收入与房价对比.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 收入增长与房价增长对比\n",
    "1. 有一个传说，程序员的收入是跟房价一起增长的，那么我们拿历史数据来分析一下吧。\n",
    "2. 收入数据来自国家统计局官网 www.stats.gov.cn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城镇单位就业人员平均工资(元)</th>\n",
       "      <th>农、林、牧、渔业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>采矿业城镇单位就业人员平均工资(元)</th>\n",
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       "      <th>交通运输、仓储和邮政业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>批发和零售业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>住宿和餐饮业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>金融业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>房地产业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>租赁和商务服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>水利、环境和公共设施管理业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>居民服务和其他服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>教育城镇单位就业人员平均工资(元)</th>\n",
       "      <th>卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>文化、体育和娱乐业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>公共管理和社会组织城镇单位就业人员平均工资(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>9333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>10834.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>12373.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>13969.0</td>\n",
       "      <td>6884.0</td>\n",
       "      <td>13627.0</td>\n",
       "      <td>12671.0</td>\n",
       "      <td>18574.0</td>\n",
       "      <td>11328.0</td>\n",
       "      <td>15753.0</td>\n",
       "      <td>30897.0</td>\n",
       "      <td>10894.0</td>\n",
       "      <td>11198.0</td>\n",
       "      <td>20780.0</td>\n",
       "      <td>17085.0</td>\n",
       "      <td>17020.0</td>\n",
       "      <td>20442.0</td>\n",
       "      <td>11774.0</td>\n",
       "      <td>12665.0</td>\n",
       "      <td>14189.0</td>\n",
       "      <td>16185.0</td>\n",
       "      <td>17098.0</td>\n",
       "      <td>15355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>15920.0</td>\n",
       "      <td>7497.0</td>\n",
       "      <td>16774.0</td>\n",
       "      <td>14251.0</td>\n",
       "      <td>21543.0</td>\n",
       "      <td>12578.0</td>\n",
       "      <td>18071.0</td>\n",
       "      <td>33449.0</td>\n",
       "      <td>13012.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>24299.0</td>\n",
       "      <td>18467.0</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>23351.0</td>\n",
       "      <td>12884.0</td>\n",
       "      <td>13680.0</td>\n",
       "      <td>16085.0</td>\n",
       "      <td>18386.0</td>\n",
       "      <td>20522.0</td>\n",
       "      <td>17372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>18200.0</td>\n",
       "      <td>8207.0</td>\n",
       "      <td>20449.0</td>\n",
       "      <td>15934.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>14112.0</td>\n",
       "      <td>20911.0</td>\n",
       "      <td>38799.0</td>\n",
       "      <td>15256.0</td>\n",
       "      <td>13876.0</td>\n",
       "      <td>29229.0</td>\n",
       "      <td>20253.0</td>\n",
       "      <td>21233.0</td>\n",
       "      <td>27155.0</td>\n",
       "      <td>14322.0</td>\n",
       "      <td>15747.0</td>\n",
       "      <td>18259.0</td>\n",
       "      <td>20808.0</td>\n",
       "      <td>22670.0</td>\n",
       "      <td>20234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>20856.0</td>\n",
       "      <td>9269.0</td>\n",
       "      <td>24125.0</td>\n",
       "      <td>18225.0</td>\n",
       "      <td>28424.0</td>\n",
       "      <td>16164.0</td>\n",
       "      <td>24111.0</td>\n",
       "      <td>43435.0</td>\n",
       "      <td>17796.0</td>\n",
       "      <td>15236.0</td>\n",
       "      <td>35495.0</td>\n",
       "      <td>22238.0</td>\n",
       "      <td>24510.0</td>\n",
       "      <td>31644.0</td>\n",
       "      <td>15630.0</td>\n",
       "      <td>18030.0</td>\n",
       "      <td>20918.0</td>\n",
       "      <td>23590.0</td>\n",
       "      <td>25847.0</td>\n",
       "      <td>22546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24721.0</td>\n",
       "      <td>10847.0</td>\n",
       "      <td>28185.0</td>\n",
       "      <td>21144.0</td>\n",
       "      <td>33470.0</td>\n",
       "      <td>18482.0</td>\n",
       "      <td>27903.0</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>21074.0</td>\n",
       "      <td>17046.0</td>\n",
       "      <td>44011.0</td>\n",
       "      <td>26085.0</td>\n",
       "      <td>27807.0</td>\n",
       "      <td>38432.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>20370.0</td>\n",
       "      <td>25908.0</td>\n",
       "      <td>27892.0</td>\n",
       "      <td>30430.0</td>\n",
       "      <td>27731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>28898.0</td>\n",
       "      <td>12560.0</td>\n",
       "      <td>34233.0</td>\n",
       "      <td>24404.0</td>\n",
       "      <td>38515.0</td>\n",
       "      <td>21223.0</td>\n",
       "      <td>32041.0</td>\n",
       "      <td>54906.0</td>\n",
       "      <td>25818.0</td>\n",
       "      <td>19321.0</td>\n",
       "      <td>53897.0</td>\n",
       "      <td>30118.0</td>\n",
       "      <td>32915.0</td>\n",
       "      <td>45512.0</td>\n",
       "      <td>21103.0</td>\n",
       "      <td>22858.0</td>\n",
       "      <td>29831.0</td>\n",
       "      <td>32185.0</td>\n",
       "      <td>34158.0</td>\n",
       "      <td>32296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>32244.0</td>\n",
       "      <td>14356.0</td>\n",
       "      <td>38038.0</td>\n",
       "      <td>26810.0</td>\n",
       "      <td>41869.0</td>\n",
       "      <td>24161.0</td>\n",
       "      <td>35315.0</td>\n",
       "      <td>58154.0</td>\n",
       "      <td>29139.0</td>\n",
       "      <td>20860.0</td>\n",
       "      <td>60398.0</td>\n",
       "      <td>32242.0</td>\n",
       "      <td>35494.0</td>\n",
       "      <td>50143.0</td>\n",
       "      <td>23159.0</td>\n",
       "      <td>25172.0</td>\n",
       "      <td>34543.0</td>\n",
       "      <td>35662.0</td>\n",
       "      <td>37755.0</td>\n",
       "      <td>35326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>36539.0</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>44196.0</td>\n",
       "      <td>30916.0</td>\n",
       "      <td>47309.0</td>\n",
       "      <td>27529.0</td>\n",
       "      <td>40466.0</td>\n",
       "      <td>64436.0</td>\n",
       "      <td>33635.0</td>\n",
       "      <td>23382.0</td>\n",
       "      <td>70146.0</td>\n",
       "      <td>35870.0</td>\n",
       "      <td>39566.0</td>\n",
       "      <td>56376.0</td>\n",
       "      <td>25544.0</td>\n",
       "      <td>28206.0</td>\n",
       "      <td>38968.0</td>\n",
       "      <td>40232.0</td>\n",
       "      <td>41428.0</td>\n",
       "      <td>38242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>41799.0</td>\n",
       "      <td>19469.0</td>\n",
       "      <td>52230.0</td>\n",
       "      <td>36665.0</td>\n",
       "      <td>52723.0</td>\n",
       "      <td>32103.0</td>\n",
       "      <td>47078.0</td>\n",
       "      <td>70918.0</td>\n",
       "      <td>40654.0</td>\n",
       "      <td>27486.0</td>\n",
       "      <td>81109.0</td>\n",
       "      <td>42837.0</td>\n",
       "      <td>46976.0</td>\n",
       "      <td>64252.0</td>\n",
       "      <td>28868.0</td>\n",
       "      <td>33169.0</td>\n",
       "      <td>43194.0</td>\n",
       "      <td>46206.0</td>\n",
       "      <td>47878.0</td>\n",
       "      <td>42062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>46769.0</td>\n",
       "      <td>22687.0</td>\n",
       "      <td>56946.0</td>\n",
       "      <td>41650.0</td>\n",
       "      <td>58202.0</td>\n",
       "      <td>36483.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>80510.0</td>\n",
       "      <td>46340.0</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>89743.0</td>\n",
       "      <td>46764.0</td>\n",
       "      <td>53162.0</td>\n",
       "      <td>69254.0</td>\n",
       "      <td>32343.0</td>\n",
       "      <td>35135.0</td>\n",
       "      <td>47734.0</td>\n",
       "      <td>52564.0</td>\n",
       "      <td>53558.0</td>\n",
       "      <td>46074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>51483.0</td>\n",
       "      <td>25820.0</td>\n",
       "      <td>60138.0</td>\n",
       "      <td>46431.0</td>\n",
       "      <td>67085.0</td>\n",
       "      <td>42072.0</td>\n",
       "      <td>57993.0</td>\n",
       "      <td>90915.0</td>\n",
       "      <td>50308.0</td>\n",
       "      <td>34044.0</td>\n",
       "      <td>99653.0</td>\n",
       "      <td>51048.0</td>\n",
       "      <td>62538.0</td>\n",
       "      <td>76602.0</td>\n",
       "      <td>36123.0</td>\n",
       "      <td>38429.0</td>\n",
       "      <td>51950.0</td>\n",
       "      <td>57979.0</td>\n",
       "      <td>59336.0</td>\n",
       "      <td>49259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>56360.0</td>\n",
       "      <td>28356.0</td>\n",
       "      <td>61677.0</td>\n",
       "      <td>51369.0</td>\n",
       "      <td>73339.0</td>\n",
       "      <td>45804.0</td>\n",
       "      <td>63416.0</td>\n",
       "      <td>100845.0</td>\n",
       "      <td>55838.0</td>\n",
       "      <td>37264.0</td>\n",
       "      <td>108273.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>67131.0</td>\n",
       "      <td>82259.0</td>\n",
       "      <td>39198.0</td>\n",
       "      <td>41882.0</td>\n",
       "      <td>56580.0</td>\n",
       "      <td>63267.0</td>\n",
       "      <td>64375.0</td>\n",
       "      <td>53110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>62029.0</td>\n",
       "      <td>31947.0</td>\n",
       "      <td>59404.0</td>\n",
       "      <td>55324.0</td>\n",
       "      <td>78886.0</td>\n",
       "      <td>48886.0</td>\n",
       "      <td>68822.0</td>\n",
       "      <td>112042.0</td>\n",
       "      <td>60328.0</td>\n",
       "      <td>40806.0</td>\n",
       "      <td>114777.0</td>\n",
       "      <td>60244.0</td>\n",
       "      <td>72489.0</td>\n",
       "      <td>89410.0</td>\n",
       "      <td>43528.0</td>\n",
       "      <td>44802.0</td>\n",
       "      <td>66592.0</td>\n",
       "      <td>71624.0</td>\n",
       "      <td>72764.0</td>\n",
       "      <td>62323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>67569.0</td>\n",
       "      <td>33612.0</td>\n",
       "      <td>60544.0</td>\n",
       "      <td>59470.0</td>\n",
       "      <td>83863.0</td>\n",
       "      <td>52082.0</td>\n",
       "      <td>73650.0</td>\n",
       "      <td>122478.0</td>\n",
       "      <td>65061.0</td>\n",
       "      <td>43382.0</td>\n",
       "      <td>117418.0</td>\n",
       "      <td>65497.0</td>\n",
       "      <td>76782.0</td>\n",
       "      <td>96638.0</td>\n",
       "      <td>47750.0</td>\n",
       "      <td>47577.0</td>\n",
       "      <td>74498.0</td>\n",
       "      <td>80026.0</td>\n",
       "      <td>79875.0</td>\n",
       "      <td>70959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>74318.0</td>\n",
       "      <td>36504.0</td>\n",
       "      <td>69500.0</td>\n",
       "      <td>64452.0</td>\n",
       "      <td>90348.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>80225.0</td>\n",
       "      <td>133150.0</td>\n",
       "      <td>71201.0</td>\n",
       "      <td>45751.0</td>\n",
       "      <td>122851.0</td>\n",
       "      <td>69277.0</td>\n",
       "      <td>81393.0</td>\n",
       "      <td>107815.0</td>\n",
       "      <td>52229.0</td>\n",
       "      <td>50552.0</td>\n",
       "      <td>83412.0</td>\n",
       "      <td>89648.0</td>\n",
       "      <td>87803.0</td>\n",
       "      <td>80372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>82461.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      城镇单位就业人员平均工资(元)  农、林、牧、渔业城镇单位就业人员平均工资(元)  采矿业城镇单位就业人员平均工资(元)  \\\n",
       "1999              NaN                      NaN                 NaN   \n",
       "2000           9333.0                      NaN                 NaN   \n",
       "2001          10834.0                      NaN                 NaN   \n",
       "2002          12373.0                      NaN                 NaN   \n",
       "2003          13969.0                   6884.0             13627.0   \n",
       "2004          15920.0                   7497.0             16774.0   \n",
       "2005          18200.0                   8207.0             20449.0   \n",
       "2006          20856.0                   9269.0             24125.0   \n",
       "2007          24721.0                  10847.0             28185.0   \n",
       "2008          28898.0                  12560.0             34233.0   \n",
       "2009          32244.0                  14356.0             38038.0   \n",
       "2010          36539.0                  16717.0             44196.0   \n",
       "2011          41799.0                  19469.0             52230.0   \n",
       "2012          46769.0                  22687.0             56946.0   \n",
       "2013          51483.0                  25820.0             60138.0   \n",
       "2014          56360.0                  28356.0             61677.0   \n",
       "2015          62029.0                  31947.0             59404.0   \n",
       "2016          67569.0                  33612.0             60544.0   \n",
       "2017          74318.0                  36504.0             69500.0   \n",
       "2018          82461.0                      NaN                 NaN   \n",
       "\n",
       "      制造业城镇单位就业人员平均工资(元)  电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)  建筑业城镇单位就业人员平均工资(元)  \\\n",
       "1999                 NaN                            NaN                 NaN   \n",
       "2000                 NaN                            NaN                 NaN   \n",
       "2001                 NaN                            NaN                 NaN   \n",
       "2002                 NaN                            NaN                 NaN   \n",
       "2003             12671.0                        18574.0             11328.0   \n",
       "2004             14251.0                        21543.0             12578.0   \n",
       "2005             15934.0                        24750.0             14112.0   \n",
       "2006             18225.0                        28424.0             16164.0   \n",
       "2007             21144.0                        33470.0             18482.0   \n",
       "2008             24404.0                        38515.0             21223.0   \n",
       "2009             26810.0                        41869.0             24161.0   \n",
       "2010             30916.0                        47309.0             27529.0   \n",
       "2011             36665.0                        52723.0             32103.0   \n",
       "2012             41650.0                        58202.0             36483.0   \n",
       "2013             46431.0                        67085.0             42072.0   \n",
       "2014             51369.0                        73339.0             45804.0   \n",
       "2015             55324.0                        78886.0             48886.0   \n",
       "2016             59470.0                        83863.0             52082.0   \n",
       "2017             64452.0                        90348.0             55568.0   \n",
       "2018                 NaN                            NaN                 NaN   \n",
       "\n",
       "      交通运输、仓储和邮政业城镇单位就业人员平均工资(元)  信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)  \\\n",
       "1999                         NaN                            NaN   \n",
       "2000                         NaN                            NaN   \n",
       "2001                         NaN                            NaN   \n",
       "2002                         NaN                            NaN   \n",
       "2003                     15753.0                        30897.0   \n",
       "2004                     18071.0                        33449.0   \n",
       "2005                     20911.0                        38799.0   \n",
       "2006                     24111.0                        43435.0   \n",
       "2007                     27903.0                        47700.0   \n",
       "2008                     32041.0                        54906.0   \n",
       "2009                     35315.0                        58154.0   \n",
       "2010                     40466.0                        64436.0   \n",
       "2011                     47078.0                        70918.0   \n",
       "2012                     53391.0                        80510.0   \n",
       "2013                     57993.0                        90915.0   \n",
       "2014                     63416.0                       100845.0   \n",
       "2015                     68822.0                       112042.0   \n",
       "2016                     73650.0                       122478.0   \n",
       "2017                     80225.0                       133150.0   \n",
       "2018                         NaN                            NaN   \n",
       "\n",
       "      批发和零售业城镇单位就业人员平均工资(元)  住宿和餐饮业城镇单位就业人员平均工资(元)  金融业城镇单位就业人员平均工资(元)  \\\n",
       "1999                    NaN                    NaN                 NaN   \n",
       "2000                    NaN                    NaN                 NaN   \n",
       "2001                    NaN                    NaN                 NaN   \n",
       "2002                    NaN                    NaN                 NaN   \n",
       "2003                10894.0                11198.0             20780.0   \n",
       "2004                13012.0                12618.0             24299.0   \n",
       "2005                15256.0                13876.0             29229.0   \n",
       "2006                17796.0                15236.0             35495.0   \n",
       "2007                21074.0                17046.0             44011.0   \n",
       "2008                25818.0                19321.0             53897.0   \n",
       "2009                29139.0                20860.0             60398.0   \n",
       "2010                33635.0                23382.0             70146.0   \n",
       "2011                40654.0                27486.0             81109.0   \n",
       "2012                46340.0                31267.0             89743.0   \n",
       "2013                50308.0                34044.0             99653.0   \n",
       "2014                55838.0                37264.0            108273.0   \n",
       "2015                60328.0                40806.0            114777.0   \n",
       "2016                65061.0                43382.0            117418.0   \n",
       "2017                71201.0                45751.0            122851.0   \n",
       "2018                    NaN                    NaN                 NaN   \n",
       "\n",
       "      房地产业城镇单位就业人员平均工资(元)  租赁和商务服务业城镇单位就业人员平均工资(元)  \\\n",
       "1999                  NaN                      NaN   \n",
       "2000                  NaN                      NaN   \n",
       "2001                  NaN                      NaN   \n",
       "2002                  NaN                      NaN   \n",
       "2003              17085.0                  17020.0   \n",
       "2004              18467.0                  18723.0   \n",
       "2005              20253.0                  21233.0   \n",
       "2006              22238.0                  24510.0   \n",
       "2007              26085.0                  27807.0   \n",
       "2008              30118.0                  32915.0   \n",
       "2009              32242.0                  35494.0   \n",
       "2010              35870.0                  39566.0   \n",
       "2011              42837.0                  46976.0   \n",
       "2012              46764.0                  53162.0   \n",
       "2013              51048.0                  62538.0   \n",
       "2014              55568.0                  67131.0   \n",
       "2015              60244.0                  72489.0   \n",
       "2016              65497.0                  76782.0   \n",
       "2017              69277.0                  81393.0   \n",
       "2018                  NaN                      NaN   \n",
       "\n",
       "      科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)  水利、环境和公共设施管理业城镇单位就业人员平均工资(元)  \\\n",
       "1999                             NaN                           NaN   \n",
       "2000                             NaN                           NaN   \n",
       "2001                             NaN                           NaN   \n",
       "2002                             NaN                           NaN   \n",
       "2003                         20442.0                       11774.0   \n",
       "2004                         23351.0                       12884.0   \n",
       "2005                         27155.0                       14322.0   \n",
       "2006                         31644.0                       15630.0   \n",
       "2007                         38432.0                       18383.0   \n",
       "2008                         45512.0                       21103.0   \n",
       "2009                         50143.0                       23159.0   \n",
       "2010                         56376.0                       25544.0   \n",
       "2011                         64252.0                       28868.0   \n",
       "2012                         69254.0                       32343.0   \n",
       "2013                         76602.0                       36123.0   \n",
       "2014                         82259.0                       39198.0   \n",
       "2015                         89410.0                       43528.0   \n",
       "2016                         96638.0                       47750.0   \n",
       "2017                        107815.0                       52229.0   \n",
       "2018                             NaN                           NaN   \n",
       "\n",
       "      居民服务和其他服务业城镇单位就业人员平均工资(元)  教育城镇单位就业人员平均工资(元)  \\\n",
       "1999                        NaN                NaN   \n",
       "2000                        NaN                NaN   \n",
       "2001                        NaN                NaN   \n",
       "2002                        NaN                NaN   \n",
       "2003                    12665.0            14189.0   \n",
       "2004                    13680.0            16085.0   \n",
       "2005                    15747.0            18259.0   \n",
       "2006                    18030.0            20918.0   \n",
       "2007                    20370.0            25908.0   \n",
       "2008                    22858.0            29831.0   \n",
       "2009                    25172.0            34543.0   \n",
       "2010                    28206.0            38968.0   \n",
       "2011                    33169.0            43194.0   \n",
       "2012                    35135.0            47734.0   \n",
       "2013                    38429.0            51950.0   \n",
       "2014                    41882.0            56580.0   \n",
       "2015                    44802.0            66592.0   \n",
       "2016                    47577.0            74498.0   \n",
       "2017                    50552.0            83412.0   \n",
       "2018                        NaN                NaN   \n",
       "\n",
       "      卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)  文化、体育和娱乐业城镇单位就业人员平均工资(元)  \\\n",
       "1999                           NaN                       NaN   \n",
       "2000                           NaN                       NaN   \n",
       "2001                           NaN                       NaN   \n",
       "2002                           NaN                       NaN   \n",
       "2003                       16185.0                   17098.0   \n",
       "2004                       18386.0                   20522.0   \n",
       "2005                       20808.0                   22670.0   \n",
       "2006                       23590.0                   25847.0   \n",
       "2007                       27892.0                   30430.0   \n",
       "2008                       32185.0                   34158.0   \n",
       "2009                       35662.0                   37755.0   \n",
       "2010                       40232.0                   41428.0   \n",
       "2011                       46206.0                   47878.0   \n",
       "2012                       52564.0                   53558.0   \n",
       "2013                       57979.0                   59336.0   \n",
       "2014                       63267.0                   64375.0   \n",
       "2015                       71624.0                   72764.0   \n",
       "2016                       80026.0                   79875.0   \n",
       "2017                       89648.0                   87803.0   \n",
       "2018                           NaN                       NaN   \n",
       "\n",
       "      公共管理和社会组织城镇单位就业人员平均工资(元)  \n",
       "1999                       NaN  \n",
       "2000                       NaN  \n",
       "2001                       NaN  \n",
       "2002                       NaN  \n",
       "2003                   15355.0  \n",
       "2004                   17372.0  \n",
       "2005                   20234.0  \n",
       "2006                   22546.0  \n",
       "2007                   27731.0  \n",
       "2008                   32296.0  \n",
       "2009                   35326.0  \n",
       "2010                   38242.0  \n",
       "2011                   42062.0  \n",
       "2012                   46074.0  \n",
       "2013                   49259.0  \n",
       "2014                   53110.0  \n",
       "2015                   62323.0  \n",
       "2016                   70959.0  \n",
       "2017                   80372.0  \n",
       "2018                       NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载收入数据\n",
    "import pandas as pd\n",
    "income = pd.read_csv('otherdata/收入数据.csv', index_col = 0)\n",
    "income.index = [x[:-1] for x in income.index]\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>指标</th>\n",
       "      <th>城镇单位就业人员平均工资(元)</th>\n",
       "      <th>农、林、牧、渔业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>采矿业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>制造业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>建筑业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>交通运输、仓储和邮政业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>批发和零售业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>住宿和餐饮业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>金融业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>房地产业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>租赁和商务服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>水利、环境和公共设施管理业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>居民服务和其他服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>教育城镇单位就业人员平均工资(元)</th>\n",
       "      <th>卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>文化、体育和娱乐业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>公共管理和社会组织城镇单位就业人员平均工资(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>9333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>10834.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>12373.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>13969.0</td>\n",
       "      <td>6884.0</td>\n",
       "      <td>13627.0</td>\n",
       "      <td>12671.0</td>\n",
       "      <td>18574.0</td>\n",
       "      <td>11328.0</td>\n",
       "      <td>15753.0</td>\n",
       "      <td>30897.0</td>\n",
       "      <td>10894.0</td>\n",
       "      <td>11198.0</td>\n",
       "      <td>20780.0</td>\n",
       "      <td>17085.0</td>\n",
       "      <td>17020.0</td>\n",
       "      <td>20442.0</td>\n",
       "      <td>11774.0</td>\n",
       "      <td>12665.0</td>\n",
       "      <td>14189.0</td>\n",
       "      <td>16185.0</td>\n",
       "      <td>17098.0</td>\n",
       "      <td>15355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>15920.0</td>\n",
       "      <td>7497.0</td>\n",
       "      <td>16774.0</td>\n",
       "      <td>14251.0</td>\n",
       "      <td>21543.0</td>\n",
       "      <td>12578.0</td>\n",
       "      <td>18071.0</td>\n",
       "      <td>33449.0</td>\n",
       "      <td>13012.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>24299.0</td>\n",
       "      <td>18467.0</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>23351.0</td>\n",
       "      <td>12884.0</td>\n",
       "      <td>13680.0</td>\n",
       "      <td>16085.0</td>\n",
       "      <td>18386.0</td>\n",
       "      <td>20522.0</td>\n",
       "      <td>17372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>18200.0</td>\n",
       "      <td>8207.0</td>\n",
       "      <td>20449.0</td>\n",
       "      <td>15934.0</td>\n",
       "      <td>24750.0</td>\n",
       "      <td>14112.0</td>\n",
       "      <td>20911.0</td>\n",
       "      <td>38799.0</td>\n",
       "      <td>15256.0</td>\n",
       "      <td>13876.0</td>\n",
       "      <td>29229.0</td>\n",
       "      <td>20253.0</td>\n",
       "      <td>21233.0</td>\n",
       "      <td>27155.0</td>\n",
       "      <td>14322.0</td>\n",
       "      <td>15747.0</td>\n",
       "      <td>18259.0</td>\n",
       "      <td>20808.0</td>\n",
       "      <td>22670.0</td>\n",
       "      <td>20234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>20856.0</td>\n",
       "      <td>9269.0</td>\n",
       "      <td>24125.0</td>\n",
       "      <td>18225.0</td>\n",
       "      <td>28424.0</td>\n",
       "      <td>16164.0</td>\n",
       "      <td>24111.0</td>\n",
       "      <td>43435.0</td>\n",
       "      <td>17796.0</td>\n",
       "      <td>15236.0</td>\n",
       "      <td>35495.0</td>\n",
       "      <td>22238.0</td>\n",
       "      <td>24510.0</td>\n",
       "      <td>31644.0</td>\n",
       "      <td>15630.0</td>\n",
       "      <td>18030.0</td>\n",
       "      <td>20918.0</td>\n",
       "      <td>23590.0</td>\n",
       "      <td>25847.0</td>\n",
       "      <td>22546.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24721.0</td>\n",
       "      <td>10847.0</td>\n",
       "      <td>28185.0</td>\n",
       "      <td>21144.0</td>\n",
       "      <td>33470.0</td>\n",
       "      <td>18482.0</td>\n",
       "      <td>27903.0</td>\n",
       "      <td>47700.0</td>\n",
       "      <td>21074.0</td>\n",
       "      <td>17046.0</td>\n",
       "      <td>44011.0</td>\n",
       "      <td>26085.0</td>\n",
       "      <td>27807.0</td>\n",
       "      <td>38432.0</td>\n",
       "      <td>18383.0</td>\n",
       "      <td>20370.0</td>\n",
       "      <td>25908.0</td>\n",
       "      <td>27892.0</td>\n",
       "      <td>30430.0</td>\n",
       "      <td>27731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>28898.0</td>\n",
       "      <td>12560.0</td>\n",
       "      <td>34233.0</td>\n",
       "      <td>24404.0</td>\n",
       "      <td>38515.0</td>\n",
       "      <td>21223.0</td>\n",
       "      <td>32041.0</td>\n",
       "      <td>54906.0</td>\n",
       "      <td>25818.0</td>\n",
       "      <td>19321.0</td>\n",
       "      <td>53897.0</td>\n",
       "      <td>30118.0</td>\n",
       "      <td>32915.0</td>\n",
       "      <td>45512.0</td>\n",
       "      <td>21103.0</td>\n",
       "      <td>22858.0</td>\n",
       "      <td>29831.0</td>\n",
       "      <td>32185.0</td>\n",
       "      <td>34158.0</td>\n",
       "      <td>32296.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>32244.0</td>\n",
       "      <td>14356.0</td>\n",
       "      <td>38038.0</td>\n",
       "      <td>26810.0</td>\n",
       "      <td>41869.0</td>\n",
       "      <td>24161.0</td>\n",
       "      <td>35315.0</td>\n",
       "      <td>58154.0</td>\n",
       "      <td>29139.0</td>\n",
       "      <td>20860.0</td>\n",
       "      <td>60398.0</td>\n",
       "      <td>32242.0</td>\n",
       "      <td>35494.0</td>\n",
       "      <td>50143.0</td>\n",
       "      <td>23159.0</td>\n",
       "      <td>25172.0</td>\n",
       "      <td>34543.0</td>\n",
       "      <td>35662.0</td>\n",
       "      <td>37755.0</td>\n",
       "      <td>35326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>36539.0</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>44196.0</td>\n",
       "      <td>30916.0</td>\n",
       "      <td>47309.0</td>\n",
       "      <td>27529.0</td>\n",
       "      <td>40466.0</td>\n",
       "      <td>64436.0</td>\n",
       "      <td>33635.0</td>\n",
       "      <td>23382.0</td>\n",
       "      <td>70146.0</td>\n",
       "      <td>35870.0</td>\n",
       "      <td>39566.0</td>\n",
       "      <td>56376.0</td>\n",
       "      <td>25544.0</td>\n",
       "      <td>28206.0</td>\n",
       "      <td>38968.0</td>\n",
       "      <td>40232.0</td>\n",
       "      <td>41428.0</td>\n",
       "      <td>38242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>41799.0</td>\n",
       "      <td>19469.0</td>\n",
       "      <td>52230.0</td>\n",
       "      <td>36665.0</td>\n",
       "      <td>52723.0</td>\n",
       "      <td>32103.0</td>\n",
       "      <td>47078.0</td>\n",
       "      <td>70918.0</td>\n",
       "      <td>40654.0</td>\n",
       "      <td>27486.0</td>\n",
       "      <td>81109.0</td>\n",
       "      <td>42837.0</td>\n",
       "      <td>46976.0</td>\n",
       "      <td>64252.0</td>\n",
       "      <td>28868.0</td>\n",
       "      <td>33169.0</td>\n",
       "      <td>43194.0</td>\n",
       "      <td>46206.0</td>\n",
       "      <td>47878.0</td>\n",
       "      <td>42062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>46769.0</td>\n",
       "      <td>22687.0</td>\n",
       "      <td>56946.0</td>\n",
       "      <td>41650.0</td>\n",
       "      <td>58202.0</td>\n",
       "      <td>36483.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>80510.0</td>\n",
       "      <td>46340.0</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>89743.0</td>\n",
       "      <td>46764.0</td>\n",
       "      <td>53162.0</td>\n",
       "      <td>69254.0</td>\n",
       "      <td>32343.0</td>\n",
       "      <td>35135.0</td>\n",
       "      <td>47734.0</td>\n",
       "      <td>52564.0</td>\n",
       "      <td>53558.0</td>\n",
       "      <td>46074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>51483.0</td>\n",
       "      <td>25820.0</td>\n",
       "      <td>60138.0</td>\n",
       "      <td>46431.0</td>\n",
       "      <td>67085.0</td>\n",
       "      <td>42072.0</td>\n",
       "      <td>57993.0</td>\n",
       "      <td>90915.0</td>\n",
       "      <td>50308.0</td>\n",
       "      <td>34044.0</td>\n",
       "      <td>99653.0</td>\n",
       "      <td>51048.0</td>\n",
       "      <td>62538.0</td>\n",
       "      <td>76602.0</td>\n",
       "      <td>36123.0</td>\n",
       "      <td>38429.0</td>\n",
       "      <td>51950.0</td>\n",
       "      <td>57979.0</td>\n",
       "      <td>59336.0</td>\n",
       "      <td>49259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>56360.0</td>\n",
       "      <td>28356.0</td>\n",
       "      <td>61677.0</td>\n",
       "      <td>51369.0</td>\n",
       "      <td>73339.0</td>\n",
       "      <td>45804.0</td>\n",
       "      <td>63416.0</td>\n",
       "      <td>100845.0</td>\n",
       "      <td>55838.0</td>\n",
       "      <td>37264.0</td>\n",
       "      <td>108273.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>67131.0</td>\n",
       "      <td>82259.0</td>\n",
       "      <td>39198.0</td>\n",
       "      <td>41882.0</td>\n",
       "      <td>56580.0</td>\n",
       "      <td>63267.0</td>\n",
       "      <td>64375.0</td>\n",
       "      <td>53110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>62029.0</td>\n",
       "      <td>31947.0</td>\n",
       "      <td>59404.0</td>\n",
       "      <td>55324.0</td>\n",
       "      <td>78886.0</td>\n",
       "      <td>48886.0</td>\n",
       "      <td>68822.0</td>\n",
       "      <td>112042.0</td>\n",
       "      <td>60328.0</td>\n",
       "      <td>40806.0</td>\n",
       "      <td>114777.0</td>\n",
       "      <td>60244.0</td>\n",
       "      <td>72489.0</td>\n",
       "      <td>89410.0</td>\n",
       "      <td>43528.0</td>\n",
       "      <td>44802.0</td>\n",
       "      <td>66592.0</td>\n",
       "      <td>71624.0</td>\n",
       "      <td>72764.0</td>\n",
       "      <td>62323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>67569.0</td>\n",
       "      <td>33612.0</td>\n",
       "      <td>60544.0</td>\n",
       "      <td>59470.0</td>\n",
       "      <td>83863.0</td>\n",
       "      <td>52082.0</td>\n",
       "      <td>73650.0</td>\n",
       "      <td>122478.0</td>\n",
       "      <td>65061.0</td>\n",
       "      <td>43382.0</td>\n",
       "      <td>117418.0</td>\n",
       "      <td>65497.0</td>\n",
       "      <td>76782.0</td>\n",
       "      <td>96638.0</td>\n",
       "      <td>47750.0</td>\n",
       "      <td>47577.0</td>\n",
       "      <td>74498.0</td>\n",
       "      <td>80026.0</td>\n",
       "      <td>79875.0</td>\n",
       "      <td>70959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>74318.0</td>\n",
       "      <td>36504.0</td>\n",
       "      <td>69500.0</td>\n",
       "      <td>64452.0</td>\n",
       "      <td>90348.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>80225.0</td>\n",
       "      <td>133150.0</td>\n",
       "      <td>71201.0</td>\n",
       "      <td>45751.0</td>\n",
       "      <td>122851.0</td>\n",
       "      <td>69277.0</td>\n",
       "      <td>81393.0</td>\n",
       "      <td>107815.0</td>\n",
       "      <td>52229.0</td>\n",
       "      <td>50552.0</td>\n",
       "      <td>83412.0</td>\n",
       "      <td>89648.0</td>\n",
       "      <td>87803.0</td>\n",
       "      <td>80372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>82413.0</td>\n",
       "      <td>36466.0</td>\n",
       "      <td>81429.0</td>\n",
       "      <td>72088.0</td>\n",
       "      <td>100162.0</td>\n",
       "      <td>60501.0</td>\n",
       "      <td>88508.0</td>\n",
       "      <td>147678.0</td>\n",
       "      <td>80551.0</td>\n",
       "      <td>48260.0</td>\n",
       "      <td>129837.0</td>\n",
       "      <td>75281.0</td>\n",
       "      <td>85147.0</td>\n",
       "      <td>123343.0</td>\n",
       "      <td>56670.0</td>\n",
       "      <td>55343.0</td>\n",
       "      <td>92383.0</td>\n",
       "      <td>98118.0</td>\n",
       "      <td>98621.0</td>\n",
       "      <td>87932.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>90501.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "指标    城镇单位就业人员平均工资(元)  农、林、牧、渔业城镇单位就业人员平均工资(元)  采矿业城镇单位就业人员平均工资(元)  \\\n",
       "2000           9333.0                      NaN                 NaN   \n",
       "2001          10834.0                      NaN                 NaN   \n",
       "2002          12373.0                      NaN                 NaN   \n",
       "2003          13969.0                   6884.0             13627.0   \n",
       "2004          15920.0                   7497.0             16774.0   \n",
       "2005          18200.0                   8207.0             20449.0   \n",
       "2006          20856.0                   9269.0             24125.0   \n",
       "2007          24721.0                  10847.0             28185.0   \n",
       "2008          28898.0                  12560.0             34233.0   \n",
       "2009          32244.0                  14356.0             38038.0   \n",
       "2010          36539.0                  16717.0             44196.0   \n",
       "2011          41799.0                  19469.0             52230.0   \n",
       "2012          46769.0                  22687.0             56946.0   \n",
       "2013          51483.0                  25820.0             60138.0   \n",
       "2014          56360.0                  28356.0             61677.0   \n",
       "2015          62029.0                  31947.0             59404.0   \n",
       "2016          67569.0                  33612.0             60544.0   \n",
       "2017          74318.0                  36504.0             69500.0   \n",
       "2018          82413.0                  36466.0             81429.0   \n",
       "2019          90501.0                      NaN                 NaN   \n",
       "\n",
       "指标    制造业城镇单位就业人员平均工资(元)  电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)  建筑业城镇单位就业人员平均工资(元)  \\\n",
       "2000                 NaN                            NaN                 NaN   \n",
       "2001                 NaN                            NaN                 NaN   \n",
       "2002                 NaN                            NaN                 NaN   \n",
       "2003             12671.0                        18574.0             11328.0   \n",
       "2004             14251.0                        21543.0             12578.0   \n",
       "2005             15934.0                        24750.0             14112.0   \n",
       "2006             18225.0                        28424.0             16164.0   \n",
       "2007             21144.0                        33470.0             18482.0   \n",
       "2008             24404.0                        38515.0             21223.0   \n",
       "2009             26810.0                        41869.0             24161.0   \n",
       "2010             30916.0                        47309.0             27529.0   \n",
       "2011             36665.0                        52723.0             32103.0   \n",
       "2012             41650.0                        58202.0             36483.0   \n",
       "2013             46431.0                        67085.0             42072.0   \n",
       "2014             51369.0                        73339.0             45804.0   \n",
       "2015             55324.0                        78886.0             48886.0   \n",
       "2016             59470.0                        83863.0             52082.0   \n",
       "2017             64452.0                        90348.0             55568.0   \n",
       "2018             72088.0                       100162.0             60501.0   \n",
       "2019                 NaN                            NaN                 NaN   \n",
       "\n",
       "指标    交通运输、仓储和邮政业城镇单位就业人员平均工资(元)  信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)  \\\n",
       "2000                         NaN                            NaN   \n",
       "2001                         NaN                            NaN   \n",
       "2002                         NaN                            NaN   \n",
       "2003                     15753.0                        30897.0   \n",
       "2004                     18071.0                        33449.0   \n",
       "2005                     20911.0                        38799.0   \n",
       "2006                     24111.0                        43435.0   \n",
       "2007                     27903.0                        47700.0   \n",
       "2008                     32041.0                        54906.0   \n",
       "2009                     35315.0                        58154.0   \n",
       "2010                     40466.0                        64436.0   \n",
       "2011                     47078.0                        70918.0   \n",
       "2012                     53391.0                        80510.0   \n",
       "2013                     57993.0                        90915.0   \n",
       "2014                     63416.0                       100845.0   \n",
       "2015                     68822.0                       112042.0   \n",
       "2016                     73650.0                       122478.0   \n",
       "2017                     80225.0                       133150.0   \n",
       "2018                     88508.0                       147678.0   \n",
       "2019                         NaN                            NaN   \n",
       "\n",
       "指标    批发和零售业城镇单位就业人员平均工资(元)  住宿和餐饮业城镇单位就业人员平均工资(元)  金融业城镇单位就业人员平均工资(元)  \\\n",
       "2000                    NaN                    NaN                 NaN   \n",
       "2001                    NaN                    NaN                 NaN   \n",
       "2002                    NaN                    NaN                 NaN   \n",
       "2003                10894.0                11198.0             20780.0   \n",
       "2004                13012.0                12618.0             24299.0   \n",
       "2005                15256.0                13876.0             29229.0   \n",
       "2006                17796.0                15236.0             35495.0   \n",
       "2007                21074.0                17046.0             44011.0   \n",
       "2008                25818.0                19321.0             53897.0   \n",
       "2009                29139.0                20860.0             60398.0   \n",
       "2010                33635.0                23382.0             70146.0   \n",
       "2011                40654.0                27486.0             81109.0   \n",
       "2012                46340.0                31267.0             89743.0   \n",
       "2013                50308.0                34044.0             99653.0   \n",
       "2014                55838.0                37264.0            108273.0   \n",
       "2015                60328.0                40806.0            114777.0   \n",
       "2016                65061.0                43382.0            117418.0   \n",
       "2017                71201.0                45751.0            122851.0   \n",
       "2018                80551.0                48260.0            129837.0   \n",
       "2019                    NaN                    NaN                 NaN   \n",
       "\n",
       "指标    房地产业城镇单位就业人员平均工资(元)  租赁和商务服务业城镇单位就业人员平均工资(元)  \\\n",
       "2000                  NaN                      NaN   \n",
       "2001                  NaN                      NaN   \n",
       "2002                  NaN                      NaN   \n",
       "2003              17085.0                  17020.0   \n",
       "2004              18467.0                  18723.0   \n",
       "2005              20253.0                  21233.0   \n",
       "2006              22238.0                  24510.0   \n",
       "2007              26085.0                  27807.0   \n",
       "2008              30118.0                  32915.0   \n",
       "2009              32242.0                  35494.0   \n",
       "2010              35870.0                  39566.0   \n",
       "2011              42837.0                  46976.0   \n",
       "2012              46764.0                  53162.0   \n",
       "2013              51048.0                  62538.0   \n",
       "2014              55568.0                  67131.0   \n",
       "2015              60244.0                  72489.0   \n",
       "2016              65497.0                  76782.0   \n",
       "2017              69277.0                  81393.0   \n",
       "2018              75281.0                  85147.0   \n",
       "2019                  NaN                      NaN   \n",
       "\n",
       "指标    科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)  水利、环境和公共设施管理业城镇单位就业人员平均工资(元)  \\\n",
       "2000                             NaN                           NaN   \n",
       "2001                             NaN                           NaN   \n",
       "2002                             NaN                           NaN   \n",
       "2003                         20442.0                       11774.0   \n",
       "2004                         23351.0                       12884.0   \n",
       "2005                         27155.0                       14322.0   \n",
       "2006                         31644.0                       15630.0   \n",
       "2007                         38432.0                       18383.0   \n",
       "2008                         45512.0                       21103.0   \n",
       "2009                         50143.0                       23159.0   \n",
       "2010                         56376.0                       25544.0   \n",
       "2011                         64252.0                       28868.0   \n",
       "2012                         69254.0                       32343.0   \n",
       "2013                         76602.0                       36123.0   \n",
       "2014                         82259.0                       39198.0   \n",
       "2015                         89410.0                       43528.0   \n",
       "2016                         96638.0                       47750.0   \n",
       "2017                        107815.0                       52229.0   \n",
       "2018                        123343.0                       56670.0   \n",
       "2019                             NaN                           NaN   \n",
       "\n",
       "指标    居民服务和其他服务业城镇单位就业人员平均工资(元)  教育城镇单位就业人员平均工资(元)  \\\n",
       "2000                        NaN                NaN   \n",
       "2001                        NaN                NaN   \n",
       "2002                        NaN                NaN   \n",
       "2003                    12665.0            14189.0   \n",
       "2004                    13680.0            16085.0   \n",
       "2005                    15747.0            18259.0   \n",
       "2006                    18030.0            20918.0   \n",
       "2007                    20370.0            25908.0   \n",
       "2008                    22858.0            29831.0   \n",
       "2009                    25172.0            34543.0   \n",
       "2010                    28206.0            38968.0   \n",
       "2011                    33169.0            43194.0   \n",
       "2012                    35135.0            47734.0   \n",
       "2013                    38429.0            51950.0   \n",
       "2014                    41882.0            56580.0   \n",
       "2015                    44802.0            66592.0   \n",
       "2016                    47577.0            74498.0   \n",
       "2017                    50552.0            83412.0   \n",
       "2018                    55343.0            92383.0   \n",
       "2019                        NaN                NaN   \n",
       "\n",
       "指标    卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)  文化、体育和娱乐业城镇单位就业人员平均工资(元)  \\\n",
       "2000                           NaN                       NaN   \n",
       "2001                           NaN                       NaN   \n",
       "2002                           NaN                       NaN   \n",
       "2003                       16185.0                   17098.0   \n",
       "2004                       18386.0                   20522.0   \n",
       "2005                       20808.0                   22670.0   \n",
       "2006                       23590.0                   25847.0   \n",
       "2007                       27892.0                   30430.0   \n",
       "2008                       32185.0                   34158.0   \n",
       "2009                       35662.0                   37755.0   \n",
       "2010                       40232.0                   41428.0   \n",
       "2011                       46206.0                   47878.0   \n",
       "2012                       52564.0                   53558.0   \n",
       "2013                       57979.0                   59336.0   \n",
       "2014                       63267.0                   64375.0   \n",
       "2015                       71624.0                   72764.0   \n",
       "2016                       80026.0                   79875.0   \n",
       "2017                       89648.0                   87803.0   \n",
       "2018                       98118.0                   98621.0   \n",
       "2019                           NaN                       NaN   \n",
       "\n",
       "指标    公共管理和社会组织城镇单位就业人员平均工资(元)  \n",
       "2000                       NaN  \n",
       "2001                       NaN  \n",
       "2002                       NaN  \n",
       "2003                   15355.0  \n",
       "2004                   17372.0  \n",
       "2005                   20234.0  \n",
       "2006                   22546.0  \n",
       "2007                   27731.0  \n",
       "2008                   32296.0  \n",
       "2009                   35326.0  \n",
       "2010                   38242.0  \n",
       "2011                   42062.0  \n",
       "2012                   46074.0  \n",
       "2013                   49259.0  \n",
       "2014                   53110.0  \n",
       "2015                   62323.0  \n",
       "2016                   70959.0  \n",
       "2017                   80372.0  \n",
       "2018                   87932.0  \n",
       "2019                       NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "income=pd.read_html('otherdata/收入数据2020.htm')\n",
    "income=income[0]\n",
    "income = income.set_index('指标').T\n",
    "income.index = [x[:-1] for x in income.index]\n",
    "income = income.sort_index()\n",
    "income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "income.to_excel('otherdata/收入数据.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alex/Quant/code/LianjiaSpider/common.py:93: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  df = pd.concat(dfs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw count: 793369\n",
      "count after drop duplicates 585016\n",
      "count after drop less than 1000 584260\n",
      "count after drop 40, 50 564941\n"
     ]
    }
   ],
   "source": [
    "#加载北京房价数据并计算每年均价\n",
    "from common import read\n",
    "df = read('北京')\n",
    "df['year'] = [x[:4] for x in df['成交时间']]\n",
    "housePrice = df.groupby('year')['成交价(元/平)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2010年之前成交量太少，房价数据不可靠\n",
    "housePriceDf = pd.DataFrame({'北京房价':housePrice.loc[housePrice.index>='2011']})\n",
    "data = housePriceDf.merge(income, left_index = True, right_index = True, how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.4 8.4 0.9340838471148376 2.3842392105884107 1.3600000000000003 1.6591615288516242\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#北京房价相对涨幅与收入相对涨幅对比\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from matplotlib.offsetbox import TextArea, DrawingArea, OffsetImage, AnnotationBbox\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "font=FontProperties(fname='font/Songti.ttc',size=18)\n",
    "title = '房价收入对比'\n",
    "columns = ['北京房价', '城镇单位就业人员平均工资(元)', \n",
    "           '信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)','金融业城镇单位就业人员平均工资(元)',\n",
    "          '卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)']\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "matplotlib.rc('font', size=18)\n",
    "matplotlib.rcParams['figure.figsize'] = [15, 10]\n",
    "for col in columns:\n",
    "    plt.plot(data[col]/data[col].iloc[0])\n",
    "plt.legend(columns, prop = font)\n",
    "plt.grid(True)\n",
    "plt.title(title, fontproperties = font)\n",
    "#插入二维码\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "qrcode = mpimg.imread('wechatqrcode.png')\n",
    "imagebox = OffsetImage(qrcode, zoom=0.5)\n",
    "x_pos = (x1 + 0.2*(x2 - x1))\n",
    "y_pos = (y1 + 0.5*(y2-y1))\n",
    "ab = AnnotationBbox(imagebox, (x_pos, y_pos ))\n",
    "plt.gca().add_artist(ab)\n",
    "print(x1, x2, y1, y2, x_pos, y_pos)\n",
    "\n",
    "#重画x轴\n",
    "dir_name = os.path.join('fig', '房价收入分析')\n",
    "if not os.path.exists(dir_name):\n",
    "    os.makedirs(dir_name)\n",
    "plt.savefig(os.path.join(dir_name, title +'.png'))\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>涨幅</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>指标</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>批发和零售业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>7.394070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育城镇单位就业人员平均工资(元)</th>\n",
       "      <td>6.510889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>金融业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>6.248171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>6.062280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>6.033803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>采矿业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.975563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文化、体育和娱乐业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.767985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>公共管理和社会组织城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.726604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>制造业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.689212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>交通运输、仓储和邮政业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.618485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.392592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>建筑业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.340837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>农、林、牧、渔业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.297211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>租赁和商务服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>5.002761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>水利、环境和公共设施管理业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>4.813148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>4.779687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>4.406263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>居民服务和其他服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>4.369759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>住宿和餐饮业城镇单位就业人员平均工资(元)</th>\n",
       "      <td>4.309698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      涨幅\n",
       "指标                                      \n",
       "批发和零售业城镇单位就业人员平均工资(元)           7.394070\n",
       "教育城镇单位就业人员平均工资(元)               6.510889\n",
       "金融业城镇单位就业人员平均工资(元)              6.248171\n",
       "卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)    6.062280\n",
       "科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)  6.033803\n",
       "采矿业城镇单位就业人员平均工资(元)              5.975563\n",
       "城镇单位就业人员平均工资(元)                 5.899706\n",
       "文化、体育和娱乐业城镇单位就业人员平均工资(元)        5.767985\n",
       "公共管理和社会组织城镇单位就业人员平均工资(元)        5.726604\n",
       "制造业城镇单位就业人员平均工资(元)              5.689212\n",
       "交通运输、仓储和邮政业城镇单位就业人员平均工资(元)      5.618485\n",
       "电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)   5.392592\n",
       "建筑业城镇单位就业人员平均工资(元)              5.340837\n",
       "农、林、牧、渔业城镇单位就业人员平均工资(元)         5.297211\n",
       "租赁和商务服务业城镇单位就业人员平均工资(元)         5.002761\n",
       "水利、环境和公共设施管理业城镇单位就业人员平均工资(元)    4.813148\n",
       "信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)   4.779687\n",
       "房地产业城镇单位就业人员平均工资(元)             4.406263\n",
       "居民服务和其他服务业城镇单位就业人员平均工资(元)       4.369759\n",
       "住宿和餐饮业城镇单位就业人员平均工资(元)           4.309698"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分析一下各行业涨幅对比\n",
    "rise_ratio = income.loc['2018']/income.loc['2003']\n",
    "pd.DataFrame({'涨幅':rise_ratio.sort_values(ascending = False)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.25 18.75 14435.099999999999 154022.9 3.9000000000000004 84229.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#金融与IT对比\n",
    "font=FontProperties(fname='font/Songti.ttc',size=18)\n",
    "title = '金融与IT收入对比'\n",
    "columns = ['信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)','金融业城镇单位就业人员平均工资(元)']\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "matplotlib.rc('font', size=18)\n",
    "matplotlib.rcParams['figure.figsize'] = [15, 10]\n",
    "for col in columns:\n",
    "    plt.plot(income[col])\n",
    "plt.legend(columns, prop = font)\n",
    "plt.grid(True)\n",
    "plt.title(title, fontproperties = font)\n",
    "\n",
    "#插入二维码\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "qrcode = mpimg.imread('wechatqrcode.png')\n",
    "imagebox = OffsetImage(qrcode, zoom=0.5)\n",
    "x_pos = (x1 + 0.1*(x2 - x1))\n",
    "y_pos = (y1 + 0.5*(y2-y1))\n",
    "ab = AnnotationBbox(imagebox, (x_pos, y_pos ))\n",
    "plt.gca().add_artist(ab)\n",
    "print(x1, x2, y1, y2, x_pos, y_pos)\n",
    "\n",
    "#重画x轴\n",
    "dir_name = os.path.join('fig', '房价收入分析')\n",
    "if not os.path.exists(dir_name):\n",
    "    os.makedirs(dir_name)\n",
    "plt.savefig(os.path.join(dir_name, title +'.png'))\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>指标</th>\n",
       "      <th>城镇单位就业人员平均工资(元)</th>\n",
       "      <th>农、林、牧、渔业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>采矿业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>制造业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>建筑业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>交通运输、仓储和邮政业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>批发和零售业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>住宿和餐饮业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>金融业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>房地产业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>租赁和商务服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>水利、环境和公共设施管理业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>居民服务和其他服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>教育城镇单位就业人员平均工资(元)</th>\n",
       "      <th>卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>文化、体育和娱乐业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>公共管理和社会组织城镇单位就业人员平均工资(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>9333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>16.1%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>14.2%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>12.9%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>14.0%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>23.1%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>14.7%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>12.7%</td>\n",
       "      <td>16.9%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>10.0%</td>\n",
       "      <td>14.2%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>13.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>14.3%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>21.9%</td>\n",
       "      <td>11.8%</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>12.2%</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>10.0%</td>\n",
       "      <td>20.3%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>16.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>14.6%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>18.0%</td>\n",
       "      <td>14.4%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>14.5%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>16.6%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>9.1%</td>\n",
       "      <td>14.5%</td>\n",
       "      <td>14.6%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>11.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>18.5%</td>\n",
       "      <td>17.0%</td>\n",
       "      <td>16.8%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>17.8%</td>\n",
       "      <td>14.3%</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>24.0%</td>\n",
       "      <td>17.3%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>21.5%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>23.9%</td>\n",
       "      <td>18.2%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>23.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>16.9%</td>\n",
       "      <td>15.8%</td>\n",
       "      <td>21.5%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>13.3%</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>15.5%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>12.2%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>12.3%</td>\n",
       "      <td>16.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11.6%</td>\n",
       "      <td>14.3%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>10.2%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>7.1%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>10.2%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>15.8%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>9.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>13.3%</td>\n",
       "      <td>16.4%</td>\n",
       "      <td>16.2%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>14.6%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>16.1%</td>\n",
       "      <td>11.3%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>12.4%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>12.8%</td>\n",
       "      <td>12.8%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>8.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>14.4%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>18.2%</td>\n",
       "      <td>18.6%</td>\n",
       "      <td>11.4%</td>\n",
       "      <td>16.6%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>20.9%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>18.7%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>10.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>11.9%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>10.4%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>9.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>10.1%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>5.6%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>8.8%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>6.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>9.5%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>2.6%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>7.3%</td>\n",
       "      <td>7.4%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>9.1%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>7.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>10.1%</td>\n",
       "      <td>12.7%</td>\n",
       "      <td>-3.7%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>7.6%</td>\n",
       "      <td>6.7%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>7.0%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>17.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>8.9%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>1.9%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>7.0%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>2.3%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>6.2%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>13.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10.0%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>6.7%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>5.5%</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>5.8%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>11.6%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>13.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>10.9%</td>\n",
       "      <td>-0.1%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>11.8%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>13.1%</td>\n",
       "      <td>5.5%</td>\n",
       "      <td>5.7%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>14.4%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>12.3%</td>\n",
       "      <td>9.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>9.8%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "指标   城镇单位就业人员平均工资(元) 农、林、牧、渔业城镇单位就业人员平均工资(元) 采矿业城镇单位就业人员平均工资(元)  \\\n",
       "2000            9333                     NaN                NaN   \n",
       "2001           16.1%                    nan%               nan%   \n",
       "2002           14.2%                    nan%               nan%   \n",
       "2003           12.9%                    nan%               nan%   \n",
       "2004           14.0%                    8.9%              23.1%   \n",
       "2005           14.3%                    9.5%              21.9%   \n",
       "2006           14.6%                   12.9%              18.0%   \n",
       "2007           18.5%                   17.0%              16.8%   \n",
       "2008           16.9%                   15.8%              21.5%   \n",
       "2009           11.6%                   14.3%              11.1%   \n",
       "2010           13.3%                   16.4%              16.2%   \n",
       "2011           14.4%                   16.5%              18.2%   \n",
       "2012           11.9%                   16.5%               9.0%   \n",
       "2013           10.1%                   13.8%               5.6%   \n",
       "2014            9.5%                    9.8%               2.6%   \n",
       "2015           10.1%                   12.7%              -3.7%   \n",
       "2016            8.9%                    5.2%               1.9%   \n",
       "2017           10.0%                    8.6%              14.8%   \n",
       "2018           10.9%                   -0.1%              17.2%   \n",
       "2019            9.8%                    nan%               nan%   \n",
       "\n",
       "指标   制造业城镇单位就业人员平均工资(元) 电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元) 建筑业城镇单位就业人员平均工资(元)  \\\n",
       "2000                NaN                           NaN                NaN   \n",
       "2001               nan%                          nan%               nan%   \n",
       "2002               nan%                          nan%               nan%   \n",
       "2003               nan%                          nan%               nan%   \n",
       "2004              12.5%                         16.0%              11.0%   \n",
       "2005              11.8%                         14.9%              12.2%   \n",
       "2006              14.4%                         14.8%              14.5%   \n",
       "2007              16.0%                         17.8%              14.3%   \n",
       "2008              15.4%                         15.1%              14.8%   \n",
       "2009               9.9%                          8.7%              13.8%   \n",
       "2010              15.3%                         13.0%              13.9%   \n",
       "2011              18.6%                         11.4%              16.6%   \n",
       "2012              13.6%                         10.4%              13.6%   \n",
       "2013              11.5%                         15.3%              15.3%   \n",
       "2014              10.6%                          9.3%               8.9%   \n",
       "2015               7.7%                          7.6%               6.7%   \n",
       "2016               7.5%                          6.3%               6.5%   \n",
       "2017               8.4%                          7.7%               6.7%   \n",
       "2018              11.8%                         10.9%               8.9%   \n",
       "2019               nan%                          nan%               nan%   \n",
       "\n",
       "指标   交通运输、仓储和邮政业城镇单位就业人员平均工资(元) 信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)  \\\n",
       "2000                        NaN                           NaN   \n",
       "2001                       nan%                          nan%   \n",
       "2002                       nan%                          nan%   \n",
       "2003                       nan%                          nan%   \n",
       "2004                      14.7%                          8.3%   \n",
       "2005                      15.7%                         16.0%   \n",
       "2006                      15.3%                         11.9%   \n",
       "2007                      15.7%                          9.8%   \n",
       "2008                      14.8%                         15.1%   \n",
       "2009                      10.2%                          5.9%   \n",
       "2010                      14.6%                         10.8%   \n",
       "2011                      16.3%                         10.1%   \n",
       "2012                      13.4%                         13.5%   \n",
       "2013                       8.6%                         12.9%   \n",
       "2014                       9.4%                         10.9%   \n",
       "2015                       8.5%                         11.1%   \n",
       "2016                       7.0%                          9.3%   \n",
       "2017                       8.9%                          8.7%   \n",
       "2018                      10.3%                         10.9%   \n",
       "2019                       nan%                          nan%   \n",
       "\n",
       "指标   批发和零售业城镇单位就业人员平均工资(元) 住宿和餐饮业城镇单位就业人员平均工资(元) 金融业城镇单位就业人员平均工资(元)  \\\n",
       "2000                   NaN                   NaN                NaN   \n",
       "2001                  nan%                  nan%               nan%   \n",
       "2002                  nan%                  nan%               nan%   \n",
       "2003                  nan%                  nan%               nan%   \n",
       "2004                 19.4%                 12.7%              16.9%   \n",
       "2005                 17.2%                 10.0%              20.3%   \n",
       "2006                 16.6%                  9.8%              21.4%   \n",
       "2007                 18.4%                 11.9%              24.0%   \n",
       "2008                 22.5%                 13.3%              22.5%   \n",
       "2009                 12.9%                  8.0%              12.1%   \n",
       "2010                 15.4%                 12.1%              16.1%   \n",
       "2011                 20.9%                 17.6%              15.6%   \n",
       "2012                 14.0%                 13.8%              10.6%   \n",
       "2013                  8.6%                  8.9%              11.0%   \n",
       "2014                 11.0%                  9.5%               8.7%   \n",
       "2015                  8.0%                  9.5%               6.0%   \n",
       "2016                  7.8%                  6.3%               2.3%   \n",
       "2017                  9.4%                  5.5%               4.6%   \n",
       "2018                 13.1%                  5.5%               5.7%   \n",
       "2019                  nan%                  nan%               nan%   \n",
       "\n",
       "指标   房地产业城镇单位就业人员平均工资(元) 租赁和商务服务业城镇单位就业人员平均工资(元)  \\\n",
       "2000                 NaN                     NaN   \n",
       "2001                nan%                    nan%   \n",
       "2002                nan%                    nan%   \n",
       "2003                nan%                    nan%   \n",
       "2004                8.1%                   10.0%   \n",
       "2005                9.7%                   13.4%   \n",
       "2006                9.8%                   15.4%   \n",
       "2007               17.3%                   13.5%   \n",
       "2008               15.5%                   18.4%   \n",
       "2009                7.1%                    7.8%   \n",
       "2010               11.3%                   11.5%   \n",
       "2011               19.4%                   18.7%   \n",
       "2012                9.2%                   13.2%   \n",
       "2013                9.2%                   17.6%   \n",
       "2014                8.9%                    7.3%   \n",
       "2015                8.4%                    8.0%   \n",
       "2016                8.7%                    5.9%   \n",
       "2017                5.8%                    6.0%   \n",
       "2018                8.7%                    4.6%   \n",
       "2019                nan%                    nan%   \n",
       "\n",
       "指标   科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元) 水利、环境和公共设施管理业城镇单位就业人员平均工资(元)  \\\n",
       "2000                            NaN                          NaN   \n",
       "2001                           nan%                         nan%   \n",
       "2002                           nan%                         nan%   \n",
       "2003                           nan%                         nan%   \n",
       "2004                          14.2%                         9.4%   \n",
       "2005                          16.3%                        11.2%   \n",
       "2006                          16.5%                         9.1%   \n",
       "2007                          21.5%                        17.6%   \n",
       "2008                          18.4%                        14.8%   \n",
       "2009                          10.2%                         9.7%   \n",
       "2010                          12.4%                        10.3%   \n",
       "2011                          14.0%                        13.0%   \n",
       "2012                           7.8%                        12.0%   \n",
       "2013                          10.6%                        11.7%   \n",
       "2014                           7.4%                         8.5%   \n",
       "2015                           8.7%                        11.0%   \n",
       "2016                           8.1%                         9.7%   \n",
       "2017                          11.6%                         9.4%   \n",
       "2018                          14.4%                         8.5%   \n",
       "2019                           nan%                         nan%   \n",
       "\n",
       "指标   居民服务和其他服务业城镇单位就业人员平均工资(元) 教育城镇单位就业人员平均工资(元) 卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)  \\\n",
       "2000                       NaN               NaN                          NaN   \n",
       "2001                      nan%              nan%                         nan%   \n",
       "2002                      nan%              nan%                         nan%   \n",
       "2003                      nan%              nan%                         nan%   \n",
       "2004                      8.0%             13.4%                        13.6%   \n",
       "2005                     15.1%             13.5%                        13.2%   \n",
       "2006                     14.5%             14.6%                        13.4%   \n",
       "2007                     13.0%             23.9%                        18.2%   \n",
       "2008                     12.2%             15.1%                        15.4%   \n",
       "2009                     10.1%             15.8%                        10.8%   \n",
       "2010                     12.1%             12.8%                        12.8%   \n",
       "2011                     17.6%             10.8%                        14.8%   \n",
       "2012                      5.9%             10.5%                        13.8%   \n",
       "2013                      9.4%              8.8%                        10.3%   \n",
       "2014                      9.0%              8.9%                         9.1%   \n",
       "2015                      7.0%             17.7%                        13.2%   \n",
       "2016                      6.2%             11.9%                        11.7%   \n",
       "2017                      6.3%             12.0%                        12.0%   \n",
       "2018                      9.5%             10.8%                         9.4%   \n",
       "2019                      nan%              nan%                         nan%   \n",
       "\n",
       "指标   文化、体育和娱乐业城镇单位就业人员平均工资(元) 公共管理和社会组织城镇单位就业人员平均工资(元)  \n",
       "2000                      NaN                      NaN  \n",
       "2001                     nan%                     nan%  \n",
       "2002                     nan%                     nan%  \n",
       "2003                     nan%                     nan%  \n",
       "2004                    20.0%                    13.1%  \n",
       "2005                    10.5%                    16.5%  \n",
       "2006                    14.0%                    11.4%  \n",
       "2007                    17.7%                    23.0%  \n",
       "2008                    12.3%                    16.5%  \n",
       "2009                    10.5%                     9.4%  \n",
       "2010                     9.7%                     8.3%  \n",
       "2011                    15.6%                    10.0%  \n",
       "2012                    11.9%                     9.5%  \n",
       "2013                    10.8%                     6.9%  \n",
       "2014                     8.5%                     7.8%  \n",
       "2015                    13.0%                    17.3%  \n",
       "2016                     9.8%                    13.9%  \n",
       "2017                     9.9%                    13.3%  \n",
       "2018                    12.3%                     9.4%  \n",
       "2019                     nan%                     nan%  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rise = income.copy()\n",
    "for col in rise.columns:\n",
    "    for i in range(len(rise) - 1):\n",
    "        rise[col].iloc[i+1] = '%.1f%%'%((income[col].iloc[i+1]/income[col].iloc[i]-1) * 100)\n",
    "rise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>指标</th>\n",
       "      <th>城镇单位就业人员平均工资(元)</th>\n",
       "      <th>信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>金融业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>制造业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>农、林、牧、渔业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>居民服务和其他服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>水利、环境和公共设施管理业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>租赁和商务服务业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>公共管理和社会组织城镇单位就业人员平均工资(元)</th>\n",
       "      <th>批发和零售业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>建筑业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>文化、体育和娱乐业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>教育城镇单位就业人员平均工资(元)</th>\n",
       "      <th>交通运输、仓储和邮政业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>住宿和餐饮业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>采矿业城镇单位就业人员平均工资(元)</th>\n",
       "      <th>房地产业城镇单位就业人员平均工资(元)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>14.0%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>16.9%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>12.5%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>10.0%</td>\n",
       "      <td>14.2%</td>\n",
       "      <td>13.1%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>20.0%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>14.7%</td>\n",
       "      <td>12.7%</td>\n",
       "      <td>23.1%</td>\n",
       "      <td>8.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>14.3%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>20.3%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>11.8%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>12.2%</td>\n",
       "      <td>14.9%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>10.0%</td>\n",
       "      <td>21.9%</td>\n",
       "      <td>9.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>14.6%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>14.4%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>14.5%</td>\n",
       "      <td>9.1%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>11.4%</td>\n",
       "      <td>16.6%</td>\n",
       "      <td>14.5%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>14.6%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>18.0%</td>\n",
       "      <td>9.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>18.5%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>24.0%</td>\n",
       "      <td>18.2%</td>\n",
       "      <td>16.0%</td>\n",
       "      <td>17.0%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>21.5%</td>\n",
       "      <td>23.0%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>14.3%</td>\n",
       "      <td>17.8%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>23.9%</td>\n",
       "      <td>15.7%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>16.8%</td>\n",
       "      <td>17.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>16.9%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>15.8%</td>\n",
       "      <td>12.2%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>18.4%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>22.5%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>12.3%</td>\n",
       "      <td>15.1%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>13.3%</td>\n",
       "      <td>21.5%</td>\n",
       "      <td>15.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>11.6%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>14.3%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>10.2%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>15.8%</td>\n",
       "      <td>10.2%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>7.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>13.3%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>16.1%</td>\n",
       "      <td>12.8%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>16.4%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>12.4%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>15.4%</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>12.8%</td>\n",
       "      <td>14.6%</td>\n",
       "      <td>12.1%</td>\n",
       "      <td>16.2%</td>\n",
       "      <td>11.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>14.4%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>18.6%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>18.7%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>10.0%</td>\n",
       "      <td>20.9%</td>\n",
       "      <td>16.6%</td>\n",
       "      <td>11.4%</td>\n",
       "      <td>15.6%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>16.3%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>18.2%</td>\n",
       "      <td>19.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>11.9%</td>\n",
       "      <td>13.5%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>16.5%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>13.6%</td>\n",
       "      <td>10.4%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>13.4%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>9.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>10.1%</td>\n",
       "      <td>12.9%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>13.8%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>17.6%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>6.9%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>15.3%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>8.8%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>5.6%</td>\n",
       "      <td>9.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>9.5%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>9.1%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>7.3%</td>\n",
       "      <td>7.4%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>2.6%</td>\n",
       "      <td>8.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>10.1%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>13.2%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>12.7%</td>\n",
       "      <td>7.0%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>17.3%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>6.7%</td>\n",
       "      <td>7.6%</td>\n",
       "      <td>13.0%</td>\n",
       "      <td>17.7%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>-3.7%</td>\n",
       "      <td>8.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>8.9%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>2.3%</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>5.2%</td>\n",
       "      <td>6.2%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>7.0%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>1.9%</td>\n",
       "      <td>8.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10.0%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>6.0%</td>\n",
       "      <td>11.6%</td>\n",
       "      <td>13.3%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>6.7%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>12.0%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>5.5%</td>\n",
       "      <td>14.8%</td>\n",
       "      <td>5.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>10.9%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>5.7%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>11.8%</td>\n",
       "      <td>-0.1%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>14.4%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>13.1%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>10.9%</td>\n",
       "      <td>12.3%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>5.5%</td>\n",
       "      <td>17.2%</td>\n",
       "      <td>8.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>9.8%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "      <td>nan%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "指标   城镇单位就业人员平均工资(元) 信息传输、计算机服务和软件业城镇单位就业人员平均工资(元) 金融业城镇单位就业人员平均工资(元)  \\\n",
       "2004           14.0%                          8.3%              16.9%   \n",
       "2005           14.3%                         16.0%              20.3%   \n",
       "2006           14.6%                         11.9%              21.4%   \n",
       "2007           18.5%                          9.8%              24.0%   \n",
       "2008           16.9%                         15.1%              22.5%   \n",
       "2009           11.6%                          5.9%              12.1%   \n",
       "2010           13.3%                         10.8%              16.1%   \n",
       "2011           14.4%                         10.1%              15.6%   \n",
       "2012           11.9%                         13.5%              10.6%   \n",
       "2013           10.1%                         12.9%              11.0%   \n",
       "2014            9.5%                         10.9%               8.7%   \n",
       "2015           10.1%                         11.1%               6.0%   \n",
       "2016            8.9%                          9.3%               2.3%   \n",
       "2017           10.0%                          8.7%               4.6%   \n",
       "2018           10.9%                         10.9%               5.7%   \n",
       "2019            9.8%                          nan%               nan%   \n",
       "\n",
       "指标   卫生、社会保障和社会福利业城镇单位就业人员平均工资(元) 制造业城镇单位就业人员平均工资(元) 农、林、牧、渔业城镇单位就业人员平均工资(元)  \\\n",
       "2004                        13.6%              12.5%                    8.9%   \n",
       "2005                        13.2%              11.8%                    9.5%   \n",
       "2006                        13.4%              14.4%                   12.9%   \n",
       "2007                        18.2%              16.0%                   17.0%   \n",
       "2008                        15.4%              15.4%                   15.8%   \n",
       "2009                        10.8%               9.9%                   14.3%   \n",
       "2010                        12.8%              15.3%                   16.4%   \n",
       "2011                        14.8%              18.6%                   16.5%   \n",
       "2012                        13.8%              13.6%                   16.5%   \n",
       "2013                        10.3%              11.5%                   13.8%   \n",
       "2014                         9.1%              10.6%                    9.8%   \n",
       "2015                        13.2%               7.7%                   12.7%   \n",
       "2016                        11.7%               7.5%                    5.2%   \n",
       "2017                        12.0%               8.4%                    8.6%   \n",
       "2018                         9.4%              11.8%                   -0.1%   \n",
       "2019                         nan%               nan%                    nan%   \n",
       "\n",
       "指标   居民服务和其他服务业城镇单位就业人员平均工资(元) 水利、环境和公共设施管理业城镇单位就业人员平均工资(元)  \\\n",
       "2004                      8.0%                         9.4%   \n",
       "2005                     15.1%                        11.2%   \n",
       "2006                     14.5%                         9.1%   \n",
       "2007                     13.0%                        17.6%   \n",
       "2008                     12.2%                        14.8%   \n",
       "2009                     10.1%                         9.7%   \n",
       "2010                     12.1%                        10.3%   \n",
       "2011                     17.6%                        13.0%   \n",
       "2012                      5.9%                        12.0%   \n",
       "2013                      9.4%                        11.7%   \n",
       "2014                      9.0%                         8.5%   \n",
       "2015                      7.0%                        11.0%   \n",
       "2016                      6.2%                         9.7%   \n",
       "2017                      6.3%                         9.4%   \n",
       "2018                      9.5%                         8.5%   \n",
       "2019                      nan%                         nan%   \n",
       "\n",
       "指标   租赁和商务服务业城镇单位就业人员平均工资(元) 科学研究、技术服务和地质勘查业城镇单位就业人员平均工资(元)  \\\n",
       "2004                   10.0%                          14.2%   \n",
       "2005                   13.4%                          16.3%   \n",
       "2006                   15.4%                          16.5%   \n",
       "2007                   13.5%                          21.5%   \n",
       "2008                   18.4%                          18.4%   \n",
       "2009                    7.8%                          10.2%   \n",
       "2010                   11.5%                          12.4%   \n",
       "2011                   18.7%                          14.0%   \n",
       "2012                   13.2%                           7.8%   \n",
       "2013                   17.6%                          10.6%   \n",
       "2014                    7.3%                           7.4%   \n",
       "2015                    8.0%                           8.7%   \n",
       "2016                    5.9%                           8.1%   \n",
       "2017                    6.0%                          11.6%   \n",
       "2018                    4.6%                          14.4%   \n",
       "2019                    nan%                           nan%   \n",
       "\n",
       "指标   公共管理和社会组织城镇单位就业人员平均工资(元) 批发和零售业城镇单位就业人员平均工资(元) 建筑业城镇单位就业人员平均工资(元)  \\\n",
       "2004                    13.1%                 19.4%              11.0%   \n",
       "2005                    16.5%                 17.2%              12.2%   \n",
       "2006                    11.4%                 16.6%              14.5%   \n",
       "2007                    23.0%                 18.4%              14.3%   \n",
       "2008                    16.5%                 22.5%              14.8%   \n",
       "2009                     9.4%                 12.9%              13.8%   \n",
       "2010                     8.3%                 15.4%              13.9%   \n",
       "2011                    10.0%                 20.9%              16.6%   \n",
       "2012                     9.5%                 14.0%              13.6%   \n",
       "2013                     6.9%                  8.6%              15.3%   \n",
       "2014                     7.8%                 11.0%               8.9%   \n",
       "2015                    17.3%                  8.0%               6.7%   \n",
       "2016                    13.9%                  7.8%               6.5%   \n",
       "2017                    13.3%                  9.4%               6.7%   \n",
       "2018                     9.4%                 13.1%               8.9%   \n",
       "2019                     nan%                  nan%               nan%   \n",
       "\n",
       "指标   电力、燃气及水的生产和供应业城镇单位就业人员平均工资(元) 文化、体育和娱乐业城镇单位就业人员平均工资(元) 教育城镇单位就业人员平均工资(元)  \\\n",
       "2004                         16.0%                    20.0%             13.4%   \n",
       "2005                         14.9%                    10.5%             13.5%   \n",
       "2006                         14.8%                    14.0%             14.6%   \n",
       "2007                         17.8%                    17.7%             23.9%   \n",
       "2008                         15.1%                    12.3%             15.1%   \n",
       "2009                          8.7%                    10.5%             15.8%   \n",
       "2010                         13.0%                     9.7%             12.8%   \n",
       "2011                         11.4%                    15.6%             10.8%   \n",
       "2012                         10.4%                    11.9%             10.5%   \n",
       "2013                         15.3%                    10.8%              8.8%   \n",
       "2014                          9.3%                     8.5%              8.9%   \n",
       "2015                          7.6%                    13.0%             17.7%   \n",
       "2016                          6.3%                     9.8%             11.9%   \n",
       "2017                          7.7%                     9.9%             12.0%   \n",
       "2018                         10.9%                    12.3%             10.8%   \n",
       "2019                          nan%                     nan%              nan%   \n",
       "\n",
       "指标   交通运输、仓储和邮政业城镇单位就业人员平均工资(元) 住宿和餐饮业城镇单位就业人员平均工资(元) 采矿业城镇单位就业人员平均工资(元)  \\\n",
       "2004                      14.7%                 12.7%              23.1%   \n",
       "2005                      15.7%                 10.0%              21.9%   \n",
       "2006                      15.3%                  9.8%              18.0%   \n",
       "2007                      15.7%                 11.9%              16.8%   \n",
       "2008                      14.8%                 13.3%              21.5%   \n",
       "2009                      10.2%                  8.0%              11.1%   \n",
       "2010                      14.6%                 12.1%              16.2%   \n",
       "2011                      16.3%                 17.6%              18.2%   \n",
       "2012                      13.4%                 13.8%               9.0%   \n",
       "2013                       8.6%                  8.9%               5.6%   \n",
       "2014                       9.4%                  9.5%               2.6%   \n",
       "2015                       8.5%                  9.5%              -3.7%   \n",
       "2016                       7.0%                  6.3%               1.9%   \n",
       "2017                       8.9%                  5.5%              14.8%   \n",
       "2018                      10.3%                  5.5%              17.2%   \n",
       "2019                       nan%                  nan%               nan%   \n",
       "\n",
       "指标   房地产业城镇单位就业人员平均工资(元)  \n",
       "2004                8.1%  \n",
       "2005                9.7%  \n",
       "2006                9.8%  \n",
       "2007               17.3%  \n",
       "2008               15.5%  \n",
       "2009                7.1%  \n",
       "2010               11.3%  \n",
       "2011               19.4%  \n",
       "2012                9.2%  \n",
       "2013                9.2%  \n",
       "2014                8.9%  \n",
       "2015                8.4%  \n",
       "2016                8.7%  \n",
       "2017                5.8%  \n",
       "2018                8.7%  \n",
       "2019                nan%  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior_cols = ['城镇单位就业人员平均工资(元)',\n",
    "              '信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)',\n",
    "                              '金融业城镇单位就业人员平均工资(元)',\n",
    "              '卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)',\n",
    "                             '制造业城镇单位就业人员平均工资(元)',\n",
    "                             '农、林、牧、渔业城镇单位就业人员平均工资(元)']\n",
    "prior_cols.extend(list(set(rise.columns) - set(prior_cols)))\n",
    "rise.loc[rise.index >='2004',prior_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       城镇单位就业人员平均工资(元)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>2019</th>\n",
       "      <th>2018</th>\n",
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       "      <td>100845.0</td>\n",
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       "      <td>80510.0</td>\n",
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       "      <td>96638.0</td>\n",
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       "      <td>69254.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>100162.0</td>\n",
       "      <td>90348.0</td>\n",
       "      <td>83863.0</td>\n",
       "      <td>78886.0</td>\n",
       "      <td>73339.0</td>\n",
       "      <td>67085.0</td>\n",
       "      <td>58202.0</td>\n",
       "      <td>52723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卫生、社会保障和社会福利业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>98118.0</td>\n",
       "      <td>89648.0</td>\n",
       "      <td>80026.0</td>\n",
       "      <td>71624.0</td>\n",
       "      <td>63267.0</td>\n",
       "      <td>57979.0</td>\n",
       "      <td>52564.0</td>\n",
       "      <td>46206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文化、体育和娱乐业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>98621.0</td>\n",
       "      <td>87803.0</td>\n",
       "      <td>79875.0</td>\n",
       "      <td>72764.0</td>\n",
       "      <td>64375.0</td>\n",
       "      <td>59336.0</td>\n",
       "      <td>53558.0</td>\n",
       "      <td>47878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>教育</th>\n",
       "      <td>NaN</td>\n",
       "      <td>92383.0</td>\n",
       "      <td>83412.0</td>\n",
       "      <td>74498.0</td>\n",
       "      <td>66592.0</td>\n",
       "      <td>56580.0</td>\n",
       "      <td>51950.0</td>\n",
       "      <td>47734.0</td>\n",
       "      <td>43194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>租赁和商务服务业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>85147.0</td>\n",
       "      <td>81393.0</td>\n",
       "      <td>76782.0</td>\n",
       "      <td>72489.0</td>\n",
       "      <td>67131.0</td>\n",
       "      <td>62538.0</td>\n",
       "      <td>53162.0</td>\n",
       "      <td>46976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>公共管理和社会组织</th>\n",
       "      <td>NaN</td>\n",
       "      <td>87932.0</td>\n",
       "      <td>80372.0</td>\n",
       "      <td>70959.0</td>\n",
       "      <td>62323.0</td>\n",
       "      <td>53110.0</td>\n",
       "      <td>49259.0</td>\n",
       "      <td>46074.0</td>\n",
       "      <td>42062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>交通运输、仓储和邮政业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>88508.0</td>\n",
       "      <td>80225.0</td>\n",
       "      <td>73650.0</td>\n",
       "      <td>68822.0</td>\n",
       "      <td>63416.0</td>\n",
       "      <td>57993.0</td>\n",
       "      <td>53391.0</td>\n",
       "      <td>47078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>全行业</th>\n",
       "      <td>90501.0</td>\n",
       "      <td>82413.0</td>\n",
       "      <td>74318.0</td>\n",
       "      <td>67569.0</td>\n",
       "      <td>62029.0</td>\n",
       "      <td>56360.0</td>\n",
       "      <td>51483.0</td>\n",
       "      <td>46769.0</td>\n",
       "      <td>41799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>批发和零售业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>80551.0</td>\n",
       "      <td>71201.0</td>\n",
       "      <td>65061.0</td>\n",
       "      <td>60328.0</td>\n",
       "      <td>55838.0</td>\n",
       "      <td>50308.0</td>\n",
       "      <td>46340.0</td>\n",
       "      <td>40654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>采矿业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>81429.0</td>\n",
       "      <td>69500.0</td>\n",
       "      <td>60544.0</td>\n",
       "      <td>59404.0</td>\n",
       "      <td>61677.0</td>\n",
       "      <td>60138.0</td>\n",
       "      <td>56946.0</td>\n",
       "      <td>52230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>房地产业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75281.0</td>\n",
       "      <td>69277.0</td>\n",
       "      <td>65497.0</td>\n",
       "      <td>60244.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>51048.0</td>\n",
       "      <td>46764.0</td>\n",
       "      <td>42837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>制造业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>72088.0</td>\n",
       "      <td>64452.0</td>\n",
       "      <td>59470.0</td>\n",
       "      <td>55324.0</td>\n",
       "      <td>51369.0</td>\n",
       "      <td>46431.0</td>\n",
       "      <td>41650.0</td>\n",
       "      <td>36665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>建筑业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60501.0</td>\n",
       "      <td>55568.0</td>\n",
       "      <td>52082.0</td>\n",
       "      <td>48886.0</td>\n",
       "      <td>45804.0</td>\n",
       "      <td>42072.0</td>\n",
       "      <td>36483.0</td>\n",
       "      <td>32103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>水利、环境和公共设施管理业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>56670.0</td>\n",
       "      <td>52229.0</td>\n",
       "      <td>47750.0</td>\n",
       "      <td>43528.0</td>\n",
       "      <td>39198.0</td>\n",
       "      <td>36123.0</td>\n",
       "      <td>32343.0</td>\n",
       "      <td>28868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>居民服务和其他服务业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>55343.0</td>\n",
       "      <td>50552.0</td>\n",
       "      <td>47577.0</td>\n",
       "      <td>44802.0</td>\n",
       "      <td>41882.0</td>\n",
       "      <td>38429.0</td>\n",
       "      <td>35135.0</td>\n",
       "      <td>33169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>住宿和餐饮业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48260.0</td>\n",
       "      <td>45751.0</td>\n",
       "      <td>43382.0</td>\n",
       "      <td>40806.0</td>\n",
       "      <td>37264.0</td>\n",
       "      <td>34044.0</td>\n",
       "      <td>31267.0</td>\n",
       "      <td>27486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>农、林、牧、渔业</th>\n",
       "      <td>NaN</td>\n",
       "      <td>36466.0</td>\n",
       "      <td>36504.0</td>\n",
       "      <td>33612.0</td>\n",
       "      <td>31947.0</td>\n",
       "      <td>28356.0</td>\n",
       "      <td>25820.0</td>\n",
       "      <td>22687.0</td>\n",
       "      <td>19469.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2019      2018      2017      2016      2015      2014  \\\n",
       "信息传输、计算机服务和软件业       NaN  147678.0  133150.0  122478.0  112042.0  100845.0   \n",
       "金融业                  NaN  129837.0  122851.0  117418.0  114777.0  108273.0   \n",
       "科学研究、技术服务和地质勘查业      NaN  123343.0  107815.0   96638.0   89410.0   82259.0   \n",
       "电力、燃气及水的生产和供应业       NaN  100162.0   90348.0   83863.0   78886.0   73339.0   \n",
       "卫生、社会保障和社会福利业        NaN   98118.0   89648.0   80026.0   71624.0   63267.0   \n",
       "文化、体育和娱乐业            NaN   98621.0   87803.0   79875.0   72764.0   64375.0   \n",
       "教育                   NaN   92383.0   83412.0   74498.0   66592.0   56580.0   \n",
       "租赁和商务服务业             NaN   85147.0   81393.0   76782.0   72489.0   67131.0   \n",
       "公共管理和社会组织            NaN   87932.0   80372.0   70959.0   62323.0   53110.0   \n",
       "交通运输、仓储和邮政业          NaN   88508.0   80225.0   73650.0   68822.0   63416.0   \n",
       "全行业              90501.0   82413.0   74318.0   67569.0   62029.0   56360.0   \n",
       "批发和零售业               NaN   80551.0   71201.0   65061.0   60328.0   55838.0   \n",
       "采矿业                  NaN   81429.0   69500.0   60544.0   59404.0   61677.0   \n",
       "房地产业                 NaN   75281.0   69277.0   65497.0   60244.0   55568.0   \n",
       "制造业                  NaN   72088.0   64452.0   59470.0   55324.0   51369.0   \n",
       "建筑业                  NaN   60501.0   55568.0   52082.0   48886.0   45804.0   \n",
       "水利、环境和公共设施管理业        NaN   56670.0   52229.0   47750.0   43528.0   39198.0   \n",
       "居民服务和其他服务业           NaN   55343.0   50552.0   47577.0   44802.0   41882.0   \n",
       "住宿和餐饮业               NaN   48260.0   45751.0   43382.0   40806.0   37264.0   \n",
       "农、林、牧、渔业             NaN   36466.0   36504.0   33612.0   31947.0   28356.0   \n",
       "\n",
       "                    2013     2012     2011  \n",
       "信息传输、计算机服务和软件业   90915.0  80510.0  70918.0  \n",
       "金融业              99653.0  89743.0  81109.0  \n",
       "科学研究、技术服务和地质勘查业  76602.0  69254.0  64252.0  \n",
       "电力、燃气及水的生产和供应业   67085.0  58202.0  52723.0  \n",
       "卫生、社会保障和社会福利业    57979.0  52564.0  46206.0  \n",
       "文化、体育和娱乐业        59336.0  53558.0  47878.0  \n",
       "教育               51950.0  47734.0  43194.0  \n",
       "租赁和商务服务业         62538.0  53162.0  46976.0  \n",
       "公共管理和社会组织        49259.0  46074.0  42062.0  \n",
       "交通运输、仓储和邮政业      57993.0  53391.0  47078.0  \n",
       "全行业              51483.0  46769.0  41799.0  \n",
       "批发和零售业           50308.0  46340.0  40654.0  \n",
       "采矿业              60138.0  56946.0  52230.0  \n",
       "房地产业             51048.0  46764.0  42837.0  \n",
       "制造业              46431.0  41650.0  36665.0  \n",
       "建筑业              42072.0  36483.0  32103.0  \n",
       "水利、环境和公共设施管理业    36123.0  32343.0  28868.0  \n",
       "居民服务和其他服务业       38429.0  35135.0  33169.0  \n",
       "住宿和餐饮业           34044.0  31267.0  27486.0  \n",
       "农、林、牧、渔业         25820.0  22687.0  19469.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_t = income.loc[income.index>='2011']\n",
    "income_t = income_t.transpose()\n",
    "print(\" \"*22,'城镇单位就业人员平均工资(元)')\n",
    "income_t.index = [x[:x.find('城镇单位就业人员平均工资(元)')] for x in income_t.index]\n",
    "income_t.index = [\"全行业\" if x == \"\" else x for x in income_t.index]\n",
    "income_t.sort_values('2017', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.062279888785913 4.779687348286242 98118.0 147678.0\n"
     ]
    }
   ],
   "source": [
    "income_yisheng = income['卫生、社会保障和社会福利业城镇单位就业人员平均工资(元)']\n",
    "income_IT = income['信息传输、计算机服务和软件业城镇单位就业人员平均工资(元)']\n",
    "start_year = 2003\n",
    "end_year = 2018\n",
    "#display(income_yisheng)\n",
    "yishengRate = income_yisheng[str(end_year)]/income_yisheng[str(start_year)]\n",
    "ITRate =  income_IT[str(end_year)]/income_IT[str(start_year)]\n",
    "yishengBase = income_yisheng[str(end_year)]\n",
    "ITBase = income_IT[str(end_year)]\n",
    "print(yishengRate, ITRate, yishengBase, ITBase)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2044\n",
      "2016.3 2053.7 -188513.03737050918 6117369.784780692 2020.04 2964428.373705092\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from matplotlib.offsetbox import TextArea, DrawingArea, OffsetImage, AnnotationBbox\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "'''\n",
    "yishengRate = 2.228276 \n",
    "ITRate = 2.066391\n",
    "yishengBase = 89648.0\n",
    "ITBase = 133150.0\n",
    "'''\n",
    "yishengYearRate = yishengRate**(1/(end_year- start_year))-1\n",
    "ITYearRate = ITRate**(1/(end_year- start_year))-1\n",
    "yishengYearRateStr, ITYearRateStr =  \"%.2f%%\"%(100*yishengYearRate), \"%.2f%%\"%(100*ITYearRate)\n",
    "def f(base, rate, year):\n",
    "    return base * rate **(year/(end_year-start_year))\n",
    "count = 35\n",
    "yisheng = [f(yishengBase, yishengRate, n) for n in range(count)]\n",
    "IT = [f(ITBase, ITRate, n) for n in range(count)]\n",
    "year = [end_year + n for n in range(count)]\n",
    "cross_year = 'inf'\n",
    "for i in range(len(year)):\n",
    "    if yisheng[i]>IT[i]:\n",
    "        print(year[i])\n",
    "        cross_year = year[i]        \n",
    "        break\n",
    "font=FontProperties(fname='font/Songti.ttc',size=18)\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "matplotlib.rc('font', size=18)\n",
    "matplotlib.rcParams['figure.figsize'] = [15, 10]\n",
    "plt.plot(year, yisheng)\n",
    "plt.plot(year, IT)\n",
    "plt.legend(['医疗卫生行业收入预测', 'IT行业收入预测'],  prop = font)\n",
    "#插入二维码\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "qrcode = mpimg.imread('wechatqrcode.png')\n",
    "imagebox = OffsetImage(qrcode, zoom=0.5)\n",
    "x_pos = (x1 + 0.1*(x2 - x1))\n",
    "y_pos = (y1 + 0.5*(y2-y1))\n",
    "ab = AnnotationBbox(imagebox, (x_pos, y_pos ))\n",
    "plt.gca().add_artist(ab)\n",
    "print(x1, x2, y1, y2, x_pos, y_pos)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "variables": {
     "\"%.2f%%\"%(100*ITYearRate)": "10.99%",
     "ITBase": "147678.0",
     "cross_year": "2044",
     "end_year": "2018",
     "income_IT[str(start_year)]": "30897.0",
     "income_yisheng[str(start_year)]": "16185.0",
     "start_year": "2003",
     "yishengBase": "98118.0",
     "yishengYearRateStr": "12.77%"
    }
   },
   "source": [
    "# 医疗行业收入什么时候超过IT\n",
    "在美国等发达国家，医疗行业收入是比IT要高的。咱们来分析一下中国的情况。\n",
    "根据 {{start_year}} 年到{{end_year}}年的涨幅，以及{{end_year}}年的基数预测，将来多少年之后医疗可以赶上IT。结论是大概{{cross_year}}年。\n",
    "- {{start_year}}, {{end_year}} 医疗行业平均分别收入为{{income_yisheng[str(start_year)]}}, {{yishengBase}}, IT行业分别平均收入为{{income_IT[str(start_year)]}}, {{ITBase}}\n",
    "- 从{{start_year}}到{{end_year}}, 医疗行业收入复合年增长率为{{yishengYearRateStr}}, IT行业收入复合年增长率为{{\"%.2f%%\"%(100*ITYearRate)}}\n",
    "- 收入数据来自国家统计局官网 www.stats.gov.cn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}