1.406306e+06  1.406306e+06       1.406306e+06   \n",
       "mean   6.439334e+03  1.209055e+01  2.875423e+03       4.809796e+02   \n",
       "std    4.561354e+03  8.509357e+00  3.527589e+03       1.432618e+03   \n",
       "min    5.040000e+02  0.000000e+00  0.000000e+00       0.000000e+00   \n",
       "25%    2.946000e+03  5.000000e+00  3.350000e+02       0.000000e+00   \n",
       "50%    5.295000e+03  1.100000e+01  1.565430e+03       1.530000e+00   \n",
       "75%    8.722000e+03  1.800000e+01  4.122030e+03       1.429375e+02   \n",
       "max    2.049000e+04  3.500000e+01  2.047574e+04       2.036944e+04   \n",
       "\n",
       "       card_request    minutes_cs  \n",
       "count  1.406306e+06  1.406306e+06  \n",
       "mean   9.294990e-02  2.788997e+00  \n",
       "std    2.903623e-01  6.637394e+00  \n",
       "min    0.000000e+00  0.000000e+00  \n",
       "25%    0.000000e+00  0.000000e+00  \n",
       "50%    0.000000e+00  0.000000e+00  \n",
       "75%    0.000000e+00  1.720000e+00  \n",
       "max    1.000000e+00  7.558000e+01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64592, 29)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.ids.isin(big_train.ids.unique())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64592, 29)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 28)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test.ids.isin(big_train.ids.unique())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35000, 28)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 7)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_train[big_train.ids.isin(test.ids.unique())].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train['end_last_loan_date']=pd.to_datetime(train['end_last_loan'], errors='coerce')\n",
    "# train['last_payment_date']=pd.to_datetime(train['last_payment'], errors='coerce')\n",
    "train['months']=train.end_last_loan_date - train.last_payment_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 32)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.months.astype('int64').isnull()].shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def_id=train[train.default==True].ids.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "nondef_id=train[train.default==False].ids.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "50456"
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     "execution_count": 25,
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    "len(nondef_id)"
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   "execution_count": 163,
   "metadata": {},
   "outputs": [
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       "                                          ids  credit_line  month  spends  \\\n",
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       "\n",
       "         revolving_balance  card_request  minutes_cs  \n",
       "1320109                0.0             1        0.00  \n",
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       "1320156                0.0             0        0.00  \n",
       "1320175                0.0             1        7.92  \n",
       "1320181                0.0             0        0.00  "
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     "execution_count": 163,
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    "big_train.loc[(big_train.ids.isin(def_id))&(big_train.revolving_balance==0)&(big_train.spends==0),:].head()"
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   "cell_type": "code",
   "execution_count": 63,
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       "                                          ids  credit_line  month   spends  \\\n",
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       "1320183  1ee49ae0-fdce-50e6-a74c-4323c84ac1fb         6667      7  3634.19   \n",
       "1320184  1ee49ae0-fdce-50e6-a74c-4323c84ac1fb         6667      8  2648.10   \n",
       "1320185  1ee49ae0-fdce-50e6-a74c-4323c84ac1fb         6667      9   206.88   \n",
       "1320186  1ee49ae0-fdce-50e6-a74c-4323c84ac1fb         6667     10  6626.42   \n",
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       "\n",
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       "1320188               0.02             0       10.28  \n",
       "1320189               0.00             0        2.71  \n",
       "1320190               8.42             0        0.00  "
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     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 141,
   "metadata": {},
   "outputs": [
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       "                                          ids  credit_line  month   spends  \\\n",
       "1320072  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      0   219.04   \n",
       "1320073  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      1   154.29   \n",
       "1320074  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      2   574.06   \n",
       "1320075  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      3  1042.38   \n",
       "1320076  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      4   303.24   \n",
       "1320077  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      5  1197.47   \n",
       "1320078  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      6    83.87   \n",
       "1320079  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      7   612.96   \n",
       "1320080  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      8  1086.06   \n",
       "1320081  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235      9  1120.70   \n",
       "1320082  acbb594c-fdfa-feee-5a57-a21f077bff9b         1235     10    96.68   \n",
       "\n",
       "         revolving_balance  card_request  minutes_cs  \n",
       "1320072               0.00             1        0.00  \n",
       "1320073               0.44             0        0.00  \n",
       "1320074               2.27             0        0.00  \n",
       "1320075               0.56             0        7.66  \n",
       "1320076             126.36             0        0.00  \n",
       "1320077            1159.88             0        0.00  \n",
       "1320078               0.00             0        0.00  \n",
       "1320079               0.00             0        0.00  \n",
       "1320080              64.80             0       38.11  \n",
       "1320081             413.59             1        2.18  \n",
       "1320082               0.00             0        0.00  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
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   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "Empty DataFrame\n",
       "Columns: [ids, credit_line, month, spends, revolving_balance, card_request, minutes_cs]\n",
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   "execution_count": 24,
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       "                                    ids default                   score_1  \\\n",
       "0  810e3277-619e-3154-7ba0-ebddfc5f7ea9   False  smzX0nxh5QlePvtVf6EAeg==   \n",
       "\n",
       "                    score_2  score_3     score_4   score_5     score_6  \\\n",
       "0  tHpS8e9F8d9zg3iOQM9tsA==    710.0  104.174961  0.661509  123.015325   \n",
       "\n",
       "   risk_rate  amount_borrowed   ...                        state  \\\n",
       "0       0.43         20024.31   ...     xsd3ZdsI3356I3xMxZeiqQ==   \n",
       "\n",
       "                        zip                   channel  \\\n",
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       "\n",
       "                                            job_name  \\\n",
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       "\n",
       "                 real_state ok_since n_bankruptcies n_defaulted_loans  \\\n",
       "0  n+xK9CfX0bCn77lClTWviw==     14.0            1.0               0.0   \n",
       "\n",
       "  n_accounts n_issues  \n",
       "0        9.0      9.0  \n",
       "\n",
       "[1 rows x 29 columns]"
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 109,
   "metadata": {},
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     "execution_count": 109,
     "metadata": {},
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   "execution_count": 159,
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      "2509 d42cd2a3-2290-bdb9-2e18-24e6dab047fa\n"
     ]
    }
   ],
   "source": [
    "defaulted['profits']=1\n",
    "for j,i in enumerate(def_id):\n",
    "#     print(i)\n",
    "#     defaulted.loc[defaulted.ids==i,'profits'].apply(lambda z: 2\\\n",
    "#                                                     if big_train[big_train.ids==i][-1:].revolving_balance.values[0]==0\\\n",
    "#                                                    else profits(big_train,i,j=2))\n",
    "#     break\n",
    "    print(j,i)\n",
    "    if big_train[big_train.ids==i][-1:].revolving_balance.values[0]==0:\n",
    "        defaulted.loc[defaulted.ids==i,'profit']=(big_train[big_train.ids==i][:-1].spends.sum())*0.05+\\\n",
    "        (big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
    "        -big_train[big_train.ids==i][-1:].spends.values[0]\n",
    "    else:# big_train[big_train.ids==i][-1:].revolving_balance.values[0]>0:\n",
    "        defaulted.loc[defaulted.ids==i,'profit']=(big_train[big_train.ids==i].spends.sum())*0.05\\\n",
    "        +(big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
    "        -big_train[big_train.ids==i][-1:].revolving_balance.values[0]*1.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "defaulted.to_csv('Defaulted.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ids                  9510\n",
       "credit_line          9510\n",
       "card_request         9510\n",
       "minutes_cs           9510\n",
       "revolving_balance    9510\n",
       "spends               9510\n",
       "month                9510\n",
       "profit               9510\n",
       "profits              9510\n",
       "dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaulted.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def profits(big_train,i,j):\n",
    "#     if j==1:\n",
    "#         p=(big_train[big_train.ids==i][:-1].spends.sum())*0.05+\\\n",
    "#         (big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
    "#         -big_train[big_train.ids==i][-1:].spends.values[0]\n",
    "#     elif j==2:\n",
    "#         p=(big_train[big_train.ids==i].spends.sum())*0.05\\\n",
    "#         +(big_train[big_train.ids==i][:-1].revolving_balance.sum())*0.17\\\n",
    "#         -big_train[big_train.ids==i][-1:].revolving_balance.values[0]*1.17\n",
    "#     return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defaulted=big_train[big_train.ids.isin(def_id)].groupby(['ids','credit_line'],as_index=False).agg({'card_request':'sum','minutes_cs':'sum','revolving_balance':'sum','spends':'sum','month':'nunique'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_defaulted=big_train[big_train.ids.isin(nondef_id)].groupby(['ids','credit_line'],as_index=False).agg({'card_request':'sum','minutes_cs':'sum','revolving_balance':'sum','spends':'sum','month':'nunique'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_defaulted['profit']=non_defaulted['revolving_balance']*0.17+ non_defaulted['spends']*0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>credit_line</th>\n",
       "      <th>card_request</th>\n",
       "      <th>minutes_cs</th>\n",
       "      <th>revolving_balance</th>\n",
       "      <th>spends</th>\n",
       "      <th>month</th>\n",
       "      <th>profit</th>\n",
       "      <th>profits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0005967a-2048-6197-a054-30954efe93b1</td>\n",
       "      <td>11728</td>\n",
       "      <td>1</td>\n",
       "      <td>54.37</td>\n",
       "      <td>3856.89</td>\n",
       "      <td>46426.93</td>\n",
       "      <td>9</td>\n",
       "      <td>2962.4118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000779f6-0dd7-ae04-80c6-fcc030b909bc</td>\n",
       "      <td>4994</td>\n",
       "      <td>3</td>\n",
       "      <td>25.84</td>\n",
       "      <td>1000.69</td>\n",
       "      <td>15947.04</td>\n",
       "      <td>8</td>\n",
       "      <td>965.5531</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00134d43-b0c7-c669-7411-af3de6ce49c2</td>\n",
       "      <td>16782</td>\n",
       "      <td>2</td>\n",
       "      <td>43.53</td>\n",
       "      <td>25658.14</td>\n",
       "      <td>132438.37</td>\n",
       "      <td>14</td>\n",
       "      <td>7426.6383</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001b5d39-e5f2-1388-0979-ce36d79253e0</td>\n",
       "      <td>8212</td>\n",
       "      <td>1</td>\n",
       "      <td>10.85</td>\n",
       "      <td>3877.92</td>\n",
       "      <td>16060.00</td>\n",
       "      <td>6</td>\n",
       "      <td>1295.8319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>002b83b9-302c-1b64-f441-cfa545803746</td>\n",
       "      <td>1366</td>\n",
       "      <td>1</td>\n",
       "      <td>87.56</td>\n",
       "      <td>1727.31</td>\n",
       "      <td>9077.43</td>\n",
       "      <td>14</td>\n",
       "      <td>351.2360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ids  credit_line  card_request  \\\n",
       "0  0005967a-2048-6197-a054-30954efe93b1        11728             1   \n",
       "1  000779f6-0dd7-ae04-80c6-fcc030b909bc         4994             3   \n",
       "2  00134d43-b0c7-c669-7411-af3de6ce49c2        16782             2   \n",
       "3  001b5d39-e5f2-1388-0979-ce36d79253e0         8212             1   \n",
       "4  002b83b9-302c-1b64-f441-cfa545803746         1366             1   \n",
       "\n",
       "   minutes_cs  revolving_balance     spends  month     profit  profits  \n",
       "0       54.37            3856.89   46426.93      9  2962.4118        1  \n",
       "1       25.84            1000.69   15947.04      8   965.5531        1  \n",
       "2       43.53           25658.14  132438.37     14  7426.6383        1  \n",
       "3       10.85            3877.92   16060.00      6  1295.8319        1  \n",
       "4       87.56            1727.31    9077.43     14   351.2360        1  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "defaulted.head()"
   ]
  }
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