03-who-runs-China.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例：Who runs China？背后的数据\n",
    "\n",
    "作品链接：https://news.cgtn.com/event/2019/whorunschina/index.html\n",
    "\n",
    "案例解读 https://github.com/data-journalism/data-journalism.github.io/discussions/48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T07:41:11.879049Z",
     "start_time": "2021-10-24T07:41:11.875889Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pylab as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号, 注意['SimHei']对应这句不行.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T07:41:13.943413Z",
     "start_time": "2021-10-24T07:41:13.899685Z"
    }
   },
   "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>var originData = [[\"Delegation\"</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>党派</th>\n",
       "      <th>Name</th>\n",
       "      <th>姓名</th>\n",
       "      <th>Gender</th>\n",
       "      <th>性别</th>\n",
       "      <th>Birth year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Generation</th>\n",
       "      <th>...</th>\n",
       "      <th>区域</th>\n",
       "      <th>Subject Department</th>\n",
       "      <th>专业分类</th>\n",
       "      <th>Major</th>\n",
       "      <th>人文社科拆后专业</th>\n",
       "      <th>Educational background</th>\n",
       "      <th>学历</th>\n",
       "      <th>Ever studied abroad</th>\n",
       "      <th>海外留学经验]</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[\"Anhui\"</td>\n",
       "      <td>CPC(Communist Party of China)</td>\n",
       "      <td>中共</td>\n",
       "      <td>Ding Shiqi</td>\n",
       "      <td>丁士启</td>\n",
       "      <td>Male</td>\n",
       "      <td>男</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1960s</td>\n",
       "      <td>...</td>\n",
       "      <td>中部</td>\n",
       "      <td>Natrual Sciences</td>\n",
       "      <td>自然科学</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>工学</td>\n",
       "      <td>PhD</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>No</td>\n",
       "      <td>无]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[\"Hunan\"</td>\n",
       "      <td>CPC(Communist Party of China)</td>\n",
       "      <td>中共</td>\n",
       "      <td>Ding Xiaobing</td>\n",
       "      <td>丁小兵</td>\n",
       "      <td>Male</td>\n",
       "      <td>男</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1960s</td>\n",
       "      <td>...</td>\n",
       "      <td>西部</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>未知</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>未知</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>未知</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>未知]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[\"Shaanxi\"</td>\n",
       "      <td>CPC(Communist Party of China)</td>\n",
       "      <td>中共</td>\n",
       "      <td>Ding Yunxiang</td>\n",
       "      <td>丁云祥</td>\n",
       "      <td>Male</td>\n",
       "      <td>男</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1960s</td>\n",
       "      <td>...</td>\n",
       "      <td>西部</td>\n",
       "      <td>Natrual Sciences</td>\n",
       "      <td>自然科学</td>\n",
       "      <td>Engineering</td>\n",
       "      <td>工学</td>\n",
       "      <td>PhD</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>No</td>\n",
       "      <td>无]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[\"Fujian\"</td>\n",
       "      <td>No party affiliation</td>\n",
       "      <td>无</td>\n",
       "      <td>Ding Shizhong</td>\n",
       "      <td>丁世忠</td>\n",
       "      <td>Male</td>\n",
       "      <td>男</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1970s</td>\n",
       "      <td>...</td>\n",
       "      <td>东部</td>\n",
       "      <td>Humanities and Social Sciences</td>\n",
       "      <td>社会科学</td>\n",
       "      <td>Management Science</td>\n",
       "      <td>管理学</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>本科</td>\n",
       "      <td>No</td>\n",
       "      <td>无]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[\"Zhejiang\"</td>\n",
       "      <td>Chinese Peasants and Workers Democratic Party</td>\n",
       "      <td>农工党</td>\n",
       "      <td>Ding Lieming</td>\n",
       "      <td>丁列明</td>\n",
       "      <td>Male</td>\n",
       "      <td>男</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1960s</td>\n",
       "      <td>...</td>\n",
       "      <td>东部</td>\n",
       "      <td>Natrual Sciences</td>\n",
       "      <td>自然科学</td>\n",
       "      <td>Medical Science</td>\n",
       "      <td>医学</td>\n",
       "      <td>PhD</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>Yes</td>\n",
       "      <td>有]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  var originData = [[\"Delegation\"  \\\n",
       "0                        [\"Anhui\"   \n",
       "1                        [\"Hunan\"   \n",
       "2                      [\"Shaanxi\"   \n",
       "3                       [\"Fujian\"   \n",
       "4                     [\"Zhejiang\"   \n",
       "\n",
       "                                        Partisan   党派           Name   姓名  \\\n",
       "0                  CPC(Communist Party of China)   中共     Ding Shiqi  丁士启   \n",
       "1                  CPC(Communist Party of China)   中共  Ding Xiaobing  丁小兵   \n",
       "2                  CPC(Communist Party of China)   中共  Ding Yunxiang  丁云祥   \n",
       "3                           No party affiliation    无  Ding Shizhong  丁世忠   \n",
       "4  Chinese Peasants and Workers Democratic Party  农工党   Ding Lieming  丁列明   \n",
       "\n",
       "  Gender 性别  Birth year   Age Generation  ...  区域  \\\n",
       "0   Male  男      1966.0  53.0      1960s  ...  中部   \n",
       "1   Male  男      1960.0  59.0      1960s  ...  西部   \n",
       "2   Male  男      1963.0  56.0      1960s  ...  西部   \n",
       "3   Male  男      1970.0  49.0      1970s  ...  东部   \n",
       "4   Male  男      1963.0  56.0      1960s  ...  东部   \n",
       "\n",
       "               Subject Department  专业分类               Major 人文社科拆后专业  \\\n",
       "0                Natrual Sciences  自然科学         Engineering       工学   \n",
       "1                         Unknown    未知             Unknown       未知   \n",
       "2                Natrual Sciences  自然科学         Engineering       工学   \n",
       "3  Humanities and Social Sciences  社会科学  Management Science      管理学   \n",
       "4                Natrual Sciences  自然科学     Medical Science       医学   \n",
       "\n",
       "  Educational background     学历 Ever studied abroad 海外留学经验] Unnamed: 25  \n",
       "0                    PhD  博士研究生                  No      无]         NaN  \n",
       "1                Unknown     未知             Unknown     未知]         NaN  \n",
       "2                    PhD  博士研究生                  No      无]         NaN  \n",
       "3               Bachelor     本科                  No      无]         NaN  \n",
       "4                    PhD  博士研究生                 Yes      有]         NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/data.js')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T07:41:25.114526Z",
     "start_time": "2021-10-24T07:41:25.110621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2976"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:14:15.122785Z",
     "start_time": "2021-10-24T01:14:15.118081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['var originData = [[\"Delegation\"', 'Partisan', '党派', 'Name', '姓名',\n",
       "       'Gender', '性别', 'Birth year', 'Age', 'Generation', '年代', 'Ethnicity',\n",
       "       '民族', 'Birthplace', '籍贯', 'Region', '区域', 'Subject Department', '专业分类',\n",
       "       'Major', '人文社科拆后专业', 'Educational background', '学历',\n",
       "       'Ever studied abroad', '海外留学经验]', 'Unnamed: 25'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 问题的提出\n",
    "\n",
    "使用这些变量，可以提出哪些问题？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 描述性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:15:36.355219Z",
     "start_time": "2021-10-24T01:15:36.342155Z"
    }
   },
   "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>Birth year</th>\n",
       "      <th>Age</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>党派</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中共</th>\n",
       "      <td>1964.739411</td>\n",
       "      <td>54.260589</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中国民主促进会</th>\n",
       "      <td>1963.814815</td>\n",
       "      <td>55.185185</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九三学社</th>\n",
       "      <td>1965.562500</td>\n",
       "      <td>53.437500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>农工党</th>\n",
       "      <td>1963.981481</td>\n",
       "      <td>55.018519</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>台盟</th>\n",
       "      <td>1965.909091</td>\n",
       "      <td>53.090909</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>无</th>\n",
       "      <td>1968.543735</td>\n",
       "      <td>50.456265</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>民主同盟</th>\n",
       "      <td>1964.327586</td>\n",
       "      <td>54.672414</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>民主建国会</th>\n",
       "      <td>1963.789474</td>\n",
       "      <td>55.210526</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>民革</th>\n",
       "      <td>1963.636364</td>\n",
       "      <td>55.363636</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>致公党</th>\n",
       "      <td>1964.947368</td>\n",
       "      <td>54.052632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Birth year        Age  Unnamed: 25\n",
       "党派                                          \n",
       "中共       1964.739411  54.260589          NaN\n",
       "中国民主促进会  1963.814815  55.185185          NaN\n",
       "九三学社     1965.562500  53.437500          NaN\n",
       "农工党      1963.981481  55.018519          NaN\n",
       "台盟       1965.909091  53.090909          NaN\n",
       "无        1968.543735  50.456265          NaN\n",
       "民主同盟     1964.327586  54.672414          NaN\n",
       "民主建国会    1963.789474  55.210526          NaN\n",
       "民革       1963.636364  55.363636          NaN\n",
       "致公党      1964.947368  54.052632          NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('党派').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:17:49.128574Z",
     "start_time": "2021-10-24T01:17:49.121665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中共         2172\n",
       "无           423\n",
       "九三学社         64\n",
       "民主同盟         58\n",
       "民主建国会        57\n",
       "中国民主促进会      54\n",
       "农工党          54\n",
       "民革           44\n",
       "致公党          38\n",
       "台盟           11\n",
       "Name: 党派, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['党派'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:18:07.443313Z",
     "start_time": "2021-10-24T01:18:07.089345Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中共         0.730084\n",
       "无          0.142185\n",
       "九三学社       0.021513\n",
       "民主同盟       0.019496\n",
       "民主建国会      0.019160\n",
       "中国民主促进会    0.018151\n",
       "农工党        0.018151\n",
       "民革         0.014790\n",
       "致公党        0.012773\n",
       "台盟         0.003697\n",
       "Name: 党派, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"党派\"].value_counts(normalize = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:19:19.383075Z",
     "start_time": "2021-10-24T01:19:19.376334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "男    0.750588\n",
       "女    0.249412\n",
       "Name: 性别, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"性别\"].value_counts(normalize = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pandas Plot**\n",
    "\n",
    "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:48:07.171194Z",
     "start_time": "2021-10-24T01:48:05.827177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "\n",
    "df[\"民族\"].value_counts(normalize = True).plot(kind = 'bar')\n",
    "plt.yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:49:10.372603Z",
     "start_time": "2021-10-24T01:49:09.624853Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号, 注意['SimHei']对应这句不行.\n",
    "\n",
    "df[\"民族\"].value_counts(normalize = True).plot(kind = 'bar');\n",
    "#plt.yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:26:32.241354Z",
     "start_time": "2021-10-24T01:26:32.155306Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "plt.figure(figsize =(4, 4))\n",
    "df[\"性别\"].value_counts(normalize = True).plot(kind = 'barh');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:24:02.386869Z",
     "start_time": "2021-10-24T01:24:01.699134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4))\n",
    "df[\"Birth year\"].value_counts(normalize = True).plot(kind = 'bar');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:24:31.496611Z",
     "start_time": "2021-10-24T01:24:31.344531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4))\n",
    "df[\"Generation\"].value_counts(normalize = True).plot(kind = 'bar');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:50:47.867837Z",
     "start_time": "2021-10-24T01:50:47.537441Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"籍贯\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:51:25.057924Z",
     "start_time": "2021-10-24T01:51:24.890160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"区域\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:53:05.478423Z",
     "start_time": "2021-10-24T01:53:05.298238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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spLsPvo9xbkzy3bN+PgAAAACwtmZdExEAAAAAeJARIgIAAAAAg4SIAAAAAMAgISIAAAAAMEiICAAAAAAMEiICAAAAAIOEiAAAAADAICEiAAAAADBIiAgAAAAADBIiAgAAAACDhIgAAAAAwCAhIgAAAAAwSIgIAAAAAAwSIgIAAAAAg4SIAAAAAMAgISIAAAAAMEiICAAAAAAMEiICAAAAAIOEiAAAAADAICEiAAAAADBIiAgAAAAADBIiAgAAAACDhIgAAAAAwCAhIgAAAAAwaOYQsUZOrqqbqurOqjqvqvZdpt8eVXViVX1y3O/TVfWaqT4HVdX7x+dvqqo3bc+XAQAAAABW3s5zvOeEJMcl+bEkX07yziTnJHnxVL+XJzkyyZuS3JTkVUl+r6qu6+6/qKqdkvxZkhuTPCvJdyU5c3z+vXPUBQAAAKvq4J//s/UuAXZI1751OjZiRzNTiFhVGzIKEU/t7gvGbccnubCqDunuaya6X5LkD7u7x68/UVWvSvKyJH+RUej4uCQv6O4vJrmyql6Q5KeTCBEBAAAA4AFi1tuZj0iyb5KLJtouTbI1ydMmO3b3zRMB4pI7Jz7zyCRXjgPEJZck+e6qqhnrAgAAAABWyawh4qHjx3tmHHb35iQ3Jzlg6I1VdXCSJ+YbAeShk+OMXZ9k9yTfusz7d6uqvZeOJHvNWDsAAAAAMIdZQ8Q9k2zt7i1T7ZsyCv+WVVU7Jzk7yQe6+0MTY21aZpxsY6wTk9w6cdwwW+kAAAAAwDxmDRG3JNkwDgUn7Z57B4KTfifJIzLajGVyrF2XGSfbGOu0JPtMHAfev5IBAAAAgO0x6+7MN44fD0xybTK6zTjJfkmuXu4NVXV6khckeVZ3f3lqrMdMdT8oya3dfcv0OOPZj/fMgLRsIgAAAACsjVlnIl6ZZHOSoybanjN+3DjduaremuRVSY7s7uumTl+W0SYq+0y0PT+jzVUAAAAAgAeImULE8SYqZyQ5paqOrqpnJDk9yZlJ7qiqi6vq1UlSVScl+akkrx+9rEePj4eNh3tvki8n+d2qekJV/Yckr0zytpX4YgAAAADAypj1duYkeUuSPZKcm+TuJO9OckKSXZIclmT/cb/XZbR5yoVT7/+VJL/c3Zuq6nuSnJXk8ox2av7B7v6bOWoCAAAAAFbJzCHieG3CY8fHpC2Z2Oykuw++H2N9MsnTZ60BAAAAAFg7s66JCAAAAAA8yAgRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAYJEQEAAACAQUJEAAAAAGCQEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABgkBARAAAAABgkRAQAAAAABgkRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAYJEQEAAACAQUJEAAAAAGCQEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABg0JqFiFW121p9FgAAAACwcmYOEWvk5Kq6qarurKrzqmrfZfrtVlVHV9U7quq6JN+3TJ9e5njcnN8FAAAAAFgF88xEPCHJcUnekOSoJI9Pcs4y/d6Y5H1J9k/yyIHxXpPkMRPHv8xREwAAAACwSnaepXNVbcgoRDy1uy8Ytx2f5MKqOqS7r5no/vtJzujuLVXVA8Pe2N1XzVo4AAAAALA2Zp2JeESSfZNcNNF2aZKtSZ422bG7v9jdW7arOgAAAABg3c0aIh46frxnxmF3b05yc5ID5qzh4qr6QlV9sKqevK1O4zUW9146kuw15+cBAAAAADOYNUTcM8nWZWYYbkqy+xyf//QkT0ry2iS7JLm0qg7eRt8Tk9w6cdwwx+cBAAAAADOaNUTckmRDVU2vpbh7RkHiTLr7Y939ye6+KMlLkmxO8kPb6H5akn0mjgNn/TwAAAAAYHYzbayS5Mbx44FJrk1Gtxkn2S/J1dtTSHdvqqqrMtrNebnzWzIKMTP+3O35OAAAAADgfpp1JuKVGc0WPGqi7Tnjx43bU0hV7ZXksUk+sz3jAAAAAAAra6aZiN29uarOSHJKVV2f5I4kpyc5M8kdVXVxkrO7+z3jUPDhE29/eFU9Oslt3f3FqnpuRjs6fzijtRZPyuiW6HO27ysBAAAAACtp1pmISfKWJOcnOTfJBRmFgG/KaGOUw/KN25FfmeSz4yNJfmP8/G3j119J8sMZzWD8gyRfTvLM7r5tjpoAAAAAgFUy65qIS2sTHjs+Jm3JxGYn3f2uJO8aGOfjSY6Y9fMBAAAAgLU1z0xEAAAAAOBBRIgIAAAAAAwSIgIAAAAAg4SIAAAAAMAgISIAAAAAMEiICAAAAAAMEiICAAAAAIOEiAAAAADAICEiAAAAADBIiAgAAAAADBIiAgAAAACDhIgAAAAAwCAhIgAAAAAwSIgIAAAAAAwSIgIAAAAAg4SIAAAAAMAgISIAAAAAMEiICAAAAAAMEiICAAAAAIOEiAAAAADAICEiAAAAADBIiAgAAAAADBIiAgAAAACD1ixErKrd1uqzAAAAAICVM3OIWCMnV9VNVXVnVZ1XVfsu02+3qjq6qt5RVdcl+b5l+hxeVRuranNVXV1Vr5nzewAAAAAAq2SemYgnJDkuyRuSHJXk8UnOWabfG5O8L8n+SR45fbKq9knyoSTXJHlqkrOTnFNVT5ujJgAAAABglew8S+eq2pBRiHhqd18wbjs+yYVVdUh3XzPR/feTnNHdW6qqlxnutRmFmK/v7ruSfKKqXpJR+PixOb4LAAAAALAKZp2JeESSfZNcNNF2aZKtSb5pBmF3f7G7twyMdWSSj44DxCUfmR5nyfj26L2XjiR7zVg7AAAAADCHWUPEQ8eP98w47O7NSW5OcsAcY10z1Xb9wDgnJrl14rhhxs8DAAAAAOYwa4i4Z5Kty8ww3JRk9znG2jTDOKcl2WfiOHDGzwMAAAAA5jDTmohJtiTZUFU7d/fXJ9p3z70Dwfsz1q5TbdscZxxc3hNeVtWMHwcAAAAAzGPWmYg3jh/vmQVYVbsl2S/J1XOMddBU20FzjAMAAAAArKJZQ8Qrk2xOctRE23PGjxtnHOuyJM+rqp0m2p6f5JIZxwEAAAAAVtFMIeJ4E5UzkpxSVUdX1TOSnJ7kzCR3VNXFVfXqJKmqvarq0VX16PHbHz5+/W/Gr89K8tAk76iqw6vqlzLa/fnt2/2tAAAAAIAVM+tMxCR5S5Lzk5yb5IIkH07ypiS7JDksyf7jfq9M8tnxkSS/MX7+tiTp7puSvDTJM5NckeQVSY7p7uvm+SIAAAAAwOqYdWOVpQ1Ojh0fk7ZkYq3E7n5Xknfdx1h/ntHsQwAAAADgAWqemYgAAAAAwIOIEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABgkBARAAAAABgkRAQAAAAABgkRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAYJEQEAAACAQUJEAAAAAGCQEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABgkBARAAAAABgkRAQAAAAABgkRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAbNHCLWyMlVdVNV3VlV51XVvtvo++yquqKqvlpVn6qqo6fO9zLH4+b9MgAAAADAyptnJuIJSY5L8oYkRyV5fJJzpjtV1SFJLkzy4SRPSfLnSc6vqkdNdX1NksdMHP8yR00AAAAAwCqZKUSsqg0ZhYindvcF3f1XSY5Pcsw4NJz0M0mu6u43d/c/ZhQ83pLkdVP9buzuqyaOr833VQAAAACA1TDrTMQjkuyb5KKJtkuTbE3ytKm+R0726+6vJ9m4TD8AAAAA4AFs1hDx0PHjNUsN3b05yc1JDlim7zVTbdcv0+/iqvpCVX2wqp68rQ+uqt2qau+lI8leM9YOAAAAAMxh1hBxzyRbu3vLVPumJLsv03fTffR7epInJXltkl2SXFpVB2/js09McuvEccOMtQMAAAAAc5g1RNySZENV7TzVvnvuHRhuSbLrUL/u/lh3f7K7L0rykiSbk/zQNj77tCT7TBwHzlg7AAAAADCH6TDwvtw4fjwwybXJ6DbjJPsluXqZvgdNtR20TL8kSXdvqqqrkuy/jfNbMgomM/7cGUsHAAAAAOYx60zEKzOaLXjURNtzxo8bp/peNtmvqnZK8twklyw3cFXtleSxST4zY00AAAAAwCqaaSZid2+uqjOSnFJV1ye5I8npSc5MckdVXZzk7O5+T5K3J7m8qk5Kcl6SYzMKLd+VJFX13Ix2av5wRusnnpTRrc7nbO+XAgAAAABWzqwzEZPkLUnOT3JukgsyCgHflNHGKIdlfDtyd/99kh9M8qNJ/i7J4Ule2N23j8f5SpIfzmgG4x8k+XKSZ3b3bXN+FwAAAABgFcy6JuLS2oTHjo9JWzK12Ul3vy/J+7YxzseTHDHr5wMAAAAAa2uemYgAAAAAwIOIEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABgkBARAAAAABgkRAQAAAAABgkRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAYJEQEAAACAQUJEAAAAAGCQEBEAAAAAGCREBAAAAAAGCREBAAAAgEFCRAAAAABgkBARAAAAABgkRAQAAAAABgkRAQAAAIBBQkQAAAAAYJAQEQAAAAAYJEQEAAAAAAYJEQEAAACAQTOHiDVyclXdVFV3VtV5VbXvNvo+u6quqKqvVtWnquroqfOHV9XGqtpcVVdX1Wvm/SIAAAAAwOqYZybiCUmOS/KGJEcleXySc6Y7VdUhSS5M8uEkT0ny50nOr6pHjc/vk+RDSa5J8tQkZyc5p6qeNkdNAAAAAMAqmSlErKoNGYWIp3b3Bd39V0mOT3LMODSc9DNJruruN3f3P2YUPN6S5HXj868df/7ru/sT3f2rSf42yRvn/zoAAAAAwEqbdSbiEUn2TXLRRNulSbYmmZ5BeORkv+7+epKNE/2OTPLR7r5r4j0fWWYcAAAAAGAd7Txj/0PHj9csNXT35qq6OckBy/S9Zqrt+iTfMXH+wmXOT4+TJKmq3ZLsNtG0V5Lcdttt97d21tjWLZvWuwTY4fg7DebjNwfm43cHZuc3B+bjN+eBaZb/LrOGiHsm2drdW6baNyXZfZm+03+7Tva7r/PTTkxy8nTjQQcddB8lA+w49jl9vSsA4MHE7w4Aa8VvzgPeXkkGE8VZQ8QtSTZU1c7j25OX7J57B4Jbkuw61TbZ777OTzstyW9OtT0so3UWgftvryQ3JDkwye3rXAsAi81vDgBrye8OzGevJDfdV6dZQ8Qbx48HJrk2uec24/2SXL1M3+lpggdN9Luv899kPPtxegakubAwo6paenp7d/szBMCq8ZsDwFryuwNzu19/XmbdWOXKJJuTHDXR9pzx48apvpdN9quqnZI8N8klE+efN25f8vyJ8wAAAADAA8BMIWJ3b05yRpJTquroqnpGktOTnJnkjqq6uKpePe7+9iTfXVUnVdXhSX57/HnvGp8/K8lDk7yjqg6vql/KaPfnt2/fVwIAAAAAVtKsMxGT5C1Jzk9ybpILknw4yZuS7JLksCT7J0l3/32SH0zyo0n+LsnhSV7Y3bePz9+U5KVJnpnkiiSvSHJMd1+3Hd8HuG9bkvxK7r08AACsNL85AKwlvzuwiqq717sGAAAAAOABbJ6ZiAAAAADAg4gQEQAAAAAYJEQEAAAAAAYJEQEAAACAQUJEAABWVVU9e71rAABg+9idGRZUVf1Mkv96f7sn6e7eexVLAmDBVdXl46dXdPcbJ9rv7u6d1qksAB6EqurZ3b1xveuARbLzehcArJpdk7w3ySkDfSrJZ5M8ek0qAmDhVNWLk3y2u/85yZOTHJXkX6e7rXlhADwobOsCVpKPJnEBC1aQEBEW263dfd1Qh6rq++oDAAOen+RdVfXyjGa1X5IkVXVSkt/r7muTuPUFgBXjAhasD2siAgAwt+4+PskvJvmBqVO/nOTXxs/9Qw6AlfT8JH9ZVf9nxhewuvvKqjqpqg4e93EBC1aYmYiw2A68j8XsK8mGqnpWRmukWjMEgJl19/9I7lmPd9KjquqI+IccACuou4+vqs9k+QtYj0vyQ3EBC1acjVVgQVXVjyQ5fqq5s+0f0+7uJ61uVQAsoqp6SpIvJblqaQOVqtqa5EVJfiHJM5MsBYybknygu7+wHrUCsFiqamt3b1h6nuSvk/zHJB+3qResLDMRYUF19/9bVV/r7vcstVXVWd39+onX+yc5McnPdffm9agTgB1XVe2U5OQkP53kdVOnu7svrqqXZhQiHjluPzTJzyU5bM0KBWDhTFzAmp4Z9StJ3jHuc+y4zQUsWAFmIsICq6q7unvX5V5X1dFJfi/J5Ule2923rFOZAOygqmpDkt9K8rbu/tz0bJCJ53dPzFDcL8n/TrJz+x9RAGa0zAWsP5r4jbm7u3eqqt9OcmyS88ZvOzTJHt3tAhZsByEiLLCq+lqSRyX5ekZX327p7l2r6heTvDmjGYhnrGeNACyOgeBw8vkjk1y71A8AZuECFqwftzPD4rsmE3/Wq+rmJA9L8u4kH1ivogBYHFX1E0n2zjffUratNXjvyGjBewCYWXdvzTfW2b3X6W2075HRJCoBImwHISIsoKr6tSSfzmg9qt3GbQ9JcmuSlyf5zvHjZ6rq/Une3N1XrVO5AOzAqurkjELBH5k6dcJy/cfLZ7xnuXMAcH+5gAVrz20ksJi+mOT1Saqq3ltVz+juTRmFin/V3f9Pdx+d5N8leUiSf6iqn1zPggHYYf1Rkid39+UZ/e6cUlU/3t3/fb0LA2AxjS9g/VySv5w6tc0LWJMbTgLzsSYiLLCqeniSn0pyWHe/arxb8y5TfV6Q5PgkD09ylA1WAJhXVf238dOruvt/TLRvtQYiACulqg5Pcl13315VW5P8apJruvt3J/rcsyYisDKEiLDgqurC7j5m/PzuJLuM1xFJVVWSf0xyaXf/9DqWCcCCqqonJflad//jetcCwOJxAQvWjjURYfEdPXDuPyX51iS/sEa1APDg8wNJNmd00QoAVlR33+sW5vEFrCesQzmw0ISIsPiWXVy4qp6Q5JQkL+7uW9e2JAAWRVV9+3102ZrksdP9uvufV68qAB7kXMCCVSBEhMV3rzULqurbkvxZkmO7+y/WviQAFsgHM/qt2daOmHtktHvm0ybaOsmhq1wXAAvKBSxYH9ZEhAU3uRbIeNHh5yY5K8nPd/f561kbAIulqp7R3X851fbMJG/v7ietU1kALJiquib37wLWFybaurtdwILtYCYiLKCqOn7g9QeTnJ3kkOl+3f2ba1AeAAuoqn4kya9W1ZO6+8tV9cIk/5TkpiSPWN/qAFgk3X3I0nMXsGDtCBFhMT1+6vXj8o2rdLsmedj4cZJpyQDMpaqekuS0JM8bB4jPTvKHSb4/yWVJ9q2qDd29dT3rBGCxuIAFa8vtzLDglrmd+fgkP5HkldYEAWAlVNUBSQ7o7sur6luTfCrJT3b3+8fnv5LksO6+aR3LBGCBjC9gnZ/RBax/Hl/A+pN84wLWbUl2cwELVs6G9S4AWFOd5O1JTkxySVUdsc71ALCDq6rHJHl2d18+bnpjkmd29/ur6ser6juTfD7J/utVIwAL6aYk3zsOEL81yblJfqS7P9Tdm5PcmeTfrmuFsGCEiLD47jXdeDwz5GeTXFxVj1rzigBYJI/PaIb7kpO6+6qq+okkp2T0/5svTPLxdagNgAXkAhasD7czw4Krqru7e6el50l2WZrSX1WnJnlFkqeMr9YBwEyq6olJLkjynzMKDN+d5IeT/HSSC5Ncvdz7uvvctaoRgMVSVS9Lclx3v2D8+q7u3nV8AeuXk7w8yc1JPt/dX1+/SmGxCBFhwVXVP3b3EePnW5PsPBEiVpK/TvLx7v6P61gmADuoqto3yReTnJfR7PfvTfKlJPsl+XKSS5d5W3f3969VjQAsFhewYH0IEeFBpKp+t7t/fKrtO5K8ort/ZZ3KAmAHV1Wbkhzc3V+sqruSPCTJSzO6zfnbk/xWkjO7++51LBOABeECFqwPISIAANulqv4kybHdfWNVXdPdh0yce2aS38xoAfxXua0MgJXgAhasPSEiAAArpqp2ymjzrrd399eqau/uvm2dywJgwbiABWtPiAgAwIqpqh9N8j+TPKG7Pz29qde4zz7dfeu6FQnAQqmqV3X3+6baXpHkA9391araPckju/uf16dCWAwb1rsAAAAWQ1X9Hxntinl2d396qXnifFXV2RktgA8Ac6uqJ1TVX1bVw5L8/tS5707yB0meWlUvSvLpJL+wDmXCQtl5vQsAAGBh/EZGF6l3m2jrZBQgZhQeHp3kiWtfGgCLoqqOTvKuJG/s7lvGvzFL556X5L1JfjHJ1zIKGN/U3X+wHrXCInE7MwAA262q/lOS05I8P8llGd/CvHQ7c5Izkjw7yd7dfcD6VQrAjm488/0R3X3V+PXXkrw4o01VnprkuO7+0/G5h3T3pnUrFhaImYgAAGyXqjo+ya9ltHj9X0/OCBnbPcndSZ6V5Lq1rg+AhXN7kq1VtTWj35edknwgySVJvqu7v7zUsbs3VdWTkzy2u//XulQLC8KaiAAAzK2qLkry80mO6e73b6PbV7v72O7+UpKuqt220Q8A7o89kuyZ5KFJHp5RkPiKJF9K8k9V9bKljlV1bpLz881LbQBzcDszAABzq6qvJPnbJC/t7q+O27Ym2Xl8O/PWJP9uaaOVqvp8kid29/9er5oBWCxV9bXu3mX8/PkZrYl4bHe/Z7x+4qXdvWVdi4QFYCYiAADb4ylJHpHkkqracxt9LpuYFXJHRjNHAGC7VNWFVfVbSf57Ve2eJN19SZKXJfmdqjqsuz/Y3Vuq6t+ua7GwAISIAADMrbs/m+SZGd0m9v6lf8RN+d4kv11VP5vROlbfsnYVArDAXpjkhiRPSnJ9Vf2Xqtq1uy/LaLOvQ6rqWVX10SQXrGehsAjczgwAwHarqm9J8hdJPpnRDs3/prt7Ynfmhye5MMlhSX6gu/94vWoFYDFU1V3dvWtVPSrJxiRXJDk8yYu6++pxn3cm+Zsk57ilGbaP3ZkBANhu3f2vVfXKJJcnOamnrlR39+fH61T9f0ketR41ArDjq6rKaBfmayea/2tGm3xdneSPM1pG43u6+x+6+yfWvEhYUG5nBgBgRXT3Z5L8bJJfq6pvGzfXxPlbMtpk5bfWoTwAFkMl+b8ymuV+d1VtTPKHSd6X5KwkL09yapI/qaqHVdXvVtUz1q1aWCBCRAAAVtI5ST6QZP/x6x/v7q0T5/9zVe269mUBsAi6e2t3X9DdP5LkwCSXJvlfSd6V5Ke7+/LuPiOj25vPTHJlkndX1cVV9ZB1KhsWgjURAQBYMVW1T5JbununbZy/O8m3dPdta1sZAItqvPPyO5Lc3t0/Pm57WJIju/uPxhevfri7f3c964QdnRARAIAVcz9CxK1JHipEBGB7VdUTM7Fsxvj5NkOO7r5y1YuCBWZjFQAAAGBHdHmSz2UUHj4yyXX5Rqg4+Xrp+aHrUCMsDCEiAABzq6rz882zPnYZt5+3XPcMzBABgFl196FJUlWbl56PX39t4tzXJs8B8xEiAgCwPS6aer1HkmOSfHCZvpXkZateEQAPFr2N5/d1DpiDNREBAFgx1kQEYK2MZxguzYDf1N0P2ca5u7p71/WqExbFhvUuAACAhVMD51zBBmCl+E2BNeR2ZgAAVtJdSc5ZelFVxyT5YHffvdS0LlUBsDCq6owkX4nfFFhTZiICALAiquqtSc5Pcvb49X9I8vYk+090e3qS29e+OgAWyHlJDopMA9aUNREBAJhbVe2W5LVJbkzyk0kekeSxSd6d5OYkV4wfv0l3X76GZQKwgKyJCGvL7cwAAGyPyzOaCXLa+PVHkvxgkj9M8l1J/iXJlqn3dJLvWKsCAVhYQ7OizJiCFSZEBABge3xPd9+UJFX1fUnS3ddU1XOTfCjJ57v7VetXHgALrKrqI+Pnu008T5KdJ17vXFUf6e7nrXF9sFDczgwAwIqoqp/JKDR83/j1Pkn+Z5KTu/uf1rU4ABZOVb0iM6yL2N1/tIrlwMITIgIAAAAAg+xkBAAAAAAMEiICAAAAAIOEiAAAAADAICEiAAAAADBIiAgAAAAADBIiAgAAAACDhIgAAAAAwCAhIgAAAAAw6P8HLeY5HjxxMjIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"专业分类\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:53:30.823516Z",
     "start_time": "2021-10-24T01:53:30.607312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"人文社科拆后专业\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:54:17.905125Z",
     "start_time": "2021-10-24T01:54:17.899907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2975"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"人文社科拆后专业\"].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:54:23.350826Z",
     "start_time": "2021-10-24T01:54:23.346806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2976"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:55:22.223639Z",
     "start_time": "2021-10-24T01:55:22.005411Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"学历\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:59:41.443588Z",
     "start_time": "2021-10-24T01:59:41.437555Z"
    }
   },
   "outputs": [],
   "source": [
    "df[\"海外留学经验\"] = [ str(i).replace(']', '')  for i in df[\"海外留学经验]\"].tolist() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"海外留学经验\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T01:59:54.889506Z",
     "start_time": "2021-10-24T01:59:54.740419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(16, 4), dpi = 100)\n",
    "df[\"海外留学经验\"].value_counts(normalize = True).plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 列联表分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:02:55.105654Z",
     "start_time": "2021-10-24T02:02:55.064446Z"
    }
   },
   "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>学历</th>\n",
       "      <th>博士研究生</th>\n",
       "      <th>大专</th>\n",
       "      <th>未知</th>\n",
       "      <th>本科</th>\n",
       "      <th>硕士研究生</th>\n",
       "      <th>高中及以下</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>94</td>\n",
       "      <td>60</td>\n",
       "      <td>270</td>\n",
       "      <td>138</td>\n",
       "      <td>129</td>\n",
       "      <td>51</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>490</td>\n",
       "      <td>78</td>\n",
       "      <td>507</td>\n",
       "      <td>388</td>\n",
       "      <td>707</td>\n",
       "      <td>63</td>\n",
       "      <td>2233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>584</td>\n",
       "      <td>138</td>\n",
       "      <td>777</td>\n",
       "      <td>526</td>\n",
       "      <td>836</td>\n",
       "      <td>114</td>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "学历   博士研究生   大专   未知   本科  硕士研究生  高中及以下   All\n",
       "性别                                           \n",
       "女       94   60  270  138    129     51   742\n",
       "男      490   78  507  388    707     63  2233\n",
       "All    584  138  777  526    836    114  2975"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['性别'],df['学历'],margins=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:03:57.769725Z",
     "start_time": "2021-10-24T02:03:57.722851Z"
    }
   },
   "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>学历</th>\n",
       "      <th>博士研究生</th>\n",
       "      <th>大专</th>\n",
       "      <th>未知</th>\n",
       "      <th>本科</th>\n",
       "      <th>硕士研究生</th>\n",
       "      <th>高中及以下</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>0.126685</td>\n",
       "      <td>0.080863</td>\n",
       "      <td>0.363881</td>\n",
       "      <td>0.185984</td>\n",
       "      <td>0.173854</td>\n",
       "      <td>0.068733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>0.219436</td>\n",
       "      <td>0.034931</td>\n",
       "      <td>0.227049</td>\n",
       "      <td>0.173757</td>\n",
       "      <td>0.316614</td>\n",
       "      <td>0.028213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.196303</td>\n",
       "      <td>0.046387</td>\n",
       "      <td>0.261176</td>\n",
       "      <td>0.176807</td>\n",
       "      <td>0.281008</td>\n",
       "      <td>0.038319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "学历      博士研究生        大专        未知        本科     硕士研究生     高中及以下\n",
       "性别                                                             \n",
       "女    0.126685  0.080863  0.363881  0.185984  0.173854  0.068733\n",
       "男    0.219436  0.034931  0.227049  0.173757  0.316614  0.028213\n",
       "All  0.196303  0.046387  0.261176  0.176807  0.281008  0.038319"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['性别'],df['学历'],margins=True, normalize='index')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:04:17.532975Z",
     "start_time": "2021-10-24T02:04:17.528894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12668463611859837"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "94/742"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:30:04.613639Z",
     "start_time": "2021-10-24T02:30:04.309599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[94, 60, 270, 138, 129, 51, 742],\n",
       " [490, 78, 507, 388, 707, 63, 2233],\n",
       " [584, 138, 777, 526, 836, 114, 2975]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "alist = np.array(pd.crosstab(df['性别'],df['学历'],margins=True)).tolist()\n",
    "alist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:30:05.320898Z",
     "start_time": "2021-10-24T02:30:05.315114Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic: 154.38952044307908\n",
      " p-value: 7.28076449137039e-27\n",
      " Degrees of Freedom: 12\n",
      "\n",
      "[[ 145.65647059   34.41882353  193.79294118  131.19058824  208.50823529\n",
      "    28.43294118  742.        ]\n",
      " [ 438.34352941  103.58117647  583.20705882  394.80941176  627.49176471\n",
      "    85.56705882 2233.        ]\n",
      " [ 584.          138.          777.          526.          836.\n",
      "   114.         2975.        ]]\n"
     ]
    }
   ],
   "source": [
    "# 卡方检验\n",
    "\n",
    "chi2, p, ddof, expected = stats.chi2_contingency( alist )\n",
    "msg = \"Test Statistic: {}\\n p-value: {}\\n Degrees of Freedom: {}\\n\"\n",
    "print( msg.format( chi2, p, ddof ) )\n",
    "print( expected )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T02:31:45.175857Z",
     "start_time": "2021-10-24T02:31:45.132495Z"
    }
   },
   "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>专业分类</th>\n",
       "      <th>未知</th>\n",
       "      <th>社会科学</th>\n",
       "      <th>自然科学</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>0.529650</td>\n",
       "      <td>0.277628</td>\n",
       "      <td>0.192722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>0.360502</td>\n",
       "      <td>0.429915</td>\n",
       "      <td>0.209584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.402689</td>\n",
       "      <td>0.391933</td>\n",
       "      <td>0.205378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "专业分类        未知      社会科学      自然科学\n",
       "性别                                \n",
       "女     0.529650  0.277628  0.192722\n",
       "男     0.360502  0.429915  0.209584\n",
       "All   0.402689  0.391933  0.205378"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['性别'],df['专业分类'],margins=True, normalize='index')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T07:42:30.304638Z",
     "start_time": "2021-10-24T07:42:30.125740Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "丁士启 丁小兵 丁云祥 丁世忠 丁列明 丁光宏 丁仲礼 丁来杭 丁宏锁 丁纯 丁国林 丁明 丁烈云 丁绣峰 丁照民 丁福浩 丁薛祥 丁巍 乃依木·亚森 刀晓勤 于飞 于天敏 于中赤 于丕涛 于立新 于伟国 于会文 于旭波 于安玲 于志刚 于志坚 于秀明 于忠福 于金明 于金镒 于泳 于晓明 于峰 于海田 于跃敏 于清明 于集华 于普松 于巍 才华 万卫星 万连步 万勇 万鄂湘 万新恒 乞国艳 卫小春 卫华 卫岗 卫彦明 习近平 习朝峰 马一德 马乙四夫 马卫光 马天龙 马少斌 马化彬 马化腾 马文芳 马玉山 马玉红 马玉璞 马玉霞 马正山 马世忠 马叶江 马兰 马汉成 马永平 马加友 马百龄 马伟明 马传先 马传凯 马华 马旭 马兴瑞 马志武 马利民 马杰 马和帕丽 马波 马学军 马宝平 马空 马春雨 马荣荣 马秋林 马顺南 马恒燕 马晓红 马晖玲 马豹子 马逢国 马银萍 马清辉 马善祥 马瑞燕 马新强 马福昌 马豪辉 马慧娟 丰晓敏 王一宏 王一君 王力 王力威 王士岭 王小万 王小云 王小鸣 王义东 王卫东 王子联 王天目 王天宇 王天琦 王少玄 王少峰 王中立 王水平 王长仁 王长江 王长林 王长河 王凤巧 王凤英 王凤朝 王文全 王文保 王文涛 王方 王予波 王书茂 王玉志 王玉玲 王世琴 王东伟 王东明 王东京 王东峰 王东新 王立 王立山 王立平 王立伟 王立彤 王立科 王立峰 王玄玉 王宁 王宁 王永兰 王吉永 王亚平 王亚东 王再杰 王成 王成男 王光亚 王光远 王刚 王刚 王刚 王刚 王刚 王刚 王廷双 王伟 王伟 王伟 王传霖 王华生 王全 王全兴 王全理 王兆 王冰 王江 王江滨 王守聪 王安忆 王安兰 王军 王军 王红军 王红星 王寿平 王进喜 王远鹤 王孝巧 王志民 王芳 王杜娟 王丽 王连灵 王连春 王连增 王岐山 王岚 王利平 王秀兰 王秀峰 王秀峰 王作英 王沪宁 王怀军 王启繁 王拥军 王奋彦 王尚典 王国生 王国新 王明孝 王忠林 王欣会 王金书 王金行 王金会 王金南 王波 王波 王波 王学成 王学斌 王宝华 王诚 王建伟 王建宇 王建军 王建军 王建国 王建清 王绍南 王玲 王玲娜 王树 王树华 王树江 王树芬 王树国 王威东 王砚蒙 王贻芳 王钦峰 王秋实 王秋玲 王复兴 王修林 王保存 王俊峰 王俊祥 王俊飚 王胜 王胜明 王庭聪 王洪尧 王洪海 王洪祥 王宪魁 王祖伟 王娜 王勇 王勇 王勇超 王耕捷 王艳 王艳凤 王艳清 王秦 王振国 王莉 王晋 王桂波 王晓东 王晓红 王晓洁 王晓菲 王晓梅 王晓霞 王峰 王健 王爱和 王玺玉 王凌 王涛 王涛 王海 王海斗 王海龙 王润 王润梅 王悦 王家胜 王家娟 王家骐 王容川 王娟 王娟玲 王能干 王绣 王教成 王培 王萌萌 王雪梅 王常松 王晨 王曼利 王跃飞 王铮 王银良 王银香 王敏刚 王随莲 王维峰 王瑛 王超 王超英 王喜良 王朝阳 王雁 王雁飞 王雅丽 王辉山 王辉青 王辉忠 王景武 王赋 王强众 王登喜 王瑞军 王瑞霞 王填 王献军 王筱虹 王新伟 王新杰 王新春 王滨梅 王静成 王嘉鹏 王翠凤 王德佳 王毅 王毅 王霞 王麒 王馨 王巍 王巍 王巍 元茂荣 韦年洲 韦庆兰 韦红梅 韦波 韦祖英 韦振益 韦朝晖 韦韬 扎西多杰 扎西江村 木合亚提·加尔木哈买提 木沙·买买提 木沙江·努尔墩 支月英 支艳茹 尤立增 尤权 尤良英 尤猛军 车秀兰 车耶 车俊 车轼 车捷 牙生·司地克 戈明 牛三平 牛书成 牛宝伟 牛朝诗 毛伟明 毛宗福 毛珍芳 毛超峰 仁青东珠 仇冰玉 丹珠昂奔 乌日图 文广 文正友 文冬 文爱华 亢德芝 方中华 方兰 方同华 方向 方红卫 方运舟 方志宏 方利旭 方金华 方建平 方复全 方剑乔 方敏 方敏 方燕 尹力 尹中卿 尹东 尹立云 尹同跃 尹兆林 尹学群 尹德健 孔凡群 孔发龙 孔庆菊 孔军 孔昌生 孔怡 孔晓宏 孔晓艳 孔涛 巴音朝鲁 邓力平 邓大玉 邓小刚 邓向阳 邓进秀 邓丽 邓秀新 邓沛然 邓凯 邓泽永 邓修明 邓振龙 邓桂芳 双少敏 玉龙 甘华田 甘荣坤 甘楚林 艾力更·依明巴海 艾克拜尔·麦提那斯尔 古小玉 古清月 左中一 左文学 左香云 左俊 厉延明 厉莉 石玉东 石光银 石丽平 石时态 石迎军 石建辉 石香元 石泰峰 石晓 石淑兰 石蓉 石嘉兴 石聚彬 石慧芬 布小林 龙正才 龙庄伟 龙晓华 龙翔 龙献文 卢天锡 卢玉胜 卢平 卢庆国 卢克平 卢林 卢凌保 卢瑞安 卢献匾 卢馨 旦正草 叶牛平 叶仁荪 叶双瑜 叶尔江·朱安汗 叶壮 叶青 叶国谦 叶诗文 叶梅芬 叶新华 叶赞平 叶露中 甲登·洛绒向巴 申纪兰 田云鹏 田北辰 田立坤 田永东 田永君 田成 田红旗 田克恭 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赵明枝 赵明翠 赵岩泉 赵季平 赵金龙 赵治海 赵宗岐 赵建社 赵春雷 赵昭 赵思学 赵俊民 赵宪庚 赵贺 赵勇 赵素萍 赵晓燕 赵峰 赵爱军 赵剡水 赵萍 赵彪 赵铭 赵鸿涛 赵超 赵辉 赵皖平 赵瑞宝 赵锡伟 赵静 赵豪志 郝世玲 郝旭 郝芳 郝茂荣 郝明金 郝春荣 郝俊海 荣久华 胡五岳 胡五清 胡少先 胡中辉 胡为义 胡玉亭 胡可明 胡亚枫 胡成中 胡伟林 胡江 胡冶 胡启生 胡阿罗 胡昌明 胡明春 胡和平 胡季强 胡建文 胡春华 胡春莲 胡春霞 胡荃 胡胜云 胡胜宝 胡美娥 胡贺波 胡桂花 胡晓明 胡海峰 胡梅英 胡道才 胡强 柯云峰 柯军 柯建华 查玉春 查艳 柳红 柳磊 咸顺女 咸辉 战厚顺 哈尼巴提·沙布开 哈明江 钟志明 钟绍军 钟海燕 钮新强 郜秀菊 种衍民 段文泉 段宇飞 段春华 段俊 修长智 信春鹰 皇甫立同 禹诚 侯二河 侯云 侯华梅 侯军 侯学元 侯建军 侯胜亮 侯艳梅 侯清国 侯湛莹 侯蓉 侯漫路 俞学文 俞章法 昝圣达 昝林森 饶开勋 饶文霖 施克辉 施家伦 姜卫东 姜四清 姜有为 姜成英 姜希猛 姜国平 姜明 姜建军 姜建萍 姜勇 姜涛 娄勤俭 洪为民 洪杰 洪波 洛桑江村 宫明杰 宫鸣 宫蒲光 祖木热提·吾布力 祝雪兰 祝淑钗 费少云 费东斌 姚劲波 姚武江 姚忠良 姚建年 姚顺武 姚奕生 姚祖辉 姚鹃 娜仁图雅 贺一诚 贺光玉 贺泓 贺荣 贺恒扬 贺懋燮 骆惠宁 秦飞 秦玉峰 秦生祥 秦光蔚 秦玥飞 秦英林 秦和 秦春成 秦顺全 秦爱玲 班华忠 班宇侠 班忠柏 袁友方 袁玉宇 袁伟霞 袁延文 袁红梅 袁远 袁志敏 袁昌选 袁周 袁建良 袁驷 袁政海 袁俊洲 袁亮 袁桐利 袁海波 袁家军 袁晶 袁斌 袁誉柏 热依扎·巴合道列提 耿学梅 耿新翠 耿福能 耿遵珠 聂永平 聂鹏举 莫小峰 莫元花 莫华福 莫俊鹏 莫恭明 莫照兰 格桑卓嘎 格桑德吉 索朗扎西 索曙辉 栗生锐 栗战书 栗翠田 贾大风 贾文勤 贾平凹 贾尔恒·阿哈提 贾廷安 贾宇 贾红涛 贾君 贾建成 贾俊明 贾润安 贾樟柯 夏文勇 夏永祥 夏伟东 夏兆纪 夏红民 夏吾卓玛 夏林茂 夏恒建 夏道虎 顾军 顾晋 顾祥兵 顾雪飞 顾想平 柴闪闪 柴会恩 柴绍良 党永富 党晓龙 党增龙 晏婉萍 钱三雄 钱万成 钱加艳 钱凯法 钱春阳 钱树民 钱铭 倪海琼 徐一平 徐元 徐云波 徐云鹏 徐文光 徐立平 徐立毅 徐西盛 徐光 徐延豪 徐华铮 徐华勤 徐向华 徐衣显 徐兴林 徐宇宁 徐如俊 徐玖平 徐远冰 徐佐 徐启方 徐贤淑 徐国权 徐征 徐建华 徐建贤 徐建国 徐建锋 徐绍史 徐珏慧 徐显明 徐科 徐济超 徐恒秋 徐冠巨 徐艳茹 徐起零 徐莹 徐晓 徐颂 徐留平 徐郭平 徐涛 徐诺金 徐萍 徐淙祥 徐辉 徐锦庚 徐滔 徐德全 徐镜人 殷一璀 殷方龙 殷红梅 殷昭举 殷勇 殷焕明 殷鲁谦 翁一岚 翁丽芬 翁杰明 翁国星 翁孟勇 凌云 凌屹 凌希 凌焕新 高卫东 高子程 高开贤 高友东 高玉葆 高世宏 高丙伟 高东垒 高永 高伟 高向秋 高红卫 高苏娟 高步明 高阿莉 高虎城 高明芹 高岩 高岭 高波 高建民 高建军 高春艳 高莉 高桂英 高钰 高祥明 高继明 高琛 高道权 高颖明 高新才 高毅进 高憬宏 郭乃硕 郭大进 郭小芹 郭凤莲 郭以录 郭玉芬 郭永红 郭永宏 郭达文 郭成宇 郭庆平 郭安 郭军 郭红静 郭红旗 郭进 郭声琨 郭迎光 郭亨孝 郭汶霞 郭玫 郭凯 郭金才 郭建仁 郭建华 郭建英 郭建增 郭洪泉 郭姣 郭艳玲 郭素萍 郭振华 郭晓东 郭晓红 郭晓燕 郭爱玲 郭浩 郭晶晶 郭锋 郭锐 郭普校 郭雷 郭锡文 郭新明 席文 席真 唐一军 唐川平 唐仁健 唐永博 唐廷波 唐农 唐良智 唐纯玉 唐岳 唐金海 唐晓明 唐海龙 唐登杰 唐锦波 唐慧娟 唐燕 涂冬 涂伟明 容永恩 谈民强 谈绪祥 陶光辉 陶治国 陶春 陶勋花 黄小军 黄小玲 黄久生 黄友嘉 黄文生 黄玉山 黄玉林 黄玉梅 黄世勇 黄龙云 黄东兵 黄业斌 黄立军 黄汉标 黄礼辉 黄达昌 黄庆学 黄志贤 黄花春 黄克超 黄连珍 黄利婷 黄坤明 黄茂兴 黄迪南 黄河 黄波 黄定承 黄建平 黄细花 黄春燕 黄政仁 黄贵松 黄俐波 黄美媚 黄炳峰 黄炳章 黄艳 黄晓渝 黄爱民 黄海龙 黄海昆 黄海桥 黄菊花 黄铭 黄望明 黄超 黄强 黄楚平 黄路生 黄毅 黄蕾 黄鑫 萧志伟 梅世文 梅世彤 梅亦 梅花 梅建华 曹仁贤 曹可凡 曹立强 曹永鸣 曹庆华 曹汝涛 曹志强 曹国侯 曹金萍 曹京宜 曹宝华 曹建明 曹清尧 曹鸿鸣 曹新元 曹慧泉 曹燕明 龚正 龚立群 龚明珠 龚定荣 龚建明 龚冠宇 龚稼立 龚曙光 雪克来提·扎克尔 常丽虹 崔久秀 崔玉玲 崔世平 崔永辉 崔兴国 崔建梅 崔荣华 崔贵海 崔洪刚 崔桂亮 崔根良 崔海霞 崔智友 崔瑜 崔鹏 崔巍 银燕 矫勇 符小琴 符之冠 符宇航 符宣朝 庹必光 庹庆明 庹勤慧 康军 康志军 康春元 康晓晖 康涛 鹿心社 鹿新弟 章伟民 章国强 章昌平 章联生 章锋 阎少泉 阎志 阎武 阎建国 阎美蓉 梁庆凯 梁阳 梁志强 梁兵 梁金辉 梁剑涛 梁桂 梁桃 梁倩娟 梁益建 梁维东 梁惠玲 梁稳根 梁黎明 梁德标 寇昉 谌贻琴 喜清江 彭石华 彭传新 彭祁 彭寿 彭际淼 彭勃 彭家瑞 彭措 彭唱英 彭清华 揣小勇 葛会波 葛明华 葛树芹 葛晓燕 葛益平 葛菲 葛道凯 董卫民 董开军 董中原 董文琴 董正宏 董传杰 董林 董明珠 董保同 董晓宇 董晓健 董彩云 敬大力 蒋小松 蒋卫平 蒋立虹 蒋永馨 蒋庆群 蒋宇霞 蒋丽英 蒋卓庆 蒋昌忠 蒋建宇 蒋建新 蒋胜男 蒋超良 蒋漠祥 蒋毓勤 韩卫国 韩正 韩立平 韩立明 韩永文 韩永进 韩再芬 韩库 韩金光 韩荣照 韩树旺 韩显明 韩秋香 韩振东 韩晓东 韩晓武 韩恩厚 韩峰 韩梅 韩鹏 韩福春 韩德云 韩德洋 朝勒孟 覃建宁 覃鸿 粟琼 景汉朝 景俊海 喻春梅 程立峰 程并强 程芳 程坚 程林 程京 程诚 程桔 程恩富 程萍 程梦醒 程鼎 程寒飞 傅自应 傅明先 傅奎 傅信平 傅莹 傅德辉 焦云 焦兰生 焦新安 储小芹 舒庆 舒健 舒婷 番跃平 释身振 鲁培军 鲁曼 童路雯 普布顿珠 普尔巴·图格杰加甫 曾力群 曾小敏 曾云英 曾文明 曾光安 曾庆洪 曾丽 曾范涛 曾香桂 曾娜 曾卿 曾静萍 温秀玲 温枢刚 温国辉 温艳嫦 温娟 温菲 温湛滨 温暖 温锦玲 温鹏程 游弋 游劝荣 富康年 谢广祥 谢正谊 谢生瑞 谢伏瞻 谢志成 谢来发 谢坚 谢国明 谢忠岩 谢金红 谢宝禄 谢建辉 谢经荣 谢贻平 谢津秋 谢勇 谢资清 谢舒雯 谢德体 鄢福初 靳伟 靳灵展 蓝佛安 蓝绍敏 蒲永能 蒲彬彬 楼阳生 裘东耀 赖秀福 甄兰芳 雷冬竹 雷光锋 雷军 雷应敏 雷金玉 雷洪 雷艳 雷健坤 雷添良 雷温芳 雷燕琴 路俊霞 詹文龙 詹国海 鲍文波 鲍守坤 鲍迎祥 解维俊 新甲旦真 雍成瀚 满慎刚 骞芳莉 窦正宝 窦延丽 窦希萍 窦贤康 窦树华 窦晓玉 褚旭亮 褚锦锋 静波 嘉木样·洛桑久美·图丹却吉尼玛 赫捷 蔡卫平 蔡达峰 蔡光洁 蔡仲光 蔡红霞 蔡丽新 蔡奇 蔡昉 蔡学恩 蔡细春 蔡玲 蔡素玉 蔡继明 蔡培辉 蔡毅 裴伟东 裴春亮 管延密 鲜铁可 廖长江 廖仁旺 廖玉英 廖正良 廖成臣 廖华歌 廖国勋 廖昌永 廖建宇 廖贵平 廖虹宇 廖晓军 廖爱莲 廖海鹰 廖朝阳 阚全程 谭本宏 谭平川 谭民 谭成旭 谭先国 谭旭光 谭志源 谭泽勇 谭建兰 谭祖安 谭琳 谭斌 谭德才 谭耀宗 肇颖斌 翟友财 翟京宋 翟清斌 熊永俊 熊建明 熊思东 熊晓冬 熊维政 熊群力 缪中 缪国乐 缪静 樊一平 樊代明 樊会涛 樊庆峰 樊芸 樊丽明 黎火辉 黎世祺 黎立璋 黎志宏 黎霞 滕佳材 滕宝贵 颜宝铃 潘文道 潘向黎 潘丽梅 潘利国 潘佳瑛 潘复生 潘保春 潘美儿 潘桂仙 潘萍 潘雪平 潘越 潘敬东 潘锋 薛玉凤 薛占海 薛江武 薛志国 薛景霞 霍好胜 霍金花 霍晓丽 霍涛 霍敏 霍照良 霍震寰 穆为民 戴天方 戴元湖 戴长冰 戴世宏 戴立忠 戴运龙 戴启远 戴继双 戴雅萍 戴源 鞠秀芹 檀结庆 魏凤和 魏立华 魏后凯 魏志民 魏明 魏金龙 魏学峰 魏春 魏树旺 魏洪义 魏琴 魏德东 魏臻 籍涛 nan\n"
     ]
    }
   ],
   "source": [
    "print(*df['姓名'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-24T07:46:46.969067Z",
     "start_time": "2021-10-24T07:46:46.760621Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://baike.baidu.com/item/丁士启\n",
      "https://baike.baidu.com/item/丁小兵\n",
      "https://baike.baidu.com/item/丁云祥\n",
      "https://baike.baidu.com/item/丁世忠\n",
      "https://baike.baidu.com/item/丁列明\n",
      "https://baike.baidu.com/item/丁光宏\n",
      "https://baike.baidu.com/item/丁仲礼\n",
      "https://baike.baidu.com/item/丁来杭\n",
      "https://baike.baidu.com/item/丁宏锁\n",
      "https://baike.baidu.com/item/丁纯\n",
      "https://baike.baidu.com/item/丁国林\n",
      "https://baike.baidu.com/item/丁明\n",
      "https://baike.baidu.com/item/丁烈云\n",
      "https://baike.baidu.com/item/丁绣峰\n",
      "https://baike.baidu.com/item/丁照民\n",
      "https://baike.baidu.com/item/丁福浩\n",
      "https://baike.baidu.com/item/丁薛祥\n",
      "https://baike.baidu.com/item/丁巍\n",
      "https://baike.baidu.com/item/乃依木·亚森\n",
      "https://baike.baidu.com/item/刀晓勤\n",
      "https://baike.baidu.com/item/于飞\n",
      "https://baike.baidu.com/item/于天敏\n",
      "https://baike.baidu.com/item/于中赤\n",
      "https://baike.baidu.com/item/于丕涛\n",
      "https://baike.baidu.com/item/于立新\n",
      "https://baike.baidu.com/item/于伟国\n",
      "https://baike.baidu.com/item/于会文\n",
      "https://baike.baidu.com/item/于旭波\n",
      "https://baike.baidu.com/item/于安玲\n",
      "https://baike.baidu.com/item/于志刚\n",
      "https://baike.baidu.com/item/于志坚\n",
      "https://baike.baidu.com/item/于秀明\n",
      "https://baike.baidu.com/item/于忠福\n",
      "https://baike.baidu.com/item/于金明\n",
      "https://baike.baidu.com/item/于金镒\n",
      "https://baike.baidu.com/item/于泳\n",
      "https://baike.baidu.com/item/于晓明\n",
      "https://baike.baidu.com/item/于峰\n",
      "https://baike.baidu.com/item/于海田\n",
      "https://baike.baidu.com/item/于跃敏\n",
      "https://baike.baidu.com/item/于清明\n",
      "https://baike.baidu.com/item/于集华\n",
      "https://baike.baidu.com/item/于普松\n",
      "https://baike.baidu.com/item/于巍\n",
      "https://baike.baidu.com/item/才华\n",
      "https://baike.baidu.com/item/万卫星\n",
      "https://baike.baidu.com/item/万连步\n",
      "https://baike.baidu.com/item/万勇\n",
      "https://baike.baidu.com/item/万鄂湘\n",
      "https://baike.baidu.com/item/万新恒\n",
      "https://baike.baidu.com/item/乞国艳\n",
      "https://baike.baidu.com/item/卫小春\n",
      "https://baike.baidu.com/item/卫华\n",
      "https://baike.baidu.com/item/卫岗\n",
      "https://baike.baidu.com/item/卫彦明\n",
      "https://baike.baidu.com/item/习近平\n",
      "https://baike.baidu.com/item/习朝峰\n",
      "https://baike.baidu.com/item/马一德\n",
      "https://baike.baidu.com/item/马乙四夫\n",
      "https://baike.baidu.com/item/马卫光\n",
      "https://baike.baidu.com/item/马天龙\n",
      "https://baike.baidu.com/item/马少斌\n",
      "https://baike.baidu.com/item/马化彬\n",
      "https://baike.baidu.com/item/马化腾\n",
      "https://baike.baidu.com/item/马文芳\n",
      "https://baike.baidu.com/item/马玉山\n",
      "https://baike.baidu.com/item/马玉红\n",
      "https://baike.baidu.com/item/马玉璞\n",
      "https://baike.baidu.com/item/马玉霞\n",
      "https://baike.baidu.com/item/马正山\n",
      "https://baike.baidu.com/item/马世忠\n",
      "https://baike.baidu.com/item/马叶江\n",
      "https://baike.baidu.com/item/马兰\n",
      "https://baike.baidu.com/item/马汉成\n",
      "https://baike.baidu.com/item/马永平\n",
      "https://baike.baidu.com/item/马加友\n",
      "https://baike.baidu.com/item/马百龄\n",
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      "https://baike.baidu.com/item/静波\n",
      "https://baike.baidu.com/item/嘉木样·洛桑久美·图丹却吉尼玛\n",
      "https://baike.baidu.com/item/赫捷\n",
      "https://baike.baidu.com/item/蔡卫平\n",
      "https://baike.baidu.com/item/蔡达峰\n",
      "https://baike.baidu.com/item/蔡光洁\n",
      "https://baike.baidu.com/item/蔡仲光\n",
      "https://baike.baidu.com/item/蔡红霞\n",
      "https://baike.baidu.com/item/蔡丽新\n",
      "https://baike.baidu.com/item/蔡奇\n",
      "https://baike.baidu.com/item/蔡昉\n",
      "https://baike.baidu.com/item/蔡学恩\n",
      "https://baike.baidu.com/item/蔡细春\n",
      "https://baike.baidu.com/item/蔡玲\n",
      "https://baike.baidu.com/item/蔡素玉\n",
      "https://baike.baidu.com/item/蔡继明\n",
      "https://baike.baidu.com/item/蔡培辉\n",
      "https://baike.baidu.com/item/蔡毅\n",
      "https://baike.baidu.com/item/裴伟东\n",
      "https://baike.baidu.com/item/裴春亮\n",
      "https://baike.baidu.com/item/管延密\n",
      "https://baike.baidu.com/item/鲜铁可\n",
      "https://baike.baidu.com/item/廖长江\n",
      "https://baike.baidu.com/item/廖仁旺\n",
      "https://baike.baidu.com/item/廖玉英\n",
      "https://baike.baidu.com/item/廖正良\n",
      "https://baike.baidu.com/item/廖成臣\n",
      "https://baike.baidu.com/item/廖华歌\n",
      "https://baike.baidu.com/item/廖国勋\n",
      "https://baike.baidu.com/item/廖昌永\n",
      "https://baike.baidu.com/item/廖建宇\n",
      "https://baike.baidu.com/item/廖贵平\n",
      "https://baike.baidu.com/item/廖虹宇\n",
      "https://baike.baidu.com/item/廖晓军\n",
      "https://baike.baidu.com/item/廖爱莲\n",
      "https://baike.baidu.com/item/廖海鹰\n",
      "https://baike.baidu.com/item/廖朝阳\n",
      "https://baike.baidu.com/item/阚全程\n",
      "https://baike.baidu.com/item/谭本宏\n",
      "https://baike.baidu.com/item/谭平川\n",
      "https://baike.baidu.com/item/谭民\n",
      "https://baike.baidu.com/item/谭成旭\n",
      "https://baike.baidu.com/item/谭先国\n",
      "https://baike.baidu.com/item/谭旭光\n",
      "https://baike.baidu.com/item/谭志源\n",
      "https://baike.baidu.com/item/谭泽勇\n",
      "https://baike.baidu.com/item/谭建兰\n",
      "https://baike.baidu.com/item/谭祖安\n",
      "https://baike.baidu.com/item/谭琳\n",
      "https://baike.baidu.com/item/谭斌\n",
      "https://baike.baidu.com/item/谭德才\n",
      "https://baike.baidu.com/item/谭耀宗\n",
      "https://baike.baidu.com/item/肇颖斌\n",
      "https://baike.baidu.com/item/翟友财\n",
      "https://baike.baidu.com/item/翟京宋\n",
      "https://baike.baidu.com/item/翟清斌\n",
      "https://baike.baidu.com/item/熊永俊\n",
      "https://baike.baidu.com/item/熊建明\n",
      "https://baike.baidu.com/item/熊思东\n",
      "https://baike.baidu.com/item/熊晓冬\n",
      "https://baike.baidu.com/item/熊维政\n",
      "https://baike.baidu.com/item/熊群力\n",
      "https://baike.baidu.com/item/缪中\n",
      "https://baike.baidu.com/item/缪国乐\n",
      "https://baike.baidu.com/item/缪静\n",
      "https://baike.baidu.com/item/樊一平\n",
      "https://baike.baidu.com/item/樊代明\n",
      "https://baike.baidu.com/item/樊会涛\n",
      "https://baike.baidu.com/item/樊庆峰\n",
      "https://baike.baidu.com/item/樊芸\n",
      "https://baike.baidu.com/item/樊丽明\n",
      "https://baike.baidu.com/item/黎火辉\n",
      "https://baike.baidu.com/item/黎世祺\n",
      "https://baike.baidu.com/item/黎立璋\n",
      "https://baike.baidu.com/item/黎志宏\n",
      "https://baike.baidu.com/item/黎霞\n",
      "https://baike.baidu.com/item/滕佳材\n",
      "https://baike.baidu.com/item/滕宝贵\n",
      "https://baike.baidu.com/item/颜宝铃\n",
      "https://baike.baidu.com/item/潘文道\n",
      "https://baike.baidu.com/item/潘向黎\n",
      "https://baike.baidu.com/item/潘丽梅\n",
      "https://baike.baidu.com/item/潘利国\n",
      "https://baike.baidu.com/item/潘佳瑛\n",
      "https://baike.baidu.com/item/潘复生\n",
      "https://baike.baidu.com/item/潘保春\n",
      "https://baike.baidu.com/item/潘美儿\n",
      "https://baike.baidu.com/item/潘桂仙\n",
      "https://baike.baidu.com/item/潘萍\n",
      "https://baike.baidu.com/item/潘雪平\n",
      "https://baike.baidu.com/item/潘越\n",
      "https://baike.baidu.com/item/潘敬东\n",
      "https://baike.baidu.com/item/潘锋\n",
      "https://baike.baidu.com/item/薛玉凤\n",
      "https://baike.baidu.com/item/薛占海\n",
      "https://baike.baidu.com/item/薛江武\n",
      "https://baike.baidu.com/item/薛志国\n",
      "https://baike.baidu.com/item/薛景霞\n",
      "https://baike.baidu.com/item/霍好胜\n",
      "https://baike.baidu.com/item/霍金花\n",
      "https://baike.baidu.com/item/霍晓丽\n",
      "https://baike.baidu.com/item/霍涛\n",
      "https://baike.baidu.com/item/霍敏\n",
      "https://baike.baidu.com/item/霍照良\n",
      "https://baike.baidu.com/item/霍震寰\n",
      "https://baike.baidu.com/item/穆为民\n",
      "https://baike.baidu.com/item/戴天方\n",
      "https://baike.baidu.com/item/戴元湖\n",
      "https://baike.baidu.com/item/戴长冰\n",
      "https://baike.baidu.com/item/戴世宏\n",
      "https://baike.baidu.com/item/戴立忠\n",
      "https://baike.baidu.com/item/戴运龙\n",
      "https://baike.baidu.com/item/戴启远\n",
      "https://baike.baidu.com/item/戴继双\n",
      "https://baike.baidu.com/item/戴雅萍\n",
      "https://baike.baidu.com/item/戴源\n",
      "https://baike.baidu.com/item/鞠秀芹\n",
      "https://baike.baidu.com/item/檀结庆\n",
      "https://baike.baidu.com/item/魏凤和\n",
      "https://baike.baidu.com/item/魏立华\n",
      "https://baike.baidu.com/item/魏后凯\n",
      "https://baike.baidu.com/item/魏志民\n",
      "https://baike.baidu.com/item/魏明\n",
      "https://baike.baidu.com/item/魏金龙\n",
      "https://baike.baidu.com/item/魏学峰\n",
      "https://baike.baidu.com/item/魏春\n",
      "https://baike.baidu.com/item/魏树旺\n",
      "https://baike.baidu.com/item/魏洪义\n",
      "https://baike.baidu.com/item/魏琴\n",
      "https://baike.baidu.com/item/魏德东\n",
      "https://baike.baidu.com/item/魏臻\n",
      "https://baike.baidu.com/item/籍涛\n",
      "https://baike.baidu.com/item/nan\n"
     ]
    }
   ],
   "source": [
    "for i in df['姓名']:\n",
    "    print('https://baike.baidu.com/item/'+str(i))"
   ]
  }
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