Bank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 6 columns):\n",
      "用户ID    150000 non-null int64\n",
      "好坏客户    150000 non-null int64\n",
      "年龄      150000 non-null int64\n",
      "负债率     150000 non-null float64\n",
      "月收入     120269 non-null float64\n",
      "家属数量    146076 non-null float64\n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "data=pd.read_csv(\"loan.csv\",encoding=\"gbk\")\n",
    "data.info()\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 6 columns):\n",
      "用户ID    150000 non-null int64\n",
      "好坏客户    150000 non-null int64\n",
      "年龄      150000 non-null int64\n",
      "负债率     150000 non-null float64\n",
      "月收入     150000 non-null float64\n",
      "家属数量    146076 non-null float64\n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 6.9 MB\n",
      "None\n",
      "          用户ID  好坏客户  年龄          负债率           月收入  家属数量\n",
      "0            1     1  45     0.802982   9120.000000   2.0\n",
      "1            2     0  40     0.121876   2600.000000   1.0\n",
      "2            3     0  38     0.085113   3042.000000   0.0\n",
      "3            4     0  30     0.036050   3300.000000   0.0\n",
      "4            5     0  49     0.024926  63588.000000   0.0\n",
      "5            6     0  74     0.375607   3500.000000   1.0\n",
      "6            7     0  57  5710.000000   6670.221237   0.0\n",
      "7            8     0  39     0.209940   3500.000000   0.0\n",
      "8            9     0  27    46.000000   6670.221237   NaN\n",
      "9           10     0  57     0.606291  23684.000000   2.0\n",
      "10          11     0  30     0.309476   2500.000000   0.0\n",
      "11          12     0  51     0.531529   6501.000000   2.0\n",
      "12          13     0  46     0.298354  12454.000000   2.0\n",
      "13          14     1  40     0.382965  13700.000000   2.0\n",
      "14          15     0  76   477.000000      0.000000   0.0\n",
      "15          16     0  64     0.209892  11362.000000   2.0\n",
      "16          17     0  78  2058.000000   6670.221237   0.0\n",
      "17          18     0  53     0.188274   8800.000000   0.0\n",
      "18          19     0  43     0.527888   3280.000000   2.0\n",
      "19          20     0  25     0.065868    333.000000   0.0\n",
      "20          21     0  43     0.430046  12300.000000   0.0\n",
      "21          22     1  38     0.475841   3000.000000   2.0\n",
      "22          23     0  39     0.241104   2500.000000   0.0\n",
      "23          24     0  32     0.085512   7916.000000   0.0\n",
      "24          25     0  58     0.241622   2416.000000   0.0\n",
      "25          26     1  50     1.595253   4676.000000   1.0\n",
      "26          27     0  58     0.097672   8333.000000   0.0\n",
      "27          28     0  69     0.042383   2500.000000   1.0\n",
      "28          29     0  24     0.011761   3400.000000   0.0\n",
      "29          30     0  58     0.436103   5500.000000   0.0\n",
      "...        ...   ...  ..          ...           ...   ...\n",
      "149970  149971     0  58     0.253855  15500.000000   2.0\n",
      "149971  149972     0  83     0.013997   5000.000000   0.0\n",
      "149972  149973     0  42     0.008638   6945.000000   1.0\n",
      "149973  149974     0  44     0.494819   5500.000000   1.0\n",
      "149974  149975     0  61     0.603479   5000.000000   0.0\n",
      "149975  149976     0  58  2716.000000   6670.221237   0.0\n",
      "149976  149977     0  76    60.000000   6670.221237   0.0\n",
      "149977  149978     0  29   349.000000   6670.221237   0.0\n",
      "149978  149979     0  52     0.259496   2500.000000   0.0\n",
      "149979  149980     1  55     0.057235   8700.000000   0.0\n",
      "149980  149981     0  64     0.254976   5525.000000   0.0\n",
      "149981  149982     0  43     0.121752   6849.000000   4.0\n",
      "149982  149983     0  37     0.250272   2760.000000   3.0\n",
      "149983  149984     0  82     0.000800   5000.000000   0.0\n",
      "149984  149985     0  84    25.000000   6670.221237   0.0\n",
      "149985  149986     0  26     0.324962   1950.000000   0.0\n",
      "149986  149987     0  49     0.080384   5000.000000   1.0\n",
      "149987  149988     0  28     0.055692   3249.000000   0.0\n",
      "149988  149989     0  31     0.347924   7515.000000   0.0\n",
      "149989  149990     0  62     0.001408   9233.000000   3.0\n",
      "149990  149991     0  46     0.609779   4335.000000   2.0\n",
      "149991  149992     0  59     0.477658  10316.000000   0.0\n",
      "149992  149993     0  50  4132.000000   6670.221237   3.0\n",
      "149993  149994     0  22     0.000000    820.000000   0.0\n",
      "149994  149995     0  50     0.404293   3400.000000   0.0\n",
      "149995  149996     0  74     0.225131   2100.000000   0.0\n",
      "149996  149997     0  44     0.716562   5584.000000   2.0\n",
      "149997  149998     0  58  3870.000000   6670.221237   0.0\n",
      "149998  149999     0  30     0.000000   5716.000000   0.0\n",
      "149999  150000     0  64     0.249908   8158.000000   0.0\n",
      "\n",
      "[150000 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# 是不是收入越高的人坏账率越低\n",
    "data=data.fillna({\"月收入\":data[\"月收入\"].mean()})\n",
    "print(data.info())\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           (5000.0, 10000.0]\n",
      "1               (0.0, 5000.0]\n",
      "2               (0.0, 5000.0]\n",
      "3               (0.0, 5000.0]\n",
      "4         (20000.0, 100000.0]\n",
      "5               (0.0, 5000.0]\n",
      "6           (5000.0, 10000.0]\n",
      "7               (0.0, 5000.0]\n",
      "8           (5000.0, 10000.0]\n",
      "9         (20000.0, 100000.0]\n",
      "10              (0.0, 5000.0]\n",
      "11          (5000.0, 10000.0]\n",
      "12         (10000.0, 15000.0]\n",
      "13         (10000.0, 15000.0]\n",
      "14                        NaN\n",
      "15         (10000.0, 15000.0]\n",
      "16          (5000.0, 10000.0]\n",
      "17          (5000.0, 10000.0]\n",
      "18              (0.0, 5000.0]\n",
      "19              (0.0, 5000.0]\n",
      "20         (10000.0, 15000.0]\n",
      "21              (0.0, 5000.0]\n",
      "22              (0.0, 5000.0]\n",
      "23          (5000.0, 10000.0]\n",
      "24              (0.0, 5000.0]\n",
      "25              (0.0, 5000.0]\n",
      "26          (5000.0, 10000.0]\n",
      "27              (0.0, 5000.0]\n",
      "28              (0.0, 5000.0]\n",
      "29          (5000.0, 10000.0]\n",
      "                 ...         \n",
      "149970     (15000.0, 20000.0]\n",
      "149971          (0.0, 5000.0]\n",
      "149972      (5000.0, 10000.0]\n",
      "149973      (5000.0, 10000.0]\n",
      "149974          (0.0, 5000.0]\n",
      "149975      (5000.0, 10000.0]\n",
      "149976      (5000.0, 10000.0]\n",
      "149977      (5000.0, 10000.0]\n",
      "149978          (0.0, 5000.0]\n",
      "149979      (5000.0, 10000.0]\n",
      "149980      (5000.0, 10000.0]\n",
      "149981      (5000.0, 10000.0]\n",
      "149982          (0.0, 5000.0]\n",
      "149983          (0.0, 5000.0]\n",
      "149984      (5000.0, 10000.0]\n",
      "149985          (0.0, 5000.0]\n",
      "149986          (0.0, 5000.0]\n",
      "149987          (0.0, 5000.0]\n",
      "149988      (5000.0, 10000.0]\n",
      "149989      (5000.0, 10000.0]\n",
      "149990          (0.0, 5000.0]\n",
      "149991     (10000.0, 15000.0]\n",
      "149992      (5000.0, 10000.0]\n",
      "149993          (0.0, 5000.0]\n",
      "149994          (0.0, 5000.0]\n",
      "149995          (0.0, 5000.0]\n",
      "149996      (5000.0, 10000.0]\n",
      "149997      (5000.0, 10000.0]\n",
      "149998      (5000.0, 10000.0]\n",
      "149999      (5000.0, 10000.0]\n",
      "Name: 月收入, Length: 150000, dtype: category\n",
      "Categories (5, interval[int64]): [(0, 5000] < (5000, 10000] < (10000, 15000] < (15000, 20000] < (20000, 100000]]\n"
     ]
    }
   ],
   "source": [
    "cut_bins=[0,5000,10000,15000,20000,100000]\n",
    "income_cut=pd.cut(data[\"月收入\"],cut_bins)\n",
    "print(income_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "月收入\n",
      "(0, 5000]          0.087543\n",
      "(5000, 10000]      0.058308\n",
      "(10000, 15000]     0.041964\n",
      "(15000, 20000]     0.041811\n",
      "(20000, 100000]    0.053615\n",
      "Name: 好坏客户, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce4ab588>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "all_income_user=data[\"好坏客户\"].groupby(income_cut).count()\n",
    "bad_income_user=data[\"好坏客户\"].groupby(income_cut).sum()\n",
    "bad_rate=bad_income_user/all_income_user\n",
    "print(bad_rate)\n",
    "\n",
    "# 绘制月收入与坏账率关系图\n",
    "bad_rate.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "年龄\n",
      "(-0.109, 18.167]    0.000000\n",
      "(18.167, 36.333]    0.110124\n",
      "(36.333, 54.5]      0.081645\n",
      "(54.5, 72.667]      0.041719\n",
      "(72.667, 90.833]    0.021585\n",
      "(90.833, 109.0]     0.022495\n",
      "Name: 好坏客户, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19c7cfe710>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄和坏账率有什么关系\n",
    "age_cut=pd.cut(data[\"年龄\"],6)\n",
    "all_age_user=data[\"好坏客户\"].groupby(age_cut).count()\n",
    "bad_age_user=data[\"好坏客户\"].groupby(age_cut).sum()\n",
    "bad_rate=bad_age_user/all_age_user\n",
    "print(bad_rate)\n",
    "\n",
    "bad_rate.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "家属数量\n",
      "0.0     0.058629\n",
      "1.0     0.073529\n",
      "2.0     0.081139\n",
      "3.0     0.088263\n",
      "4.0     0.103774\n",
      "5.0     0.091153\n",
      "6.0     0.151899\n",
      "7.0     0.098039\n",
      "8.0     0.083333\n",
      "9.0     0.000000\n",
      "10.0    0.000000\n",
      "13.0    0.000000\n",
      "20.0    0.000000\n",
      "Name: 好坏客户, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 家庭人口数量和坏账率有什么关系\n",
    "all_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].count()\n",
    "bad_age_user=data.groupby(\"家属数量\")[\"好坏客户\"].sum()\n",
    "bad_rate=bad_age_user/all_age_user\n",
    "print(bad_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ce71a1d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bad_rate.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}