{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "中国以生产法与收入法来核算GDP。生产法就是算各行业的added value加总。分类是根据《国民经济行业分类标准》来核算，但季度频率上无法获取全部行业的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df=pd.read_excel('China Annual GDP.xls')\n",
    "df.insert(loc = 0, column = 'Year', value = range(1978,2020))\n",
    "df.drop(['指标'], axis=1, inplace = True)\n",
    "df.set_index('Year', inplace = True) \n",
    "df.set_index(pd.to_datetime(df.index, format = '%Y')) # convert integer index to timestamp\n",
    "pd.set_option('display.max_columns', None) # show all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>GDP(亿元)</th>\n",
       "      <th>GDP(可比价格，亿元)</th>\n",
       "      <th>GDP增长指数(上年＝100)</th>\n",
       "      <th>GDP增长指数(1978年＝100)</th>\n",
       "      <th>GDP增长指数(1990年＝100)</th>\n",
       "      <th>GDP增长指数(2000年＝100)</th>\n",
       "      <th>第一产业增加值</th>\n",
       "      <th>第一产业增加值(可比价格)</th>\n",
       "      <th>第一产业增加值增长指数(上年＝100)</th>\n",
       "      <th>第一产业增加值增长指数(1978年＝100)</th>\n",
       "      <th>第一产业增加值增长指数(1990年＝100)</th>\n",
       "      <th>第一产业增加值增长指数(2000年＝100)</th>\n",
       "      <th>第二产业增加值</th>\n",
       "      <th>第二产业增加值(可比价格)</th>\n",
       "      <th>第二产业增加值增长指数(上年＝100)</th>\n",
       "      <th>第二产业增加值增长指数(1978年＝100)</th>\n",
       "      <th>第二产业增加值增长指数(1990年＝100)</th>\n",
       "      <th>第二产业增加值增长指数(2000年＝100)</th>\n",
       "      <th>第三产业增加值</th>\n",
       "      <th>第三产业增加值(可比价格)</th>\n",
       "      <th>第三产业增加值增长指数(上年＝100)</th>\n",
       "      <th>第三产业增加值增长指数(1978年＝100)</th>\n",
       "      <th>第三产业增加值增长指数(1990年＝100)</th>\n",
       "      <th>第三产业增加值增长指数(2000年＝100)</th>\n",
       "      <th>人均GDP(元)</th>\n",
       "      <th>人均GDP增长指数(上年＝100)</th>\n",
       "      <th>人均GDP增长指数(1978年＝100)</th>\n",
       "      <th>国民总收入</th>\n",
       "      <th>国民总收入指数(上年=100)</th>\n",
       "      <th>国民总收入指数(1978年=100)</th>\n",
       "      <th>行业增加值_农、林、牧、渔业</th>\n",
       "      <th>工业增加值</th>\n",
       "      <th>行业增加值_建筑业</th>\n",
       "      <th>行业增加值_批发和零售业</th>\n",
       "      <th>行业增加值_交通运输、仓储和邮政业</th>\n",
       "      <th>行业增加值_住宿和餐饮业</th>\n",
       "      <th>行业增加值_金融业</th>\n",
       "      <th>行业增加值_房地产业</th>\n",
       "      <th>第一产业增加值占GDP比重</th>\n",
       "      <th>第二产业增加值占GDP比重</th>\n",
       "      <th>第三产业增加值占GDP比重</th>\n",
       "      <th>农、林、牧、渔业增加值占GDP比重</th>\n",
       "      <th>工业增加值占GDP比重</th>\n",
       "      <th>建筑业增加值占GDP比重</th>\n",
       "      <th>批发和零售业增加值占GDP比重</th>\n",
       "      <th>交通运输、仓储和邮政业增加值占GDP比重</th>\n",
       "      <th>住宿和餐饮业增加值占GDP比重</th>\n",
       "      <th>金融业增加值占GDP比重</th>\n",
       "      <th>房地产业增加值占GDP比重</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_农、林、牧、渔业</th>\n",
       "      <th>工业增加值增长指数(上年＝100)</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_建筑业</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_批发和零售业</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_交通运输、仓储和邮政业</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_住宿和餐饮业</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_金融业</th>\n",
       "      <th>行业增加值增长指数(上年＝100)_房地产业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_农、林、牧、渔业</th>\n",
       "      <th>工业增加值增长指数(1978年＝100)</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_建筑业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_批发和零售业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_交通运输、仓储和邮政业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_住宿和餐饮业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_金融业</th>\n",
       "      <th>行业增加值增长指数(1978年＝100)_房地产业</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1978</td>\n",
       "      <td>3678.7</td>\n",
       "      <td>3593.0</td>\n",
       "      <td>111.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.5</td>\n",
       "      <td>927.8</td>\n",
       "      <td>104.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1755.1</td>\n",
       "      <td>1776.5</td>\n",
       "      <td>115.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>905.1</td>\n",
       "      <td>888.8</td>\n",
       "      <td>113.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>385</td>\n",
       "      <td>110.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3678.7</td>\n",
       "      <td>111.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1027.5</td>\n",
       "      <td>1621.4</td>\n",
       "      <td>138.9</td>\n",
       "      <td>242.4</td>\n",
       "      <td>182.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>76.5</td>\n",
       "      <td>79.9</td>\n",
       "      <td>27.69</td>\n",
       "      <td>47.71</td>\n",
       "      <td>24.6</td>\n",
       "      <td>27.9</td>\n",
       "      <td>44.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>104.1</td>\n",
       "      <td>116.4</td>\n",
       "      <td>99.5</td>\n",
       "      <td>123.1</td>\n",
       "      <td>108.9</td>\n",
       "      <td>118.1</td>\n",
       "      <td>110.1</td>\n",
       "      <td>105.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1979</td>\n",
       "      <td>4100.5</td>\n",
       "      <td>3865.8</td>\n",
       "      <td>107.6</td>\n",
       "      <td>107.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1259.0</td>\n",
       "      <td>984.7</td>\n",
       "      <td>106.1</td>\n",
       "      <td>106.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1925.3</td>\n",
       "      <td>1922.6</td>\n",
       "      <td>108.2</td>\n",
       "      <td>108.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>916.1</td>\n",
       "      <td>958.5</td>\n",
       "      <td>107.8</td>\n",
       "      <td>107.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>423</td>\n",
       "      <td>106.2</td>\n",
       "      <td>106.2</td>\n",
       "      <td>4100.5</td>\n",
       "      <td>107.6</td>\n",
       "      <td>107.6</td>\n",
       "      <td>1270.2</td>\n",
       "      <td>1786.5</td>\n",
       "      <td>144.6</td>\n",
       "      <td>200.9</td>\n",
       "      <td>193.7</td>\n",
       "      <td>44.0</td>\n",
       "      <td>75.9</td>\n",
       "      <td>86.3</td>\n",
       "      <td>30.70</td>\n",
       "      <td>47.00</td>\n",
       "      <td>22.3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>106.1</td>\n",
       "      <td>108.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>108.3</td>\n",
       "      <td>111.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>104.1</td>\n",
       "      <td>106.1</td>\n",
       "      <td>108.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>108.3</td>\n",
       "      <td>111.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>104.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1980</td>\n",
       "      <td>4587.6</td>\n",
       "      <td>4587.6</td>\n",
       "      <td>107.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1359.5</td>\n",
       "      <td>1359.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>104.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2204.7</td>\n",
       "      <td>2204.7</td>\n",
       "      <td>113.5</td>\n",
       "      <td>122.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.4</td>\n",
       "      <td>1023.4</td>\n",
       "      <td>106.1</td>\n",
       "      <td>114.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>468</td>\n",
       "      <td>106.5</td>\n",
       "      <td>113.1</td>\n",
       "      <td>4587.6</td>\n",
       "      <td>107.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1371.6</td>\n",
       "      <td>2014.8</td>\n",
       "      <td>196.3</td>\n",
       "      <td>193.8</td>\n",
       "      <td>213.4</td>\n",
       "      <td>47.4</td>\n",
       "      <td>85.8</td>\n",
       "      <td>96.4</td>\n",
       "      <td>29.60</td>\n",
       "      <td>48.10</td>\n",
       "      <td>22.3</td>\n",
       "      <td>29.9</td>\n",
       "      <td>43.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>98.5</td>\n",
       "      <td>112.6</td>\n",
       "      <td>126.6</td>\n",
       "      <td>98.1</td>\n",
       "      <td>104.3</td>\n",
       "      <td>103.9</td>\n",
       "      <td>107.3</td>\n",
       "      <td>107.9</td>\n",
       "      <td>104.6</td>\n",
       "      <td>122.4</td>\n",
       "      <td>129.2</td>\n",
       "      <td>106.7</td>\n",
       "      <td>112.9</td>\n",
       "      <td>115.5</td>\n",
       "      <td>105.2</td>\n",
       "      <td>112.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1981</td>\n",
       "      <td>4935.8</td>\n",
       "      <td>4822.1</td>\n",
       "      <td>105.1</td>\n",
       "      <td>122.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1545.7</td>\n",
       "      <td>1454.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2269.1</td>\n",
       "      <td>2246.3</td>\n",
       "      <td>101.9</td>\n",
       "      <td>125.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1121.1</td>\n",
       "      <td>1121.5</td>\n",
       "      <td>109.6</td>\n",
       "      <td>125.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>497</td>\n",
       "      <td>103.8</td>\n",
       "      <td>117.3</td>\n",
       "      <td>4933.7</td>\n",
       "      <td>105.1</td>\n",
       "      <td>121.9</td>\n",
       "      <td>1559.4</td>\n",
       "      <td>2067.7</td>\n",
       "      <td>208.0</td>\n",
       "      <td>231.2</td>\n",
       "      <td>220.8</td>\n",
       "      <td>54.1</td>\n",
       "      <td>91.6</td>\n",
       "      <td>99.9</td>\n",
       "      <td>31.30</td>\n",
       "      <td>46.00</td>\n",
       "      <td>22.7</td>\n",
       "      <td>31.6</td>\n",
       "      <td>41.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>101.7</td>\n",
       "      <td>103.2</td>\n",
       "      <td>129.5</td>\n",
       "      <td>101.9</td>\n",
       "      <td>117.5</td>\n",
       "      <td>104.7</td>\n",
       "      <td>96.5</td>\n",
       "      <td>111.9</td>\n",
       "      <td>124.5</td>\n",
       "      <td>133.3</td>\n",
       "      <td>138.2</td>\n",
       "      <td>115.0</td>\n",
       "      <td>135.6</td>\n",
       "      <td>110.2</td>\n",
       "      <td>108.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1982</td>\n",
       "      <td>5373.4</td>\n",
       "      <td>5257.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>132.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1761.7</td>\n",
       "      <td>1622.1</td>\n",
       "      <td>111.5</td>\n",
       "      <td>124.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2397.6</td>\n",
       "      <td>2371.5</td>\n",
       "      <td>105.6</td>\n",
       "      <td>132.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1214.0</td>\n",
       "      <td>1263.4</td>\n",
       "      <td>112.7</td>\n",
       "      <td>141.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>533</td>\n",
       "      <td>107.4</td>\n",
       "      <td>126.0</td>\n",
       "      <td>5380.5</td>\n",
       "      <td>109.2</td>\n",
       "      <td>133.1</td>\n",
       "      <td>1777.3</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>221.6</td>\n",
       "      <td>171.5</td>\n",
       "      <td>246.9</td>\n",
       "      <td>62.3</td>\n",
       "      <td>130.6</td>\n",
       "      <td>110.8</td>\n",
       "      <td>32.80</td>\n",
       "      <td>44.60</td>\n",
       "      <td>22.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>111.5</td>\n",
       "      <td>105.8</td>\n",
       "      <td>103.4</td>\n",
       "      <td>99.3</td>\n",
       "      <td>111.4</td>\n",
       "      <td>131.6</td>\n",
       "      <td>143.1</td>\n",
       "      <td>109.1</td>\n",
       "      <td>124.8</td>\n",
       "      <td>131.7</td>\n",
       "      <td>137.9</td>\n",
       "      <td>137.2</td>\n",
       "      <td>128.1</td>\n",
       "      <td>178.5</td>\n",
       "      <td>157.6</td>\n",
       "      <td>118.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GDP(亿元)  GDP(可比价格，亿元)  GDP增长指数(上年＝100)  GDP增长指数(1978年＝100)  \\\n",
       "Year                                                               \n",
       "1978   3678.7        3593.0            111.7               100.0   \n",
       "1979   4100.5        3865.8            107.6               107.6   \n",
       "1980   4587.6        4587.6            107.8               116.0   \n",
       "1981   4935.8        4822.1            105.1               122.0   \n",
       "1982   5373.4        5257.0            109.0               132.9   \n",
       "\n",
       "      GDP增长指数(1990年＝100)  GDP增长指数(2000年＝100)  第一产业增加值  第一产业增加值(可比价格)  \\\n",
       "Year                                                                   \n",
       "1978                 NaN                 NaN   1018.5          927.8   \n",
       "1979                 NaN                 NaN   1259.0          984.7   \n",
       "1980                 NaN                 NaN   1359.5         1359.5   \n",
       "1981                 NaN                 NaN   1545.7         1454.4   \n",
       "1982                 NaN                 NaN   1761.7         1622.1   \n",
       "\n",
       "      第一产业增加值增长指数(上年＝100)  第一产业增加值增长指数(1978年＝100)  第一产业增加值增长指数(1990年＝100)  \\\n",
       "Year                                                                        \n",
       "1978                104.1                   100.0                     NaN   \n",
       "1979                106.1                   106.1                     NaN   \n",
       "1980                 98.5                   104.6                     NaN   \n",
       "1981                107.0                   111.9                     NaN   \n",
       "1982                111.5                   124.8                     NaN   \n",
       "\n",
       "      第一产业增加值增长指数(2000年＝100)  第二产业增加值  第二产业增加值(可比价格)  第二产业增加值增长指数(上年＝100)  \\\n",
       "Year                                                                        \n",
       "1978                     NaN   1755.1         1776.5                115.0   \n",
       "1979                     NaN   1925.3         1922.6                108.2   \n",
       "1980                     NaN   2204.7         2204.7                113.5   \n",
       "1981                     NaN   2269.1         2246.3                101.9   \n",
       "1982                     NaN   2397.6         2371.5                105.6   \n",
       "\n",
       "      第二产业增加值增长指数(1978年＝100)  第二产业增加值增长指数(1990年＝100)  第二产业增加值增长指数(2000年＝100)  \\\n",
       "Year                                                                           \n",
       "1978                   100.0                     NaN                     NaN   \n",
       "1979                   108.2                     NaN                     NaN   \n",
       "1980                   122.8                     NaN                     NaN   \n",
       "1981                   125.1                     NaN                     NaN   \n",
       "1982                   132.1                     NaN                     NaN   \n",
       "\n",
       "      第三产业增加值  第三产业增加值(可比价格)  第三产业增加值增长指数(上年＝100)  第三产业增加值增长指数(1978年＝100)  \\\n",
       "Year                                                                        \n",
       "1978    905.1          888.8                113.6                   100.0   \n",
       "1979    916.1          958.5                107.8                   107.8   \n",
       "1980   1023.4         1023.4                106.1                   114.4   \n",
       "1981   1121.1         1121.5                109.6                   125.3   \n",
       "1982   1214.0         1263.4                112.7                   141.2   \n",
       "\n",
       "      第三产业增加值增长指数(1990年＝100)  第三产业增加值增长指数(2000年＝100)  人均GDP(元)  \\\n",
       "Year                                                             \n",
       "1978                     NaN                     NaN       385   \n",
       "1979                     NaN                     NaN       423   \n",
       "1980                     NaN                     NaN       468   \n",
       "1981                     NaN                     NaN       497   \n",
       "1982                     NaN                     NaN       533   \n",
       "\n",
       "      人均GDP增长指数(上年＝100)  人均GDP增长指数(1978年＝100)   国民总收入  国民总收入指数(上年=100)  \\\n",
       "Year                                                                     \n",
       "1978              110.2                 100.0  3678.7            111.7   \n",
       "1979              106.2                 106.2  4100.5            107.6   \n",
       "1980              106.5                 113.1  4587.6            107.8   \n",
       "1981              103.8                 117.3  4933.7            105.1   \n",
       "1982              107.4                 126.0  5380.5            109.2   \n",
       "\n",
       "      国民总收入指数(1978年=100)  行业增加值_农、林、牧、渔业   工业增加值  行业增加值_建筑业  行业增加值_批发和零售业  \\\n",
       "Year                                                                        \n",
       "1978               100.0          1027.5  1621.4      138.9         242.4   \n",
       "1979               107.6          1270.2  1786.5      144.6         200.9   \n",
       "1980               116.0          1371.6  2014.8      196.3         193.8   \n",
       "1981               121.9          1559.4  2067.7      208.0         231.2   \n",
       "1982               133.1          1777.3  2183.0      221.6         171.5   \n",
       "\n",
       "      行业增加值_交通运输、仓储和邮政业  行业增加值_住宿和餐饮业  行业增加值_金融业  行业增加值_房地产业  第一产业增加值占GDP比重  \\\n",
       "Year                                                                          \n",
       "1978              182.0          44.6       76.5        79.9          27.69   \n",
       "1979              193.7          44.0       75.9        86.3          30.70   \n",
       "1980              213.4          47.4       85.8        96.4          29.60   \n",
       "1981              220.8          54.1       91.6        99.9          31.30   \n",
       "1982              246.9          62.3      130.6       110.8          32.80   \n",
       "\n",
       "      第二产业增加值占GDP比重  第三产业增加值占GDP比重  农、林、牧、渔业增加值占GDP比重  工业增加值占GDP比重  \\\n",
       "Year                                                                 \n",
       "1978          47.71           24.6               27.9         44.1   \n",
       "1979          47.00           22.3               31.0         43.6   \n",
       "1980          48.10           22.3               29.9         43.9   \n",
       "1981          46.00           22.7               31.6         41.9   \n",
       "1982          44.60           22.6               33.1         40.6   \n",
       "\n",
       "      建筑业增加值占GDP比重  批发和零售业增加值占GDP比重  交通运输、仓储和邮政业增加值占GDP比重  住宿和餐饮业增加值占GDP比重  \\\n",
       "Year                                                                         \n",
       "1978           3.8              6.6                   4.9              1.2   \n",
       "1979           3.5              4.9                   4.7              1.1   \n",
       "1980           4.3              4.2                   4.7              1.0   \n",
       "1981           4.2              4.7                   4.5              1.1   \n",
       "1982           4.1              3.2                   4.6              1.2   \n",
       "\n",
       "      金融业增加值占GDP比重  房地产业增加值占GDP比重  行业增加值增长指数(上年＝100)_农、林、牧、渔业  \\\n",
       "Year                                                            \n",
       "1978           2.1            2.2                       104.1   \n",
       "1979           1.9            2.1                       106.1   \n",
       "1980           1.9            2.1                        98.5   \n",
       "1981           1.9            2.0                       107.0   \n",
       "1982           2.4            2.1                       111.5   \n",
       "\n",
       "      工业增加值增长指数(上年＝100)  行业增加值增长指数(上年＝100)_建筑业  行业增加值增长指数(上年＝100)_批发和零售业  \\\n",
       "Year                                                                       \n",
       "1978              116.4                   99.5                     123.1   \n",
       "1979              108.7                  102.0                     108.7   \n",
       "1980              112.6                  126.6                      98.1   \n",
       "1981              101.7                  103.2                     129.5   \n",
       "1982              105.8                  103.4                      99.3   \n",
       "\n",
       "      行业增加值增长指数(上年＝100)_交通运输、仓储和邮政业  行业增加值增长指数(上年＝100)_住宿和餐饮业  \\\n",
       "Year                                                            \n",
       "1978                          108.9                     118.1   \n",
       "1979                          108.3                     111.1   \n",
       "1980                          104.3                     103.9   \n",
       "1981                          101.9                     117.5   \n",
       "1982                          111.4                     131.6   \n",
       "\n",
       "      行业增加值增长指数(上年＝100)_金融业  行业增加值增长指数(上年＝100)_房地产业  \\\n",
       "Year                                                  \n",
       "1978                  110.1                   105.7   \n",
       "1979                   98.0                   104.1   \n",
       "1980                  107.3                   107.9   \n",
       "1981                  104.7                    96.5   \n",
       "1982                  143.1                   109.1   \n",
       "\n",
       "      行业增加值增长指数(1978年＝100)_农、林、牧、渔业  工业增加值增长指数(1978年＝100)  \\\n",
       "Year                                                        \n",
       "1978                          100.0                 100.0   \n",
       "1979                          106.1                 108.7   \n",
       "1980                          104.6                 122.4   \n",
       "1981                          111.9                 124.5   \n",
       "1982                          124.8                 131.7   \n",
       "\n",
       "      行业增加值增长指数(1978年＝100)_建筑业  行业增加值增长指数(1978年＝100)_批发和零售业  \\\n",
       "Year                                                          \n",
       "1978                     100.0                        100.0   \n",
       "1979                     102.0                        108.7   \n",
       "1980                     129.2                        106.7   \n",
       "1981                     133.3                        138.2   \n",
       "1982                     137.9                        137.2   \n",
       "\n",
       "      行业增加值增长指数(1978年＝100)_交通运输、仓储和邮政业  行业增加值增长指数(1978年＝100)_住宿和餐饮业  \\\n",
       "Year                                                                  \n",
       "1978                             100.0                        100.0   \n",
       "1979                             108.3                        111.1   \n",
       "1980                             112.9                        115.5   \n",
       "1981                             115.0                        135.6   \n",
       "1982                             128.1                        178.5   \n",
       "\n",
       "      行业增加值增长指数(1978年＝100)_金融业  行业增加值增长指数(1978年＝100)_房地产业  \n",
       "Year                                                       \n",
       "1978                     100.0                      100.0  \n",
       "1979                      98.0                      104.1  \n",
       "1980                     105.2                      112.3  \n",
       "1981                     110.2                      108.4  \n",
       "1982                     157.6                      118.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['GDP(亿元)'].plot(figsize = (12, 7)); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_Industries = df[['第一产业增加值占GDP比重', '第二产业增加值占GDP比重','第三产业增加值占GDP比重']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_Industries.columns = ['Primary Pct', 'Secondary Pct', 'Tertiary Pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Primary Pct       7.1\n",
       "Secondary Pct    39.0\n",
       "Tertiary Pct     53.9\n",
       "Name: 2019, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GDP_Industries.loc[2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = GDP_Industries.plot(kind='bar', figsize = (14, 7),\n",
    "                    stacked=True, rot=1,\n",
    "                    title='')\n",
    "#ax.xaxis.set_major_locator(ticker.MultipleLocator(3))\n",
    "#ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(45)"
   ]
  },
  {
   "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}