xhtd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            AGE                                  \n",
      "           mean    max   min median    std  count\n",
      "GOOD/BAD                                         \n",
      "0         44.13  121.0   0.0   44.0   9.82  37201\n",
      "1         40.94   68.0  22.0   39.0  10.04    200\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "age = pd.read_excel(r'D:\\EXIMBANK\\Document\\XHTDNB\\Data\\age.xlsx')\n",
    "df= age.groupby('GOOD/BAD').agg({'AGE':['mean','max','min','median','std','count']}).round(decimals = 2)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "x = age['AGE'].loc[age['GOOD/BAD'].isin([0])]\n",
    "y = age['AGE'].loc[age['GOOD/BAD'].isin([1])]\n",
    "\n",
    "\n",
    "\n",
    "bins = 300\n",
    "\n",
    "\n",
    "figure(figsize=(12,6 ), dpi=80)\n",
    "pyplot.hist(x, bins, alpha=0.5, label='Good',color = 'mediumblue')\n",
    "pyplot.hist(y, bins, alpha=0.5, label='Bad',color = 'darkorange')\n",
    "pyplot.title(\"Tiêu chí tuổi khách háng\")\n",
    "#pyplot.grid()\n",
    "pyplot.legend(loc='upper right')\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequenly\")\n",
    "pyplot.axvline(age['AGE'].mean(), color='red', linestyle='dashed', linewidth=1.5)\n",
    "plt.text(45, 1400, r'<-- Mean',color = 'red')\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}