data_pre_processing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PryaP3CpADYW",
    "outputId": "4ed53962-558b-4197-a0e1-a3e83a034d9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "#挂载云盘 \n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "7FazK2zZADLo",
    "outputId": "e49aba68-f6b6-4dec-cf59-0ca4837dc571"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_analysis.ipynb  数据处理.xlsx\n",
      "random_forest.ipynb  测试集进项.xlsx\n",
      "results.csv\t     测试集销项.xlsx\n",
      "test_in.npy\t     训练集进项.xlsx\n",
      "test_out.npy\t     训练集销项.xlsx\n",
      "train_in.npy\t     附件1：123家有信贷记录企业的相关数据2.xlsx\n",
      "train_out.npy\t     附件1：123家有信贷记录企业的相关数据.xlsx\n",
      "信贷数据分析.7z      附件2：302家无信贷记录企业的相关数据.xlsx\n",
      "data_analysis.ipynb  数据处理.xlsx\n",
      "random_forest.ipynb  测试集进项.xlsx\n",
      "results.csv\t     测试集销项.xlsx\n",
      "test_in.npy\t     训练集进项.xlsx\n",
      "test_out.npy\t     训练集销项.xlsx\n",
      "train_in.npy\t     附件1：123家有信贷记录企业的相关数据2.xlsx\n",
      "train_out.npy\t     附件1：123家有信贷记录企业的相关数据.xlsx\n",
      "信贷数据分析.7z      附件2：302家无信贷记录企业的相关数据.xlsx\n"
     ]
    }
   ],
   "source": [
    "#进入项目目录\n",
    "!ls\n",
    "import os\n",
    "os.chdir('/content/drive/My Drive/colab/信贷数据分析')\n",
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "colab_type": "code",
    "id": "9VrdWHGaEWD0",
    "outputId": "0ae239e9-b595-43a6-ccee-ec606dbabace"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>企业代号</th>\n",
       "      <th>金额</th>\n",
       "      <th>税额</th>\n",
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       "      <th>0</th>\n",
       "      <td>E1</td>\n",
       "      <td>-943.40</td>\n",
       "      <td>-56.60</td>\n",
       "      <td>-1000.00</td>\n",
       "      <td>有效发票</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E1</td>\n",
       "      <td>-4780.24</td>\n",
       "      <td>-286.81</td>\n",
       "      <td>-5067.05</td>\n",
       "      <td>有效发票</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E1</td>\n",
       "      <td>943.40</td>\n",
       "      <td>56.60</td>\n",
       "      <td>1000.00</td>\n",
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       "      <td>4780.24</td>\n",
       "      <td>286.81</td>\n",
       "      <td>5067.05</td>\n",
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       "      <td>223.30</td>\n",
       "      <td>6.70</td>\n",
       "      <td>230.00</td>\n",
       "      <td>有效发票</td>\n",
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       "      <td>264.15</td>\n",
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       "      <td>264.15</td>\n",
       "      <td>15.85</td>\n",
       "      <td>280.00</td>\n",
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       "      <th>210946</th>\n",
       "      <td>E123</td>\n",
       "      <td>264.15</td>\n",
       "      <td>15.85</td>\n",
       "      <td>280.00</td>\n",
       "      <td>有效发票</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210947 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "        企业代号       金额      税额     价税合计  发票状态\n",
       "0         E1  -943.40  -56.60 -1000.00  有效发票\n",
       "1         E1 -4780.24 -286.81 -5067.05  有效发票\n",
       "2         E1   943.40   56.60  1000.00  有效发票\n",
       "3         E1  4780.24  286.81  5067.05  有效发票\n",
       "4         E1   326.21    9.79   336.00  有效发票\n",
       "...      ...      ...     ...      ...   ...\n",
       "210942  E122   223.30    6.70   230.00  有效发票\n",
       "210943  E122   264.15   15.85   280.00  有效发票\n",
       "210944  E123   264.15   15.85   280.00  有效发票\n",
       "210945  E123   264.15   15.85   280.00  有效发票\n",
       "210946  E123   264.15   15.85   280.00  有效发票\n",
       "\n",
       "[210947 rows x 5 columns]"
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     },
     "execution_count": 93,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
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   ],
   "source": [
    "pd.read_excel('训练集进项.xlsx',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
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    "colab_type": "code",
    "id": "gAW74JU1EYkU",
    "outputId": "e3cee84c-f553-4c4a-dbaa-6e8211f508a9"
   },
   "outputs": [
    {
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       "      <th>金额</th>\n",
       "      <th>税额</th>\n",
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       "      <td>396.0</td>\n",
       "      <td>有效发票</td>\n",
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       "      <td>E124</td>\n",
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       "      <td>73.22</td>\n",
       "      <td>6.58</td>\n",
       "      <td>79.8</td>\n",
       "      <td>有效发票</td>\n",
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       "      <td>E425</td>\n",
       "      <td>132.04</td>\n",
       "      <td>3.96</td>\n",
       "      <td>136.0</td>\n",
       "      <td>有效发票</td>\n",
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       "        企业代号        金额       税额      价税合计  发票状态\n",
       "0       E124    338.46    57.54     396.0  有效发票\n",
       "1       E124    230.10     6.90     237.0  有效发票\n",
       "2       E124    223.30     6.70     230.0  有效发票\n",
       "3       E124  90090.09  9909.91  100000.0  有效发票\n",
       "4       E124  90090.09  9909.91  100000.0  有效发票\n",
       "...      ...       ...      ...       ...   ...\n",
       "395170  E425    663.11    19.89     683.0  有效发票\n",
       "395171  E425     45.41     4.09      49.5  有效发票\n",
       "395172  E425    110.14     1.16     111.3  有效发票\n",
       "395173  E425     73.22     6.58      79.8  有效发票\n",
       "395174  E425    132.04     3.96     136.0  有效发票\n",
       "\n",
       "[395175 rows x 5 columns]"
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     },
     "execution_count": 94,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('测试集进项.xlsx',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "36t6ttB1_2ZY"
   },
   "outputs": [],
   "source": [
    "'''\n",
    "一些函数\n",
    "'''\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "def pre_processing(train_in):\n",
    "  record=[]\n",
    "  for i in train_in['发票状态']:\n",
    "      if(i=='有效发票'):\n",
    "          record.append(1)\n",
    "      else:\n",
    "          record.append(0)\n",
    "  train_in['发票状态']=record \n",
    "  return train_in \n",
    "#汇总每个公司的数据 略慢\n",
    "def sumfun(get_in,name,feature_num):#'E%d'%1\n",
    "    '''\n",
    "    输入：原始数据，当前企业名称，第几个特征\n",
    "    输出：该企业该特征的总和\n",
    "    '''\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.iloc[i,feature_num])\n",
    "    return sum(record)\n",
    "#汇总每一列的数据\n",
    "def convert(name_list,train_in):\n",
    "    '''\n",
    "    输入：企业名称的列表，输入的表格\n",
    "    输出：该表格汇总好的数据\n",
    "    '''   \n",
    "    train_in1=[]\n",
    "    for name in name_list:\n",
    "        train_in1.append(sumfun(train_in,name,1))\n",
    "    train_in2=[]\n",
    "    for name in name_list:\n",
    "        train_in2.append(sumfun(train_in,name,2))\n",
    "    train_in3=[]\n",
    "    for name in name_list:\n",
    "        train_in3.append(sumfun(train_in,name,3))\n",
    "    train_in4=[]\n",
    "    for name in name_list:\n",
    "        train_in4.append(sumfun(train_in,name,4))    \n",
    "    train_in1,train_in2,train_in3,train_in4=np.array(train_in1),np.array(train_in2),np.array(train_in3),np.array(train_in4)    \n",
    "    return np.vstack((train_in1,train_in2,train_in3,train_in4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "id": "pnhMKPhD_2Ze",
    "outputId": "b8abce74-6347-4c17-fa23-21958a1d3300"
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       "        6.70879000e+03, 1.51970750e+05, 2.19792140e+05, 2.82570700e+04,\n",
       "        1.07348618e+06, 5.15796600e+04, 7.92450000e+02],\n",
       "       [9.28666459e+08, 7.19230533e+06, 2.33001404e+06, 5.22408513e+07,\n",
       "        3.01094522e+07, 2.03711513e+07, 3.95104972e+06, 8.54508548e+06,\n",
       "        1.81120283e+06, 3.21183320e+05, 1.20221729e+07, 7.09255255e+06,\n",
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       "        1.36398028e+07, 1.07655648e+07, 3.02378543e+07, 2.94237240e+07,\n",
       "        9.29954059e+06, 9.94470244e+06, 2.74621042e+07, 1.11468665e+07,\n",
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       "        2.60735400e+04, 3.21121275e+06, 6.51302600e+05, 1.92357920e+05,\n",
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       "        2.47316866e+06, 6.99056170e+05, 8.11001280e+05, 4.09653049e+06,\n",
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       "        4.84325080e+05, 3.20218960e+05, 6.94197600e+04, 4.18900000e+01,\n",
       "        9.46600000e+01, 8.05373660e+05, 1.22558670e+05, 1.32512720e+05,\n",
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       "        5.86945900e+04, 5.29422340e+05, 3.98068080e+05, 1.37644890e+05,\n",
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       "        2.39129500e+04, 1.00674700e+04, 1.69753450e+05, 3.53894000e+04,\n",
       "        1.35740570e+05, 2.75399800e+04, 2.10178810e+05, 1.07074500e+04,\n",
       "        1.80853000e+04, 2.29613000e+03, 6.44200000e+01, 1.16468590e+05,\n",
       "        1.12530000e+03, 9.97563000e+03, 6.17104100e+05, 2.25422000e+03,\n",
       "        7.25623000e+03, 4.86776470e+05, 5.73220400e+04, 1.58500000e+01,\n",
       "        1.04308000e+03, 2.38797000e+03, 6.27262000e+03, 6.92595000e+03,\n",
       "        1.13560000e+02, 7.66100000e+01, 5.53396300e+04, 1.39319200e+05,\n",
       "        6.76673600e+04, 8.29009100e+04, 3.17000000e+01, 2.00380000e+02,\n",
       "        4.11210000e+02, 1.71720400e+04, 3.00051900e+04, 1.93927000e+03,\n",
       "        1.77633700e+05, 3.12860000e+03, 4.75500000e+01],\n",
       "       [6.89269384e+09, 1.71736466e+08, 5.75208383e+07, 3.80407448e+08,\n",
       "        2.32050214e+08, 3.41136856e+08, 6.66535968e+07, 1.81067507e+08,\n",
       "        2.67023701e+07, 6.47861055e+06, 1.55449569e+08, 1.11222715e+08,\n",
       "        1.10124168e+08, 1.38371076e+08, 4.80394444e+06, 2.93248420e+05,\n",
       "        1.56248941e+08, 1.48426890e+08, 2.21345740e+08, 2.24188361e+08,\n",
       "        1.39254206e+08, 8.24704727e+07, 2.02890430e+08, 8.82058441e+07,\n",
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       "        1.86918440e+05, 2.53320744e+07, 5.10657158e+06, 2.60786272e+06,\n",
       "        8.46517458e+07, 6.37527406e+07, 3.76954158e+07, 2.37119736e+07,\n",
       "        5.21526507e+07, 1.59638523e+07, 3.74123460e+05, 4.84702063e+06,\n",
       "        5.88801549e+06, 1.17690000e+04, 4.89652975e+06, 3.48897366e+07,\n",
       "        2.71649933e+07, 5.51398528e+07, 5.08438107e+07, 1.48688455e+07,\n",
       "        2.86210959e+07, 3.00123529e+06, 2.27522919e+07, 1.92190225e+07,\n",
       "        3.72080909e+07, 9.50509981e+06, 1.23578935e+07, 3.17627354e+07,\n",
       "        3.31426706e+07, 8.10902868e+06, 2.23680952e+07, 5.49552757e+06,\n",
       "        9.42920800e+05, 1.29959098e+06, 4.01716823e+07, 9.06340500e+04,\n",
       "        5.19911509e+06, 5.45655831e+06, 1.37641055e+06, 7.40000000e+02,\n",
       "        3.25000000e+03, 6.50561719e+06, 1.19322301e+07, 1.54858277e+06,\n",
       "        8.70778460e+05, 3.37315000e+05, 2.75868025e+06, 9.37510610e+05,\n",
       "        8.09775000e+05, 3.74896862e+06, 3.32445258e+06, 2.60303573e+06,\n",
       "        2.35823653e+06, 1.77700832e+06, 4.11861202e+07, 1.66286365e+06,\n",
       "        4.36011420e+05, 1.76278358e+06, 1.79825011e+06, 6.83458460e+05,\n",
       "        2.82344269e+06, 7.87876570e+05, 1.61518218e+06, 1.59766410e+05,\n",
       "        3.35386830e+05, 1.63460000e+04, 1.13800000e+03, 2.43546996e+06,\n",
       "        1.98800000e+04, 2.09488980e+05, 7.00201961e+06, 2.33230200e+04,\n",
       "        5.31580000e+04, 3.70888384e+06, 7.69561210e+05, 2.80000000e+02,\n",
       "        8.48000000e+03, 6.14611000e+04, 1.10910000e+05, 1.06222950e+05,\n",
       "        3.37100000e+03, 1.04000000e+03, 6.53296620e+05, 9.88653890e+05,\n",
       "        1.35798873e+06, 7.00442000e+05, 5.60000000e+02, 2.80625000e+03,\n",
       "        7.12000000e+03, 1.69142790e+05, 2.49797330e+05, 3.01963400e+04,\n",
       "        1.25111988e+06, 5.47082600e+04, 8.40000000e+02],\n",
       "       [3.24900000e+03, 3.14350000e+04, 4.36700000e+03, 5.21000000e+02,\n",
       "        2.08400000e+03, 1.08140000e+04, 1.26430000e+04, 2.17770000e+04,\n",
       "        4.19900000e+03, 3.86000000e+03, 2.71500000e+03, 1.75700000e+03,\n",
       "        1.32550000e+04, 6.52700000e+03, 9.20000000e+01, 2.89000000e+02,\n",
       "        7.14300000e+03, 5.45500000e+03, 1.48400000e+03, 1.04100000e+03,\n",
       "        2.14600000e+03, 1.52000000e+03, 4.80200000e+03, 2.00400000e+03,\n",
       "        1.39100000e+03, 7.58000000e+02, 2.47500000e+03, 1.56000000e+02,\n",
       "        4.80000000e+01, 7.40000000e+02, 4.04000000e+02, 2.81100000e+03,\n",
       "        1.13300000e+03, 2.07100000e+03, 6.33000000e+02, 1.56200000e+03,\n",
       "        5.92000000e+02, 9.58000000e+02, 2.79000000e+02, 3.06800000e+03,\n",
       "        1.96800000e+03, 4.70000000e+01, 6.80000000e+01, 1.22200000e+03,\n",
       "        5.92000000e+02, 2.49200000e+03, 2.81100000e+03, 1.21000000e+03,\n",
       "        3.51800000e+03, 1.86000000e+02, 4.51000000e+02, 4.09000000e+02,\n",
       "        5.99000000e+02, 1.55500000e+03, 7.52000000e+02, 1.15200000e+03,\n",
       "        1.22000000e+02, 1.31300000e+03, 5.32000000e+02, 5.30000000e+02,\n",
       "        2.09000000e+02, 5.27000000e+02, 1.52600000e+03, 3.20000000e+01,\n",
       "        5.79000000e+02, 5.75000000e+02, 6.27000000e+02, 3.00000000e+00,\n",
       "        1.00000000e+00, 1.78700000e+03, 1.20500000e+03, 1.14000000e+02,\n",
       "        6.00000000e+02, 1.90000000e+01, 5.38000000e+02, 3.46000000e+02,\n",
       "        5.30000000e+01, 5.20000000e+01, 1.52000000e+02, 3.90000000e+02,\n",
       "        2.05000000e+02, 3.11000000e+02, 2.69900000e+03, 5.30000000e+01,\n",
       "        2.28000000e+02, 1.37000000e+02, 3.71000000e+02, 2.14000000e+02,\n",
       "        6.30000000e+01, 1.35000000e+02, 1.68000000e+02, 2.12000000e+02,\n",
       "        2.08000000e+02, 4.00000000e+00, 4.00000000e+00, 1.10000000e+01,\n",
       "        9.00000000e+00, 5.00000000e+01, 8.71000000e+02, 5.80000000e+01,\n",
       "        1.60000000e+01, 7.10000000e+02, 1.69000000e+02, 1.00000000e+00,\n",
       "        6.00000000e+00, 3.50000000e+01, 9.00000000e+00, 2.18000000e+02,\n",
       "        1.00000000e+01, 3.00000000e+00, 2.64000000e+02, 1.02000000e+02,\n",
       "        1.02000000e+02, 6.90000000e+01, 2.00000000e+00, 1.00000000e+01,\n",
       "        4.00000000e+00, 2.70000000e+01, 3.14000000e+02, 3.50000000e+01,\n",
       "        5.00000000e+01, 4.70000000e+01, 3.00000000e+00]])"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#处理训练集的数据，并保存 很慢 一个小时以内\n",
    "train_in=pd.read_excel('训练集进项.xlsx',0)\n",
    "train_in=pre_processing(train_in)     \n",
    "name_list=train_in['企业代号'].drop_duplicates()\n",
    "name_list=name_list.values\n",
    "train_in=convert(name_list,train_in)\n",
    "# 保存.npy文件\n",
    "np.save(\"train_in.npy\", train_in)\n",
    "train_out=pd.read_excel('训练集销项.xlsx',0)\n",
    "train_out=pre_processing(train_out)  \n",
    "train_out=convert(name_list,train_out)\n",
    "# 保存.npy文件\n",
    "np.save(\"train_out.npy\", train_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "colab_type": "code",
    "id": "pDIMJmfFFxbQ",
    "outputId": "1c4755ff-278e-49d8-a798-ebd138b4c38d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.22278530e+08 1.09590419e+09 1.17327184e+08 ... 6.10461400e+04\n",
      "  2.54329900e+05 5.95195200e+04]\n",
      " [6.99027333e+07 8.19090379e+07 1.66791566e+07 ... 8.00556000e+03\n",
      "  1.76694900e+04 5.24072000e+03]\n",
      " [9.92181263e+08 1.17781323e+09 1.34006341e+08 ... 6.90517000e+04\n",
      "  2.71999390e+05 6.47602400e+04]\n",
      " [1.52640000e+04 1.77760000e+04 5.15000000e+02 ... 2.10000000e+01\n",
      "  2.50000000e+01 1.16000000e+02]] (4, 302)\n"
     ]
    }
   ],
   "source": [
    "#处理测试集的数据，并保存---很慢，2-3个小时\n",
    "test_in=pd.read_excel('测试集进项.xlsx',0)\n",
    "test_in=pre_processing(test_in)  \n",
    "name_list=test_in['企业代号'].drop_duplicates()\n",
    "name_list=name_list.values\n",
    "test_in=convert(name_list,test_in)\n",
    "print(test_in,test_in.shape)\n",
    "# 保存.npy文件\n",
    "np.save(\"test_in.npy\", test_in)\n",
    "test_out=pd.read_excel('测试集销项.xlsx',0)\n",
    "test_out=pre_processing(test_out)  \n",
    "test_out=convert(name_list,test_out)\n",
    "# 保存.npy文件\n",
    "np.save(\"test_out.npy\", test_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J3zbAJOgEvum"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OJYY5Fj0EvYP"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "random_forest.ipynb",
   "provenance": []
  },
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}