data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "get_in=pd.read_excel('附件1：123家有信贷记录企业的相关数据.xlsx',1)\n",
    "get_out=pd.read_excel('附件1：123家有信贷记录企业的相关数据.xlsx',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#汇总每个公司的数据 略慢\n",
    "def sumfun(get_in,name):#'E%d'%1\n",
    "    record=[]\n",
    "    for i in range(len(get_in)):\n",
    "        if(get_in['企业代号'][i]==name):\n",
    "            record.append(get_in['价税合计'][i])\n",
    "    return sum(record)\n",
    "name_list=get_in['企业代号'].drop_duplicates()\n",
    "name_list=name_list.values\n",
    "get_in_price=[]\n",
    "for name in name_list:\n",
    "    get_in_price.append(sumfun(get_in,name))\n",
    "get_out_price=[]\n",
    "for name in name_list:\n",
    "    get_out_price.append(sumfun(get_out,name))    \n",
    "get_in_price,get_out_price=np.array(get_in_price),np.array(get_out_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图 num控制从第几个公司开始 因为前几个公司的值太大 会影响热力图效果\n",
    "num=10\n",
    "dataset=pd.DataFrame([get_in_price,get_out_price])\n",
    "plt.figure(figsize=(20,1))\n",
    "sns.heatmap(dataset.iloc[:,num:],annot=False,cmap=\"YlGnBu\")\n",
    "plt.show()#展示下原始数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导出数据到result.csv\n",
    "record=pd.DataFrame([name_list,get_in_price,get_out_price,get_in_price-get_out_price],index=['name','get_in','get_out','get_in-get_out'])\n",
    "record.to_csv('results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}