ThesisDataCleaning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Undergraduate Thesis Data Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create MySQL Connection to Original Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_conn = pymysql.connect(\n",
    "    host='localhost',\n",
    "    user='root',\n",
    "    password='Lc15503539901',\n",
    "    port=3306,\n",
    "    database='zsdatak_v1'\n",
    ")\n",
    "\n",
    "target_conn = pymysql.connect(\n",
    "    host='localhost',\n",
    "    user='root',\n",
    "    password='Lc15503539901',\n",
    "    port=3306,\n",
    "    database='admission'\n",
    ")\n",
    "\n",
    "\n",
    "engine = create_engine('mysql+pymysql://root:Lc15503539901@localhost/admission')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing School Table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Province"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/2631906848.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select gxsfdm, gxsfmc from table_yssj_gxxx group by gxsfdm, gxsfmc order by gxsfdm;'\n",
    "\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'gxsfdm': 'province_id', 'gxsfmc': 'province_name'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province_id</th>\n",
       "      <th>province_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>天津</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>河北</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>山西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>内蒙古</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  province_id province_name\n",
       "0          11            北京\n",
       "1          12            天津\n",
       "2          13            河北\n",
       "3          14            山西\n",
       "4          15           内蒙古"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('province', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## School"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/2798480820.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select * from table_yssj_gxxx;'\n",
    "\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2823 entries, 0 to 2822\n",
      "Data columns (total 27 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   id            2823 non-null   int64         \n",
      " 1   gxsfmc        2823 non-null   object        \n",
      " 2   gxsfdm        2823 non-null   object        \n",
      " 3   gxid          2823 non-null   object        \n",
      " 4   gxmc          2823 non-null   object        \n",
      " 5   bxxz          2823 non-null   object        \n",
      " 6   cclx          2823 non-null   object        \n",
      " 7   gxdj985       2823 non-null   object        \n",
      " 8   gxdj211       2823 non-null   object        \n",
      " 9   gxdjsyl       2823 non-null   object        \n",
      " 10  site          0 non-null      object        \n",
      " 11  email         0 non-null      object        \n",
      " 12  phone         0 non-null      object        \n",
      " 13  school_batch  0 non-null      object        \n",
      " 14  address       0 non-null      object        \n",
      " 15  postcode      0 non-null      object        \n",
      " 16  gxlx          0 non-null      object        \n",
      " 17  gxlxdm        0 non-null      object        \n",
      " 18  school_desc   0 non-null      object        \n",
      " 19  create_by     0 non-null      object        \n",
      " 20  create_date   0 non-null      object        \n",
      " 21  update_by     0 non-null      object        \n",
      " 22  update_date   2823 non-null   datetime64[ns]\n",
      " 23  remarks       0 non-null      object        \n",
      " 24  del_flag      0 non-null      object        \n",
      " 25  cjzt          2823 non-null   object        \n",
      " 26  cjflag        2823 non-null   object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(25)\n",
      "memory usage: 595.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>gxsfmc</th>\n",
       "      <th>gxsfdm</th>\n",
       "      <th>gxid</th>\n",
       "      <th>gxmc</th>\n",
       "      <th>bxxz</th>\n",
       "      <th>cclx</th>\n",
       "      <th>gxdj985</th>\n",
       "      <th>gxdj211</th>\n",
       "      <th>gxdjsyl</th>\n",
       "      <th>...</th>\n",
       "      <th>gxlxdm</th>\n",
       "      <th>school_desc</th>\n",
       "      <th>create_by</th>\n",
       "      <th>create_date</th>\n",
       "      <th>update_by</th>\n",
       "      <th>update_date</th>\n",
       "      <th>remarks</th>\n",
       "      <th>del_flag</th>\n",
       "      <th>cjzt</th>\n",
       "      <th>cjflag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>北京</td>\n",
       "      <td>11</td>\n",
       "      <td>140</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>北京</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>北京大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>浙江</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>上海</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>上海</td>\n",
       "      <td>31</td>\n",
       "      <td>132</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id gxsfmc gxsfdm gxid    gxmc bxxz  cclx gxdj985 gxdj211 gxdjsyl  ...  \\\n",
       "0  33     北京     11  140    清华大学   公办  普通本科       1       1   38000  ...   \n",
       "1  34     北京     11   31    北京大学   公办  普通本科       1       1   38000  ...   \n",
       "2  35     浙江     33  114    浙江大学   公办  普通本科       1       1   38000  ...   \n",
       "3  36     上海     31  125  上海交通大学   公办  普通本科       1       1   38000  ...   \n",
       "4  37     上海     31  132    复旦大学   公办  普通本科       1       1   38000  ...   \n",
       "\n",
       "  gxlxdm school_desc create_by create_date update_by         update_date  \\\n",
       "0   None        None      None        None      None 2022-07-02 10:30:49   \n",
       "1   None        None      None        None      None 2022-07-02 10:30:49   \n",
       "2   None        None      None        None      None 2022-07-02 10:30:49   \n",
       "3   None        None      None        None      None 2022-07-02 10:30:49   \n",
       "4   None        None      None        None      None 2022-07-02 10:30:49   \n",
       "\n",
       "  remarks del_flag cjzt cjflag  \n",
       "0    None     None    1         \n",
       "1    None     None    1         \n",
       "2    None     None    1         \n",
       "3    None     None    1         \n",
       "4    None     None    1         \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'gxsfdm': 'province_id', \n",
    "                   'gxid': 'school_id', \n",
    "                   'gxmc': 'school_name',\n",
    "                   'bxxz': 'school_type',\n",
    "                   'cclx': 'school_level',\n",
    "                   'gxdj985': 'is_985',\n",
    "                   'gxdj211': 'is_211',\n",
    "                   'gxdjsyl': 'is_doubletoptier',},\n",
    "                   inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th>create_date</th>\n",
       "      <th>update_by</th>\n",
       "      <th>update_date</th>\n",
       "      <th>remarks</th>\n",
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       "      <th>cjzt</th>\n",
       "      <th>cjflag</th>\n",
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       "      <th>0</th>\n",
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       "      <td>北京</td>\n",
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       "      <td>清华大学</td>\n",
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       "      <td>None</td>\n",
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       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>北京</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>北京大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>浙江</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>上海</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>上海</td>\n",
       "      <td>31</td>\n",
       "      <td>132</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2022-07-02 10:30:49</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id gxsfmc province_id school_id school_name school_type school_level  \\\n",
       "0  33     北京          11       140        清华大学          公办         普通本科   \n",
       "1  34     北京          11        31        北京大学          公办         普通本科   \n",
       "2  35     浙江          33       114        浙江大学          公办         普通本科   \n",
       "3  36     上海          31       125      上海交通大学          公办         普通本科   \n",
       "4  37     上海          31       132        复旦大学          公办         普通本科   \n",
       "\n",
       "  is_985 is_211 is_doubletoptier  ... gxlxdm school_desc create_by  \\\n",
       "0      1      1            38000  ...   None        None      None   \n",
       "1      1      1            38000  ...   None        None      None   \n",
       "2      1      1            38000  ...   None        None      None   \n",
       "3      1      1            38000  ...   None        None      None   \n",
       "4      1      1            38000  ...   None        None      None   \n",
       "\n",
       "  create_date update_by         update_date remarks del_flag cjzt cjflag  \n",
       "0        None      None 2022-07-02 10:30:49    None     None    1         \n",
       "1        None      None 2022-07-02 10:30:49    None     None    1         \n",
       "2        None      None 2022-07-02 10:30:49    None     None    1         \n",
       "3        None      None 2022-07-02 10:30:49    None     None    1         \n",
       "4        None      None 2022-07-02 10:30:49    None     None    1         \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['gxsfmc', 'site', 'email', 'phone', 'school_batch', 'address', 'postcode', 'gxlx', 'gxlxdm', 'school_desc', 'create_by', 'create_date', 'update_by', 'update_date', 'remarks', 'del_flag', 'cjzt', 'cjflag'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>province_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>school_name</th>\n",
       "      <th>school_type</th>\n",
       "      <th>school_level</th>\n",
       "      <th>is_985</th>\n",
       "      <th>is_211</th>\n",
       "      <th>is_doubletoptier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>140</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>北京大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>132</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id province_id school_id school_name school_type school_level is_985  \\\n",
       "0  33          11       140        清华大学          公办         普通本科      1   \n",
       "1  34          11        31        北京大学          公办         普通本科      1   \n",
       "2  35          33       114        浙江大学          公办         普通本科      1   \n",
       "3  36          31       125      上海交通大学          公办         普通本科      1   \n",
       "4  37          31       132        复旦大学          公办         普通本科      1   \n",
       "\n",
       "  is_211 is_doubletoptier  \n",
       "0      1            38000  \n",
       "1      1            38000  \n",
       "2      1            38000  \n",
       "3      1            38000  \n",
       "4      1            38000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2823 entries, 0 to 2822\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   id                2823 non-null   int64 \n",
      " 1   province_id       2823 non-null   object\n",
      " 2   school_id         2823 non-null   object\n",
      " 3   school_name       2823 non-null   object\n",
      " 4   school_type       2823 non-null   object\n",
      " 5   school_level      2823 non-null   object\n",
      " 6   is_985            2823 non-null   object\n",
      " 7   is_211            2823 non-null   object\n",
      " 8   is_doubletoptier  2823 non-null   object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 198.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_211'] = df['is_211'].astype('int')\n",
    "df['is_985'] = df['is_985'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_211'] = df['is_211'].where(df['is_211'] == 1, other=int(0))\n",
    "df['is_985'] = df['is_985'].where(df['is_985'] == 1, other=int(0))\n",
    "df['is_211'] = df['is_211'].astype('bool')\n",
    "df['is_985'] = df['is_985'].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_doubletoptier'] = df['is_doubletoptier'].astype('int')\n",
    "\n",
    "df['is_doubletoptier'] = df['is_doubletoptier'].where(df['is_doubletoptier'] == 38000, other=int(0))\n",
    "df['is_doubletoptier'] = df['is_doubletoptier'].where(df['is_doubletoptier'] == 0, other=int(1))\n",
    "df['is_doubletoptier'] = df['is_doubletoptier'].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2823 entries, 0 to 2822\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   id                2823 non-null   int64 \n",
      " 1   province_id       2823 non-null   object\n",
      " 2   school_id         2823 non-null   object\n",
      " 3   school_name       2823 non-null   object\n",
      " 4   school_type       2823 non-null   object\n",
      " 5   school_level      2823 non-null   object\n",
      " 6   is_985            2823 non-null   bool  \n",
      " 7   is_211            2823 non-null   bool  \n",
      " 8   is_doubletoptier  2823 non-null   bool  \n",
      "dtypes: bool(3), int64(1), object(5)\n",
      "memory usage: 140.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>school_level</th>\n",
       "      <th>is_985</th>\n",
       "      <th>is_211</th>\n",
       "      <th>is_doubletoptier</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>140</td>\n",
       "      <td>清华大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
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       "      <td>北京大学</td>\n",
       "      <td>公办</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>114</td>\n",
       "      <td>浙江大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>31</td>\n",
       "      <td>125</td>\n",
       "      <td>上海交通大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>31</td>\n",
       "      <td>132</td>\n",
       "      <td>复旦大学</td>\n",
       "      <td>公办</td>\n",
       "      <td>普通本科</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id province_id school_id school_name school_type school_level  is_985  \\\n",
       "0  33          11       140        清华大学          公办         普通本科    True   \n",
       "1  34          11        31        北京大学          公办         普通本科    True   \n",
       "2  35          33       114        浙江大学          公办         普通本科    True   \n",
       "3  36          31       125      上海交通大学          公办         普通本科    True   \n",
       "4  37          31       132        复旦大学          公办         普通本科    True   \n",
       "\n",
       "   is_211  is_doubletoptier  \n",
       "0    True              True  \n",
       "1    True              True  \n",
       "2    True              True  \n",
       "3    True              True  \n",
       "4    True              True  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2823"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('school', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score Sorted By Province"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/1369504060.py:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select * from table_yssj_sftdfs'\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>nf</th>\n",
       "      <th>cjgxdm</th>\n",
       "      <th>sfdm</th>\n",
       "      <th>zslb</th>\n",
       "      <th>zslbdm</th>\n",
       "      <th>pcmc</th>\n",
       "      <th>pcdm</th>\n",
       "      <th>klmc</th>\n",
       "      <th>kldm</th>\n",
       "      <th>...</th>\n",
       "      <th>zxkmdm</th>\n",
       "      <th>xkkmlx</th>\n",
       "      <th>zygroup</th>\n",
       "      <th>xkxx</th>\n",
       "      <th>fskzx</th>\n",
       "      <th>xclevel</th>\n",
       "      <th>xclevel_name</th>\n",
       "      <th>filing</th>\n",
       "      <th>first_km</th>\n",
       "      <th>update_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
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       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:45</td>\n",
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       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
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       "      <td>2022-07-10 01:01:19</td>\n",
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       "      <td>15</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>466</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>489</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>513</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>507</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    nf cjgxdm sfdm zslb zslbdm    pcmc pcdm klmc kldm  ... zxkmdm xkkmlx  \\\n",
       "0   1  2017    100   13  普通类      0    本科一批    7         1  ...                 \n",
       "1   2  2017    100   14  普通类      0  本科一批A段   51         1  ...                 \n",
       "2   3  2017    100   15  普通类      0    本科一批    7         1  ...                 \n",
       "3   4  2017    100   21  普通类      0    本科一批    7         1  ...                 \n",
       "4   5  2017    100   22  普通类      0  本科一批A段   51         1  ...                 \n",
       "\n",
       "  zygroup xkxx fskzx xclevel xclevel_name filing first_km         update_date  \n",
       "0                485       0            -    552        0 2022-07-10 00:59:45  \n",
       "1                481       0            -    492        0 2022-07-10 01:01:19  \n",
       "2                466       0            -    489        0 2022-07-10 00:59:39  \n",
       "3                480       0            -    513        0 2022-07-10 00:59:51  \n",
       "4                507       0            -    512        0 2022-07-10 00:59:15  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 342028 entries, 0 to 342027\n",
      "Data columns (total 25 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   id            342028 non-null  int64         \n",
      " 1   nf            342028 non-null  object        \n",
      " 2   cjgxdm        342028 non-null  object        \n",
      " 3   sfdm          342028 non-null  object        \n",
      " 4   zslb          342028 non-null  object        \n",
      " 5   zslbdm        342028 non-null  object        \n",
      " 6   pcmc          342028 non-null  object        \n",
      " 7   pcdm          342028 non-null  object        \n",
      " 8   klmc          342028 non-null  object        \n",
      " 9   kldm          342028 non-null  object        \n",
      " 10  zgf           342028 non-null  object        \n",
      " 11  pjf           342028 non-null  object        \n",
      " 12  zdf           342024 non-null  object        \n",
      " 13  zdwc          342028 non-null  object        \n",
      " 14  sxkmdm        342028 non-null  object        \n",
      " 15  zxkmdm        342028 non-null  object        \n",
      " 16  xkkmlx        342028 non-null  object        \n",
      " 17  zygroup       342028 non-null  object        \n",
      " 18  xkxx          342028 non-null  object        \n",
      " 19  fskzx         342028 non-null  object        \n",
      " 20  xclevel       342028 non-null  object        \n",
      " 21  xclevel_name  342028 non-null  object        \n",
      " 22  filing        341996 non-null  object        \n",
      " 23  first_km      342028 non-null  object        \n",
      " 24  update_date   342028 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1), object(23)\n",
      "memory usage: 65.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       140690\n",
       "2       104060\n",
       "3        36791\n",
       "2073     36223\n",
       "2074     21551\n",
       "5          559\n",
       "4          521\n",
       "24         382\n",
       "33         213\n",
       "23         198\n",
       "32         175\n",
       "138        142\n",
       "25         131\n",
       "140         96\n",
       "137         85\n",
       "143         66\n",
       "26          63\n",
       "142         29\n",
       "1079        16\n",
       "31           5\n",
       "141          5\n",
       "139          4\n",
       "144          3\n",
       "1144         3\n",
       "1150         3\n",
       "1145         2\n",
       "1146         2\n",
       "1149         2\n",
       "1151         2\n",
       "1154         2\n",
       "1148         2\n",
       "1152         1\n",
       "1156         1\n",
       "Name: kldm, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['kldm'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'nf': 'year', \n",
    "                   'cjgxdm': 'school_id', \n",
    "                   'sfdm': 'province_id', \n",
    "                   'zslb': 'category', \n",
    "                   'zslbdm': 'category_id',\n",
    "                   'pcmc': 'round',\n",
    "                   'pcdm': 'round_id',\n",
    "                   'xkxx': 'selection',\n",
    "                   'zgf': 'highest',\n",
    "                   'pjf': 'average',\n",
    "                   'zdf': 'lowest',\n",
    "                   'zdwc': 'lowest_rank',\n",
    "                   'kldm': 'group_id',\n",
    "                   'fskzx': 'line'}, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>year</th>\n",
       "      <th>school_id</th>\n",
       "      <th>province_id</th>\n",
       "      <th>category</th>\n",
       "      <th>category_id</th>\n",
       "      <th>round</th>\n",
       "      <th>round_id</th>\n",
       "      <th>klmc</th>\n",
       "      <th>group_id</th>\n",
       "      <th>...</th>\n",
       "      <th>zxkmdm</th>\n",
       "      <th>xkkmlx</th>\n",
       "      <th>zygroup</th>\n",
       "      <th>selection</th>\n",
       "      <th>line</th>\n",
       "      <th>xclevel</th>\n",
       "      <th>xclevel_name</th>\n",
       "      <th>filing</th>\n",
       "      <th>first_km</th>\n",
       "      <th>update_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>13</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>485</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>552</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>492</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>466</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>489</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>513</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>507</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 00:59:15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year school_id province_id category category_id   round round_id klmc  \\\n",
       "0   1  2017       100          13      普通类           0    本科一批        7        \n",
       "1   2  2017       100          14      普通类           0  本科一批A段       51        \n",
       "2   3  2017       100          15      普通类           0    本科一批        7        \n",
       "3   4  2017       100          21      普通类           0    本科一批        7        \n",
       "4   5  2017       100          22      普通类           0  本科一批A段       51        \n",
       "\n",
       "  group_id  ... zxkmdm xkkmlx zygroup selection line xclevel xclevel_name  \\\n",
       "0        1  ...                                  485       0            -   \n",
       "1        1  ...                                  481       0            -   \n",
       "2        1  ...                                  466       0            -   \n",
       "3        1  ...                                  480       0            -   \n",
       "4        1  ...                                  507       0            -   \n",
       "\n",
       "  filing first_km         update_date  \n",
       "0    552        0 2022-07-10 00:59:45  \n",
       "1    492        0 2022-07-10 01:01:19  \n",
       "2    489        0 2022-07-10 00:59:39  \n",
       "3    513        0 2022-07-10 00:59:51  \n",
       "4    512        0 2022-07-10 00:59:15  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['klmc', 'sxkmdm', 'zxkmdm', 'xkkmlx', 'zygroup', 'xclevel', 'xclevel_name', 'filing', 'first_km', 'update_date'], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>492</td>\n",
       "      <td>24054</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>521</td>\n",
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       "      <td>17175</td>\n",
       "      <td></td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>普通类</td>\n",
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       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>513</td>\n",
       "      <td>27828</td>\n",
       "      <td></td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "      <td>523</td>\n",
       "      <td>512</td>\n",
       "      <td>15120</td>\n",
       "      <td></td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year school_id province_id category category_id   round round_id  \\\n",
       "0   1  2017       100          13      普通类           0    本科一批        7   \n",
       "1   2  2017       100          14      普通类           0  本科一批A段       51   \n",
       "2   3  2017       100          15      普通类           0    本科一批        7   \n",
       "3   4  2017       100          21      普通类           0    本科一批        7   \n",
       "4   5  2017       100          22      普通类           0  本科一批A段       51   \n",
       "\n",
       "  group_id highest average lowest lowest_rank selection line  \n",
       "0        1       -       -    552       30485            485  \n",
       "1        1       -       -    492       24054            481  \n",
       "2        1       -     521    489       17175            466  \n",
       "3        1       -       -    513       27828            480  \n",
       "4        1       -     523    512       15120            507  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         0.857892\n",
       "545       0.000836\n",
       "533       0.000804\n",
       "535       0.000789\n",
       "542       0.000784\n",
       "            ...   \n",
       "551.60    0.000003\n",
       "362.40    0.000003\n",
       "345.60    0.000003\n",
       "299.80    0.000003\n",
       "163       0.000003\n",
       "Name: highest, Length: 977, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['highest'].value_counts() / df['highest'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         0.906511\n",
       "492       0.000462\n",
       "495       0.000456\n",
       "497       0.000453\n",
       "510       0.000444\n",
       "            ...   \n",
       "577.13    0.000003\n",
       "265.85    0.000003\n",
       "339.25    0.000003\n",
       "535.39    0.000003\n",
       "355.29    0.000003\n",
       "Name: average, Length: 5024, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['average'].value_counts() / df['average'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_selection'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['highest', 'average'], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>id</th>\n",
       "      <th>year</th>\n",
       "      <th>school_id</th>\n",
       "      <th>province_id</th>\n",
       "      <th>category</th>\n",
       "      <th>category_id</th>\n",
       "      <th>round</th>\n",
       "      <th>round_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>lowest</th>\n",
       "      <th>lowest_rank</th>\n",
       "      <th>selection</th>\n",
       "      <th>line</th>\n",
       "      <th>is_selection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>13</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>30485</td>\n",
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       "      <td>485</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>14</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>492</td>\n",
       "      <td>24054</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>489</td>\n",
       "      <td>17175</td>\n",
       "      <td></td>\n",
       "      <td>466</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>513</td>\n",
       "      <td>27828</td>\n",
       "      <td></td>\n",
       "      <td>480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>512</td>\n",
       "      <td>15120</td>\n",
       "      <td></td>\n",
       "      <td>507</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year school_id province_id category category_id   round round_id  \\\n",
       "0   1  2017       100          13      普通类           0    本科一批        7   \n",
       "1   2  2017       100          14      普通类           0  本科一批A段       51   \n",
       "2   3  2017       100          15      普通类           0    本科一批        7   \n",
       "3   4  2017       100          21      普通类           0    本科一批        7   \n",
       "4   5  2017       100          22      普通类           0  本科一批A段       51   \n",
       "\n",
       "  group_id lowest lowest_rank selection line  is_selection  \n",
       "0        1    552       30485            485         False  \n",
       "1        1    492       24054            481         False  \n",
       "2        1    489       17175            466         False  \n",
       "3        1    513       27828            480         False  \n",
       "4        1    512       15120            507         False  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_selection'] = df['is_selection'].where(df['selection'] == '', other= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      90\n",
       "year                  2017\n",
       "school_id             1007\n",
       "province_id             31\n",
       "category               普通类\n",
       "category_id              0\n",
       "round                  本科批\n",
       "round_id                14\n",
       "group_id                 3\n",
       "lowest                 460\n",
       "lowest_rank          24235\n",
       "selection       物/化/生(3选1)\n",
       "line                   402\n",
       "is_selection          True\n",
       "Name: 89, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[89, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 342028 entries, 0 to 342027\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            342028 non-null  int64 \n",
      " 1   year          342028 non-null  object\n",
      " 2   school_id     342028 non-null  object\n",
      " 3   province_id   342028 non-null  object\n",
      " 4   category      342028 non-null  object\n",
      " 5   category_id   342028 non-null  object\n",
      " 6   round         342028 non-null  object\n",
      " 7   round_id      342028 non-null  object\n",
      " 8   group_id      342028 non-null  object\n",
      " 9   lowest        342024 non-null  object\n",
      " 10  lowest_rank   342028 non-null  object\n",
      " 11  selection     342028 non-null  object\n",
      " 12  line          342028 non-null  object\n",
      " 13  is_selection  342028 non-null  bool  \n",
      "dtypes: bool(1), int64(1), object(12)\n",
      "memory usage: 34.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lowest'] = df['lowest'].where(df['lowest'] != '-', other= '0')\n",
    "df['lowest_rank'] = df['lowest_rank'].where(df['lowest_rank'] != '-', other= '0')\n",
    "df['line'] = df['line'].where(df['line'] != '-', other= '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lowest'] = df['lowest'].astype('float')\n",
    "df['lowest_rank'] = df['lowest_rank'].astype('int')\n",
    "df['line'] = df['line'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>category</th>\n",
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       "      <th>0</th>\n",
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       "      <td>本科一批</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
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       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>492.0</td>\n",
       "      <td>24054</td>\n",
       "      <td></td>\n",
       "      <td>481</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>15</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>489.0</td>\n",
       "      <td>17175</td>\n",
       "      <td></td>\n",
       "      <td>466</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2017</td>\n",
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       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>513.0</td>\n",
       "      <td>27828</td>\n",
       "      <td></td>\n",
       "      <td>480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>22</td>\n",
       "      <td>普通类</td>\n",
       "      <td>0</td>\n",
       "      <td>本科一批A段</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>512.0</td>\n",
       "      <td>15120</td>\n",
       "      <td></td>\n",
       "      <td>507</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year school_id province_id category category_id   round round_id  \\\n",
       "0   1  2017       100          13      普通类           0    本科一批        7   \n",
       "1   2  2017       100          14      普通类           0  本科一批A段       51   \n",
       "2   3  2017       100          15      普通类           0    本科一批        7   \n",
       "3   4  2017       100          21      普通类           0    本科一批        7   \n",
       "4   5  2017       100          22      普通类           0  本科一批A段       51   \n",
       "\n",
       "  group_id  lowest  lowest_rank selection  line  is_selection  \n",
       "0        1   552.0        30485             485         False  \n",
       "1        1   492.0        24054             481         False  \n",
       "2        1   489.0        17175             466         False  \n",
       "3        1   513.0        27828             480         False  \n",
       "4        1   512.0        15120             507         False  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 342028 entries, 0 to 342027\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            342028 non-null  int64  \n",
      " 1   year          342028 non-null  object \n",
      " 2   school_id     342028 non-null  object \n",
      " 3   province_id   342028 non-null  object \n",
      " 4   category      342028 non-null  object \n",
      " 5   category_id   342028 non-null  object \n",
      " 6   round         342028 non-null  object \n",
      " 7   round_id      342028 non-null  object \n",
      " 8   group_id      342028 non-null  object \n",
      " 9   lowest        342024 non-null  float64\n",
      " 10  lowest_rank   342028 non-null  int64  \n",
      " 11  selection     342028 non-null  object \n",
      " 12  line          342028 non-null  int64  \n",
      " 13  is_selection  342028 non-null  bool   \n",
      "dtypes: bool(1), float64(1), int64(3), object(9)\n",
      "memory usage: 34.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342028"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('schoolscore', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Major Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/732458871.py:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select * from table_yssj_zyfs'\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1763177 entries, 0 to 1763176\n",
      "Data columns (total 31 columns):\n",
      " #   Column         Dtype         \n",
      "---  ------         -----         \n",
      " 0   id             int64         \n",
      " 1   nf             object        \n",
      " 2   sfdm           object        \n",
      " 3   cjgxdm         object        \n",
      " 4   kldm           object        \n",
      " 5   klmc           object        \n",
      " 6   pcdm           object        \n",
      " 7   pcmc           object        \n",
      " 8   zslbdm         object        \n",
      " 9   zslb           object        \n",
      " 10  zymldm         object        \n",
      " 11  zyml           object        \n",
      " 12  zydldm         object        \n",
      " 13  zydl           object        \n",
      " 14  zydm           object        \n",
      " 15  zymc           object        \n",
      " 16  zgf            object        \n",
      " 17  pjf            object        \n",
      " 18  zdf            object        \n",
      " 19  zdwc           object        \n",
      " 20  ccdm           object        \n",
      " 21  ccmc           object        \n",
      " 22  sxkmdm         object        \n",
      " 23  zxkmdm         object        \n",
      " 24  xkkmlx         object        \n",
      " 25  zygroup        object        \n",
      " 26  xkxx           object        \n",
      " 27  special_id     object        \n",
      " 28  spe_id         object        \n",
      " 29  special_group  object        \n",
      " 30  update_date    datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1), object(29)\n",
      "memory usage: 417.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                 1\n",
       "nf                              2017\n",
       "sfdm                              13\n",
       "cjgxdm                           100\n",
       "kldm                               1\n",
       "klmc                                \n",
       "pcdm                               7\n",
       "pcmc                            本科一批\n",
       "zslbdm                             -\n",
       "zslb                             普通类\n",
       "zymldm                            13\n",
       "zyml                             管理学\n",
       "zydldm                           113\n",
       "zydl                           工商管理类\n",
       "zydm                            None\n",
       "zymc                          人力资源管理\n",
       "zgf                                -\n",
       "pjf                              554\n",
       "zdf                              553\n",
       "zdwc                           29972\n",
       "ccdm                               1\n",
       "ccmc                              本科\n",
       "sxkmdm                              \n",
       "zxkmdm                              \n",
       "xkkmlx                              \n",
       "zygroup                             \n",
       "xkxx                                \n",
       "special_id                    143458\n",
       "spe_id                           302\n",
       "special_group                      0\n",
       "update_date      2022-07-10 01:01:49\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>nf</th>\n",
       "      <th>sfdm</th>\n",
       "      <th>cjgxdm</th>\n",
       "      <th>kldm</th>\n",
       "      <th>klmc</th>\n",
       "      <th>pcdm</th>\n",
       "      <th>pcmc</th>\n",
       "      <th>zslbdm</th>\n",
       "      <th>zslb</th>\n",
       "      <th>...</th>\n",
       "      <th>ccmc</th>\n",
       "      <th>sxkmdm</th>\n",
       "      <th>zxkmdm</th>\n",
       "      <th>xkkmlx</th>\n",
       "      <th>zygroup</th>\n",
       "      <th>xkxx</th>\n",
       "      <th>special_id</th>\n",
       "      <th>spe_id</th>\n",
       "      <th>special_group</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
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       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143458</td>\n",
       "      <td>302</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143469</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
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       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143511</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143512</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143519</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    nf sfdm cjgxdm kldm klmc pcdm  pcmc zslbdm zslb  ... ccmc sxkmdm  \\\n",
       "0   1  2017   13    100    1         7  本科一批      -  普通类  ...   本科          \n",
       "1   2  2017   13    100    1         7  本科一批      -  普通类  ...   本科          \n",
       "2   3  2017   13    100    1         7  本科一批      -  普通类  ...   本科          \n",
       "3   4  2017   13    100    1         7  本科一批      -  普通类  ...   本科          \n",
       "4   5  2017   13    100    1         7  本科一批      -  普通类  ...   本科          \n",
       "\n",
       "  zxkmdm xkkmlx zygroup xkxx special_id spe_id special_group  \\\n",
       "0                                143458    302             0   \n",
       "1                                143469    323             0   \n",
       "2                                143511    224             0   \n",
       "3                                143512    227             0   \n",
       "4                                143519    248             0   \n",
       "\n",
       "          update_date  \n",
       "0 2022-07-10 01:01:49  \n",
       "1 2022-07-10 01:01:47  \n",
       "2 2022-07-10 01:01:47  \n",
       "3 2022-07-10 01:01:49  \n",
       "4 2022-07-10 01:01:47  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'nf': 'year',\n",
    "                   'sfdm': 'province_id',\n",
    "                   'cjgxdm': 'school_id',\n",
    "                   'kldm': 'group_id',\n",
    "                   'pcdm': 'level_id',\n",
    "                   'pcmc': 'level',\n",
    "                   'zslb': 'category',\n",
    "                   'zslbdm': 'category_id',\n",
    "                   'zyml': 'class_name',\n",
    "                   'zymldm': 'class_id',\n",
    "                   'zydl': 'majorcategory',\n",
    "                   'zydldm': 'majorcategory_id',\n",
    "                   'zymc': 'major',\n",
    "                   'zgf': 'highest',\n",
    "                   'pjf': 'average',\n",
    "                   'zdf': 'lowest',\n",
    "                   'zdwc': 'lowest_rank',\n",
    "                   'ccdm': 'degree_id',\n",
    "                   'ccmc': 'degree',\n",
    "                   'xkxx': 'selection'\n",
    "                   }, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
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       "      <td>7</td>\n",
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       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
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       "      <td>143469</td>\n",
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       "      <th>2</th>\n",
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       "      <td>2017</td>\n",
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       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
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       "      <td>...</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143511</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143512</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>...</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>143519</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-07-10 01:01:47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year province_id school_id group_id klmc level_id level category_id  \\\n",
       "0   1  2017          13       100        1             7  本科一批           -   \n",
       "1   2  2017          13       100        1             7  本科一批           -   \n",
       "2   3  2017          13       100        1             7  本科一批           -   \n",
       "3   4  2017          13       100        1             7  本科一批           -   \n",
       "4   5  2017          13       100        1             7  本科一批           -   \n",
       "\n",
       "  category  ... degree sxkmdm zxkmdm xkkmlx zygroup selection special_id  \\\n",
       "0      普通类  ...     本科                                            143458   \n",
       "1      普通类  ...     本科                                            143469   \n",
       "2      普通类  ...     本科                                            143511   \n",
       "3      普通类  ...     本科                                            143512   \n",
       "4      普通类  ...     本科                                            143519   \n",
       "\n",
       "  spe_id special_group         update_date  \n",
       "0    302             0 2022-07-10 01:01:49  \n",
       "1    323             0 2022-07-10 01:01:47  \n",
       "2    224             0 2022-07-10 01:01:47  \n",
       "3    227             0 2022-07-10 01:01:49  \n",
       "4    248             0 2022-07-10 01:01:47  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'year', 'province_id', 'school_id', 'group_id', 'klmc',\n",
       "       'level_id', 'level', 'category_id', 'category', 'class_id',\n",
       "       'class_name', 'majorcategory_id', 'majorcategory', 'zydm', 'major',\n",
       "       'highest', 'average', 'lowest', 'lowest_rank', 'degree_id', 'degree',\n",
       "       'sxkmdm', 'zxkmdm', 'xkkmlx', 'zygroup', 'selection', 'special_id',\n",
       "       'spe_id', 'special_group', 'update_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['klmc', 'sxkmdm', 'zxkmdm', 'zydm', 'xkkmlx', 'zygroup', 'special_id', 'spe_id', 'special_group', 'update_date'], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>school_id</th>\n",
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       "      <th>...</th>\n",
       "      <th>majorcategory_id</th>\n",
       "      <th>majorcategory</th>\n",
       "      <th>major</th>\n",
       "      <th>highest</th>\n",
       "      <th>average</th>\n",
       "      <th>lowest</th>\n",
       "      <th>lowest_rank</th>\n",
       "      <th>degree_id</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>工商管理类</td>\n",
       "      <td>人力资源管理</td>\n",
       "      <td>-</td>\n",
       "      <td>554</td>\n",
       "      <td>553</td>\n",
       "      <td>29972</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>旅游管理类</td>\n",
       "      <td>会展经济与管理</td>\n",
       "      <td>-</td>\n",
       "      <td>559</td>\n",
       "      <td>556</td>\n",
       "      <td>28435</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>农业工程类</td>\n",
       "      <td>农业机械化及其自动化</td>\n",
       "      <td>-</td>\n",
       "      <td>559</td>\n",
       "      <td>557</td>\n",
       "      <td>27890</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>农业工程类</td>\n",
       "      <td>农业水利工程</td>\n",
       "      <td>-</td>\n",
       "      <td>553</td>\n",
       "      <td>552</td>\n",
       "      <td>30485</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>植物生产类</td>\n",
       "      <td>农学</td>\n",
       "      <td>-</td>\n",
       "      <td>560</td>\n",
       "      <td>556</td>\n",
       "      <td>28435</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year province_id school_id group_id level_id level category_id  \\\n",
       "0   1  2017          13       100        1        7  本科一批           -   \n",
       "1   2  2017          13       100        1        7  本科一批           -   \n",
       "2   3  2017          13       100        1        7  本科一批           -   \n",
       "3   4  2017          13       100        1        7  本科一批           -   \n",
       "4   5  2017          13       100        1        7  本科一批           -   \n",
       "\n",
       "  category class_id  ... majorcategory_id majorcategory       major highest  \\\n",
       "0      普通类       13  ...              113         工商管理类      人力资源管理       -   \n",
       "1      普通类       13  ...              120         旅游管理类     会展经济与管理       -   \n",
       "2      普通类       10  ...               85         农业工程类  农业机械化及其自动化       -   \n",
       "3      普通类       10  ...               85         农业工程类      农业水利工程       -   \n",
       "4      普通类       11  ...               94         植物生产类          农学       -   \n",
       "\n",
       "  average lowest lowest_rank degree_id degree selection  \n",
       "0     554    553       29972         1     本科            \n",
       "1     559    556       28435         1     本科            \n",
       "2     559    557       27890         1     本科            \n",
       "3     553    552       30485         1     本科            \n",
       "4     560    556       28435         1     本科            \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1763177 entries, 0 to 1763176\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Dtype \n",
      "---  ------            ----- \n",
      " 0   id                int64 \n",
      " 1   year              object\n",
      " 2   province_id       object\n",
      " 3   school_id         object\n",
      " 4   group_id          object\n",
      " 5   level_id          object\n",
      " 6   level             object\n",
      " 7   category_id       object\n",
      " 8   category          object\n",
      " 9   class_id          object\n",
      " 10  class_name        object\n",
      " 11  majorcategory_id  object\n",
      " 12  majorcategory     object\n",
      " 13  major             object\n",
      " 14  highest           object\n",
      " 15  average           object\n",
      " 16  lowest            object\n",
      " 17  lowest_rank       object\n",
      " 18  degree_id         object\n",
      " 19  degree            object\n",
      " 20  selection         object\n",
      "dtypes: int64(1), object(20)\n",
      "memory usage: 282.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         78.079966\n",
       "518        0.129709\n",
       "519        0.129539\n",
       "520        0.127497\n",
       "526        0.127157\n",
       "            ...    \n",
       "608.02     0.000057\n",
       "589.06     0.000057\n",
       "613.02     0.000057\n",
       "576.01     0.000057\n",
       "242.99     0.000057\n",
       "Name: highest, Length: 3925, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['highest'].value_counts() / df['highest'].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         58.485677\n",
       "559        0.240929\n",
       "557        0.240759\n",
       "515        0.238320\n",
       "516        0.237866\n",
       "            ...    \n",
       "476.32     0.000057\n",
       "443.82     0.000057\n",
       "451.21     0.000057\n",
       "443.02     0.000057\n",
       "439.20     0.000057\n",
       "Name: average, Length: 11785, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['average'].value_counts() / df['average'].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         0.555134\n",
       "549       0.547875\n",
       "505       0.541693\n",
       "548       0.540445\n",
       "561       0.538346\n",
       "            ...   \n",
       "300.59    0.000057\n",
       "504.98    0.000057\n",
       "527.52    0.000057\n",
       "466.28    0.000057\n",
       "385.90    0.000057\n",
       "Name: lowest, Length: 3969, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['lowest'].value_counts() / df['lowest'].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     94.349405\n",
       "首选物理，再选不限             1.679525\n",
       "不限                    0.876599\n",
       "物理必选                  0.848128\n",
       "首选历史，再选不限             0.751541\n",
       "                       ...    \n",
       "政/化/生(3选1)            0.000057\n",
       "化/地/生(3选1)            0.000057\n",
       "首选历史，再选生物/地理(2选1)     0.000057\n",
       "化/生/地(3选1)            0.000057\n",
       "地史政(3科必选)             0.000057\n",
       "Name: selection, Length: 116, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['selection'].value_counts() / df['selection'].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['is_selection'] = False\n",
    "\n",
    "df.drop(['highest', 'average'], inplace= True, axis=1)\n",
    "\n",
    "df['is_selection'] = df['is_selection'].where(df['selection'] == '', other= True)\n",
    "\n",
    "\n",
    "df['lowest'] = df['lowest'].where(df['lowest'] != '-', other= '0')\n",
    "df['lowest_rank'] = df['lowest_rank'].where(df['lowest_rank'] != '-', other= '0')\n",
    "\n",
    "df['lowest'] = df['lowest'].astype('float')\n",
    "df['lowest_rank'] = df['lowest_rank'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>id</th>\n",
       "      <th>year</th>\n",
       "      <th>province_id</th>\n",
       "      <th>school_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>level_id</th>\n",
       "      <th>level</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category</th>\n",
       "      <th>class_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>majorcategory_id</th>\n",
       "      <th>majorcategory</th>\n",
       "      <th>major</th>\n",
       "      <th>lowest</th>\n",
       "      <th>lowest_rank</th>\n",
       "      <th>degree_id</th>\n",
       "      <th>degree</th>\n",
       "      <th>selection</th>\n",
       "      <th>is_selection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>13</td>\n",
       "      <td>管理学</td>\n",
       "      <td>113</td>\n",
       "      <td>工商管理类</td>\n",
       "      <td>人力资源管理</td>\n",
       "      <td>553.0</td>\n",
       "      <td>29972</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>13</td>\n",
       "      <td>管理学</td>\n",
       "      <td>120</td>\n",
       "      <td>旅游管理类</td>\n",
       "      <td>会展经济与管理</td>\n",
       "      <td>556.0</td>\n",
       "      <td>28435</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>10</td>\n",
       "      <td>工学</td>\n",
       "      <td>85</td>\n",
       "      <td>农业工程类</td>\n",
       "      <td>农业机械化及其自动化</td>\n",
       "      <td>557.0</td>\n",
       "      <td>27890</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>10</td>\n",
       "      <td>工学</td>\n",
       "      <td>85</td>\n",
       "      <td>农业工程类</td>\n",
       "      <td>农业水利工程</td>\n",
       "      <td>552.0</td>\n",
       "      <td>30485</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>13</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>本科一批</td>\n",
       "      <td>-</td>\n",
       "      <td>普通类</td>\n",
       "      <td>11</td>\n",
       "      <td>农学</td>\n",
       "      <td>94</td>\n",
       "      <td>植物生产类</td>\n",
       "      <td>农学</td>\n",
       "      <td>556.0</td>\n",
       "      <td>28435</td>\n",
       "      <td>1</td>\n",
       "      <td>本科</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  year province_id school_id group_id level_id level category_id  \\\n",
       "0   1  2017          13       100        1        7  本科一批           -   \n",
       "1   2  2017          13       100        1        7  本科一批           -   \n",
       "2   3  2017          13       100        1        7  本科一批           -   \n",
       "3   4  2017          13       100        1        7  本科一批           -   \n",
       "4   5  2017          13       100        1        7  本科一批           -   \n",
       "\n",
       "  category class_id class_name majorcategory_id majorcategory       major  \\\n",
       "0      普通类       13        管理学              113         工商管理类      人力资源管理   \n",
       "1      普通类       13        管理学              120         旅游管理类     会展经济与管理   \n",
       "2      普通类       10         工学               85         农业工程类  农业机械化及其自动化   \n",
       "3      普通类       10         工学               85         农业工程类      农业水利工程   \n",
       "4      普通类       11         农学               94         植物生产类          农学   \n",
       "\n",
       "   lowest  lowest_rank degree_id degree selection  is_selection  \n",
       "0   553.0        29972         1     本科                   False  \n",
       "1   556.0        28435         1     本科                   False  \n",
       "2   557.0        27890         1     本科                   False  \n",
       "3   552.0        30485         1     本科                   False  \n",
       "4   556.0        28435         1     本科                   False  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1763177 entries, 0 to 1763176\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Dtype  \n",
      "---  ------            -----  \n",
      " 0   id                int64  \n",
      " 1   year              object \n",
      " 2   province_id       object \n",
      " 3   school_id         object \n",
      " 4   group_id          object \n",
      " 5   level_id          object \n",
      " 6   level             object \n",
      " 7   category_id       object \n",
      " 8   category          object \n",
      " 9   class_id          object \n",
      " 10  class_name        object \n",
      " 11  majorcategory_id  object \n",
      " 12  majorcategory     object \n",
      " 13  major             object \n",
      " 14  lowest            float64\n",
      " 15  lowest_rank       int64  \n",
      " 16  degree_id         object \n",
      " 17  degree            object \n",
      " 18  selection         object \n",
      " 19  is_selection      bool   \n",
      "dtypes: bool(1), float64(1), int64(2), object(16)\n",
      "memory usage: 257.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1763177"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('majorscore', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaokao Enrollment People Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/3624820289.py:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select * from table_fsbmrs_xxk;'\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217 entries, 0 to 216\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   id           217 non-null    object        \n",
      " 1   nf           217 non-null    object        \n",
      " 2   sf           217 non-null    object        \n",
      " 3   bmrs         217 non-null    float64       \n",
      " 4   create_by    217 non-null    object        \n",
      " 5   create_date  217 non-null    datetime64[ns]\n",
      " 6   update_by    217 non-null    object        \n",
      " 7   update_date  217 non-null    datetime64[ns]\n",
      " 8   remarks      0 non-null      object        \n",
      " 9   del_flag     217 non-null    object        \n",
      "dtypes: datetime64[ns](2), float64(1), object(7)\n",
      "memory usage: 17.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>006c058c7b1142449c849a5f6657f4c1</td>\n",
       "      <td>2019</td>\n",
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       "      <td>50.00</td>\n",
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       "      <td>2021-01-03 02:32:01</td>\n",
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       "      <td>77.20</td>\n",
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       "      <td>1</td>\n",
       "      <td>2021-12-21 12:18:50</td>\n",
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       "      <td>2015</td>\n",
       "      <td>32</td>\n",
       "      <td>39.29</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-21 12:18:50</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-21 12:18:50</td>\n",
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       "      <th>4</th>\n",
       "      <td>03bf41cb7bf0426d8df25fbb56be8d87</td>\n",
       "      <td>2019</td>\n",
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       "      <td>2021-01-03 02:32:01</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-03 02:32:01</td>\n",
       "      <td>None</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id    nf  sf   bmrs create_by  \\\n",
       "0  006c058c7b1142449c849a5f6657f4c1  2019  43  50.00         1   \n",
       "1  024e833de4fb4a2fa4245cd4d9dbb67b  2015  41  77.20         1   \n",
       "2  027f6ba6a7344e83aec6c61926e08293  2020  43  53.70         1   \n",
       "3  0287f28c9c014522aa3aa5aabfe0e19c  2015  32  39.29         1   \n",
       "4  03bf41cb7bf0426d8df25fbb56be8d87  2019  11   5.90         1   \n",
       "\n",
       "          create_date update_by         update_date remarks del_flag  \n",
       "0 2021-01-03 02:32:01         1 2021-01-03 02:32:01    None        0  \n",
       "1 2021-12-21 12:18:50         1 2021-12-21 12:18:50    None        0  \n",
       "2 2021-01-03 02:32:00         1 2021-01-03 02:32:00    None        0  \n",
       "3 2021-12-21 12:18:50         1 2021-12-21 12:18:50    None        0  \n",
       "4 2021-01-03 02:32:01         1 2021-01-03 02:32:01    None        0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'nf': 'year',\n",
    "                   'sf': 'province_id',\n",
    "                   'bmrs': 'enrollment'},\n",
    "          inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "                                 id  year province_id  enrollment create_by  \\\n",
       "0  006c058c7b1142449c849a5f6657f4c1  2019          43       50.00         1   \n",
       "1  024e833de4fb4a2fa4245cd4d9dbb67b  2015          41       77.20         1   \n",
       "2  027f6ba6a7344e83aec6c61926e08293  2020          43       53.70         1   \n",
       "3  0287f28c9c014522aa3aa5aabfe0e19c  2015          32       39.29         1   \n",
       "4  03bf41cb7bf0426d8df25fbb56be8d87  2019          11        5.90         1   \n",
       "\n",
       "          create_date update_by         update_date remarks del_flag  \n",
       "0 2021-01-03 02:32:01         1 2021-01-03 02:32:01    None        0  \n",
       "1 2021-12-21 12:18:50         1 2021-12-21 12:18:50    None        0  \n",
       "2 2021-01-03 02:32:00         1 2021-01-03 02:32:00    None        0  \n",
       "3 2021-12-21 12:18:50         1 2021-12-21 12:18:50    None        0  \n",
       "4 2021-01-03 02:32:01         1 2021-01-03 02:32:01    None        0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id', 'create_by', 'create_date', 'update_by', 'update_date', 'remarks', 'del_flag'], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217 entries, 0 to 216\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   year         217 non-null    object \n",
      " 1   province_id  217 non-null    object \n",
      " 2   enrollment   217 non-null    float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 5.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>43</td>\n",
       "      <td>53.70</td>\n",
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "   year province_id  enrollment\n",
       "0  2019          43       50.00\n",
       "1  2015          41       77.20\n",
       "2  2020          43       53.70\n",
       "3  2015          32       39.29\n",
       "4  2019          11        5.90"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['enrollment'] = df['enrollment'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 217 entries, 0 to 216\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   year         217 non-null    object \n",
      " 1   province_id  217 non-null    object \n",
      " 2   enrollment   217 non-null    float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 5.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('enrollmentstatistics', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Score Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lc/tt04hd_n7l15qpmt9xjhtfcc0000gn/T/ipykernel_83335/3489829004.py:2: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, origin_conn)\n"
     ]
    }
   ],
   "source": [
    "query = 'select * from table_gxxx_fstowc'\n",
    "df = pd.read_sql(query, origin_conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157148 entries, 0 to 157147\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   id           157148 non-null  object        \n",
      " 1   nf           157148 non-null  object        \n",
      " 2   sf           157148 non-null  object        \n",
      " 3   klmc         157148 non-null  object        \n",
      " 4   gkfs         157148 non-null  int64         \n",
      " 5   bdrs         157148 non-null  int64         \n",
      " 6   ljrs         157148 non-null  int64         \n",
      " 7   bz           157148 non-null  object        \n",
      " 8   startfs      0 non-null       object        \n",
      " 9   endfs        0 non-null       object        \n",
      " 10  create_by    157148 non-null  object        \n",
      " 11  create_date  157148 non-null  datetime64[ns]\n",
      " 12  update_by    157148 non-null  object        \n",
      " 13  update_date  157148 non-null  datetime64[ns]\n",
      " 14  remarks      0 non-null       object        \n",
      " 15  del_flag     157148 non-null  object        \n",
      "dtypes: datetime64[ns](2), int64(3), object(11)\n",
      "memory usage: 19.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00012d29c86d4738ad2fb9462f118abf</td>\n",
       "      <td>2020</td>\n",
       "      <td>46</td>\n",
       "      <td>03</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:57:07</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:57:07</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00018ed310714fbaab6818973a44ca41</td>\n",
       "      <td>2016</td>\n",
       "      <td>51</td>\n",
       "      <td>01</td>\n",
       "      <td>325</td>\n",
       "      <td>415</td>\n",
       "      <td>236196</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:47:04</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:47:04</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000280ce7b2042a2a7ea65b6fa7cd9bc</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>01</td>\n",
       "      <td>478</td>\n",
       "      <td>90</td>\n",
       "      <td>23546</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:26:46</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:26:46</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002b19909f940829e856794763d2ae8</td>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "      <td>01</td>\n",
       "      <td>367</td>\n",
       "      <td>564</td>\n",
       "      <td>177600</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:35:03</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:35:03</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id    nf  sf klmc  gkfs  bdrs    ljrs bz  \\\n",
       "0  00009d1acf93409789ae4515cb9451bc  2020  44   01   364   898  264445      \n",
       "1  00012d29c86d4738ad2fb9462f118abf  2020  46   03   888     1       4      \n",
       "2  00018ed310714fbaab6818973a44ca41  2016  51   01   325   415  236196      \n",
       "3  000280ce7b2042a2a7ea65b6fa7cd9bc  2018  11   01   478    90   23546      \n",
       "4  0002b19909f940829e856794763d2ae8  2018  13   01   367   564  177600      \n",
       "\n",
       "  startfs endfs create_by         create_date update_by         update_date  \\\n",
       "0    None  None         1 2020-07-27 00:56:21         1 2020-07-27 00:56:21   \n",
       "1    None  None         1 2020-07-27 00:57:07         1 2020-07-27 00:57:07   \n",
       "2    None  None         1 2020-05-10 00:47:04         1 2020-05-10 00:47:04   \n",
       "3    None  None         1 2020-05-10 00:26:46         1 2020-05-10 00:26:46   \n",
       "4    None  None         1 2020-05-10 00:35:03         1 2020-05-10 00:35:03   \n",
       "\n",
       "  remarks del_flag  \n",
       "0    None        0  \n",
       "1    None        0  \n",
       "2    None        0  \n",
       "3    None        0  \n",
       "4    None        0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "01    73997\n",
       "02    68028\n",
       "03     7401\n",
       "04     3987\n",
       "05     3735\n",
       "Name: klmc, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['klmc'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['klmc'] = df['klmc'].astype('int')\n",
    "df['klmc'] = df['klmc'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157148 entries, 0 to 157147\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count   Dtype         \n",
      "---  ------       --------------   -----         \n",
      " 0   id           157148 non-null  object        \n",
      " 1   nf           157148 non-null  object        \n",
      " 2   sf           157148 non-null  object        \n",
      " 3   klmc         157148 non-null  object        \n",
      " 4   gkfs         157148 non-null  int64         \n",
      " 5   bdrs         157148 non-null  int64         \n",
      " 6   ljrs         157148 non-null  int64         \n",
      " 7   bz           157148 non-null  object        \n",
      " 8   startfs      0 non-null       object        \n",
      " 9   endfs        0 non-null       object        \n",
      " 10  create_by    157148 non-null  object        \n",
      " 11  create_date  157148 non-null  datetime64[ns]\n",
      " 12  update_by    157148 non-null  object        \n",
      " 13  update_date  157148 non-null  datetime64[ns]\n",
      " 14  remarks      0 non-null       object        \n",
      " 15  del_flag     157148 non-null  object        \n",
      "dtypes: datetime64[ns](2), int64(3), object(11)\n",
      "memory usage: 19.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'sf': 'province_id',\n",
    "                   'nf': 'year',\n",
    "                   'klmc': 'group_id',\n",
    "                   'gkfs': 'score',\n",
    "                   'bdrs': 'number_in_this_score',\n",
    "                   'ljrs': 'number_not_less_than_this_score'},\n",
    "          inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>id</th>\n",
       "      <th>year</th>\n",
       "      <th>province_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>score</th>\n",
       "      <th>number_in_this_score</th>\n",
       "      <th>number_not_less_than_this_score</th>\n",
       "      <th>bz</th>\n",
       "      <th>startfs</th>\n",
       "      <th>endfs</th>\n",
       "      <th>create_by</th>\n",
       "      <th>create_date</th>\n",
       "      <th>update_by</th>\n",
       "      <th>update_date</th>\n",
       "      <th>remarks</th>\n",
       "      <th>del_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00009d1acf93409789ae4515cb9451bc</td>\n",
       "      <td>2020</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>898</td>\n",
       "      <td>264445</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:56:21</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:56:21</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00012d29c86d4738ad2fb9462f118abf</td>\n",
       "      <td>2020</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:57:07</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-27 00:57:07</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00018ed310714fbaab6818973a44ca41</td>\n",
       "      <td>2016</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>325</td>\n",
       "      <td>415</td>\n",
       "      <td>236196</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:47:04</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:47:04</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000280ce7b2042a2a7ea65b6fa7cd9bc</td>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>478</td>\n",
       "      <td>90</td>\n",
       "      <td>23546</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:26:46</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:26:46</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002b19909f940829e856794763d2ae8</td>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>564</td>\n",
       "      <td>177600</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:35:03</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-05-10 00:35:03</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id  year province_id group_id  score  \\\n",
       "0  00009d1acf93409789ae4515cb9451bc  2020          44        1    364   \n",
       "1  00012d29c86d4738ad2fb9462f118abf  2020          46        3    888   \n",
       "2  00018ed310714fbaab6818973a44ca41  2016          51        1    325   \n",
       "3  000280ce7b2042a2a7ea65b6fa7cd9bc  2018          11        1    478   \n",
       "4  0002b19909f940829e856794763d2ae8  2018          13        1    367   \n",
       "\n",
       "   number_in_this_score  number_not_less_than_this_score bz startfs endfs  \\\n",
       "0                   898                           264445       None  None   \n",
       "1                     1                                4       None  None   \n",
       "2                   415                           236196       None  None   \n",
       "3                    90                            23546       None  None   \n",
       "4                   564                           177600       None  None   \n",
       "\n",
       "  create_by         create_date update_by         update_date remarks del_flag  \n",
       "0         1 2020-07-27 00:56:21         1 2020-07-27 00:56:21    None        0  \n",
       "1         1 2020-07-27 00:57:07         1 2020-07-27 00:57:07    None        0  \n",
       "2         1 2020-05-10 00:47:04         1 2020-05-10 00:47:04    None        0  \n",
       "3         1 2020-05-10 00:26:46         1 2020-05-10 00:26:46    None        0  \n",
       "4         1 2020-05-10 00:35:03         1 2020-05-10 00:35:03    None        0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['id', 'bz', 'startfs', 'endfs',  'create_by', 'create_date', 'update_by', 'update_date', 'remarks', 'del_flag'], inplace= True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157148 entries, 0 to 157147\n",
      "Data columns (total 6 columns):\n",
      " #   Column                           Non-Null Count   Dtype \n",
      "---  ------                           --------------   ----- \n",
      " 0   year                             157148 non-null  object\n",
      " 1   province_id                      157148 non-null  object\n",
      " 2   group_id                         157148 non-null  object\n",
      " 3   score                            157148 non-null  int64 \n",
      " 4   number_in_this_score             157148 non-null  int64 \n",
      " 5   number_not_less_than_this_score  157148 non-null  int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>year</th>\n",
       "      <th>province_id</th>\n",
       "      <th>group_id</th>\n",
       "      <th>score</th>\n",
       "      <th>number_in_this_score</th>\n",
       "      <th>number_not_less_than_this_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>898</td>\n",
       "      <td>264445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>325</td>\n",
       "      <td>415</td>\n",
       "      <td>236196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>478</td>\n",
       "      <td>90</td>\n",
       "      <td>23546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>367</td>\n",
       "      <td>564</td>\n",
       "      <td>177600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year province_id group_id  score  number_in_this_score  \\\n",
       "0  2020          44        1    364                   898   \n",
       "1  2020          46        3    888                     1   \n",
       "2  2016          51        1    325                   415   \n",
       "3  2018          11        1    478                    90   \n",
       "4  2018          13        1    367                   564   \n",
       "\n",
       "   number_not_less_than_this_score  \n",
       "0                           264445  \n",
       "1                                4  \n",
       "2                           236196  \n",
       "3                            23546  \n",
       "4                           177600  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157148"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql('scorestatistics', engine, if_exists='replace', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_axis = ['pre-preprocessing', 'post-preprocessing']\n",
    "\n",
    "y1_axis = [595.6, 198.6]\n",
    "y2_axis = [65.2, 34.2]\n",
    "y3_axis = [417.0, 257.3]\n",
    "y4_axis = [19.2, 7.2]\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n",
    "axs[0,0].bar(x_axis, y1_axis)\n",
    "axs[0,0].set_title('table_yssj_gxxx')\n",
    "axs[0,0].set_ylabel('storage/MB')\n",
    "for i in range(len(y1_axis)):\n",
    "    axs[0,0].text(x=i, y=y1_axis[i]+1, s=y1_axis[i], ha=\"center\")\n",
    "\n",
    "axs[0,1].bar(x_axis, y2_axis)\n",
    "axs[0,1].set_title('table_yssj_sftdfs')\n",
    "axs[0,1].set_ylabel('storage/MB')\n",
    "for i in range(len(y1_axis)):\n",
    "    axs[0,1].text(x=i, y=y2_axis[i]+0.25, s=y2_axis[i], ha=\"center\")\n",
    "\n",
    "axs[1,0].bar(x_axis, y3_axis)\n",
    "axs[1,0].set_title('table_yssj_zyfs')\n",
    "axs[1,0].set_ylabel('storage/MB')\n",
    "for i in range(len(y3_axis)):\n",
    "    axs[1,0].text(x=i, y=y3_axis[i]+1, s=y3_axis[i], ha=\"center\")\n",
    "\n",
    "axs[1,1].bar(x_axis, y4_axis)\n",
    "axs[1,1].set_title('table_yssj_zsjh')\n",
    "axs[1,1].set_ylabel('storage/MB')\n",
    "for i in range(len(y4_axis)):\n",
    "    axs[1,1].text(x=i, y=y4_axis[i]+0.125, s=y4_axis[i], ha=\"center\")\n",
    "\n",
    "plt.savefig(\"preprocessing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['database I/O', 'server', 'document generate & conversion', 'browser']\n",
    "cpu = [0.2, 1.2, 0.3, 0.1]\n",
    "ram = [520.9, 334.5, 95.8, 322.7]\n",
    "\n",
    "plt.pie(ram, labels=x)\n",
    "\n",
    "plt.title('RAM Usage')\n",
    "plt.savefig(\"RAM.png\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MachineLearning",
   "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": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}