From 81c7d17ff1e6e08a8b357432d99572ec3e90e09c Mon Sep 17 00:00:00 2001 From: zhouwei713 <736552883@qq.com> Date: Mon, 18 Nov 2019 18:10:26 +0800 Subject: [PATCH] add gdp code --- .idea/workspace.xml | 9 +- .../GDP_analyse-checkpoint.ipynb | 13289 ++++++++++++---- ...1\345\261\225\346\214\207\346\240\207.zip" | Bin 0 -> 30526 bytes GDP_analyse/GDP_analyse.ipynb | 13289 ++++++++++++---- GDP_analyse/Untitled.ipynb | 6 - GDP_analyse/auto_gdp.csv | 1181 ++ GDP_analyse/countries.json | 2225 +++ ...f6-8f9e-5aff034c24b5_Series - Metadata.csv | 14 + GDP_analyse/growth_Data.csv | 270 + GDP_analyse/test.txt | 3 - boss_spider/analyse.py | 7 - 11 files changed, 23792 insertions(+), 6501 deletions(-) create mode 100644 "GDP_analyse/Data_Extract_From_\344\270\226\347\225\214\345\217\221\345\261\225\346\214\207\346\240\207.zip" delete mode 100644 GDP_analyse/Untitled.ipynb create mode 100644 GDP_analyse/auto_gdp.csv create mode 100644 GDP_analyse/countries.json create mode 100644 GDP_analyse/f51403fc-f4b4-47f6-8f9e-5aff034c24b5_Series - Metadata.csv create mode 100644 GDP_analyse/growth_Data.csv delete mode 100644 GDP_analyse/test.txt diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 48cc8db..c46f074 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,7 +3,9 @@ - + + + @@ -273,7 +275,8 @@ - + + diff --git a/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb b/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb index 7b87c81..55ce3aa 100644 --- a/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb +++ b/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 209, "metadata": {}, "outputs": [], "source": [ @@ -11,17 +11,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 290, "metadata": {}, "outputs": [], "source": [ "from pyecharts import options as opts\n", - "from pyecharts.charts import Pie, Bar" + "from pyecharts.charts import Pie, Bar, Map, Geo, Liquid, Line" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -48,162 +48,7 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4
0阿鲁巴ABWNaN高收入国家NaN
1阿富汗AFG南亚低收入国家NaN
2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaN
4安道尔共和国ANDNaN高收入国家NaN
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259科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaN
260也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaN
261南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaN
262赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
263津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN\n", - "1 阿富汗 AFG 南亚 低收入国家 NaN\n", - "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN\n", - "4 安道尔共和国 AND NaN 高收入国家 NaN\n", - ".. ... ... ... ... ...\n", - "259 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN\n", - "260 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN\n", - "261 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN\n", - "262 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "263 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "\n", - "[217 rows x 5 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_data" - ] - }, - { - "cell_type": "code", - "execution_count": 73, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -212,27 +57,7 @@ }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[47, 60, 31, 79]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_data.groupby('Income_Group').size().values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -248,14 +73,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 81, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -383,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -392,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 9, "metadata": { "scrolled": true }, @@ -406,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -422,14 +247,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 101, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -604,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -620,14 +445,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 113, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -824,21 +649,23 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ - "# 中高等收入国家\n", - "mid_high = country_data[country_data['Income_Group'] == '中高等收入国家']\n", - "mid_high_gdp = pd.merge(mid_high, gdp, how='inner')\n", - "mid_high_gdp['2018'] = mid_high_gdp['2018'].apply(lambda x: x/1000000000000)\n", - "mid_high_gdp_top10 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", - "mid_high_gdp_top20 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + "world_gdp = 85.8\n", + "def liquid_base(country, gdp) -> Liquid:\n", + " c = (\n", + " Liquid()\n", + " .add(\"lq\", [gdp/world_gdp])\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"%s GDP 总量占比世界\" % country))\n", + " )\n", + " return c" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 234, "metadata": { "scrolled": true }, @@ -849,19 +676,19 @@ "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 117, + "execution_count": 234, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bar = Bar()\n", - "bar.add_xaxis(mid_high_gdp_top10['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_high_gdp_top10['2018'].values.tolist())\n", - "bar.reversal_axis()\n", - "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top10\", subtitle=\"\"),\n", - " xaxis_opts=opts.AxisOpts(\n", - " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", - " ),)\n", - "bar.render_notebook()" + "liquid_base(\"美国\", 20.4941).render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 中高等收入国家\n", + "mid_high = country_data[country_data['Income_Group'] == '中高等收入国家']\n", + "mid_high_gdp = pd.merge(mid_high, gdp, how='inner')\n", + "mid_high_gdp['2018'] = mid_high_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "mid_high_gdp_top10 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "mid_high_gdp_top20 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -1052,14 +817,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 120, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar()\n", - "bar.add_xaxis(mid_high_gdp_top20['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_high_gdp_top20['2018'].values.tolist())\n", + "bar.add_xaxis(mid_high_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_high_gdp_top10['2018'].values.tolist())\n", "bar.reversal_axis()\n", "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top20\", subtitle=\"\"),\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top10\", subtitle=\"\"),\n", " xaxis_opts=opts.AxisOpts(\n", " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", " ),)\n", @@ -1254,21 +999,129 @@ }, { "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], + "execution_count": 235, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 中低等收入国家\n", - "mid_low = country_data[country_data['Income_Group'] == '中低等收入国家']\n", - "mid_low_gdp = pd.merge(mid_low, gdp, how='inner')\n", - "mid_low_gdp['2018'] = mid_low_gdp['2018'].apply(lambda x: x/1000000000000)\n", - "mid_low_gdp_top10 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", - "mid_low_gdp_top20 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + "liquid_base(\"中美\", 34.1022).render_notebook()" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -1284,14 +1137,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 122, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar()\n", - "bar.add_xaxis(mid_low_gdp_top10['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_low_gdp_top10['2018'].values.tolist())\n", + "bar.add_xaxis(mid_high_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_high_gdp_top20['2018'].values.tolist())\n", "bar.reversal_axis()\n", "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中低等收入国家GDP Top10\", subtitle=\"\"),\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top20\", subtitle=\"\"),\n", " xaxis_opts=opts.AxisOpts(\n", " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", " ),)\n", @@ -1466,7 +1339,21 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# 中低等收入国家\n", + "mid_low = country_data[country_data['Income_Group'] == '中低等收入国家']\n", + "mid_low_gdp = pd.merge(mid_low, gdp, how='inner')\n", + "mid_low_gdp['2018'] = mid_low_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "mid_low_gdp_top10 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "mid_low_gdp_top20 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "scrolled": true }, @@ -1482,14 +1369,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(mid_low_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_low_gdp_top10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中低等收入国家GDP Top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 123, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1769,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1698,7 +1783,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -1714,14 +1799,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 127, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1896,7 +1981,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -1912,14 +1997,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 129, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2114,7 +2199,16 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "country_gdp = pd.merge(country_data, gdp, how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, "metadata": { "scrolled": true }, @@ -2177,47 +2271,47 @@ " NaN\n", " NaN\n", " ...\n", - " 2.549721e+09\n", - " 2.534637e+09\n", - " 2.581564e+09\n", - " 2.649721e+09\n", - " 2.691620e+09\n", - " 2.646927e+09\n", - " 2.700559e+09\n", + " 0.002550\n", + " 0.002535\n", + " 0.002582\n", + " 0.002650\n", + " 0.002692\n", + " 0.002647\n", + " 0.002701\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", - " 安道尔共和国\n", - " AND\n", - " NaN\n", - " 高收入国家\n", + " 阿富汗\n", + " AFG\n", + " 南亚\n", + " 低收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000538\n", + " 0.000549\n", + " 0.000547\n", " ...\n", - " 3.442063e+09\n", - " 3.164615e+09\n", - " 3.281585e+09\n", - " 3.350736e+09\n", - " 2.811489e+09\n", - " 2.877312e+09\n", - " 3.013387e+09\n", - " 3.236544e+09\n", + " 0.017804\n", + " 0.020002\n", + " 0.020561\n", + " 0.020485\n", + " 0.019907\n", + " 0.019363\n", + " 0.020192\n", + " 0.019363\n", " NaN\n", " NaN\n", " \n", " \n", " 2\n", - " 阿拉伯联合酋长国\n", - " ARE\n", - " NaN\n", - " 高收入国家\n", + " 安哥拉\n", + " AGO\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", @@ -2225,23 +2319,23 @@ " NaN\n", " NaN\n", " ...\n", - " 3.506660e+11\n", - " 3.745906e+11\n", - " 3.901076e+11\n", - " 4.031371e+11\n", - " 3.581351e+11\n", - " 3.570451e+11\n", - " 3.825751e+11\n", - " 4.141789e+11\n", + " 0.111790\n", + " 0.128053\n", + " 0.136710\n", + " 0.145712\n", + " 0.116194\n", + " 0.101124\n", + " 0.122124\n", + " 0.105751\n", " NaN\n", " NaN\n", " \n", " \n", " 3\n", - " 安提瓜和巴布达\n", - " ATG\n", - " NaN\n", - " 高收入国家\n", + " 阿尔巴尼亚\n", + " ALB\n", + " 欧洲与中亚地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", @@ -2249,38 +2343,38 @@ " NaN\n", " NaN\n", " ...\n", - " 1.142043e+09\n", - " 1.211412e+09\n", - " 1.192920e+09\n", - " 1.275577e+09\n", - " 1.359195e+09\n", - " 1.464630e+09\n", - " 1.510085e+09\n", - " 1.623804e+09\n", + " 0.012891\n", + " 0.012320\n", + " 0.012776\n", + " 0.013228\n", + " 0.011387\n", + " 0.011861\n", + " 0.013025\n", + " 0.015059\n", " NaN\n", " NaN\n", " \n", " \n", " 4\n", - " 澳大利亚\n", - " AUS\n", + " 安道尔共和国\n", + " AND\n", " NaN\n", " 高收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 1.857767e+10\n", - " 1.965394e+10\n", - " 1.989249e+10\n", - " ...\n", - " 1.396650e+12\n", - " 1.546152e+12\n", - " 1.576184e+12\n", - " 1.467484e+12\n", - " 1.351520e+12\n", - " 1.210028e+12\n", - " 1.330803e+12\n", - " 1.432195e+12\n", + " NaN\n", + " NaN\n", + " NaN\n", + " ...\n", + " 0.003442\n", + " 0.003165\n", + " 0.003282\n", + " 0.003351\n", + " 0.002811\n", + " 0.002877\n", + " 0.003013\n", + " 0.003237\n", " NaN\n", " NaN\n", " \n", @@ -2309,198 +2403,198 @@ " ...\n", " \n", " \n", - " 74\n", - " 特立尼达和多巴哥\n", - " TTO\n", - " NaN\n", - " 高收入国家\n", + " 211\n", + " 科索沃\n", + " XKX\n", + " 欧洲与中亚地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 5.356701e+08\n", - " 5.849612e+08\n", - " 6.193192e+08\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 2.543301e+10\n", - " 2.576933e+10\n", - " 2.711026e+10\n", - " 2.747797e+10\n", - " 2.512152e+10\n", - " 2.174639e+10\n", - " 2.225046e+10\n", - " 2.341035e+10\n", + " 0.006692\n", + " 0.006500\n", + " 0.007072\n", + " 0.007387\n", + " 0.006441\n", + " 0.006715\n", + " 0.007228\n", + " 0.007900\n", " NaN\n", " NaN\n", " \n", " \n", - " 75\n", - " 乌拉圭\n", - " URY\n", - " NaN\n", - " 高收入国家\n", + " 212\n", + " 也门共和国\n", + " YEM\n", + " 中东与北非地区（不包括高收入）\n", + " 低收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 1.242289e+09\n", - " 1.547389e+09\n", - " 1.710004e+09\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 4.796244e+10\n", - " 5.126439e+10\n", - " 5.753123e+10\n", - " 5.723601e+10\n", - " 5.327430e+10\n", - " 5.268761e+10\n", - " 5.648899e+10\n", - " 5.959689e+10\n", + " 0.032726\n", + " 0.035401\n", + " 0.040415\n", + " 0.043229\n", + " 0.042628\n", + " 0.030968\n", + " 0.026819\n", + " 0.026914\n", " NaN\n", " NaN\n", " \n", " \n", - " 76\n", - " 美国\n", - " USA\n", - " NaN\n", - " 高收入国家\n", + " 213\n", + " 南非\n", + " ZAF\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 5.433000e+11\n", - " 5.633000e+11\n", - " 6.051000e+11\n", + " 0.007575\n", + " 0.007973\n", + " 0.008498\n", " ...\n", - " 1.554258e+13\n", - " 1.619701e+13\n", - " 1.678485e+13\n", - " 1.752175e+13\n", - " 1.821930e+13\n", - " 1.870719e+13\n", - " 1.948539e+13\n", - " 2.049410e+13\n", + " 0.416417\n", + " 0.396329\n", + " 0.366645\n", + " 0.350638\n", + " 0.317416\n", + " 0.296341\n", + " 0.349268\n", + " 0.368288\n", " NaN\n", " NaN\n", " \n", " \n", - " 77\n", - " 英屬維爾京群島\n", - " VGB\n", - " NaN\n", - " 高收入国家\n", + " 214\n", + " 赞比亚\n", + " ZMB\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000713\n", + " 0.000696\n", + " 0.000693\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.023460\n", + " 0.025503\n", + " 0.028045\n", + " 0.027151\n", + " 0.021154\n", + " 0.020955\n", + " 0.025868\n", + " 0.026720\n", " NaN\n", " NaN\n", " \n", " \n", - " 78\n", - " 美属维京群岛\n", - " VIR\n", - " NaN\n", - " 高收入国家\n", + " 215\n", + " 津巴布韦\n", + " ZWE\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.001053\n", + " 0.001097\n", + " 0.001118\n", " ...\n", - " 4.239000e+09\n", - " 4.095000e+09\n", - " 3.762000e+09\n", - " 3.622000e+09\n", - " 3.748000e+09\n", - " 3.863000e+09\n", - " 3.855000e+09\n", - " NaN\n", + " 0.014102\n", + " 0.017115\n", + " 0.019091\n", + " 0.019496\n", + " 0.019963\n", + " 0.020549\n", + " 0.022813\n", + " 0.031001\n", " NaN\n", " NaN\n", " \n", " \n", "\n", - "

79 rows × 68 columns

\n", + "

216 rows × 68 columns

\n", "" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN GDP（现价美元） \n", - "1 安道尔共和国 AND NaN 高收入国家 NaN GDP（现价美元） \n", - "2 阿拉伯联合酋长国 ARE NaN 高收入国家 NaN GDP（现价美元） \n", - "3 安提瓜和巴布达 ATG NaN 高收入国家 NaN GDP（现价美元） \n", - "4 澳大利亚 AUS NaN 高收入国家 NaN GDP（现价美元） \n", - ".. ... ... ... ... ... ... \n", - "74 特立尼达和多巴哥 TTO NaN 高收入国家 NaN GDP（现价美元） \n", - "75 乌拉圭 URY NaN 高收入国家 NaN GDP（现价美元） \n", - "76 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", - "77 英屬維爾京群島 VGB NaN 高收入国家 NaN GDP（现价美元） \n", - "78 美属维京群岛 VIR NaN 高收入国家 NaN GDP（现价美元） \n", + " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", + "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", + "1 阿富汗 AFG 南亚 低收入国家 NaN \n", + "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "4 安道尔共和国 AND NaN 高收入国家 NaN \n", + ".. ... ... ... ... ... \n", + "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", + "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", + "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", "\n", - " Indicator Code 1960 1961 1962 ... \\\n", - "0 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "1 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "2 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "3 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "4 NY.GDP.MKTP.CD 1.857767e+10 1.965394e+10 1.989249e+10 ... \n", - ".. ... ... ... ... ... \n", - "74 NY.GDP.MKTP.CD 5.356701e+08 5.849612e+08 6.193192e+08 ... \n", - "75 NY.GDP.MKTP.CD 1.242289e+09 1.547389e+09 1.710004e+09 ... \n", - "76 NY.GDP.MKTP.CD 5.433000e+11 5.633000e+11 6.051000e+11 ... \n", - "77 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "78 NY.GDP.MKTP.CD NaN NaN NaN ... \n", + " Indicator Name Indicator Code 1960 1961 1962 ... \\\n", + "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "1 GDP（现价美元） NY.GDP.MKTP.CD 0.000538 0.000549 0.000547 ... \n", + "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + ".. ... ... ... ... ... ... \n", + "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "213 GDP（现价美元） NY.GDP.MKTP.CD 0.007575 0.007973 0.008498 ... \n", + "214 GDP（现价美元） NY.GDP.MKTP.CD 0.000713 0.000696 0.000693 ... \n", + "215 GDP（现价美元） NY.GDP.MKTP.CD 0.001053 0.001097 0.001118 ... \n", "\n", - " 2011 2012 2013 2014 2015 \\\n", - "0 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 2.691620e+09 \n", - "1 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 2.811489e+09 \n", - "2 3.506660e+11 3.745906e+11 3.901076e+11 4.031371e+11 3.581351e+11 \n", - "3 1.142043e+09 1.211412e+09 1.192920e+09 1.275577e+09 1.359195e+09 \n", - "4 1.396650e+12 1.546152e+12 1.576184e+12 1.467484e+12 1.351520e+12 \n", - ".. ... ... ... ... ... \n", - "74 2.543301e+10 2.576933e+10 2.711026e+10 2.747797e+10 2.512152e+10 \n", - "75 4.796244e+10 5.126439e+10 5.753123e+10 5.723601e+10 5.327430e+10 \n", - "76 1.554258e+13 1.619701e+13 1.678485e+13 1.752175e+13 1.821930e+13 \n", - "77 NaN NaN NaN NaN NaN \n", - "78 4.239000e+09 4.095000e+09 3.762000e+09 3.622000e+09 3.748000e+09 \n", + " 2011 2012 2013 2014 2015 2016 2017 \\\n", + "0 0.002550 0.002535 0.002582 0.002650 0.002692 0.002647 0.002701 \n", + "1 0.017804 0.020002 0.020561 0.020485 0.019907 0.019363 0.020192 \n", + "2 0.111790 0.128053 0.136710 0.145712 0.116194 0.101124 0.122124 \n", + "3 0.012891 0.012320 0.012776 0.013228 0.011387 0.011861 0.013025 \n", + "4 0.003442 0.003165 0.003282 0.003351 0.002811 0.002877 0.003013 \n", + ".. ... ... ... ... ... ... ... \n", + "211 0.006692 0.006500 0.007072 0.007387 0.006441 0.006715 0.007228 \n", + "212 0.032726 0.035401 0.040415 0.043229 0.042628 0.030968 0.026819 \n", + "213 0.416417 0.396329 0.366645 0.350638 0.317416 0.296341 0.349268 \n", + "214 0.023460 0.025503 0.028045 0.027151 0.021154 0.020955 0.025868 \n", + "215 0.014102 0.017115 0.019091 0.019496 0.019963 0.020549 0.022813 \n", "\n", - " 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - "2 3.570451e+11 3.825751e+11 4.141789e+11 NaN NaN \n", - "3 1.464630e+09 1.510085e+09 1.623804e+09 NaN NaN \n", - "4 1.210028e+12 1.330803e+12 1.432195e+12 NaN NaN \n", - ".. ... ... ... ... ... \n", - "74 2.174639e+10 2.225046e+10 2.341035e+10 NaN NaN \n", - "75 5.268761e+10 5.648899e+10 5.959689e+10 NaN NaN \n", - "76 1.870719e+13 1.948539e+13 2.049410e+13 NaN NaN \n", - "77 NaN NaN NaN NaN NaN \n", - "78 3.863000e+09 3.855000e+09 NaN NaN NaN \n", + " 2018 2019 Unnamed: 64 \n", + "0 NaN NaN NaN \n", + "1 0.019363 NaN NaN \n", + "2 0.105751 NaN NaN \n", + "3 0.015059 NaN NaN \n", + "4 0.003237 NaN NaN \n", + ".. ... ... ... \n", + "211 0.007900 NaN NaN \n", + "212 0.026914 NaN NaN \n", + "213 0.368288 NaN NaN \n", + "214 0.026720 NaN NaN \n", + "215 0.031001 NaN NaN \n", "\n", - "[79 rows x 68 columns]" + "[216 rows x 68 columns]" ] }, - "execution_count": 91, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "high_gdp" + "country_gdp" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 66, "metadata": { "scrolled": true }, @@ -2528,913 +2622,123 @@ " \n", " Country Name\n", " Country Code\n", - " Indicator Name\n", - " Indicator Code\n", - " 1960\n", - " 1961\n", - " 1962\n", - " 1963\n", - " 1964\n", - " 1965\n", - " ...\n", - " 2011\n", - " 2012\n", - " 2013\n", - " 2014\n", - " 2015\n", - " 2016\n", - " 2017\n", " 2018\n", - " 2019\n", - " Unnamed: 64\n", " \n", " \n", " \n", " \n", - " 0\n", - " 阿鲁巴\n", - " ABW\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 2.549721e+09\n", - " 2.534637e+09\n", - " 2.581564e+09\n", - " 2.649721e+09\n", - " 2.691620e+09\n", - " 2.646927e+09\n", - " 2.700559e+09\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 202\n", + " 美国\n", + " USA\n", + " 20.494100\n", " \n", " \n", - " 1\n", - " 阿富汗\n", - " AFG\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 5.377778e+08\n", - " 5.488889e+08\n", - " 5.466667e+08\n", - " 7.511112e+08\n", - " 8.000000e+08\n", - " 1.006667e+09\n", - " ...\n", - " 1.780428e+10\n", - " 2.000162e+10\n", - " 2.056105e+10\n", - " 2.048487e+10\n", - " 1.990711e+10\n", - " 1.936264e+10\n", - " 2.019176e+10\n", - " 1.936297e+10\n", - " NaN\n", - " NaN\n", + " 36\n", + " 中国\n", + " CHN\n", + " 13.608152\n", " \n", " \n", - " 2\n", - " 安哥拉\n", - " AGO\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 1.117897e+11\n", - " 1.280529e+11\n", - " 1.367099e+11\n", - " 1.457122e+11\n", - " 1.161936e+11\n", - " 1.011239e+11\n", - " 1.221238e+11\n", - " 1.057510e+11\n", - " NaN\n", - " NaN\n", + " 97\n", + " 日本\n", + " JPN\n", + " 4.970916\n", " \n", " \n", - " 3\n", - " 阿尔巴尼亚\n", - " ALB\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 1.289087e+10\n", - " 1.231978e+10\n", - " 1.277628e+10\n", - " 1.322825e+10\n", - " 1.138693e+10\n", - " 1.186135e+10\n", - " 1.302506e+10\n", - " 1.505888e+10\n", - " NaN\n", - " NaN\n", + " 50\n", + " 德国\n", + " DEU\n", + " 3.996759\n", " \n", " \n", - " 4\n", - " 安道尔共和国\n", - " AND\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 3.442063e+09\n", - " 3.164615e+09\n", - " 3.281585e+09\n", - " 3.350736e+09\n", - " 2.811489e+09\n", - " 2.877312e+09\n", - " 3.013387e+09\n", - " 3.236544e+09\n", - " NaN\n", - " NaN\n", + " 68\n", + " 英国\n", + " GBR\n", + " 2.825208\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 64\n", + " 法国\n", + " FRA\n", + " 2.777535\n", " \n", " \n", - " 259\n", - " 科索沃\n", - " XKX\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 6.691827e+09\n", - " 6.499936e+09\n", - " 7.071960e+09\n", - " 7.386891e+09\n", - " 6.440612e+09\n", - " 6.714712e+09\n", - " 7.227765e+09\n", - " 7.900269e+09\n", - " NaN\n", - " NaN\n", + " 89\n", + " 印度\n", + " IND\n", + " 2.726323\n", " \n", " \n", - " 260\n", - " 也门共和国\n", - " YEM\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 3.272642e+10\n", - " 3.540134e+10\n", - " 4.041523e+10\n", - " 4.322859e+10\n", - " 4.262833e+10\n", - " 3.096824e+10\n", - " 2.681870e+10\n", - " 2.691440e+10\n", - " NaN\n", - " NaN\n", + " 94\n", + " 意大利\n", + " ITA\n", + " 2.073902\n", " \n", " \n", - " 261\n", - " 南非\n", - " ZAF\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 7.575397e+09\n", - " 7.972997e+09\n", - " 8.497997e+09\n", - " 9.423396e+09\n", - " 1.037400e+10\n", - " 1.133440e+10\n", - " ...\n", - " 4.164170e+11\n", - " 3.963294e+11\n", - " 3.666449e+11\n", - " 3.506376e+11\n", - " 3.174156e+11\n", - " 2.963409e+11\n", - " 3.492681e+11\n", - " 3.682882e+11\n", - " NaN\n", - " NaN\n", + " 26\n", + " 巴西\n", + " BRA\n", + " 1.868626\n", " \n", " \n", - " 262\n", - " 赞比亚\n", - " ZMB\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 7.130000e+08\n", - " 6.962857e+08\n", - " 6.931429e+08\n", - " 7.187143e+08\n", - " 8.394286e+08\n", - " 1.082857e+09\n", - " ...\n", - " 2.346010e+10\n", - " 2.550337e+10\n", - " 2.804546e+10\n", - " 2.715063e+10\n", - " 2.115439e+10\n", - " 2.095475e+10\n", - " 2.586814e+10\n", - " 2.672007e+10\n", - " NaN\n", - " NaN\n", + " 32\n", + " 加拿大\n", + " CAN\n", + " 1.712510\n", " \n", " \n", - " 263\n", - " 津巴布韦\n", - " ZWE\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 1.052990e+09\n", - " 1.096647e+09\n", - " 1.117602e+09\n", - " 1.159512e+09\n", - " 1.217138e+09\n", - " 1.311436e+09\n", - " ...\n", - " 1.410192e+10\n", - " 1.711485e+10\n", - " 1.909102e+10\n", - " 1.949552e+10\n", - " 1.996312e+10\n", - " 2.054868e+10\n", - " 2.281301e+10\n", - " 3.100052e+10\n", - " NaN\n", - " NaN\n", + " 164\n", + " 俄罗斯联邦\n", + " RUS\n", + " 1.657554\n", " \n", - " \n", - "\n", - "

264 rows × 65 columns

\n", - "" - ], - "text/plain": [ - " Country Name Country Code Indicator Name Indicator Code 1960 \\\n", - "0 阿鲁巴 ABW GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "1 阿富汗 AFG GDP（现价美元） NY.GDP.MKTP.CD 5.377778e+08 \n", - "2 安哥拉 AGO GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "3 阿尔巴尼亚 ALB GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "4 安道尔共和国 AND GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - ".. ... ... ... ... ... \n", - "259 科索沃 XKX GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "260 也门共和国 YEM GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "261 南非 ZAF GDP（现价美元） NY.GDP.MKTP.CD 7.575397e+09 \n", - "262 赞比亚 ZMB GDP（现价美元） NY.GDP.MKTP.CD 7.130000e+08 \n", - "263 津巴布韦 ZWE GDP（现价美元） NY.GDP.MKTP.CD 1.052990e+09 \n", - "\n", - " 1961 1962 1963 1964 1965 \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 5.488889e+08 5.466667e+08 7.511112e+08 8.000000e+08 1.006667e+09 \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - ".. ... ... ... ... ... \n", - "259 NaN NaN NaN NaN NaN \n", - "260 NaN NaN NaN NaN NaN \n", - "261 7.972997e+09 8.497997e+09 9.423396e+09 1.037400e+10 1.133440e+10 \n", - "262 6.962857e+08 6.931429e+08 7.187143e+08 8.394286e+08 1.082857e+09 \n", - "263 1.096647e+09 1.117602e+09 1.159512e+09 1.217138e+09 1.311436e+09 \n", - "\n", - " ... 2011 2012 2013 2014 \\\n", - "0 ... 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 \n", - "1 ... 1.780428e+10 2.000162e+10 2.056105e+10 2.048487e+10 \n", - "2 ... 1.117897e+11 1.280529e+11 1.367099e+11 1.457122e+11 \n", - "3 ... 1.289087e+10 1.231978e+10 1.277628e+10 1.322825e+10 \n", - "4 ... 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 \n", - ".. ... ... ... ... ... \n", - "259 ... 6.691827e+09 6.499936e+09 7.071960e+09 7.386891e+09 \n", - "260 ... 3.272642e+10 3.540134e+10 4.041523e+10 4.322859e+10 \n", - "261 ... 4.164170e+11 3.963294e+11 3.666449e+11 3.506376e+11 \n", - "262 ... 2.346010e+10 2.550337e+10 2.804546e+10 2.715063e+10 \n", - "263 ... 1.410192e+10 1.711485e+10 1.909102e+10 1.949552e+10 \n", - "\n", - " 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.691620e+09 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 1.990711e+10 1.936264e+10 2.019176e+10 1.936297e+10 NaN NaN \n", - "2 1.161936e+11 1.011239e+11 1.221238e+11 1.057510e+11 NaN NaN \n", - "3 1.138693e+10 1.186135e+10 1.302506e+10 1.505888e+10 NaN NaN \n", - "4 2.811489e+09 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - ".. ... ... ... ... ... ... \n", - "259 6.440612e+09 6.714712e+09 7.227765e+09 7.900269e+09 NaN NaN \n", - "260 4.262833e+10 3.096824e+10 2.681870e+10 2.691440e+10 NaN NaN \n", - "261 3.174156e+11 2.963409e+11 3.492681e+11 3.682882e+11 NaN NaN \n", - "262 2.115439e+10 2.095475e+10 2.586814e+10 2.672007e+10 NaN NaN \n", - "263 1.996312e+10 2.054868e+10 2.281301e+10 3.100052e+10 NaN NaN \n", - "\n", - "[264 rows x 65 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdp" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "country_gdp = pd.merge(country_data, gdp, how='inner')" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
104大韩民国KOR1.619424
0阿鲁巴ABWNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...2.549721e+092.534637e+092.581564e+092.649721e+092.691620e+092.646927e+092.700559e+09NaNNaNNaN10澳大利亚AUS1.432195
1阿富汗AFG南亚低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.377778e+085.488889e+085.466667e+08...1.780428e+102.000162e+102.056105e+102.048487e+101.990711e+101.936264e+102.019176e+101.936297e+10NaNNaN59西班牙ESP1.426189
2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.117897e+111.280529e+111.367099e+111.457122e+111.161936e+111.011239e+111.221238e+111.057510e+11NaNNaN124墨西哥MEX1.223809
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.289087e+101.231978e+101.277628e+101.322825e+101.138693e+101.186135e+101.302506e+101.505888e+10NaNNaN87印度尼西亚IDN1.042173
4安道尔共和国ANDNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.442063e+093.164615e+093.281585e+093.350736e+092.811489e+092.877312e+093.013387e+093.236544e+09NaNNaN143荷兰NLD0.913658
..................................................................166沙特阿拉伯SAU0.782483
211科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...6.691827e+096.499936e+097.071960e+097.386891e+096.440612e+096.714712e+097.227765e+097.900269e+09NaNNaN
212也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.272642e+103.540134e+104.041523e+104.322859e+104.262833e+103.096824e+102.681870e+102.691440e+10NaNNaN
213南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD7.575397e+097.972997e+098.497997e+09...4.164170e+113.963294e+113.666449e+113.506376e+113.174156e+112.963409e+113.492681e+113.682882e+11NaNNaN
214赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD7.130000e+086.962857e+086.931429e+08...2.346010e+102.550337e+102.804546e+102.715063e+102.115439e+102.095475e+102.586814e+102.672007e+10NaNNaN
215津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.052990e+091.096647e+091.117602e+09...1.410192e+101.711485e+101.909102e+101.949552e+101.996312e+102.054868e+102.281301e+103.100052e+10NaNNaN
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216 rows × 68 columns

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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", - "1 阿富汗 AFG 南亚 低收入国家 NaN \n", - "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", - "4 安道尔共和国 AND NaN 高收入国家 NaN \n", - ".. ... ... ... ... ... \n", - "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", - "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", - "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", - "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "\n", - " Indicator Name Indicator Code 1960 1961 1962 \\\n", - "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "1 GDP（现价美元） NY.GDP.MKTP.CD 5.377778e+08 5.488889e+08 5.466667e+08 \n", - "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - ".. ... ... ... ... ... \n", - "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "213 GDP（现价美元） NY.GDP.MKTP.CD 7.575397e+09 7.972997e+09 8.497997e+09 \n", - "214 GDP（现价美元） NY.GDP.MKTP.CD 7.130000e+08 6.962857e+08 6.931429e+08 \n", - "215 GDP（现价美元） NY.GDP.MKTP.CD 1.052990e+09 1.096647e+09 1.117602e+09 \n", - "\n", - " ... 2011 2012 2013 2014 \\\n", - "0 ... 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 \n", - "1 ... 1.780428e+10 2.000162e+10 2.056105e+10 2.048487e+10 \n", - "2 ... 1.117897e+11 1.280529e+11 1.367099e+11 1.457122e+11 \n", - "3 ... 1.289087e+10 1.231978e+10 1.277628e+10 1.322825e+10 \n", - "4 ... 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 \n", - ".. ... ... ... ... ... \n", - "211 ... 6.691827e+09 6.499936e+09 7.071960e+09 7.386891e+09 \n", - "212 ... 3.272642e+10 3.540134e+10 4.041523e+10 4.322859e+10 \n", - "213 ... 4.164170e+11 3.963294e+11 3.666449e+11 3.506376e+11 \n", - "214 ... 2.346010e+10 2.550337e+10 2.804546e+10 2.715063e+10 \n", - "215 ... 1.410192e+10 1.711485e+10 1.909102e+10 1.949552e+10 \n", - "\n", - " 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.691620e+09 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 1.990711e+10 1.936264e+10 2.019176e+10 1.936297e+10 NaN NaN \n", - "2 1.161936e+11 1.011239e+11 1.221238e+11 1.057510e+11 NaN NaN \n", - "3 1.138693e+10 1.186135e+10 1.302506e+10 1.505888e+10 NaN NaN \n", - "4 2.811489e+09 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - ".. ... ... ... ... ... ... \n", - "211 6.440612e+09 6.714712e+09 7.227765e+09 7.900269e+09 NaN NaN \n", - "212 4.262833e+10 3.096824e+10 2.681870e+10 2.691440e+10 NaN NaN \n", - "213 3.174156e+11 2.963409e+11 3.492681e+11 3.682882e+11 NaN NaN \n", - "214 2.115439e+10 2.095475e+10 2.586814e+10 2.672007e+10 NaN NaN \n", - "215 1.996312e+10 2.054868e+10 2.281301e+10 3.100052e+10 NaN NaN \n", - "\n", - "[216 rows x 68 columns]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_gdp" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# country_gdp['2018'] = country_gdp['2018'].apply(lambda x: x/1000000000000)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry Code2018
202美国USA20.494100
36中国CHN13.608152
97日本JPN4.970916
50德国DEU3.996759
68英国GBR2.825208
64法国FRA2.777535
89印度IND2.726323
94意大利ITA2.073902
26巴西BRA1.868626
32加拿大CAN1.712510
164俄罗斯联邦RUS1.657554
104大韩民国KOR1.619424
10澳大利亚AUS1.432195
59西班牙ESP1.426189
124墨西哥MEX1.223809
87印度尼西亚IDN1.042173
143荷兰NLD0.913658
166沙特阿拉伯SAU0.782483
196土耳其TUR0.766509196土耳其TUR0.766509
33Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
202美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.54330.56330.6051...15.54258116.19700716.78484917.52174718.21929818.70718819.48539420.4941NaNNaN9安提瓜和巴布达ATG0.001624
184塞舌尔SYC0.001590
74几内亚比绍共和国GNB0.001458
170所罗门群岛SLB0.001412
77格林纳达GRD0.001207
42科摩罗COM0.001203
103圣基茨和尼维斯KNA0.001040
186特克斯科斯群岛TCA0.001022
209瓦努阿图VUT0.000888
210萨摩亚WSM0.000861
204圣文森特和格林纳丁斯VCT0.000813
52多米尼克DMA0.000504
193汤加TON0.000450
177圣多美和普林西比STP0.000422
66密克罗尼西亚联邦FSM0.000345
153帕劳PLW0.000310
125马绍尔群岛MHL0.000212
102基里巴斯KIR0.000188
146瑙魯NRU0.000115
197图瓦卢TUV0.000043
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1 rows × 68 columns

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" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "202 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", - "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 \\\n", - "202 NY.GDP.MKTP.CD 0.5433 0.5633 0.6051 ... 15.542581 16.197007 \n", - "\n", - " 2013 2014 2015 2016 2017 2018 2019 \\\n", - "202 16.784849 17.521747 18.219298 18.707188 19.485394 20.4941 NaN \n", - "\n", - " Unnamed: 64 \n", - "202 NaN \n", - "\n", - "[1 rows x 68 columns]" + " Country Name Country Code 2018\n", + "9 安提瓜和巴布达 ATG 0.001624\n", + "184 塞舌尔 SYC 0.001590\n", + "74 几内亚比绍共和国 GNB 0.001458\n", + "170 所罗门群岛 SLB 0.001412\n", + "77 格林纳达 GRD 0.001207\n", + "42 科摩罗 COM 0.001203\n", + "103 圣基茨和尼维斯 KNA 0.001040\n", + "186 特克斯科斯群岛 TCA 0.001022\n", + "209 瓦努阿图 VUT 0.000888\n", + "210 萨摩亚 WSM 0.000861\n", + "204 圣文森特和格林纳丁斯 VCT 0.000813\n", + "52 多米尼克 DMA 0.000504\n", + "193 汤加 TON 0.000450\n", + "177 圣多美和普林西比 STP 0.000422\n", + "66 密克罗尼西亚联邦 FSM 0.000345\n", + "153 帕劳 PLW 0.000310\n", + "125 马绍尔群岛 MHL 0.000212\n", + "102 基里巴斯 KIR 0.000188\n", + "146 瑙魯 NRU 0.000115\n", + "197 图瓦卢 TUV 0.000043" ] }, - "execution_count": 13, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_usa = country_gdp[country_gdp['Country Name']=='美国']\n", - "for i in range(1960, 2019):\n", - " df_usa[str(i)] = df_usa[str(i)].apply(lambda x: x/1000000000000)\n", - "df_usa" + "# 查看倒数排名\n", + "country_gdp.dropna(subset=['2018'])[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[-20:]" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, { "data": { "text/html": [ @@ -3989,130 +3308,105 @@ " \n", " \n", " \n", - " 97\n", - " 日本\n", - " JPN\n", + " 0\n", + " 阿鲁巴\n", + " ABW\n", " NaN\n", " 高收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 0.044307\n", - " 0.053509\n", - " 0.060723\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 6.15746\n", - " 6.203213\n", - " 5.155717\n", - " 4.850414\n", - " 4.389476\n", - " 4.926667\n", - " 4.859951\n", - " 4.970916\n", + " 0.002550\n", + " 0.002535\n", + " 0.002582\n", + " 0.002650\n", + " 0.002692\n", + " 0.002647\n", + " 0.002701\n", + " NaN\n", " NaN\n", " NaN\n", " \n", - " \n", - "\n", - "

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\n", - "" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "97 日本 JPN NaN 高收入国家 NaN GDP（现价美元） \n", - "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 \\\n", - "97 NY.GDP.MKTP.CD 0.044307 0.053509 0.060723 ... 6.15746 6.203213 \n", - "\n", - " 2013 2014 2015 2016 2017 2018 2019 \\\n", - "97 5.155717 4.850414 4.389476 4.926667 4.859951 4.970916 NaN \n", - "\n", - " Unnamed: 64 \n", - "97 NaN \n", - "\n", - "[1 rows x 68 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_jpn = country_gdp[country_gdp['Country Name']=='日本']\n", - "for i in range(1960, 2019):\n", - " df_jpn[str(i)] = df_jpn[str(i)].apply(lambda x: x/1000000000000)\n", - "df_jpn" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
1阿富汗AFG南亚低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0005380.0005490.000547...0.0178040.0200020.0205610.0204850.0199070.0193630.0201920.019363NaNNaN
50德国DEU2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.1117900.1280530.1367100.1457120.1161940.1011240.1221240.105751NaNNaN
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.0128910.0123200.0127760.0132280.0113870.0118610.0130250.015059NaNNaN
4安道尔共和国ANDNaN高收入国家NaNNaNNaN...3.7576983.5439843.7525143.8987273.3813893.4951633.6932043.9967590.0034420.0031650.0032820.0033510.0028110.0028770.0030130.003237NaNNaN
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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "50 德国 DEU NaN 高收入国家 NaN GDP（现价美元） \n", - "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 2013 \\\n", - "50 NY.GDP.MKTP.CD NaN NaN NaN ... 3.757698 3.543984 3.752514 \n", - "\n", - " 2014 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "50 3.898727 3.381389 3.495163 3.693204 3.996759 NaN NaN \n", - "\n", - "[1 rows x 68 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_de = country_gdp[country_gdp['Country Name']=='德国']\n", - "for i in range(1960, 2019):\n", - " df_de[str(i)] = df_de[str(i)].apply(lambda x: x/1000000000000)\n", - "df_de" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
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68英国GBR211科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaN高收入国家GDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.0066920.0065000.0070720.0073870.0064410.0067150.0072280.007900NaNNaN
212也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0723280.0766940.080602NaNNaNNaN...2.6348962.6766052.7535653.0347292.8964212.6592392.6378662.8252080.0327260.0354010.0404150.0432290.0426280.0309680.0268190.026914NaNNaN
213南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0075750.0079730.008498...0.4164170.3963290.3666450.3506380.3174160.2963410.3492680.368288NaNNaN
214赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0007130.0006960.000693...0.0234600.0255030.0280450.0271510.0211540.0209550.0258680.026720NaNNaN
215津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0010530.0010970.001118...0.0141020.0171150.0190910.0194960.0199630.0205490.0228130.031001NaNNaN
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" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "68 英国 GBR NaN 高收入国家 NaN GDP（现价美元） \n", + " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", + "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", + "1 阿富汗 AFG 南亚 低收入国家 NaN \n", + "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "4 安道尔共和国 AND NaN 高收入国家 NaN \n", + ".. ... ... ... ... ... \n", + "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", + "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", + "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 \\\n", - "68 NY.GDP.MKTP.CD 0.072328 0.076694 0.080602 ... 2.634896 2.676605 \n", + " Indicator Name Indicator Code 1960 1961 1962 ... \\\n", + "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "1 GDP（现价美元） NY.GDP.MKTP.CD 0.000538 0.000549 0.000547 ... \n", + "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + ".. ... ... ... ... ... ... \n", + "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "213 GDP（现价美元） NY.GDP.MKTP.CD 0.007575 0.007973 0.008498 ... \n", + "214 GDP（现价美元） NY.GDP.MKTP.CD 0.000713 0.000696 0.000693 ... \n", + "215 GDP（现价美元） NY.GDP.MKTP.CD 0.001053 0.001097 0.001118 ... \n", "\n", - " 2013 2014 2015 2016 2017 2018 2019 \\\n", - "68 2.753565 3.034729 2.896421 2.659239 2.637866 2.825208 NaN \n", + " 2011 2012 2013 2014 2015 2016 2017 \\\n", + "0 0.002550 0.002535 0.002582 0.002650 0.002692 0.002647 0.002701 \n", + "1 0.017804 0.020002 0.020561 0.020485 0.019907 0.019363 0.020192 \n", + "2 0.111790 0.128053 0.136710 0.145712 0.116194 0.101124 0.122124 \n", + "3 0.012891 0.012320 0.012776 0.013228 0.011387 0.011861 0.013025 \n", + "4 0.003442 0.003165 0.003282 0.003351 0.002811 0.002877 0.003013 \n", + ".. ... ... ... ... ... ... ... \n", + "211 0.006692 0.006500 0.007072 0.007387 0.006441 0.006715 0.007228 \n", + "212 0.032726 0.035401 0.040415 0.043229 0.042628 0.030968 0.026819 \n", + "213 0.416417 0.396329 0.366645 0.350638 0.317416 0.296341 0.349268 \n", + "214 0.023460 0.025503 0.028045 0.027151 0.021154 0.020955 0.025868 \n", + "215 0.014102 0.017115 0.019091 0.019496 0.019963 0.020549 0.022813 \n", "\n", - " Unnamed: 64 \n", - "68 NaN \n", + " 2018 2019 Unnamed: 64 \n", + "0 NaN NaN NaN \n", + "1 0.019363 NaN NaN \n", + "2 0.105751 NaN NaN \n", + "3 0.015059 NaN NaN \n", + "4 0.003237 NaN NaN \n", + ".. ... ... ... \n", + "211 0.007900 NaN NaN \n", + "212 0.026914 NaN NaN \n", + "213 0.368288 NaN NaN \n", + "214 0.026720 NaN NaN \n", + "215 0.031001 NaN NaN \n", "\n", - "[1 rows x 68 columns]" + "[216 rows x 68 columns]" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_uk = country_gdp[country_gdp['Country Name']=='英国']\n", + "# GDP转换成万亿单位\n", "for i in range(1960, 2019):\n", - " df_uk[str(i)] = df_uk[str(i)].apply(lambda x: x/1000000000000)\n", - "df_uk" + " country_gdp[str(i)] = country_gdp[str(i)].apply(lambda x: x/1000000000000)\n", + "\n", + "country_gdp" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 99, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "year_str = [str(i) for i in range(1960, 2019)]\n", - "\n", - "china_gdp = df_china[year_str].values.tolist()[0]\n", - "usa_gdp = df_usa[year_str].values.tolist()[0]\n", - "jpn_gdp = df_jpn[year_str].values.tolist()[0]\n", - "de_gdp = df_de[year_str].values.tolist()[0]\n", - "uk_gdp = df_uk[year_str].values.tolist()[0]" + "# 2018年GDP前十名\n", + "country_gdp_top10 = country_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 101, "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [ { "data": { "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
36中国CHN东亚与太平洋地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0597160.0500570.047209...7.55158.5322319.57040610.43852911.01554211.13794612.14349113.608152NaNNaN
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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", - "36 中国 CHN 东亚与太平洋地区（不包括高收入） 中高等收入国家 NaN \n", - "\n", - " Indicator Name Indicator Code 1960 1961 1962 ... 2011 \\\n", - "36 GDP（现价美元） NY.GDP.MKTP.CD 0.059716 0.050057 0.047209 ... 7.5515 \n", - "\n", - " 2012 2013 2014 2015 2016 2017 2018 \\\n", - "36 8.532231 9.570406 10.438529 11.015542 11.137946 12.143491 13.608152 \n", - "\n", - " 2019 Unnamed: 64 \n", - "36 NaN NaN \n", - "\n", - "[1 rows x 68 columns]" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_china" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from pyecharts.charts import Scatter\n", - "\n", - "def scatter_base(choose, values, country) -> Scatter:\n", - " c = (\n", - " Scatter()\n", - " .add_xaxis(choose)\n", - " .add_yaxis(\"%s历年GDP\" % country, values)\n", - " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", - " # datazoom_opts=opts.DataZoomOpts(),\n", - " yaxis_opts=opts.AxisOpts(\n", - " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", - " )\n", - " )\n", - " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", - " )\n", - " return c" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, china_gdp, '中国').render_notebook()" + "bar = Bar()\n", + "bar.add_xaxis(country_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_gdp_top10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP Top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 278, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/html": [ - "\n", - "\n", - "
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Country NameCountry Code19601961196219631964196519661967...2009201020112012201320142015201620172018
202美国USA5.433000e+115.633000e+116.051000e+116.386000e+116.858000e+117.437000e+118.150000e+118.617000e+11...1.444893e+131.499205e+131.554258e+131.619701e+131.678485e+131.752175e+131.821930e+131.870719e+131.948539e+132.049410e+13
36中国CHN5.971647e+105.005687e+104.720936e+105.070680e+105.970834e+107.043627e+107.672029e+107.288163e+10...5.101702e+126.087165e+127.551500e+128.532231e+129.570406e+121.043853e+131.101554e+131.113795e+131.214349e+131.360815e+13
97日本JPN4.430734e+105.350862e+106.072302e+106.949813e+108.174901e+109.095028e+101.056281e+111.237819e+11...5.231383e+125.700098e+126.157460e+126.203213e+125.155717e+124.850414e+124.389476e+124.926667e+124.859951e+124.970916e+12
50德国DEUNaNNaNNaNNaNNaNNaNNaNNaN...3.418005e+123.417095e+123.757698e+123.543984e+123.752514e+123.898727e+123.381389e+123.495163e+123.693204e+123.996759e+12
68英国GBR7.232805e+107.669436e+108.060194e+108.544377e+109.338760e+101.005958e+111.070907e+111.111854e+11...2.394786e+122.452900e+122.634896e+122.676605e+122.753565e+123.034729e+122.896421e+122.659239e+122.637866e+122.825208e+12
64法国FRA6.265147e+106.834674e+107.631378e+108.555111e+109.490659e+101.021606e+111.105975e+111.194661e+11...2.690222e+122.642610e+122.861408e+122.683825e+122.811078e+122.852166e+122.438208e+122.471286e+122.586285e+122.777535e+12
89印度IND3.702988e+103.923244e+104.216148e+104.842192e+105.648029e+105.955486e+104.586546e+105.013494e+10...1.341887e+121.675615e+121.823050e+121.827638e+121.856722e+122.039127e+122.103588e+122.290432e+122.652551e+122.726323e+12
94意大利ITA4.038529e+104.484276e+105.038389e+105.771074e+106.317542e+106.797815e+107.365487e+108.113312e+10...2.185160e+122.125058e+122.276292e+122.072823e+122.130491e+122.151733e+121.832273e+121.869202e+121.946570e+122.073902e+12
26巴西BRA1.516557e+101.523685e+101.992629e+102.302148e+102.121189e+102.179004e+102.706272e+103.059183e+10...1.667020e+122.208872e+122.616202e+122.465189e+122.472806e+122.455994e+121.802214e+121.796275e+122.053595e+121.868626e+12
32加拿大CANNaN4.155599e+104.286809e+104.571315e+105.012664e+105.534224e+106.201517e+106.666493e+10...1.371153e+121.613543e+121.789141e+121.823967e+121.842018e+121.801480e+121.552900e+121.526706e+121.646867e+121.712510e+12
164俄罗斯联邦RUSNaNNaNNaNNaNNaNNaNNaNNaN...1.222644e+121.524917e+122.051662e+122.210257e+122.297128e+122.059984e+121.363594e+121.282724e+121.578624e+121.657554e+12
104大韩民国KOR3.957240e+092.417638e+092.813857e+093.988477e+093.458565e+093.120495e+093.928282e+094.854724e+09...9.019350e+111.094499e+121.202464e+121.222807e+121.305605e+121.411334e+121.382764e+121.414804e+121.530751e+121.619424e+12
10澳大利亚AUS1.857767e+101.965394e+101.989249e+102.150745e+102.376414e+102.593795e+102.726845e+103.039758e+10...9.278052e+111.146138e+121.396650e+121.546152e+121.576184e+121.467484e+121.351520e+121.210028e+121.330803e+121.432195e+12
59西班牙ESP1.207213e+101.383430e+101.613855e+101.907491e+102.134384e+102.475696e+102.872106e+103.164712e+10...1.499100e+121.431617e+121.488067e+121.336019e+121.361854e+121.376911e+121.199084e+121.237499e+121.314314e+121.426189e+12
124墨西哥MEX1.304000e+101.416000e+101.520000e+101.696000e+102.008000e+102.184000e+102.432000e+102.656000e+10...9.000454e+111.057801e+121.180490e+121.201090e+121.274443e+121.314564e+121.170565e+121.077828e+121.158071e+121.223809e+12
87印度尼西亚IDNNaNNaNNaNNaNNaNNaNNaN5.667757e+09...5.395801e+117.550942e+118.929691e+119.178699e+119.125241e+118.908148e+118.608542e+119.318774e+111.015423e+121.042173e+12
143荷兰NLD1.227673e+101.349383e+101.464706e+101.589124e+101.869938e+102.100059e+102.286720e+102.508756e+10...8.680772e+118.465549e+119.040860e+118.389713e+118.769235e+118.909813e+117.652649e+117.835282e+118.318099e+119.136585e+11
166沙特阿拉伯SAUNaNNaNNaNNaNNaNNaNNaNNaN...4.290979e+115.282072e+116.712388e+117.359748e+117.466471e+117.563503e+116.542699e+116.449355e+116.885861e+117.824835e+11
196土耳其TUR1.399507e+107.988889e+098.922222e+091.035556e+101.117778e+101.196667e+101.410000e+101.564444e+10...6.446399e+117.719018e+118.325237e+118.739822e+119.505794e+119.341859e+118.597969e+118.637216e+118.515492e+117.665091e+11
33瑞士CHE9.522747e+091.071271e+101.187998e+101.306364e+101.448056e+101.534674e+101.648006e+101.774001e+10...5.415065e+115.837830e+116.995796e+116.680436e+116.885042e+117.091826e+116.798323e+116.701811e+116.789654e+117.055013e+11
\n", + "

20 rows × 61 columns

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" + ], + "text/plain": [ + " Country Name Country Code 1960 1961 1962 \\\n", + "202 美国 USA 5.433000e+11 5.633000e+11 6.051000e+11 \n", + "36 中国 CHN 5.971647e+10 5.005687e+10 4.720936e+10 \n", + "97 日本 JPN 4.430734e+10 5.350862e+10 6.072302e+10 \n", + "50 德国 DEU NaN NaN NaN \n", + "68 英国 GBR 7.232805e+10 7.669436e+10 8.060194e+10 \n", + "64 法国 FRA 6.265147e+10 6.834674e+10 7.631378e+10 \n", + "89 印度 IND 3.702988e+10 3.923244e+10 4.216148e+10 \n", + "94 意大利 ITA 4.038529e+10 4.484276e+10 5.038389e+10 \n", + "26 巴西 BRA 1.516557e+10 1.523685e+10 1.992629e+10 \n", + "32 加拿大 CAN NaN 4.155599e+10 4.286809e+10 \n", + "164 俄罗斯联邦 RUS NaN NaN NaN \n", + "104 大韩民国 KOR 3.957240e+09 2.417638e+09 2.813857e+09 \n", + "10 澳大利亚 AUS 1.857767e+10 1.965394e+10 1.989249e+10 \n", + "59 西班牙 ESP 1.207213e+10 1.383430e+10 1.613855e+10 \n", + "124 墨西哥 MEX 1.304000e+10 1.416000e+10 1.520000e+10 \n", + "87 印度尼西亚 IDN NaN NaN NaN \n", + "143 荷兰 NLD 1.227673e+10 1.349383e+10 1.464706e+10 \n", + "166 沙特阿拉伯 SAU NaN NaN NaN \n", + "196 土耳其 TUR 1.399507e+10 7.988889e+09 8.922222e+09 \n", + "33 瑞士 CHE 9.522747e+09 1.071271e+10 1.187998e+10 \n", + "\n", + " 1963 1964 1965 1966 1967 \\\n", + "202 6.386000e+11 6.858000e+11 7.437000e+11 8.150000e+11 8.617000e+11 \n", + "36 5.070680e+10 5.970834e+10 7.043627e+10 7.672029e+10 7.288163e+10 \n", + "97 6.949813e+10 8.174901e+10 9.095028e+10 1.056281e+11 1.237819e+11 \n", + "50 NaN NaN NaN NaN NaN \n", + "68 8.544377e+10 9.338760e+10 1.005958e+11 1.070907e+11 1.111854e+11 \n", + "64 8.555111e+10 9.490659e+10 1.021606e+11 1.105975e+11 1.194661e+11 \n", + "89 4.842192e+10 5.648029e+10 5.955486e+10 4.586546e+10 5.013494e+10 \n", + "94 5.771074e+10 6.317542e+10 6.797815e+10 7.365487e+10 8.113312e+10 \n", + "26 2.302148e+10 2.121189e+10 2.179004e+10 2.706272e+10 3.059183e+10 \n", + "32 4.571315e+10 5.012664e+10 5.534224e+10 6.201517e+10 6.666493e+10 \n", + "164 NaN NaN NaN NaN NaN \n", + "104 3.988477e+09 3.458565e+09 3.120495e+09 3.928282e+09 4.854724e+09 \n", + "10 2.150745e+10 2.376414e+10 2.593795e+10 2.726845e+10 3.039758e+10 \n", + "59 1.907491e+10 2.134384e+10 2.475696e+10 2.872106e+10 3.164712e+10 \n", + "124 1.696000e+10 2.008000e+10 2.184000e+10 2.432000e+10 2.656000e+10 \n", + "87 NaN NaN NaN NaN 5.667757e+09 \n", + "143 1.589124e+10 1.869938e+10 2.100059e+10 2.286720e+10 2.508756e+10 \n", + "166 NaN NaN NaN NaN NaN \n", + "196 1.035556e+10 1.117778e+10 1.196667e+10 1.410000e+10 1.564444e+10 \n", + "33 1.306364e+10 1.448056e+10 1.534674e+10 1.648006e+10 1.774001e+10 \n", + "\n", + " ... 2009 2010 2011 2012 \\\n", + "202 ... 1.444893e+13 1.499205e+13 1.554258e+13 1.619701e+13 \n", + "36 ... 5.101702e+12 6.087165e+12 7.551500e+12 8.532231e+12 \n", + "97 ... 5.231383e+12 5.700098e+12 6.157460e+12 6.203213e+12 \n", + "50 ... 3.418005e+12 3.417095e+12 3.757698e+12 3.543984e+12 \n", + "68 ... 2.394786e+12 2.452900e+12 2.634896e+12 2.676605e+12 \n", + "64 ... 2.690222e+12 2.642610e+12 2.861408e+12 2.683825e+12 \n", + "89 ... 1.341887e+12 1.675615e+12 1.823050e+12 1.827638e+12 \n", + "94 ... 2.185160e+12 2.125058e+12 2.276292e+12 2.072823e+12 \n", + "26 ... 1.667020e+12 2.208872e+12 2.616202e+12 2.465189e+12 \n", + "32 ... 1.371153e+12 1.613543e+12 1.789141e+12 1.823967e+12 \n", + "164 ... 1.222644e+12 1.524917e+12 2.051662e+12 2.210257e+12 \n", + "104 ... 9.019350e+11 1.094499e+12 1.202464e+12 1.222807e+12 \n", + "10 ... 9.278052e+11 1.146138e+12 1.396650e+12 1.546152e+12 \n", + "59 ... 1.499100e+12 1.431617e+12 1.488067e+12 1.336019e+12 \n", + "124 ... 9.000454e+11 1.057801e+12 1.180490e+12 1.201090e+12 \n", + "87 ... 5.395801e+11 7.550942e+11 8.929691e+11 9.178699e+11 \n", + "143 ... 8.680772e+11 8.465549e+11 9.040860e+11 8.389713e+11 \n", + "166 ... 4.290979e+11 5.282072e+11 6.712388e+11 7.359748e+11 \n", + "196 ... 6.446399e+11 7.719018e+11 8.325237e+11 8.739822e+11 \n", + "33 ... 5.415065e+11 5.837830e+11 6.995796e+11 6.680436e+11 \n", + "\n", + " 2013 2014 2015 2016 2017 \\\n", + "202 1.678485e+13 1.752175e+13 1.821930e+13 1.870719e+13 1.948539e+13 \n", + "36 9.570406e+12 1.043853e+13 1.101554e+13 1.113795e+13 1.214349e+13 \n", + "97 5.155717e+12 4.850414e+12 4.389476e+12 4.926667e+12 4.859951e+12 \n", + "50 3.752514e+12 3.898727e+12 3.381389e+12 3.495163e+12 3.693204e+12 \n", + "68 2.753565e+12 3.034729e+12 2.896421e+12 2.659239e+12 2.637866e+12 \n", + "64 2.811078e+12 2.852166e+12 2.438208e+12 2.471286e+12 2.586285e+12 \n", + "89 1.856722e+12 2.039127e+12 2.103588e+12 2.290432e+12 2.652551e+12 \n", + "94 2.130491e+12 2.151733e+12 1.832273e+12 1.869202e+12 1.946570e+12 \n", + "26 2.472806e+12 2.455994e+12 1.802214e+12 1.796275e+12 2.053595e+12 \n", + "32 1.842018e+12 1.801480e+12 1.552900e+12 1.526706e+12 1.646867e+12 \n", + "164 2.297128e+12 2.059984e+12 1.363594e+12 1.282724e+12 1.578624e+12 \n", + "104 1.305605e+12 1.411334e+12 1.382764e+12 1.414804e+12 1.530751e+12 \n", + "10 1.576184e+12 1.467484e+12 1.351520e+12 1.210028e+12 1.330803e+12 \n", + "59 1.361854e+12 1.376911e+12 1.199084e+12 1.237499e+12 1.314314e+12 \n", + "124 1.274443e+12 1.314564e+12 1.170565e+12 1.077828e+12 1.158071e+12 \n", + "87 9.125241e+11 8.908148e+11 8.608542e+11 9.318774e+11 1.015423e+12 \n", + "143 8.769235e+11 8.909813e+11 7.652649e+11 7.835282e+11 8.318099e+11 \n", + "166 7.466471e+11 7.563503e+11 6.542699e+11 6.449355e+11 6.885861e+11 \n", + "196 9.505794e+11 9.341859e+11 8.597969e+11 8.637216e+11 8.515492e+11 \n", + "33 6.885042e+11 7.091826e+11 6.798323e+11 6.701811e+11 6.789654e+11 \n", + "\n", + " 2018 \n", + "202 2.049410e+13 \n", + "36 1.360815e+13 \n", + "97 4.970916e+12 \n", + "50 3.996759e+12 \n", + "68 2.825208e+12 \n", + "64 2.777535e+12 \n", + "89 2.726323e+12 \n", + "94 2.073902e+12 \n", + "26 1.868626e+12 \n", + "32 1.712510e+12 \n", + "164 1.657554e+12 \n", + "104 1.619424e+12 \n", + "10 1.432195e+12 \n", + "59 1.426189e+12 \n", + "124 1.223809e+12 \n", + "87 1.042173e+12 \n", + "143 9.136585e+11 \n", + "166 7.824835e+11 \n", + "196 7.665091e+11 \n", + "33 7.055013e+11 \n", + "\n", + "[20 rows x 61 columns]" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# top20 国家历年GDP\n", + "country_gdp_top20 = country_gdp.drop(columns=['Region', 'Income_Group', 'Unnamed: 4', 'Indicator Name', 'Indicator Code', '2019', 'Unnamed: 64']).sort_values(by='2018', ascending=False)[:20]\n", + "# GDP转换成万亿单位\n", + "for i in range(1960, 2019):\n", + " country_gdp_top20[str(i)] = country_gdp_top20[str(i)].apply(lambda x: x*1000000000000)\n", + "\n", + "country_gdp_top20" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1180\n", + "1180\n" + ] + } + ], + "source": [ + "country_list = []\n", + "year_list = []\n", + "value_list = []\n", + "type_list = []\n", + "c_list = ['美国', '中国', '日本', '德国', '英国', '法国', '印度', '意大利', '巴西', '加拿大', '俄罗斯联邦', '大韩民国', '澳大利亚', \n", + " '西班牙', '墨西哥', '印度尼西亚', '荷兰', '沙特阿拉伯', '土耳其', '瑞士']\n", + "for c in c_list:\n", + " for i in range(1960, 2019):\n", + " country_list.append(c)\n", + " type_list.append(country_gdp_top20[country_gdp_top20['Country Name'] == c]['Country Code'].values.tolist()[0])\n", + " value_list.append(country_gdp_top20[country_gdp_top20['Country Name'] == c][str(i)].values.tolist()[0])\n", + " year_list.append(str(i))\n", + "\n", + "print(len(value_list))\n", + "print(len(country_list))\n", + "d = {'name': country_list, 'type': type_list, 'value': value_list, 'date': year_list}\n", + "pd.DataFrame(d).to_csv('auto_gdp.csv', index=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [], + "source": [ + "# 2018年GDP后十名\n", + "country_gdp_bottom10 = country_gdp.dropna(subset=['2018'])[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(country_gdp_bottom10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_gdp_bottom10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP bottom10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP倒数分析" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_tuwalu = country_gdp[country_gdp['Country Name']=='图瓦卢']\n", + "\n", + "df_naolu = country_gdp[country_gdp['Country Name']=='瑙魯']\n", + "\n", + "df_jilibasi = country_gdp[country_gdp['Country Name']=='基里巴斯']\n", + "\n", + "df_mashaoerqundao = country_gdp[country_gdp['Country Name']=='马绍尔群岛']\n", + " \n", + "df_palao = country_gdp[country_gdp['Country Name']=='帕劳']" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "tuwalu_gdp = df_tuwalu[year_str].values.tolist()[0]\n", + "naolu_gdp = df_naolu[year_str].values.tolist()[0]\n", + "jilibasi_gdp = df_jilibasi[year_str].values.tolist()[0]\n", + "mashaoerqundao_gdp = df_mashaoerqundao[year_str].values.tolist()[0]\n", + "palao_gdp = df_palao[year_str].values.tolist()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP正数分析" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "df_china = country_gdp[country_gdp['Country Name']=='中国']\n", + "\n", + "df_usa = country_gdp[country_gdp['Country Name']=='美国']\n", + "\n", + "df_jpn = country_gdp[country_gdp['Country Name']=='日本']\n", + "\n", + "df_de = country_gdp[country_gdp['Country Name']=='德国']\n", + "\n", + "df_uk = country_gdp[country_gdp['Country Name']=='英国']" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "year_str = [str(i) for i in range(1960, 2019)]\n", + "\n", + "china_gdp = df_china[year_str].values.tolist()[0]\n", + "usa_gdp = df_usa[year_str].values.tolist()[0]\n", + "jpn_gdp = df_jpn[year_str].values.tolist()[0]\n", + "de_gdp = df_de[year_str].values.tolist()[0]\n", + "uk_gdp = df_uk[year_str].values.tolist()[0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "from pyecharts.charts import Scatter\n", + "\n", + "def scatter_base(choose, values, country) -> Scatter:\n", + " c = (\n", + " Scatter()\n", + " .add_xaxis(choose)\n", + " .add_yaxis(\"%s历年GDP\" % country, values)\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " # datazoom_opts=opts.DataZoomOpts(),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " )\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, china_gdp, '中国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, usa_gdp, '美国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, jpn_gdp, '日本').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, de_gdp, '德国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, uk_gdp, '英国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, tuwalu_gdp, '图瓦卢').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, naolu_gdp, '瑙魯').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, jilibasi_gdp, '基里帕斯').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, usa_gdp, '美国').render_notebook()" + "scatter_base(year_str, mashaoerqundao_gdp, '马绍尔群岛').render_notebook()" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 85, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -5369,14 +9060,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, palao_gdp, '帕劳').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "country_code = pd.read_json('countries.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "country_code.rename(columns={'iso3': 'Country Code'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "conutry_code_name = country_code[['name', 'Country Code']]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "country_gdp_code = pd.merge(country_gdp, conutry_code_name, on='Country Code', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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199美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.54330.56330.6051...16.19700716.78484917.52174718.21929818.70718819.48539420.4941NaNNaNUnited States
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" + ], + "text/plain": [ + " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", + "199 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", + "\n", + " Indicator Code 1960 1961 1962 ... 2012 2013 \\\n", + "199 NY.GDP.MKTP.CD 0.5433 0.5633 0.6051 ... 16.197007 16.784849 \n", + "\n", + " 2014 2015 2016 2017 2018 2019 Unnamed: 64 \\\n", + "199 17.521747 18.219298 18.707188 19.485394 20.4941 NaN NaN \n", + "\n", + " name \n", + "199 United States \n", + "\n", + "[1 rows x 69 columns]" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_gdp_code[country_gdp_code['Country Name'] == '美国']" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "212 212\n" + ] + } + ], + "source": [ + "map_country = country_gdp_code['name'].values.tolist()\n", + "map_country_gdp = country_gdp_code['2018'].values.tolist()\n", + "print(len(map_country), len(map_country_gdp))" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "def map_world() -> Map:\n", + " c = (\n", + " Map()\n", + " .add(\"GDP总量\", [list(z) for z in zip(map_country, map_country_gdp)], \"world\")\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " .set_global_opts(\n", + " title_opts=opts.TitleOpts(title=\"GDP总量\"),\n", + " visualmap_opts=opts.VisualMapOpts(max_=5, is_piecewise=True),\n", + " )\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map_world().render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# 增长率分析\n", + "growth = pd.read_csv('growth_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "growth_data = pd.merge(country_data, growth, how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "country_growth_top10 = growth_data[['Country Name', 'Country Code', '2018 [YR2018]']].sort_values(by='2018 [YR2018]', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, jpn_gdp, '日本').render_notebook()" + "bar = Bar()\n", + "bar.add_xaxis(country_growth_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_growth_top10['2018 [YR2018]'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP增长率top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\")\n", + " ),)\n", + "bar.render_notebook()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 111, "metadata": {}, + "outputs": [], + "source": [ + "country_growth_bottom10 = growth_data[['Country Name', 'Country Code', '2018 [YR2018]']].sort_values(by='2018 [YR2018]', ascending=False)[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -5830,14 +10939,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(country_growth_bottom10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_growth_bottom10['2018 [YR2018]'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP增长率bottom10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [], + "source": [ + "growth_china = growth_data[growth_data['Country Name']=='中国']\n", + "growth_usa = growth_data[growth_data['Country Name']=='美国']\n", + "growth_ind = growth_data[growth_data['Country Name']=='印度']" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "def scatter_growth(choose, values, country) -> Scatter:\n", + " c = (\n", + " Scatter()\n", + " .add_xaxis(choose)\n", + " .add_yaxis(\"%s历年GDP增长率\" % country, values)\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\"),\n", + " ),\n", + " xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10.6361404632299, 9.55091409001014, 7.8596274932851, 7.76861528412806, 7.29951892117124, 6.90531667019702, 6.73667525262536, 6.75700761091511, 6.60000000000001]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year_str_new = [str(i)+ \" [YR%s]\" % i for i in range(2010, 2019)]\n", + "year_str_new1 = [str(i) for i in range(2010, 2019)]\n", + "\n", + "china_growth = growth_china[year_str_new].values.tolist()[0]\n", + "china_growth = list(map(float, china_growth))\n", + "print(china_growth)\n", + "scatter_growth(year_str_new1, china_growth, '中国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['2.56376655847168', '1.55083550620974', '2.24954585216848', '1.8420810704697', '2.4519730360895', '2.88091046576689', '1.56721516988685', '2.21701033035224', '2.8569878160516']\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, de_gdp, '德国').render_notebook()" + "usa_growth = growth_usa[year_str_new].values.tolist()[0]\n", + "print(usa_growth)\n", + "scatter_growth(year_str_new1, usa_growth, '美国').render_notebook()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 318, "metadata": {}, + "outputs": [], + "source": [ + "ind_growth = growth_ind[year_str_new].values.tolist()[0]\n", + "df_ind = country_gdp[country_gdp['Country Name']=='印度']\n", + "ind_gdp = df_ind[year_str].values.tolist()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -6291,14 +11787,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def overlap_line_scatter() -> Bar:\n", + " scatter = (\n", + " Scatter()\n", + " .add_xaxis(year_str_new1)\n", + " .add_yaxis(\"中国历年GDP增长率\", china_growth)\n", + " .add_yaxis(\"美国历年GDP增长率\", usa_growth)\n", + " .add_yaxis(\"印度历年GDP增长率\", ind_growth)\n", + " .extend_axis(\n", + " yaxis=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} 万亿\"), interval=5\n", + " )\n", + " )\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\"),\n", + " ),\n", + " xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " line = (\n", + " Line()\n", + " .add_xaxis(year_str[-9:])\n", + " .add_yaxis(\"中国历年 GDP 总量\", china_gdp[-9:], yaxis_index=1)\n", + " .add_yaxis(\"美国历年 GDP 总量\", usa_gdp[-9:], yaxis_index=1)\n", + " .add_yaxis(\"印度历年 GDP 总量\", ind_gdp[-9:], yaxis_index=1)\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " scatter.overlap(line)\n", + " return scatter\n", + "\n", + "overlap_line_scatter().render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "country_growth_code = pd.merge(growth_data, conutry_code_name, on='Country Code', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "212 212\n" + ] + } + ], + "source": [ + "map_country = country_growth_code['name'].values.tolist()\n", + "map_country_gdp = country_growth_code['2018 [YR2018]'].values.tolist()\n", + "print(len(map_country), len(map_country_gdp))\n", + "def map_world_growth() -> Map:\n", + " c = (\n", + " Map()\n", + " .add(\"GDP增长率\", [list(z) for z in zip(map_country, map_country_gdp)], \"world\")\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " .set_global_opts(\n", + " title_opts=opts.TitleOpts(title=\"GDP增长率\"),\n", + " visualmap_opts=opts.VisualMapOpts(max_=10, min_=-5, is_piecewise=True),\n", + " )\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, uk_gdp, '英国').render_notebook()" + "map_world_growth().render_notebook()" ] }, { @@ -6858,39 +13778,6 @@ "pred_y = model.predict(X)" ] }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.67561513, 0.64550236, 0.63069893, 0.6310778 , 0.64651146,\n", - " 0.67687193, 0.7220308 , 0.78185916, 0.85622766, 0.94500648,\n", - " 1.04806535, 1.16527352, 1.29649978, 1.44161246, 1.60047942,\n", - " 1.77296806, 1.9589453 , 2.15827761, 2.37083099, 2.59647097,\n", - " 2.8350626 , 3.08647047, 3.35055872, 3.62719099, 3.91623047,\n", - " 4.21753986, 4.53098142, 4.8564169 , 5.19370761, 5.54271437,\n", - " 5.90329754, 6.27531699, 6.65863212, 7.05310188, 7.4585847 ,\n", - " 7.87493857, 8.302021 , 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Country NameCountry CodeRegionIncome_GroupUnnamed: 4
0阿鲁巴ABWNaN高收入国家NaN
1阿富汗AFG南亚低收入国家NaN
2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaN
4安道尔共和国ANDNaN高收入国家NaN
..................
259科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaN
260也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaN
261南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaN
262赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
263津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaN
\n", - "

217 rows × 5 columns

\n", - "" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN\n", - "1 阿富汗 AFG 南亚 低收入国家 NaN\n", - "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN\n", - "4 安道尔共和国 AND NaN 高收入国家 NaN\n", - ".. ... ... ... ... ...\n", - "259 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN\n", - "260 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN\n", - "261 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN\n", - "262 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "263 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN\n", - "\n", - "[217 rows x 5 columns]" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_data" - ] - }, - { - "cell_type": "code", - "execution_count": 73, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -212,27 +57,7 @@ }, { "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[47, 60, 31, 79]" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_data.groupby('Income_Group').size().values.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -248,14 +73,14 @@ " });\n", "\n", "\n", - "
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\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 101, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -604,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -620,14 +445,14 @@ " });\n", "\n", "\n", - "
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\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 113, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -824,21 +649,23 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ - "# 中高等收入国家\n", - "mid_high = country_data[country_data['Income_Group'] == '中高等收入国家']\n", - "mid_high_gdp = pd.merge(mid_high, gdp, how='inner')\n", - "mid_high_gdp['2018'] = mid_high_gdp['2018'].apply(lambda x: x/1000000000000)\n", - "mid_high_gdp_top10 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", - "mid_high_gdp_top20 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + "world_gdp = 85.8\n", + "def liquid_base(country, gdp) -> Liquid:\n", + " c = (\n", + " Liquid()\n", + " .add(\"lq\", [gdp/world_gdp])\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"%s GDP 总量占比世界\" % country))\n", + " )\n", + " return c" ] }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 234, "metadata": { "scrolled": true }, @@ -849,19 +676,19 @@ "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 117, + "execution_count": 234, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bar = Bar()\n", - "bar.add_xaxis(mid_high_gdp_top10['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_high_gdp_top10['2018'].values.tolist())\n", - "bar.reversal_axis()\n", - "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top10\", subtitle=\"\"),\n", - " xaxis_opts=opts.AxisOpts(\n", - " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", - " ),)\n", - "bar.render_notebook()" + "liquid_base(\"美国\", 20.4941).render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 中高等收入国家\n", + "mid_high = country_data[country_data['Income_Group'] == '中高等收入国家']\n", + "mid_high_gdp = pd.merge(mid_high, gdp, how='inner')\n", + "mid_high_gdp['2018'] = mid_high_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "mid_high_gdp_top10 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "mid_high_gdp_top20 = mid_high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" ] }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -1052,14 +817,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 120, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar()\n", - "bar.add_xaxis(mid_high_gdp_top20['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_high_gdp_top20['2018'].values.tolist())\n", + "bar.add_xaxis(mid_high_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_high_gdp_top10['2018'].values.tolist())\n", "bar.reversal_axis()\n", "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top20\", subtitle=\"\"),\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top10\", subtitle=\"\"),\n", " xaxis_opts=opts.AxisOpts(\n", " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", " ),)\n", @@ -1254,21 +999,129 @@ }, { "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], + "execution_count": 235, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 中低等收入国家\n", - "mid_low = country_data[country_data['Income_Group'] == '中低等收入国家']\n", - "mid_low_gdp = pd.merge(mid_low, gdp, how='inner')\n", - "mid_low_gdp['2018'] = mid_low_gdp['2018'].apply(lambda x: x/1000000000000)\n", - "mid_low_gdp_top10 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", - "mid_low_gdp_top20 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + "liquid_base(\"中美\", 34.1022).render_notebook()" ] }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -1284,14 +1137,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 122, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bar = Bar()\n", - "bar.add_xaxis(mid_low_gdp_top10['Country Name'].values.tolist())\n", - "bar.add_yaxis(\"\", mid_low_gdp_top10['2018'].values.tolist())\n", + "bar.add_xaxis(mid_high_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_high_gdp_top20['2018'].values.tolist())\n", "bar.reversal_axis()\n", "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", - "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中低等收入国家GDP Top10\", subtitle=\"\"),\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中高等收入国家GDP Top20\", subtitle=\"\"),\n", " xaxis_opts=opts.AxisOpts(\n", " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", " ),)\n", @@ -1466,7 +1339,21 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# 中低等收入国家\n", + "mid_low = country_data[country_data['Income_Group'] == '中低等收入国家']\n", + "mid_low_gdp = pd.merge(mid_low, gdp, how='inner')\n", + "mid_low_gdp['2018'] = mid_low_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "mid_low_gdp_top10 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "mid_low_gdp_top20 = mid_low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "scrolled": true }, @@ -1482,14 +1369,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(mid_low_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_low_gdp_top10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"中低等收入国家GDP Top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 123, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1684,7 +1769,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1698,7 +1783,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -1714,14 +1799,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 127, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1896,7 +1981,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -1912,14 +1997,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 129, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2114,7 +2199,16 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "country_gdp = pd.merge(country_data, gdp, how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 94, "metadata": { "scrolled": true }, @@ -2177,47 +2271,47 @@ " NaN\n", " NaN\n", " ...\n", - " 2.549721e+09\n", - " 2.534637e+09\n", - " 2.581564e+09\n", - " 2.649721e+09\n", - " 2.691620e+09\n", - " 2.646927e+09\n", - " 2.700559e+09\n", + " 0.002550\n", + " 0.002535\n", + " 0.002582\n", + " 0.002650\n", + " 0.002692\n", + " 0.002647\n", + " 0.002701\n", " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", - " 安道尔共和国\n", - " AND\n", - " NaN\n", - " 高收入国家\n", + " 阿富汗\n", + " AFG\n", + " 南亚\n", + " 低收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000538\n", + " 0.000549\n", + " 0.000547\n", " ...\n", - " 3.442063e+09\n", - " 3.164615e+09\n", - " 3.281585e+09\n", - " 3.350736e+09\n", - " 2.811489e+09\n", - " 2.877312e+09\n", - " 3.013387e+09\n", - " 3.236544e+09\n", + " 0.017804\n", + " 0.020002\n", + " 0.020561\n", + " 0.020485\n", + " 0.019907\n", + " 0.019363\n", + " 0.020192\n", + " 0.019363\n", " NaN\n", " NaN\n", " \n", " \n", " 2\n", - " 阿拉伯联合酋长国\n", - " ARE\n", - " NaN\n", - " 高收入国家\n", + " 安哥拉\n", + " AGO\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", @@ -2225,23 +2319,23 @@ " NaN\n", " NaN\n", " ...\n", - " 3.506660e+11\n", - " 3.745906e+11\n", - " 3.901076e+11\n", - " 4.031371e+11\n", - " 3.581351e+11\n", - " 3.570451e+11\n", - " 3.825751e+11\n", - " 4.141789e+11\n", + " 0.111790\n", + " 0.128053\n", + " 0.136710\n", + " 0.145712\n", + " 0.116194\n", + " 0.101124\n", + " 0.122124\n", + " 0.105751\n", " NaN\n", " NaN\n", " \n", " \n", " 3\n", - " 安提瓜和巴布达\n", - " ATG\n", - " NaN\n", - " 高收入国家\n", + " 阿尔巴尼亚\n", + " ALB\n", + " 欧洲与中亚地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", @@ -2249,38 +2343,38 @@ " NaN\n", " NaN\n", " ...\n", - " 1.142043e+09\n", - " 1.211412e+09\n", - " 1.192920e+09\n", - " 1.275577e+09\n", - " 1.359195e+09\n", - " 1.464630e+09\n", - " 1.510085e+09\n", - " 1.623804e+09\n", + " 0.012891\n", + " 0.012320\n", + " 0.012776\n", + " 0.013228\n", + " 0.011387\n", + " 0.011861\n", + " 0.013025\n", + " 0.015059\n", " NaN\n", " NaN\n", " \n", " \n", " 4\n", - " 澳大利亚\n", - " AUS\n", + " 安道尔共和国\n", + " AND\n", " NaN\n", " 高收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 1.857767e+10\n", - " 1.965394e+10\n", - " 1.989249e+10\n", - " ...\n", - " 1.396650e+12\n", - " 1.546152e+12\n", - " 1.576184e+12\n", - " 1.467484e+12\n", - " 1.351520e+12\n", - " 1.210028e+12\n", - " 1.330803e+12\n", - " 1.432195e+12\n", + " NaN\n", + " NaN\n", + " NaN\n", + " ...\n", + " 0.003442\n", + " 0.003165\n", + " 0.003282\n", + " 0.003351\n", + " 0.002811\n", + " 0.002877\n", + " 0.003013\n", + " 0.003237\n", " NaN\n", " NaN\n", " \n", @@ -2309,198 +2403,198 @@ " ...\n", " \n", " \n", - " 74\n", - " 特立尼达和多巴哥\n", - " TTO\n", - " NaN\n", - " 高收入国家\n", + " 211\n", + " 科索沃\n", + " XKX\n", + " 欧洲与中亚地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 5.356701e+08\n", - " 5.849612e+08\n", - " 6.193192e+08\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 2.543301e+10\n", - " 2.576933e+10\n", - " 2.711026e+10\n", - " 2.747797e+10\n", - " 2.512152e+10\n", - " 2.174639e+10\n", - " 2.225046e+10\n", - " 2.341035e+10\n", + " 0.006692\n", + " 0.006500\n", + " 0.007072\n", + " 0.007387\n", + " 0.006441\n", + " 0.006715\n", + " 0.007228\n", + " 0.007900\n", " NaN\n", " NaN\n", " \n", " \n", - " 75\n", - " 乌拉圭\n", - " URY\n", - " NaN\n", - " 高收入国家\n", + " 212\n", + " 也门共和国\n", + " YEM\n", + " 中东与北非地区（不包括高收入）\n", + " 低收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 1.242289e+09\n", - " 1.547389e+09\n", - " 1.710004e+09\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 4.796244e+10\n", - " 5.126439e+10\n", - " 5.753123e+10\n", - " 5.723601e+10\n", - " 5.327430e+10\n", - " 5.268761e+10\n", - " 5.648899e+10\n", - " 5.959689e+10\n", + " 0.032726\n", + " 0.035401\n", + " 0.040415\n", + " 0.043229\n", + " 0.042628\n", + " 0.030968\n", + " 0.026819\n", + " 0.026914\n", " NaN\n", " NaN\n", " \n", " \n", - " 76\n", - " 美国\n", - " USA\n", - " NaN\n", - " 高收入国家\n", + " 213\n", + " 南非\n", + " ZAF\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中高等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 5.433000e+11\n", - " 5.633000e+11\n", - " 6.051000e+11\n", + " 0.007575\n", + " 0.007973\n", + " 0.008498\n", " ...\n", - " 1.554258e+13\n", - " 1.619701e+13\n", - " 1.678485e+13\n", - " 1.752175e+13\n", - " 1.821930e+13\n", - " 1.870719e+13\n", - " 1.948539e+13\n", - " 2.049410e+13\n", + " 0.416417\n", + " 0.396329\n", + " 0.366645\n", + " 0.350638\n", + " 0.317416\n", + " 0.296341\n", + " 0.349268\n", + " 0.368288\n", " NaN\n", " NaN\n", " \n", " \n", - " 77\n", - " 英屬維爾京群島\n", - " VGB\n", - " NaN\n", - " 高收入国家\n", + " 214\n", + " 赞比亚\n", + " ZMB\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000713\n", + " 0.000696\n", + " 0.000693\n", " ...\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.023460\n", + " 0.025503\n", + " 0.028045\n", + " 0.027151\n", + " 0.021154\n", + " 0.020955\n", + " 0.025868\n", + " 0.026720\n", " NaN\n", " NaN\n", " \n", " \n", - " 78\n", - " 美属维京群岛\n", - " VIR\n", - " NaN\n", - " 高收入国家\n", + " 215\n", + " 津巴布韦\n", + " ZWE\n", + " 撒哈拉以南非洲地区（不包括高收入）\n", + " 中低等收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.001053\n", + " 0.001097\n", + " 0.001118\n", " ...\n", - " 4.239000e+09\n", - " 4.095000e+09\n", - " 3.762000e+09\n", - " 3.622000e+09\n", - " 3.748000e+09\n", - " 3.863000e+09\n", - " 3.855000e+09\n", - " NaN\n", + " 0.014102\n", + " 0.017115\n", + " 0.019091\n", + " 0.019496\n", + " 0.019963\n", + " 0.020549\n", + " 0.022813\n", + " 0.031001\n", " NaN\n", " NaN\n", " \n", " \n", "\n", - "

79 rows × 68 columns

\n", + "

216 rows × 68 columns

\n", "" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN GDP（现价美元） \n", - "1 安道尔共和国 AND NaN 高收入国家 NaN GDP（现价美元） \n", - "2 阿拉伯联合酋长国 ARE NaN 高收入国家 NaN GDP（现价美元） \n", - "3 安提瓜和巴布达 ATG NaN 高收入国家 NaN GDP（现价美元） \n", - "4 澳大利亚 AUS NaN 高收入国家 NaN GDP（现价美元） \n", - ".. ... ... ... ... ... ... \n", - "74 特立尼达和多巴哥 TTO NaN 高收入国家 NaN GDP（现价美元） \n", - "75 乌拉圭 URY NaN 高收入国家 NaN GDP（现价美元） \n", - "76 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", - "77 英屬維爾京群島 VGB NaN 高收入国家 NaN GDP（现价美元） \n", - "78 美属维京群岛 VIR NaN 高收入国家 NaN GDP（现价美元） \n", + " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", + "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", + "1 阿富汗 AFG 南亚 低收入国家 NaN \n", + "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "4 安道尔共和国 AND NaN 高收入国家 NaN \n", + ".. ... ... ... ... ... \n", + "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", + "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", + "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", "\n", - " Indicator Code 1960 1961 1962 ... \\\n", - "0 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "1 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "2 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "3 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "4 NY.GDP.MKTP.CD 1.857767e+10 1.965394e+10 1.989249e+10 ... \n", - ".. ... ... ... ... ... \n", - "74 NY.GDP.MKTP.CD 5.356701e+08 5.849612e+08 6.193192e+08 ... \n", - "75 NY.GDP.MKTP.CD 1.242289e+09 1.547389e+09 1.710004e+09 ... \n", - "76 NY.GDP.MKTP.CD 5.433000e+11 5.633000e+11 6.051000e+11 ... \n", - "77 NY.GDP.MKTP.CD NaN NaN NaN ... \n", - "78 NY.GDP.MKTP.CD NaN NaN NaN ... \n", + " Indicator Name Indicator Code 1960 1961 1962 ... \\\n", + "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "1 GDP（现价美元） NY.GDP.MKTP.CD 0.000538 0.000549 0.000547 ... \n", + "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + ".. ... ... ... ... ... ... \n", + "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "213 GDP（现价美元） NY.GDP.MKTP.CD 0.007575 0.007973 0.008498 ... \n", + "214 GDP（现价美元） NY.GDP.MKTP.CD 0.000713 0.000696 0.000693 ... \n", + "215 GDP（现价美元） NY.GDP.MKTP.CD 0.001053 0.001097 0.001118 ... \n", "\n", - " 2011 2012 2013 2014 2015 \\\n", - "0 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 2.691620e+09 \n", - "1 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 2.811489e+09 \n", - "2 3.506660e+11 3.745906e+11 3.901076e+11 4.031371e+11 3.581351e+11 \n", - "3 1.142043e+09 1.211412e+09 1.192920e+09 1.275577e+09 1.359195e+09 \n", - "4 1.396650e+12 1.546152e+12 1.576184e+12 1.467484e+12 1.351520e+12 \n", - ".. ... ... ... ... ... \n", - "74 2.543301e+10 2.576933e+10 2.711026e+10 2.747797e+10 2.512152e+10 \n", - "75 4.796244e+10 5.126439e+10 5.753123e+10 5.723601e+10 5.327430e+10 \n", - "76 1.554258e+13 1.619701e+13 1.678485e+13 1.752175e+13 1.821930e+13 \n", - "77 NaN NaN NaN NaN NaN \n", - "78 4.239000e+09 4.095000e+09 3.762000e+09 3.622000e+09 3.748000e+09 \n", + " 2011 2012 2013 2014 2015 2016 2017 \\\n", + "0 0.002550 0.002535 0.002582 0.002650 0.002692 0.002647 0.002701 \n", + "1 0.017804 0.020002 0.020561 0.020485 0.019907 0.019363 0.020192 \n", + "2 0.111790 0.128053 0.136710 0.145712 0.116194 0.101124 0.122124 \n", + "3 0.012891 0.012320 0.012776 0.013228 0.011387 0.011861 0.013025 \n", + "4 0.003442 0.003165 0.003282 0.003351 0.002811 0.002877 0.003013 \n", + ".. ... ... ... ... ... ... ... \n", + "211 0.006692 0.006500 0.007072 0.007387 0.006441 0.006715 0.007228 \n", + "212 0.032726 0.035401 0.040415 0.043229 0.042628 0.030968 0.026819 \n", + "213 0.416417 0.396329 0.366645 0.350638 0.317416 0.296341 0.349268 \n", + "214 0.023460 0.025503 0.028045 0.027151 0.021154 0.020955 0.025868 \n", + "215 0.014102 0.017115 0.019091 0.019496 0.019963 0.020549 0.022813 \n", "\n", - " 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - "2 3.570451e+11 3.825751e+11 4.141789e+11 NaN NaN \n", - "3 1.464630e+09 1.510085e+09 1.623804e+09 NaN NaN \n", - "4 1.210028e+12 1.330803e+12 1.432195e+12 NaN NaN \n", - ".. ... ... ... ... ... \n", - "74 2.174639e+10 2.225046e+10 2.341035e+10 NaN NaN \n", - "75 5.268761e+10 5.648899e+10 5.959689e+10 NaN NaN \n", - "76 1.870719e+13 1.948539e+13 2.049410e+13 NaN NaN \n", - "77 NaN NaN NaN NaN NaN \n", - "78 3.863000e+09 3.855000e+09 NaN NaN NaN \n", + " 2018 2019 Unnamed: 64 \n", + "0 NaN NaN NaN \n", + "1 0.019363 NaN NaN \n", + "2 0.105751 NaN NaN \n", + "3 0.015059 NaN NaN \n", + "4 0.003237 NaN NaN \n", + ".. ... ... ... \n", + "211 0.007900 NaN NaN \n", + "212 0.026914 NaN NaN \n", + "213 0.368288 NaN NaN \n", + "214 0.026720 NaN NaN \n", + "215 0.031001 NaN NaN \n", "\n", - "[79 rows x 68 columns]" + "[216 rows x 68 columns]" ] }, - "execution_count": 91, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "high_gdp" + "country_gdp" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 66, "metadata": { "scrolled": true }, @@ -2528,913 +2622,123 @@ " \n", " Country Name\n", " Country Code\n", - " Indicator Name\n", - " Indicator Code\n", - " 1960\n", - " 1961\n", - " 1962\n", - " 1963\n", - " 1964\n", - " 1965\n", - " ...\n", - " 2011\n", - " 2012\n", - " 2013\n", - " 2014\n", - " 2015\n", - " 2016\n", - " 2017\n", " 2018\n", - " 2019\n", - " Unnamed: 64\n", " \n", " \n", " \n", " \n", - " 0\n", - " 阿鲁巴\n", - " ABW\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 2.549721e+09\n", - " 2.534637e+09\n", - " 2.581564e+09\n", - " 2.649721e+09\n", - " 2.691620e+09\n", - " 2.646927e+09\n", - " 2.700559e+09\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 202\n", + " 美国\n", + " USA\n", + " 20.494100\n", " \n", " \n", - " 1\n", - " 阿富汗\n", - " AFG\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 5.377778e+08\n", - " 5.488889e+08\n", - " 5.466667e+08\n", - " 7.511112e+08\n", - " 8.000000e+08\n", - " 1.006667e+09\n", - " ...\n", - " 1.780428e+10\n", - " 2.000162e+10\n", - " 2.056105e+10\n", - " 2.048487e+10\n", - " 1.990711e+10\n", - " 1.936264e+10\n", - " 2.019176e+10\n", - " 1.936297e+10\n", - " NaN\n", - " NaN\n", + " 36\n", + " 中国\n", + " CHN\n", + " 13.608152\n", " \n", " \n", - " 2\n", - " 安哥拉\n", - " AGO\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 1.117897e+11\n", - " 1.280529e+11\n", - " 1.367099e+11\n", - " 1.457122e+11\n", - " 1.161936e+11\n", - " 1.011239e+11\n", - " 1.221238e+11\n", - " 1.057510e+11\n", - " NaN\n", - " NaN\n", + " 97\n", + " 日本\n", + " JPN\n", + " 4.970916\n", " \n", " \n", - " 3\n", - " 阿尔巴尼亚\n", - " ALB\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 1.289087e+10\n", - " 1.231978e+10\n", - " 1.277628e+10\n", - " 1.322825e+10\n", - " 1.138693e+10\n", - " 1.186135e+10\n", - " 1.302506e+10\n", - " 1.505888e+10\n", - " NaN\n", - " NaN\n", + " 50\n", + " 德国\n", + " DEU\n", + " 3.996759\n", " \n", " \n", - " 4\n", - " 安道尔共和国\n", - " AND\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 3.442063e+09\n", - " 3.164615e+09\n", - " 3.281585e+09\n", - " 3.350736e+09\n", - " 2.811489e+09\n", - " 2.877312e+09\n", - " 3.013387e+09\n", - " 3.236544e+09\n", - " NaN\n", - " NaN\n", + " 68\n", + " 英国\n", + " GBR\n", + " 2.825208\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " 64\n", + " 法国\n", + " FRA\n", + " 2.777535\n", " \n", " \n", - " 259\n", - " 科索沃\n", - " XKX\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 6.691827e+09\n", - " 6.499936e+09\n", - " 7.071960e+09\n", - " 7.386891e+09\n", - " 6.440612e+09\n", - " 6.714712e+09\n", - " 7.227765e+09\n", - " 7.900269e+09\n", - " NaN\n", - " NaN\n", + " 89\n", + " 印度\n", + " IND\n", + " 2.726323\n", " \n", " \n", - " 260\n", - " 也门共和国\n", - " YEM\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " 3.272642e+10\n", - " 3.540134e+10\n", - " 4.041523e+10\n", - " 4.322859e+10\n", - " 4.262833e+10\n", - " 3.096824e+10\n", - " 2.681870e+10\n", - " 2.691440e+10\n", - " NaN\n", - " NaN\n", + " 94\n", + " 意大利\n", + " ITA\n", + " 2.073902\n", " \n", " \n", - " 261\n", - " 南非\n", - " ZAF\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 7.575397e+09\n", - " 7.972997e+09\n", - " 8.497997e+09\n", - " 9.423396e+09\n", - " 1.037400e+10\n", - " 1.133440e+10\n", - " ...\n", - " 4.164170e+11\n", - " 3.963294e+11\n", - " 3.666449e+11\n", - " 3.506376e+11\n", - " 3.174156e+11\n", - " 2.963409e+11\n", - " 3.492681e+11\n", - " 3.682882e+11\n", - " NaN\n", - " NaN\n", + " 26\n", + " 巴西\n", + " BRA\n", + " 1.868626\n", " \n", " \n", - " 262\n", - " 赞比亚\n", - " ZMB\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 7.130000e+08\n", - " 6.962857e+08\n", - " 6.931429e+08\n", - " 7.187143e+08\n", - " 8.394286e+08\n", - " 1.082857e+09\n", - " ...\n", - " 2.346010e+10\n", - " 2.550337e+10\n", - " 2.804546e+10\n", - " 2.715063e+10\n", - " 2.115439e+10\n", - " 2.095475e+10\n", - " 2.586814e+10\n", - " 2.672007e+10\n", - " NaN\n", - " NaN\n", + " 32\n", + " 加拿大\n", + " CAN\n", + " 1.712510\n", " \n", " \n", - " 263\n", - " 津巴布韦\n", - " ZWE\n", - " GDP（现价美元）\n", - " NY.GDP.MKTP.CD\n", - " 1.052990e+09\n", - " 1.096647e+09\n", - " 1.117602e+09\n", - " 1.159512e+09\n", - " 1.217138e+09\n", - " 1.311436e+09\n", - " ...\n", - " 1.410192e+10\n", - " 1.711485e+10\n", - " 1.909102e+10\n", - " 1.949552e+10\n", - " 1.996312e+10\n", - " 2.054868e+10\n", - " 2.281301e+10\n", - " 3.100052e+10\n", - " NaN\n", - " NaN\n", + " 164\n", + " 俄罗斯联邦\n", + " RUS\n", + " 1.657554\n", " \n", - " \n", - "\n", - "

264 rows × 65 columns

\n", - "" - ], - "text/plain": [ - " Country Name Country Code Indicator Name Indicator Code 1960 \\\n", - "0 阿鲁巴 ABW GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "1 阿富汗 AFG GDP（现价美元） NY.GDP.MKTP.CD 5.377778e+08 \n", - "2 安哥拉 AGO GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "3 阿尔巴尼亚 ALB GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "4 安道尔共和国 AND GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - ".. ... ... ... ... ... \n", - "259 科索沃 XKX GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "260 也门共和国 YEM GDP（现价美元） NY.GDP.MKTP.CD NaN \n", - "261 南非 ZAF GDP（现价美元） NY.GDP.MKTP.CD 7.575397e+09 \n", - "262 赞比亚 ZMB GDP（现价美元） NY.GDP.MKTP.CD 7.130000e+08 \n", - "263 津巴布韦 ZWE GDP（现价美元） NY.GDP.MKTP.CD 1.052990e+09 \n", - "\n", - " 1961 1962 1963 1964 1965 \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 5.488889e+08 5.466667e+08 7.511112e+08 8.000000e+08 1.006667e+09 \n", - "2 NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN \n", - ".. ... ... ... ... ... \n", - "259 NaN NaN NaN NaN NaN \n", - "260 NaN NaN NaN NaN NaN \n", - "261 7.972997e+09 8.497997e+09 9.423396e+09 1.037400e+10 1.133440e+10 \n", - "262 6.962857e+08 6.931429e+08 7.187143e+08 8.394286e+08 1.082857e+09 \n", - "263 1.096647e+09 1.117602e+09 1.159512e+09 1.217138e+09 1.311436e+09 \n", - "\n", - " ... 2011 2012 2013 2014 \\\n", - "0 ... 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 \n", - "1 ... 1.780428e+10 2.000162e+10 2.056105e+10 2.048487e+10 \n", - "2 ... 1.117897e+11 1.280529e+11 1.367099e+11 1.457122e+11 \n", - "3 ... 1.289087e+10 1.231978e+10 1.277628e+10 1.322825e+10 \n", - "4 ... 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 \n", - ".. ... ... ... ... ... \n", - "259 ... 6.691827e+09 6.499936e+09 7.071960e+09 7.386891e+09 \n", - "260 ... 3.272642e+10 3.540134e+10 4.041523e+10 4.322859e+10 \n", - "261 ... 4.164170e+11 3.963294e+11 3.666449e+11 3.506376e+11 \n", - "262 ... 2.346010e+10 2.550337e+10 2.804546e+10 2.715063e+10 \n", - "263 ... 1.410192e+10 1.711485e+10 1.909102e+10 1.949552e+10 \n", - "\n", - " 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.691620e+09 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 1.990711e+10 1.936264e+10 2.019176e+10 1.936297e+10 NaN NaN \n", - "2 1.161936e+11 1.011239e+11 1.221238e+11 1.057510e+11 NaN NaN \n", - "3 1.138693e+10 1.186135e+10 1.302506e+10 1.505888e+10 NaN NaN \n", - "4 2.811489e+09 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - ".. ... ... ... ... ... ... \n", - "259 6.440612e+09 6.714712e+09 7.227765e+09 7.900269e+09 NaN NaN \n", - "260 4.262833e+10 3.096824e+10 2.681870e+10 2.691440e+10 NaN NaN \n", - "261 3.174156e+11 2.963409e+11 3.492681e+11 3.682882e+11 NaN NaN \n", - "262 2.115439e+10 2.095475e+10 2.586814e+10 2.672007e+10 NaN NaN \n", - "263 1.996312e+10 2.054868e+10 2.281301e+10 3.100052e+10 NaN NaN \n", - "\n", - "[264 rows x 65 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdp" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "country_gdp = pd.merge(country_data, gdp, how='inner')" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
104大韩民国KOR1.619424
0阿鲁巴ABWNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...2.549721e+092.534637e+092.581564e+092.649721e+092.691620e+092.646927e+092.700559e+09NaNNaNNaN10澳大利亚AUS1.432195
1阿富汗AFG南亚低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.377778e+085.488889e+085.466667e+08...1.780428e+102.000162e+102.056105e+102.048487e+101.990711e+101.936264e+102.019176e+101.936297e+10NaNNaN59西班牙ESP1.426189
2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.117897e+111.280529e+111.367099e+111.457122e+111.161936e+111.011239e+111.221238e+111.057510e+11NaNNaN124墨西哥MEX1.223809
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.289087e+101.231978e+101.277628e+101.322825e+101.138693e+101.186135e+101.302506e+101.505888e+10NaNNaN87印度尼西亚IDN1.042173
4安道尔共和国ANDNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.442063e+093.164615e+093.281585e+093.350736e+092.811489e+092.877312e+093.013387e+093.236544e+09NaNNaN143荷兰NLD0.913658
..................................................................166沙特阿拉伯SAU0.782483
211科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...6.691827e+096.499936e+097.071960e+097.386891e+096.440612e+096.714712e+097.227765e+097.900269e+09NaNNaN
212也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.272642e+103.540134e+104.041523e+104.322859e+104.262833e+103.096824e+102.681870e+102.691440e+10NaNNaN
213南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD7.575397e+097.972997e+098.497997e+09...4.164170e+113.963294e+113.666449e+113.506376e+113.174156e+112.963409e+113.492681e+113.682882e+11NaNNaN
214赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD7.130000e+086.962857e+086.931429e+08...2.346010e+102.550337e+102.804546e+102.715063e+102.115439e+102.095475e+102.586814e+102.672007e+10NaNNaN
215津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.052990e+091.096647e+091.117602e+09...1.410192e+101.711485e+101.909102e+101.949552e+101.996312e+102.054868e+102.281301e+103.100052e+10NaNNaN
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216 rows × 68 columns

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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", - "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", - "1 阿富汗 AFG 南亚 低收入国家 NaN \n", - "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", - "4 安道尔共和国 AND NaN 高收入国家 NaN \n", - ".. ... ... ... ... ... \n", - "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", - "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", - "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", - "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", - "\n", - " Indicator Name Indicator Code 1960 1961 1962 \\\n", - "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "1 GDP（现价美元） NY.GDP.MKTP.CD 5.377778e+08 5.488889e+08 5.466667e+08 \n", - "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - ".. ... ... ... ... ... \n", - "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN \n", - "213 GDP（现价美元） NY.GDP.MKTP.CD 7.575397e+09 7.972997e+09 8.497997e+09 \n", - "214 GDP（现价美元） NY.GDP.MKTP.CD 7.130000e+08 6.962857e+08 6.931429e+08 \n", - "215 GDP（现价美元） NY.GDP.MKTP.CD 1.052990e+09 1.096647e+09 1.117602e+09 \n", - "\n", - " ... 2011 2012 2013 2014 \\\n", - "0 ... 2.549721e+09 2.534637e+09 2.581564e+09 2.649721e+09 \n", - "1 ... 1.780428e+10 2.000162e+10 2.056105e+10 2.048487e+10 \n", - "2 ... 1.117897e+11 1.280529e+11 1.367099e+11 1.457122e+11 \n", - "3 ... 1.289087e+10 1.231978e+10 1.277628e+10 1.322825e+10 \n", - "4 ... 3.442063e+09 3.164615e+09 3.281585e+09 3.350736e+09 \n", - ".. ... ... ... ... ... \n", - "211 ... 6.691827e+09 6.499936e+09 7.071960e+09 7.386891e+09 \n", - "212 ... 3.272642e+10 3.540134e+10 4.041523e+10 4.322859e+10 \n", - "213 ... 4.164170e+11 3.963294e+11 3.666449e+11 3.506376e+11 \n", - "214 ... 2.346010e+10 2.550337e+10 2.804546e+10 2.715063e+10 \n", - "215 ... 1.410192e+10 1.711485e+10 1.909102e+10 1.949552e+10 \n", - "\n", - " 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "0 2.691620e+09 2.646927e+09 2.700559e+09 NaN NaN NaN \n", - "1 1.990711e+10 1.936264e+10 2.019176e+10 1.936297e+10 NaN NaN \n", - "2 1.161936e+11 1.011239e+11 1.221238e+11 1.057510e+11 NaN NaN \n", - "3 1.138693e+10 1.186135e+10 1.302506e+10 1.505888e+10 NaN NaN \n", - "4 2.811489e+09 2.877312e+09 3.013387e+09 3.236544e+09 NaN NaN \n", - ".. ... ... ... ... ... ... \n", - "211 6.440612e+09 6.714712e+09 7.227765e+09 7.900269e+09 NaN NaN \n", - "212 4.262833e+10 3.096824e+10 2.681870e+10 2.691440e+10 NaN NaN \n", - "213 3.174156e+11 2.963409e+11 3.492681e+11 3.682882e+11 NaN NaN \n", - "214 2.115439e+10 2.095475e+10 2.586814e+10 2.672007e+10 NaN NaN \n", - "215 1.996312e+10 2.054868e+10 2.281301e+10 3.100052e+10 NaN NaN \n", - "\n", - "[216 rows x 68 columns]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "country_gdp" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "# country_gdp['2018'] = country_gdp['2018'].apply(lambda x: x/1000000000000)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Country NameCountry Code2018
202美国USA20.494100
36中国CHN13.608152
97日本JPN4.970916
50德国DEU3.996759
68英国GBR2.825208
64法国FRA2.777535
89印度IND2.726323
94意大利ITA2.073902
26巴西BRA1.868626
32加拿大CAN1.712510
164俄罗斯联邦RUS1.657554
104大韩民国KOR1.619424
10澳大利亚AUS1.432195
59西班牙ESP1.426189
124墨西哥MEX1.223809
87印度尼西亚IDN1.042173
143荷兰NLD0.913658
166沙特阿拉伯SAU0.782483
196土耳其TUR0.766509196土耳其TUR0.766509
33Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
202美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.54330.56330.6051...15.54258116.19700716.78484917.52174718.21929818.70718819.48539420.4941NaNNaN9安提瓜和巴布达ATG0.001624
184塞舌尔SYC0.001590
74几内亚比绍共和国GNB0.001458
170所罗门群岛SLB0.001412
77格林纳达GRD0.001207
42科摩罗COM0.001203
103圣基茨和尼维斯KNA0.001040
186特克斯科斯群岛TCA0.001022
209瓦努阿图VUT0.000888
210萨摩亚WSM0.000861
204圣文森特和格林纳丁斯VCT0.000813
52多米尼克DMA0.000504
193汤加TON0.000450
177圣多美和普林西比STP0.000422
66密克罗尼西亚联邦FSM0.000345
153帕劳PLW0.000310
125马绍尔群岛MHL0.000212
102基里巴斯KIR0.000188
146瑙魯NRU0.000115
197图瓦卢TUV0.000043
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1 rows × 68 columns

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" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "202 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", - "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 \\\n", - "202 NY.GDP.MKTP.CD 0.5433 0.5633 0.6051 ... 15.542581 16.197007 \n", - "\n", - " 2013 2014 2015 2016 2017 2018 2019 \\\n", - "202 16.784849 17.521747 18.219298 18.707188 19.485394 20.4941 NaN \n", - "\n", - " Unnamed: 64 \n", - "202 NaN \n", - "\n", - "[1 rows x 68 columns]" + " Country Name Country Code 2018\n", + "9 安提瓜和巴布达 ATG 0.001624\n", + "184 塞舌尔 SYC 0.001590\n", + "74 几内亚比绍共和国 GNB 0.001458\n", + "170 所罗门群岛 SLB 0.001412\n", + "77 格林纳达 GRD 0.001207\n", + "42 科摩罗 COM 0.001203\n", + "103 圣基茨和尼维斯 KNA 0.001040\n", + "186 特克斯科斯群岛 TCA 0.001022\n", + "209 瓦努阿图 VUT 0.000888\n", + "210 萨摩亚 WSM 0.000861\n", + "204 圣文森特和格林纳丁斯 VCT 0.000813\n", + "52 多米尼克 DMA 0.000504\n", + "193 汤加 TON 0.000450\n", + "177 圣多美和普林西比 STP 0.000422\n", + "66 密克罗尼西亚联邦 FSM 0.000345\n", + "153 帕劳 PLW 0.000310\n", + "125 马绍尔群岛 MHL 0.000212\n", + "102 基里巴斯 KIR 0.000188\n", + "146 瑙魯 NRU 0.000115\n", + "197 图瓦卢 TUV 0.000043" ] }, - "execution_count": 13, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_usa = country_gdp[country_gdp['Country Name']=='美国']\n", - "for i in range(1960, 2019):\n", - " df_usa[str(i)] = df_usa[str(i)].apply(lambda x: x/1000000000000)\n", - "df_usa" + "# 查看倒数排名\n", + "country_gdp.dropna(subset=['2018'])[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[-20:]" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, { "data": { "text/html": [ @@ -3989,130 +3308,105 @@ " \n", " \n", " \n", - " 97\n", - " 日本\n", - " JPN\n", + " 0\n", + " 阿鲁巴\n", + " ABW\n", " NaN\n", " 高收入国家\n", " NaN\n", " GDP（现价美元）\n", " NY.GDP.MKTP.CD\n", - " 0.044307\n", - " 0.053509\n", - " 0.060723\n", + " NaN\n", + " NaN\n", + " NaN\n", " ...\n", - " 6.15746\n", - " 6.203213\n", - " 5.155717\n", - " 4.850414\n", - " 4.389476\n", - " 4.926667\n", - " 4.859951\n", - " 4.970916\n", + " 0.002550\n", + " 0.002535\n", + " 0.002582\n", + " 0.002650\n", + " 0.002692\n", + " 0.002647\n", + " 0.002701\n", + " NaN\n", " NaN\n", " NaN\n", " \n", - " \n", - "\n", - "

1 rows × 68 columns

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1阿富汗AFG南亚低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0005380.0005490.000547...0.0178040.0200020.0205610.0204850.0199070.0193630.0201920.019363NaNNaN
50德国DEU2安哥拉AGO撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.1117900.1280530.1367100.1457120.1161940.1011240.1221240.105751NaNNaN
3阿尔巴尼亚ALB欧洲与中亚地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.0128910.0123200.0127760.0132280.0113870.0118610.0130250.015059NaNNaN
4安道尔共和国ANDNaN高收入国家NaNNaNNaN...3.7576983.5439843.7525143.8987273.3813893.4951633.6932043.9967590.0034420.0031650.0032820.0033510.0028110.0028770.0030130.003237NaNNaN
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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "50 德国 DEU NaN 高收入国家 NaN GDP（现价美元） \n", - "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 2013 \\\n", - "50 NY.GDP.MKTP.CD NaN NaN NaN ... 3.757698 3.543984 3.752514 \n", - "\n", - " 2014 2015 2016 2017 2018 2019 Unnamed: 64 \n", - "50 3.898727 3.381389 3.495163 3.693204 3.996759 NaN NaN \n", - "\n", - "[1 rows x 68 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_de = country_gdp[country_gdp['Country Name']=='德国']\n", - "for i in range(1960, 2019):\n", - " df_de[str(i)] = df_de[str(i)].apply(lambda x: x/1000000000000)\n", - "df_de" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - }, - { - "data": { - "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
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68英国GBR211科索沃XKX欧洲与中亚地区（不包括高收入）中高等收入国家NaN高收入国家GDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...0.0066920.0065000.0070720.0073870.0064410.0067150.0072280.007900NaNNaN
212也门共和国YEM中东与北非地区（不包括高收入）低收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0723280.0766940.080602NaNNaNNaN...2.6348962.6766052.7535653.0347292.8964212.6592392.6378662.8252080.0327260.0354010.0404150.0432290.0426280.0309680.0268190.026914NaNNaN
213南非ZAF撒哈拉以南非洲地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0075750.0079730.008498...0.4164170.3963290.3666450.3506380.3174160.2963410.3492680.368288NaNNaN
214赞比亚ZMB撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0007130.0006960.000693...0.0234600.0255030.0280450.0271510.0211540.0209550.0258680.026720NaNNaN
215津巴布韦ZWE撒哈拉以南非洲地区（不包括高收入）中低等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0010530.0010970.001118...0.0141020.0171150.0190910.0194960.0199630.0205490.0228130.031001NaNNaN
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" ], "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", - "68 英国 GBR NaN 高收入国家 NaN GDP（现价美元） \n", + " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", + "0 阿鲁巴 ABW NaN 高收入国家 NaN \n", + "1 阿富汗 AFG 南亚 低收入国家 NaN \n", + "2 安哥拉 AGO 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "3 阿尔巴尼亚 ALB 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "4 安道尔共和国 AND NaN 高收入国家 NaN \n", + ".. ... ... ... ... ... \n", + "211 科索沃 XKX 欧洲与中亚地区（不包括高收入） 中高等收入国家 NaN \n", + "212 也门共和国 YEM 中东与北非地区（不包括高收入） 低收入国家 NaN \n", + "213 南非 ZAF 撒哈拉以南非洲地区（不包括高收入） 中高等收入国家 NaN \n", + "214 赞比亚 ZMB 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", + "215 津巴布韦 ZWE 撒哈拉以南非洲地区（不包括高收入） 中低等收入国家 NaN \n", "\n", - " Indicator Code 1960 1961 1962 ... 2011 2012 \\\n", - "68 NY.GDP.MKTP.CD 0.072328 0.076694 0.080602 ... 2.634896 2.676605 \n", + " Indicator Name Indicator Code 1960 1961 1962 ... \\\n", + "0 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "1 GDP（现价美元） NY.GDP.MKTP.CD 0.000538 0.000549 0.000547 ... \n", + "2 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "3 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "4 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + ".. ... ... ... ... ... ... \n", + "211 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "212 GDP（现价美元） NY.GDP.MKTP.CD NaN NaN NaN ... \n", + "213 GDP（现价美元） NY.GDP.MKTP.CD 0.007575 0.007973 0.008498 ... \n", + "214 GDP（现价美元） NY.GDP.MKTP.CD 0.000713 0.000696 0.000693 ... \n", + "215 GDP（现价美元） NY.GDP.MKTP.CD 0.001053 0.001097 0.001118 ... \n", "\n", - " 2013 2014 2015 2016 2017 2018 2019 \\\n", - "68 2.753565 3.034729 2.896421 2.659239 2.637866 2.825208 NaN \n", + " 2011 2012 2013 2014 2015 2016 2017 \\\n", + "0 0.002550 0.002535 0.002582 0.002650 0.002692 0.002647 0.002701 \n", + "1 0.017804 0.020002 0.020561 0.020485 0.019907 0.019363 0.020192 \n", + "2 0.111790 0.128053 0.136710 0.145712 0.116194 0.101124 0.122124 \n", + "3 0.012891 0.012320 0.012776 0.013228 0.011387 0.011861 0.013025 \n", + "4 0.003442 0.003165 0.003282 0.003351 0.002811 0.002877 0.003013 \n", + ".. ... ... ... ... ... ... ... \n", + "211 0.006692 0.006500 0.007072 0.007387 0.006441 0.006715 0.007228 \n", + "212 0.032726 0.035401 0.040415 0.043229 0.042628 0.030968 0.026819 \n", + "213 0.416417 0.396329 0.366645 0.350638 0.317416 0.296341 0.349268 \n", + "214 0.023460 0.025503 0.028045 0.027151 0.021154 0.020955 0.025868 \n", + "215 0.014102 0.017115 0.019091 0.019496 0.019963 0.020549 0.022813 \n", "\n", - " Unnamed: 64 \n", - "68 NaN \n", + " 2018 2019 Unnamed: 64 \n", + "0 NaN NaN NaN \n", + "1 0.019363 NaN NaN \n", + "2 0.105751 NaN NaN \n", + "3 0.015059 NaN NaN \n", + "4 0.003237 NaN NaN \n", + ".. ... ... ... \n", + "211 0.007900 NaN NaN \n", + "212 0.026914 NaN NaN \n", + "213 0.368288 NaN NaN \n", + "214 0.026720 NaN NaN \n", + "215 0.031001 NaN NaN \n", "\n", - "[1 rows x 68 columns]" + "[216 rows x 68 columns]" ] }, - "execution_count": 16, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_uk = country_gdp[country_gdp['Country Name']=='英国']\n", + "# GDP转换成万亿单位\n", "for i in range(1960, 2019):\n", - " df_uk[str(i)] = df_uk[str(i)].apply(lambda x: x/1000000000000)\n", - "df_uk" + " country_gdp[str(i)] = country_gdp[str(i)].apply(lambda x: x/1000000000000)\n", + "\n", + "country_gdp" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 99, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "year_str = [str(i) for i in range(1960, 2019)]\n", - "\n", - "china_gdp = df_china[year_str].values.tolist()[0]\n", - "usa_gdp = df_usa[year_str].values.tolist()[0]\n", - "jpn_gdp = df_jpn[year_str].values.tolist()[0]\n", - "de_gdp = df_de[year_str].values.tolist()[0]\n", - "uk_gdp = df_uk[year_str].values.tolist()[0]" + "# 2018年GDP前十名\n", + "country_gdp_top10 = country_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]" ] }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 101, "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [ { "data": { "text/html": [ - "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
36中国CHN东亚与太平洋地区（不包括高收入）中高等收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.0597160.0500570.047209...7.55158.5322319.57040610.43852911.01554211.13794612.14349113.608152NaNNaN
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" - ], - "text/plain": [ - " Country Name Country Code Region Income_Group Unnamed: 4 \\\n", - "36 中国 CHN 东亚与太平洋地区（不包括高收入） 中高等收入国家 NaN \n", - "\n", - " Indicator Name Indicator Code 1960 1961 1962 ... 2011 \\\n", - "36 GDP（现价美元） NY.GDP.MKTP.CD 0.059716 0.050057 0.047209 ... 7.5515 \n", - "\n", - " 2012 2013 2014 2015 2016 2017 2018 \\\n", - "36 8.532231 9.570406 10.438529 11.015542 11.137946 12.143491 13.608152 \n", - "\n", - " 2019 Unnamed: 64 \n", - "36 NaN NaN \n", - "\n", - "[1 rows x 68 columns]" - ] - }, - "execution_count": 154, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_china" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from pyecharts.charts import Scatter\n", - "\n", - "def scatter_base(choose, values, country) -> Scatter:\n", - " c = (\n", - " Scatter()\n", - " .add_xaxis(choose)\n", - " .add_yaxis(\"%s历年GDP\" % country, values)\n", - " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", - " # datazoom_opts=opts.DataZoomOpts(),\n", - " yaxis_opts=opts.AxisOpts(\n", - " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", - " )\n", - " )\n", - " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", - " )\n", - " return c" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, china_gdp, '中国').render_notebook()" + "bar = Bar()\n", + "bar.add_xaxis(country_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_gdp_top10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP Top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 278, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/html": [ - "\n", - "\n", - "
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Country NameCountry Code19601961196219631964196519661967...2009201020112012201320142015201620172018
202美国USA5.433000e+115.633000e+116.051000e+116.386000e+116.858000e+117.437000e+118.150000e+118.617000e+11...1.444893e+131.499205e+131.554258e+131.619701e+131.678485e+131.752175e+131.821930e+131.870719e+131.948539e+132.049410e+13
36中国CHN5.971647e+105.005687e+104.720936e+105.070680e+105.970834e+107.043627e+107.672029e+107.288163e+10...5.101702e+126.087165e+127.551500e+128.532231e+129.570406e+121.043853e+131.101554e+131.113795e+131.214349e+131.360815e+13
97日本JPN4.430734e+105.350862e+106.072302e+106.949813e+108.174901e+109.095028e+101.056281e+111.237819e+11...5.231383e+125.700098e+126.157460e+126.203213e+125.155717e+124.850414e+124.389476e+124.926667e+124.859951e+124.970916e+12
50德国DEUNaNNaNNaNNaNNaNNaNNaNNaN...3.418005e+123.417095e+123.757698e+123.543984e+123.752514e+123.898727e+123.381389e+123.495163e+123.693204e+123.996759e+12
68英国GBR7.232805e+107.669436e+108.060194e+108.544377e+109.338760e+101.005958e+111.070907e+111.111854e+11...2.394786e+122.452900e+122.634896e+122.676605e+122.753565e+123.034729e+122.896421e+122.659239e+122.637866e+122.825208e+12
64法国FRA6.265147e+106.834674e+107.631378e+108.555111e+109.490659e+101.021606e+111.105975e+111.194661e+11...2.690222e+122.642610e+122.861408e+122.683825e+122.811078e+122.852166e+122.438208e+122.471286e+122.586285e+122.777535e+12
89印度IND3.702988e+103.923244e+104.216148e+104.842192e+105.648029e+105.955486e+104.586546e+105.013494e+10...1.341887e+121.675615e+121.823050e+121.827638e+121.856722e+122.039127e+122.103588e+122.290432e+122.652551e+122.726323e+12
94意大利ITA4.038529e+104.484276e+105.038389e+105.771074e+106.317542e+106.797815e+107.365487e+108.113312e+10...2.185160e+122.125058e+122.276292e+122.072823e+122.130491e+122.151733e+121.832273e+121.869202e+121.946570e+122.073902e+12
26巴西BRA1.516557e+101.523685e+101.992629e+102.302148e+102.121189e+102.179004e+102.706272e+103.059183e+10...1.667020e+122.208872e+122.616202e+122.465189e+122.472806e+122.455994e+121.802214e+121.796275e+122.053595e+121.868626e+12
32加拿大CANNaN4.155599e+104.286809e+104.571315e+105.012664e+105.534224e+106.201517e+106.666493e+10...1.371153e+121.613543e+121.789141e+121.823967e+121.842018e+121.801480e+121.552900e+121.526706e+121.646867e+121.712510e+12
164俄罗斯联邦RUSNaNNaNNaNNaNNaNNaNNaNNaN...1.222644e+121.524917e+122.051662e+122.210257e+122.297128e+122.059984e+121.363594e+121.282724e+121.578624e+121.657554e+12
104大韩民国KOR3.957240e+092.417638e+092.813857e+093.988477e+093.458565e+093.120495e+093.928282e+094.854724e+09...9.019350e+111.094499e+121.202464e+121.222807e+121.305605e+121.411334e+121.382764e+121.414804e+121.530751e+121.619424e+12
10澳大利亚AUS1.857767e+101.965394e+101.989249e+102.150745e+102.376414e+102.593795e+102.726845e+103.039758e+10...9.278052e+111.146138e+121.396650e+121.546152e+121.576184e+121.467484e+121.351520e+121.210028e+121.330803e+121.432195e+12
59西班牙ESP1.207213e+101.383430e+101.613855e+101.907491e+102.134384e+102.475696e+102.872106e+103.164712e+10...1.499100e+121.431617e+121.488067e+121.336019e+121.361854e+121.376911e+121.199084e+121.237499e+121.314314e+121.426189e+12
124墨西哥MEX1.304000e+101.416000e+101.520000e+101.696000e+102.008000e+102.184000e+102.432000e+102.656000e+10...9.000454e+111.057801e+121.180490e+121.201090e+121.274443e+121.314564e+121.170565e+121.077828e+121.158071e+121.223809e+12
87印度尼西亚IDNNaNNaNNaNNaNNaNNaNNaN5.667757e+09...5.395801e+117.550942e+118.929691e+119.178699e+119.125241e+118.908148e+118.608542e+119.318774e+111.015423e+121.042173e+12
143荷兰NLD1.227673e+101.349383e+101.464706e+101.589124e+101.869938e+102.100059e+102.286720e+102.508756e+10...8.680772e+118.465549e+119.040860e+118.389713e+118.769235e+118.909813e+117.652649e+117.835282e+118.318099e+119.136585e+11
166沙特阿拉伯SAUNaNNaNNaNNaNNaNNaNNaNNaN...4.290979e+115.282072e+116.712388e+117.359748e+117.466471e+117.563503e+116.542699e+116.449355e+116.885861e+117.824835e+11
196土耳其TUR1.399507e+107.988889e+098.922222e+091.035556e+101.117778e+101.196667e+101.410000e+101.564444e+10...6.446399e+117.719018e+118.325237e+118.739822e+119.505794e+119.341859e+118.597969e+118.637216e+118.515492e+117.665091e+11
33瑞士CHE9.522747e+091.071271e+101.187998e+101.306364e+101.448056e+101.534674e+101.648006e+101.774001e+10...5.415065e+115.837830e+116.995796e+116.680436e+116.885042e+117.091826e+116.798323e+116.701811e+116.789654e+117.055013e+11
\n", + "

20 rows × 61 columns

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" + ], + "text/plain": [ + " Country Name Country Code 1960 1961 1962 \\\n", + "202 美国 USA 5.433000e+11 5.633000e+11 6.051000e+11 \n", + "36 中国 CHN 5.971647e+10 5.005687e+10 4.720936e+10 \n", + "97 日本 JPN 4.430734e+10 5.350862e+10 6.072302e+10 \n", + "50 德国 DEU NaN NaN NaN \n", + "68 英国 GBR 7.232805e+10 7.669436e+10 8.060194e+10 \n", + "64 法国 FRA 6.265147e+10 6.834674e+10 7.631378e+10 \n", + "89 印度 IND 3.702988e+10 3.923244e+10 4.216148e+10 \n", + "94 意大利 ITA 4.038529e+10 4.484276e+10 5.038389e+10 \n", + "26 巴西 BRA 1.516557e+10 1.523685e+10 1.992629e+10 \n", + "32 加拿大 CAN NaN 4.155599e+10 4.286809e+10 \n", + "164 俄罗斯联邦 RUS NaN NaN NaN \n", + "104 大韩民国 KOR 3.957240e+09 2.417638e+09 2.813857e+09 \n", + "10 澳大利亚 AUS 1.857767e+10 1.965394e+10 1.989249e+10 \n", + "59 西班牙 ESP 1.207213e+10 1.383430e+10 1.613855e+10 \n", + "124 墨西哥 MEX 1.304000e+10 1.416000e+10 1.520000e+10 \n", + "87 印度尼西亚 IDN NaN NaN NaN \n", + "143 荷兰 NLD 1.227673e+10 1.349383e+10 1.464706e+10 \n", + "166 沙特阿拉伯 SAU NaN NaN NaN \n", + "196 土耳其 TUR 1.399507e+10 7.988889e+09 8.922222e+09 \n", + "33 瑞士 CHE 9.522747e+09 1.071271e+10 1.187998e+10 \n", + "\n", + " 1963 1964 1965 1966 1967 \\\n", + "202 6.386000e+11 6.858000e+11 7.437000e+11 8.150000e+11 8.617000e+11 \n", + "36 5.070680e+10 5.970834e+10 7.043627e+10 7.672029e+10 7.288163e+10 \n", + "97 6.949813e+10 8.174901e+10 9.095028e+10 1.056281e+11 1.237819e+11 \n", + "50 NaN NaN NaN NaN NaN \n", + "68 8.544377e+10 9.338760e+10 1.005958e+11 1.070907e+11 1.111854e+11 \n", + "64 8.555111e+10 9.490659e+10 1.021606e+11 1.105975e+11 1.194661e+11 \n", + "89 4.842192e+10 5.648029e+10 5.955486e+10 4.586546e+10 5.013494e+10 \n", + "94 5.771074e+10 6.317542e+10 6.797815e+10 7.365487e+10 8.113312e+10 \n", + "26 2.302148e+10 2.121189e+10 2.179004e+10 2.706272e+10 3.059183e+10 \n", + "32 4.571315e+10 5.012664e+10 5.534224e+10 6.201517e+10 6.666493e+10 \n", + "164 NaN NaN NaN NaN NaN \n", + "104 3.988477e+09 3.458565e+09 3.120495e+09 3.928282e+09 4.854724e+09 \n", + "10 2.150745e+10 2.376414e+10 2.593795e+10 2.726845e+10 3.039758e+10 \n", + "59 1.907491e+10 2.134384e+10 2.475696e+10 2.872106e+10 3.164712e+10 \n", + "124 1.696000e+10 2.008000e+10 2.184000e+10 2.432000e+10 2.656000e+10 \n", + "87 NaN NaN NaN NaN 5.667757e+09 \n", + "143 1.589124e+10 1.869938e+10 2.100059e+10 2.286720e+10 2.508756e+10 \n", + "166 NaN NaN NaN NaN NaN \n", + "196 1.035556e+10 1.117778e+10 1.196667e+10 1.410000e+10 1.564444e+10 \n", + "33 1.306364e+10 1.448056e+10 1.534674e+10 1.648006e+10 1.774001e+10 \n", + "\n", + " ... 2009 2010 2011 2012 \\\n", + "202 ... 1.444893e+13 1.499205e+13 1.554258e+13 1.619701e+13 \n", + "36 ... 5.101702e+12 6.087165e+12 7.551500e+12 8.532231e+12 \n", + "97 ... 5.231383e+12 5.700098e+12 6.157460e+12 6.203213e+12 \n", + "50 ... 3.418005e+12 3.417095e+12 3.757698e+12 3.543984e+12 \n", + "68 ... 2.394786e+12 2.452900e+12 2.634896e+12 2.676605e+12 \n", + "64 ... 2.690222e+12 2.642610e+12 2.861408e+12 2.683825e+12 \n", + "89 ... 1.341887e+12 1.675615e+12 1.823050e+12 1.827638e+12 \n", + "94 ... 2.185160e+12 2.125058e+12 2.276292e+12 2.072823e+12 \n", + "26 ... 1.667020e+12 2.208872e+12 2.616202e+12 2.465189e+12 \n", + "32 ... 1.371153e+12 1.613543e+12 1.789141e+12 1.823967e+12 \n", + "164 ... 1.222644e+12 1.524917e+12 2.051662e+12 2.210257e+12 \n", + "104 ... 9.019350e+11 1.094499e+12 1.202464e+12 1.222807e+12 \n", + "10 ... 9.278052e+11 1.146138e+12 1.396650e+12 1.546152e+12 \n", + "59 ... 1.499100e+12 1.431617e+12 1.488067e+12 1.336019e+12 \n", + "124 ... 9.000454e+11 1.057801e+12 1.180490e+12 1.201090e+12 \n", + "87 ... 5.395801e+11 7.550942e+11 8.929691e+11 9.178699e+11 \n", + "143 ... 8.680772e+11 8.465549e+11 9.040860e+11 8.389713e+11 \n", + "166 ... 4.290979e+11 5.282072e+11 6.712388e+11 7.359748e+11 \n", + "196 ... 6.446399e+11 7.719018e+11 8.325237e+11 8.739822e+11 \n", + "33 ... 5.415065e+11 5.837830e+11 6.995796e+11 6.680436e+11 \n", + "\n", + " 2013 2014 2015 2016 2017 \\\n", + "202 1.678485e+13 1.752175e+13 1.821930e+13 1.870719e+13 1.948539e+13 \n", + "36 9.570406e+12 1.043853e+13 1.101554e+13 1.113795e+13 1.214349e+13 \n", + "97 5.155717e+12 4.850414e+12 4.389476e+12 4.926667e+12 4.859951e+12 \n", + "50 3.752514e+12 3.898727e+12 3.381389e+12 3.495163e+12 3.693204e+12 \n", + "68 2.753565e+12 3.034729e+12 2.896421e+12 2.659239e+12 2.637866e+12 \n", + "64 2.811078e+12 2.852166e+12 2.438208e+12 2.471286e+12 2.586285e+12 \n", + "89 1.856722e+12 2.039127e+12 2.103588e+12 2.290432e+12 2.652551e+12 \n", + "94 2.130491e+12 2.151733e+12 1.832273e+12 1.869202e+12 1.946570e+12 \n", + "26 2.472806e+12 2.455994e+12 1.802214e+12 1.796275e+12 2.053595e+12 \n", + "32 1.842018e+12 1.801480e+12 1.552900e+12 1.526706e+12 1.646867e+12 \n", + "164 2.297128e+12 2.059984e+12 1.363594e+12 1.282724e+12 1.578624e+12 \n", + "104 1.305605e+12 1.411334e+12 1.382764e+12 1.414804e+12 1.530751e+12 \n", + "10 1.576184e+12 1.467484e+12 1.351520e+12 1.210028e+12 1.330803e+12 \n", + "59 1.361854e+12 1.376911e+12 1.199084e+12 1.237499e+12 1.314314e+12 \n", + "124 1.274443e+12 1.314564e+12 1.170565e+12 1.077828e+12 1.158071e+12 \n", + "87 9.125241e+11 8.908148e+11 8.608542e+11 9.318774e+11 1.015423e+12 \n", + "143 8.769235e+11 8.909813e+11 7.652649e+11 7.835282e+11 8.318099e+11 \n", + "166 7.466471e+11 7.563503e+11 6.542699e+11 6.449355e+11 6.885861e+11 \n", + "196 9.505794e+11 9.341859e+11 8.597969e+11 8.637216e+11 8.515492e+11 \n", + "33 6.885042e+11 7.091826e+11 6.798323e+11 6.701811e+11 6.789654e+11 \n", + "\n", + " 2018 \n", + "202 2.049410e+13 \n", + "36 1.360815e+13 \n", + "97 4.970916e+12 \n", + "50 3.996759e+12 \n", + "68 2.825208e+12 \n", + "64 2.777535e+12 \n", + "89 2.726323e+12 \n", + "94 2.073902e+12 \n", + "26 1.868626e+12 \n", + "32 1.712510e+12 \n", + "164 1.657554e+12 \n", + "104 1.619424e+12 \n", + "10 1.432195e+12 \n", + "59 1.426189e+12 \n", + "124 1.223809e+12 \n", + "87 1.042173e+12 \n", + "143 9.136585e+11 \n", + "166 7.824835e+11 \n", + "196 7.665091e+11 \n", + "33 7.055013e+11 \n", + "\n", + "[20 rows x 61 columns]" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# top20 国家历年GDP\n", + "country_gdp_top20 = country_gdp.drop(columns=['Region', 'Income_Group', 'Unnamed: 4', 'Indicator Name', 'Indicator Code', '2019', 'Unnamed: 64']).sort_values(by='2018', ascending=False)[:20]\n", + "# GDP转换成万亿单位\n", + "for i in range(1960, 2019):\n", + " country_gdp_top20[str(i)] = country_gdp_top20[str(i)].apply(lambda x: x*1000000000000)\n", + "\n", + "country_gdp_top20" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1180\n", + "1180\n" + ] + } + ], + "source": [ + "country_list = []\n", + "year_list = []\n", + "value_list = []\n", + "type_list = []\n", + "c_list = ['美国', '中国', '日本', '德国', '英国', '法国', '印度', '意大利', '巴西', '加拿大', '俄罗斯联邦', '大韩民国', '澳大利亚', \n", + " '西班牙', '墨西哥', '印度尼西亚', '荷兰', '沙特阿拉伯', '土耳其', '瑞士']\n", + "for c in c_list:\n", + " for i in range(1960, 2019):\n", + " country_list.append(c)\n", + " type_list.append(country_gdp_top20[country_gdp_top20['Country Name'] == c]['Country Code'].values.tolist()[0])\n", + " value_list.append(country_gdp_top20[country_gdp_top20['Country Name'] == c][str(i)].values.tolist()[0])\n", + " year_list.append(str(i))\n", + "\n", + "print(len(value_list))\n", + "print(len(country_list))\n", + "d = {'name': country_list, 'type': type_list, 'value': value_list, 'date': year_list}\n", + "pd.DataFrame(d).to_csv('auto_gdp.csv', index=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [], + "source": [ + "# 2018年GDP后十名\n", + "country_gdp_bottom10 = country_gdp.dropna(subset=['2018'])[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(country_gdp_bottom10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_gdp_bottom10['2018'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP bottom10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP倒数分析" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_tuwalu = country_gdp[country_gdp['Country Name']=='图瓦卢']\n", + "\n", + "df_naolu = country_gdp[country_gdp['Country Name']=='瑙魯']\n", + "\n", + "df_jilibasi = country_gdp[country_gdp['Country Name']=='基里巴斯']\n", + "\n", + "df_mashaoerqundao = country_gdp[country_gdp['Country Name']=='马绍尔群岛']\n", + " \n", + "df_palao = country_gdp[country_gdp['Country Name']=='帕劳']" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "tuwalu_gdp = df_tuwalu[year_str].values.tolist()[0]\n", + "naolu_gdp = df_naolu[year_str].values.tolist()[0]\n", + "jilibasi_gdp = df_jilibasi[year_str].values.tolist()[0]\n", + "mashaoerqundao_gdp = df_mashaoerqundao[year_str].values.tolist()[0]\n", + "palao_gdp = df_palao[year_str].values.tolist()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP正数分析" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [], + "source": [ + "df_china = country_gdp[country_gdp['Country Name']=='中国']\n", + "\n", + "df_usa = country_gdp[country_gdp['Country Name']=='美国']\n", + "\n", + "df_jpn = country_gdp[country_gdp['Country Name']=='日本']\n", + "\n", + "df_de = country_gdp[country_gdp['Country Name']=='德国']\n", + "\n", + "df_uk = country_gdp[country_gdp['Country Name']=='英国']" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "year_str = [str(i) for i in range(1960, 2019)]\n", + "\n", + "china_gdp = df_china[year_str].values.tolist()[0]\n", + "usa_gdp = df_usa[year_str].values.tolist()[0]\n", + "jpn_gdp = df_jpn[year_str].values.tolist()[0]\n", + "de_gdp = df_de[year_str].values.tolist()[0]\n", + "uk_gdp = df_uk[year_str].values.tolist()[0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "from pyecharts.charts import Scatter\n", + "\n", + "def scatter_base(choose, values, country) -> Scatter:\n", + " c = (\n", + " Scatter()\n", + " .add_xaxis(choose)\n", + " .add_yaxis(\"%s历年GDP\" % country, values)\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " # datazoom_opts=opts.DataZoomOpts(),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " )\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, china_gdp, '中国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, usa_gdp, '美国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, jpn_gdp, '日本').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, de_gdp, '德国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, uk_gdp, '英国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, tuwalu_gdp, '图瓦卢').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, naolu_gdp, '瑙魯').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, jilibasi_gdp, '基里帕斯').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, usa_gdp, '美国').render_notebook()" + "scatter_base(year_str, mashaoerqundao_gdp, '马绍尔群岛').render_notebook()" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 85, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -5369,14 +9060,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter_base(year_str, palao_gdp, '帕劳').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "country_code = pd.read_json('countries.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "country_code.rename(columns={'iso3': 'Country Code'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "conutry_code_name = country_code[['name', 'Country Code']]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "country_gdp_code = pd.merge(country_gdp, conutry_code_name, on='Country Code', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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199美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.54330.56330.6051...16.19700716.78484917.52174718.21929818.70718819.48539420.4941NaNNaNUnited States
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" + ], + "text/plain": [ + " Country Name Country Code Region Income_Group Unnamed: 4 Indicator Name \\\n", + "199 美国 USA NaN 高收入国家 NaN GDP（现价美元） \n", + "\n", + " Indicator Code 1960 1961 1962 ... 2012 2013 \\\n", + "199 NY.GDP.MKTP.CD 0.5433 0.5633 0.6051 ... 16.197007 16.784849 \n", + "\n", + " 2014 2015 2016 2017 2018 2019 Unnamed: 64 \\\n", + "199 17.521747 18.219298 18.707188 19.485394 20.4941 NaN NaN \n", + "\n", + " name \n", + "199 United States \n", + "\n", + "[1 rows x 69 columns]" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_gdp_code[country_gdp_code['Country Name'] == '美国']" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "212 212\n" + ] + } + ], + "source": [ + "map_country = country_gdp_code['name'].values.tolist()\n", + "map_country_gdp = country_gdp_code['2018'].values.tolist()\n", + "print(len(map_country), len(map_country_gdp))" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "def map_world() -> Map:\n", + " c = (\n", + " Map()\n", + " .add(\"GDP总量\", [list(z) for z in zip(map_country, map_country_gdp)], \"world\")\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " .set_global_opts(\n", + " title_opts=opts.TitleOpts(title=\"GDP总量\"),\n", + " visualmap_opts=opts.VisualMapOpts(max_=5, is_piecewise=True),\n", + " )\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map_world().render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# 增长率分析\n", + "growth = pd.read_csv('growth_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "growth_data = pd.merge(country_data, growth, how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "country_growth_top10 = growth_data[['Country Name', 'Country Code', '2018 [YR2018]']].sort_values(by='2018 [YR2018]', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, jpn_gdp, '日本').render_notebook()" + "bar = Bar()\n", + "bar.add_xaxis(country_growth_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_growth_top10['2018 [YR2018]'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP增长率top10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\")\n", + " ),)\n", + "bar.render_notebook()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 111, "metadata": {}, + "outputs": [], + "source": [ + "country_growth_bottom10 = growth_data[['Country Name', 'Country Code', '2018 [YR2018]']].sort_values(by='2018 [YR2018]', ascending=False)[-10:]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -5830,14 +10939,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(country_growth_bottom10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", country_growth_bottom10['2018 [YR2018]'].values.tolist())\n", + "bar.reversal_axis()\n", + "bar.set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n", + "bar.set_global_opts(title_opts=opts.TitleOpts(title=\"2018年GDP增长率bottom10\", subtitle=\"\"),\n", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [], + "source": [ + "growth_china = growth_data[growth_data['Country Name']=='中国']\n", + "growth_usa = growth_data[growth_data['Country Name']=='美国']\n", + "growth_ind = growth_data[growth_data['Country Name']=='印度']" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "def scatter_growth(choose, values, country) -> Scatter:\n", + " c = (\n", + " Scatter()\n", + " .add_xaxis(choose)\n", + " .add_yaxis(\"%s历年GDP增长率\" % country, values)\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\"),\n", + " ),\n", + " xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10.6361404632299, 9.55091409001014, 7.8596274932851, 7.76861528412806, 7.29951892117124, 6.90531667019702, 6.73667525262536, 6.75700761091511, 6.60000000000001]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year_str_new = [str(i)+ \" [YR%s]\" % i for i in range(2010, 2019)]\n", + "year_str_new1 = [str(i) for i in range(2010, 2019)]\n", + "\n", + "china_growth = growth_china[year_str_new].values.tolist()[0]\n", + "china_growth = list(map(float, china_growth))\n", + "print(china_growth)\n", + "scatter_growth(year_str_new1, china_growth, '中国').render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['2.56376655847168', '1.55083550620974', '2.24954585216848', '1.8420810704697', '2.4519730360895', '2.88091046576689', '1.56721516988685', '2.21701033035224', '2.8569878160516']\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, de_gdp, '德国').render_notebook()" + "usa_growth = growth_usa[year_str_new].values.tolist()[0]\n", + "print(usa_growth)\n", + "scatter_growth(year_str_new1, usa_growth, '美国').render_notebook()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 318, "metadata": {}, + "outputs": [], + "source": [ + "ind_growth = growth_ind[year_str_new].values.tolist()[0]\n", + "df_ind = country_gdp[country_gdp['Country Name']=='印度']\n", + "ind_gdp = df_ind[year_str].values.tolist()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -6291,14 +11787,14 @@ " });\n", "\n", "\n", - "
\n", + "
\n", "\n", "\n", "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 322, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def overlap_line_scatter() -> Bar:\n", + " scatter = (\n", + " Scatter()\n", + " .add_xaxis(year_str_new1)\n", + " .add_yaxis(\"中国历年GDP增长率\", china_growth)\n", + " .add_yaxis(\"美国历年GDP增长率\", usa_growth)\n", + " .add_yaxis(\"印度历年GDP增长率\", ind_growth)\n", + " .extend_axis(\n", + " yaxis=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} 万亿\"), interval=5\n", + " )\n", + " )\n", + " .set_global_opts(title_opts=opts.TitleOpts(title=\"\"),\n", + " yaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /年百分比\"),\n", + " ),\n", + " xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),\n", + " )\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " line = (\n", + " Line()\n", + " .add_xaxis(year_str[-9:])\n", + " .add_yaxis(\"中国历年 GDP 总量\", china_gdp[-9:], yaxis_index=1)\n", + " .add_yaxis(\"美国历年 GDP 总量\", usa_gdp[-9:], yaxis_index=1)\n", + " .add_yaxis(\"印度历年 GDP 总量\", ind_gdp[-9:], yaxis_index=1)\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " )\n", + " scatter.overlap(line)\n", + " return scatter\n", + "\n", + "overlap_line_scatter().render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "country_growth_code = pd.merge(growth_data, conutry_code_name, on='Country Code', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "212 212\n" + ] + } + ], + "source": [ + "map_country = country_growth_code['name'].values.tolist()\n", + "map_country_gdp = country_growth_code['2018 [YR2018]'].values.tolist()\n", + "print(len(map_country), len(map_country_gdp))\n", + "def map_world_growth() -> Map:\n", + " c = (\n", + " Map()\n", + " .add(\"GDP增长率\", [list(z) for z in zip(map_country, map_country_gdp)], \"world\")\n", + " .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n", + " .set_global_opts(\n", + " title_opts=opts.TitleOpts(title=\"GDP增长率\"),\n", + " visualmap_opts=opts.VisualMapOpts(max_=10, min_=-5, is_piecewise=True),\n", + " )\n", + " )\n", + " return c" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 208, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "scatter_base(year_str, uk_gdp, '英国').render_notebook()" + "map_world_growth().render_notebook()" ] }, { @@ -6858,39 +13778,6 @@ "pred_y = model.predict(X)" ] }, - { - "cell_type": "code", - "execution_count": 244, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0.67561513, 0.64550236, 0.63069893, 0.6310778 , 0.64651146,\n", - " 0.67687193, 0.7220308 , 0.78185916, 0.85622766, 0.94500648,\n", - " 1.04806535, 1.16527352, 1.29649978, 1.44161246, 1.60047942,\n", - " 1.77296806, 1.9589453 , 2.15827761, 2.37083099, 2.59647097,\n", - " 2.8350626 , 3.08647047, 3.35055872, 3.62719099, 3.91623047,\n", - " 4.21753986, 4.53098142, 4.8564169 , 5.19370761, 5.54271437,\n", - " 5.90329754, 6.27531699, 6.65863212, 7.05310188, 7.4585847 ,\n", - " 7.87493857, 8.302021 , 8.739689 , 9.18779914, 9.64620748,\n", - " 10.11476961, 10.59334065, 11.08177525, 11.57992755, 12.08765123,\n", - " 12.60479949, 13.13122504, 13.66678012, 14.21131649, 14.7646854 ,\n", - " 15.32673766, 15.89732355, 16.47629291, 17.06349506, 17.65877886,\n", - " 18.26199267, 18.87298437, 19.49160136, 20.11769053])" - ] - }, - "execution_count": 244, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pred_y" - ] - }, { "cell_type": "code", "execution_count": 34, @@ -6929,86 +13816,6 @@ "p_pred_y" ] }, - { - "cell_type": "code", - "execution_count": 245, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.5433,\n", - " 0.5633,\n", - " 0.6051,\n", - " 0.6386,\n", - " 0.6858,\n", - " 0.7437,\n", - " 0.815,\n", - " 0.8617,\n", - " 0.9425,\n", - " 1.0199,\n", - " 1.073303,\n", - " 1.16485,\n", - " 1.27911,\n", - " 1.425376,\n", - " 1.545243,\n", - " 1.684904,\n", - " 1.873412,\n", - " 2.081826,\n", - " 2.351599,\n", - " 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b/GDP_analyse/Untitled.ipynb deleted file mode 100644 index 2fd6442..0000000 --- a/GDP_analyse/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/GDP_analyse/auto_gdp.csv b/GDP_analyse/auto_gdp.csv new file mode 100644 index 0000000..7c755be --- /dev/null +++ b/GDP_analyse/auto_gdp.csv @@ -0,0 +1,1181 @@ +name,type,value,date +美国,USA,543300000000.0,1960 +美国,USA,563300000000.0,1961 +美国,USA,605100000000.0,1962 +美国,USA,638600000000.0,1963 +美国,USA,685800000000.0,1964 +美国,USA,743700000000.0,1965 +美国,USA,815000000000.0,1966 +美国,USA,861700000000.0,1967 +美国,USA,942500000000.0,1968 +美国,USA,1019900000000.0,1969 +美国,USA,1073302999999.9999,1970 +美国,USA,1164850000000.0,1971 +美国,USA,1279110000000.0,1972 +美国,USA,1425376000000.0,1973 +美国,USA,1545243000000.0,1974 +美国,USA,1684904000000.0,1975 +美国,USA,1873412000000.0,1976 +美国,USA,2081826000000.0,1977 +美国,USA,2351599000000.0,1978 +美国,USA,2627334000000.0,1979 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"capital": "Yaounde", + "currency": "XAF" + }, + { + "id": 39, + "name": "Canada", + "iso3": "CAN", + "iso2": "CA", + "phone_code": "1", + "capital": "Ottawa", + "currency": "CAD" + }, + { + "id": 40, + "name": "Cape Verde", + "iso3": "CPV", + "iso2": "CV", + "phone_code": "238", + "capital": "Praia", + "currency": "CVE" + }, + { + "id": 41, + "name": "Cayman Islands", + "iso3": "CYM", + "iso2": "KY", + "phone_code": "+1-345", + "capital": "George Town", + "currency": "KYD" + }, + { + "id": 42, + "name": "Central African Republic", + "iso3": "CAF", + "iso2": "CF", + "phone_code": "236", + "capital": "Bangui", + "currency": "XAF" + }, + { + "id": 43, + "name": "Chad", + "iso3": "TCD", + "iso2": "TD", + "phone_code": "235", + "capital": "N'Djamena", + "currency": "XAF" + }, + { + "id": 44, + "name": "Chile", + "iso3": "CHL", + "iso2": "CL", + "phone_code": "56", + "capital": "Santiago", + "currency": "CLP" + }, + { + "id": 45, + "name": "China", + "iso3": "CHN", + "iso2": "CN", + "phone_code": "86", + "capital": "Beijing", + "currency": "CNY" + }, + { + "id": 46, + "name": "Christmas Island", + "iso3": "CXR", + "iso2": "CX", + "phone_code": "61", + "capital": "Flying Fish Cove", + "currency": "AUD" + }, + { + "id": 47, + "name": "Cocos (Keeling) Islands", + "iso3": "CCK", + "iso2": "CC", + "phone_code": "61", + "capital": "West Island", + "currency": "AUD" + }, + { + "id": 48, + "name": "Colombia", + "iso3": "COL", + "iso2": "CO", + "phone_code": "57", + "capital": "Bogota", + "currency": "COP" + }, + { + "id": 49, + "name": "Comoros", + "iso3": "COM", + "iso2": "KM", + "phone_code": "269", + "capital": "Moroni", + "currency": "KMF" + }, + { + "id": 50, + "name": "Congo", + "iso3": "COG", + "iso2": "CG", + "phone_code": "242", + "capital": "Brazzaville", + "currency": "XAF" + }, + { + "id": 51, + "name": "Congo The Democratic Republic Of The", + "iso3": "COD", + "iso2": "CD", + "phone_code": "243", + "capital": "Kinshasa", + "currency": "CDF" + }, + { + "id": 52, + "name": "Cook Islands", + "iso3": "COK", + "iso2": "CK", + "phone_code": "682", + "capital": "Avarua", + "currency": "NZD" + }, + { + "id": 53, + "name": "Costa Rica", + "iso3": "CRI", + "iso2": "CR", + "phone_code": "506", + "capital": "San Jose", + "currency": "CRC" + }, + { + "id": 54, + "name": "Cote D'Ivoire (Ivory Coast)", + "iso3": "CIV", + "iso2": "CI", + "phone_code": "225", + "capital": "Yamoussoukro", + "currency": "XOF" + }, + { + "id": 55, + "name": "Croatia (Hrvatska)", + "iso3": "HRV", + "iso2": "HR", + "phone_code": "385", + "capital": "Zagreb", + "currency": "HRK" + }, + { + "id": 56, + "name": "Cuba", + "iso3": "CUB", + "iso2": "CU", + "phone_code": "53", + "capital": "Havana", + "currency": "CUP" + }, + { + "id": 57, + "name": "Cyprus", + "iso3": "CYP", + "iso2": "CY", + "phone_code": "357", + "capital": "Nicosia", + "currency": "EUR" + }, + { + "id": 58, + "name": "Czech Republic", + "iso3": "CZE", + "iso2": "CZ", + "phone_code": "420", + "capital": "Prague", + "currency": "CZK" + }, + { + "id": 59, + "name": "Denmark", + "iso3": "DNK", + "iso2": "DK", + "phone_code": "45", + "capital": "Copenhagen", + "currency": "DKK" + }, + { + "id": 60, + "name": "Djibouti", + "iso3": "DJI", + "iso2": "DJ", + "phone_code": "253", + "capital": "Djibouti", + "currency": "DJF" + }, + { + "id": 61, + "name": "Dominica", + "iso3": "DMA", + "iso2": "DM", + "phone_code": "+1-767", + "capital": "Roseau", + "currency": "XCD" + }, + { + "id": 62, + "name": "Dominican Republic", + "iso3": "DOM", + "iso2": "DO", + "phone_code": "+1-809 and 1-829", + "capital": "Santo Domingo", + "currency": "DOP" + }, + { + "id": 63, + "name": "East Timor", + "iso3": "TLS", + "iso2": "TL", + "phone_code": "670", + "capital": "Dili", + "currency": "USD" + }, + { + "id": 64, + "name": "Ecuador", + "iso3": "ECU", + "iso2": "EC", + "phone_code": "593", + "capital": "Quito", + "currency": "USD" + }, + { + "id": 65, + "name": "Egypt", + "iso3": "EGY", + "iso2": "EG", + "phone_code": 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"iso2": "FO", + "phone_code": "298", + "capital": "Torshavn", + "currency": "DKK" + }, + { + "id": 73, + "name": "Fiji Islands", + "iso3": "FJI", + "iso2": "FJ", + "phone_code": "679", + "capital": "Suva", + "currency": "FJD" + }, + { + "id": 74, + "name": "Finland", + "iso3": "FIN", + "iso2": "FI", + "phone_code": "358", + "capital": "Helsinki", + "currency": "EUR" + }, + { + "id": 75, + "name": "France", + "iso3": "FRA", + "iso2": "FR", + "phone_code": "33", + "capital": "Paris", + "currency": "EUR" + }, + { + "id": 76, + "name": "French Guiana", + "iso3": "GUF", + "iso2": "GF", + "phone_code": "594", + "capital": "Cayenne", + "currency": "EUR" + }, + { + "id": 77, + "name": "French Polynesia", + "iso3": "PYF", + "iso2": "PF", + "phone_code": "689", + "capital": "Papeete", + "currency": "XPF" + }, + { + "id": 78, + "name": "French Southern Territories", + "iso3": "ATF", + "iso2": "TF", + "phone_code": "", + "capital": "Port-aux-Francais", + "currency": "EUR" + }, + { + "id": 79, + "name": "Gabon", + "iso3": "GAB", + "iso2": "GA", + "phone_code": "241", + "capital": "Libreville", + "currency": "XAF" + }, + { + "id": 80, + "name": "Gambia The", + "iso3": "GMB", + "iso2": "GM", + "phone_code": "220", + "capital": "Banjul", + "currency": "GMD" + }, + { + "id": 81, + "name": "Georgia", + "iso3": "GEO", + "iso2": "GE", + "phone_code": "995", + "capital": "Tbilisi", + "currency": "GEL" + }, + { + "id": 82, + "name": "Germany", + "iso3": "DEU", + "iso2": "DE", + "phone_code": "49", + "capital": "Berlin", + "currency": "EUR" + }, + { + "id": 83, + "name": "Ghana", + "iso3": "GHA", + "iso2": "GH", + "phone_code": "233", + "capital": "Accra", + "currency": "GHS" + }, + { + "id": 84, + "name": "Gibraltar", + "iso3": "GIB", + "iso2": "GI", + "phone_code": "350", + "capital": "Gibraltar", + "currency": "GIP" + }, + { + "id": 85, + "name": "Greece", + "iso3": "GRC", + "iso2": "GR", + "phone_code": "30", + "capital": "Athens", + "currency": "EUR" + }, + { + "id": 86, + "name": 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"currency": "GNF" + }, + { + "id": 93, + "name": "Guinea-Bissau", + "iso3": "GNB", + "iso2": "GW", + "phone_code": "245", + "capital": "Bissau", + "currency": "XOF" + }, + { + "id": 94, + "name": "Guyana", + "iso3": "GUY", + "iso2": "GY", + "phone_code": "592", + "capital": "Georgetown", + "currency": "GYD" + }, + { + "id": 95, + "name": "Haiti", + "iso3": "HTI", + "iso2": "HT", + "phone_code": "509", + "capital": "Port-au-Prince", + "currency": "HTG" + }, + { + "id": 96, + "name": "Heard and McDonald Islands", + "iso3": "HMD", + "iso2": "HM", + "phone_code": " ", + "capital": "", + "currency": "AUD" + }, + { + "id": 97, + "name": "Honduras", + "iso3": "HND", + "iso2": "HN", + "phone_code": "504", + "capital": "Tegucigalpa", + "currency": "HNL" + }, + { + "id": 98, + "name": "Hong Kong S.A.R.", + "iso3": "HKG", + "iso2": "HK", + "phone_code": "852", + "capital": "Hong Kong", + "currency": "HKD" + }, + { + "id": 99, + "name": "Hungary", + "iso3": "HUN", + "iso2": "HU", + "phone_code": 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+ "capital": "Jerusalem", + "currency": "ILS" + }, + { + "id": 107, + "name": "Italy", + "iso3": "ITA", + "iso2": "IT", + "phone_code": "39", + "capital": "Rome", + "currency": "EUR" + }, + { + "id": 108, + "name": "Jamaica", + "iso3": "JAM", + "iso2": "JM", + "phone_code": "+1-876", + "capital": "Kingston", + "currency": "JMD" + }, + { + "id": 109, + "name": "Japan", + "iso3": "JPN", + "iso2": "JP", + "phone_code": "81", + "capital": "Tokyo", + "currency": "JPY" + }, + { + "id": 110, + "name": "Jersey", + "iso3": "JEY", + "iso2": "JE", + "phone_code": "+44-1534", + "capital": "Saint Helier", + "currency": "GBP" + }, + { + "id": 111, + "name": "Jordan", + "iso3": "JOR", + "iso2": "JO", + "phone_code": "962", + "capital": "Amman", + "currency": "JOD" + }, + { + "id": 112, + "name": "Kazakhstan", + "iso3": "KAZ", + "iso2": "KZ", + "phone_code": "7", + "capital": "Astana", + "currency": "KZT" + }, + { + "id": 113, + "name": "Kenya", + "iso3": "KEN", + "iso2": "KE", + "phone_code": "254", + "capital": "Nairobi", + "currency": "KES" + }, + { + "id": 114, + "name": "Kiribati", + "iso3": "KIR", + "iso2": "KI", + "phone_code": "686", + "capital": "Tarawa", + "currency": "AUD" + }, + { + "id": 115, + "name": "Korea North\n", + "iso3": "PRK", + "iso2": "KP", + "phone_code": "850", + "capital": "Pyongyang", + "currency": "KPW" + }, + { + "id": 116, + "name": "Korea South", + "iso3": "KOR", + "iso2": "KR", + "phone_code": "82", + "capital": "Seoul", + "currency": "KRW" + }, + { + "id": 117, + "name": "Kuwait", + "iso3": "KWT", + "iso2": "KW", + "phone_code": "965", + "capital": "Kuwait City", + "currency": "KWD" + }, + { + "id": 118, + "name": "Kyrgyzstan", + "iso3": "KGZ", + "iso2": "KG", + "phone_code": "996", + "capital": "Bishkek", + "currency": "KGS" + }, + { + "id": 119, + "name": "Laos", + "iso3": "LAO", + "iso2": "LA", + "phone_code": "856", + "capital": "Vientiane", + "currency": "LAK" + }, + { + "id": 120, + "name": "Latvia", + "iso3": "LVA", + "iso2": "LV", + "phone_code": "371", + "capital": "Riga", + "currency": "EUR" + }, + { + "id": 121, + "name": "Lebanon", + "iso3": "LBN", + "iso2": "LB", + "phone_code": "961", + "capital": "Beirut", + "currency": "LBP" + }, + { + "id": 122, + "name": "Lesotho", + "iso3": "LSO", + "iso2": "LS", + "phone_code": "266", + "capital": "Maseru", + "currency": "LSL" + }, + { + "id": 123, + "name": "Liberia", + "iso3": "LBR", + "iso2": "LR", + "phone_code": "231", + "capital": "Monrovia", + "currency": "LRD" + }, + { + "id": 124, + "name": "Libya", + "iso3": "LBY", + "iso2": "LY", + "phone_code": "218", + "capital": "Tripolis", + "currency": "LYD" + }, + { + "id": 125, + "name": "Liechtenstein", + "iso3": "LIE", + "iso2": "LI", + "phone_code": "423", + "capital": "Vaduz", + "currency": "CHF" + }, + { + "id": 126, + "name": "Lithuania", + "iso3": "LTU", + "iso2": "LT", + "phone_code": "370", + "capital": "Vilnius", + "currency": "LTL" + }, + { + "id": 127, + "name": "Luxembourg", + "iso3": "LUX", + "iso2": "LU", + "phone_code": "352", + "capital": "Luxembourg", + "currency": "EUR" + }, + { + "id": 128, + "name": "Macau S.A.R.", + "iso3": "MAC", + "iso2": "MO", + "phone_code": "853", + "capital": "Macao", + "currency": "MOP" + }, + { + "id": 129, + "name": "Macedonia", + "iso3": "MKD", + "iso2": "MK", + "phone_code": "389", + "capital": "Skopje", + "currency": "MKD" + }, + { + "id": 130, + "name": "Madagascar", + "iso3": "MDG", + "iso2": "MG", + "phone_code": "261", + "capital": "Antananarivo", + "currency": "MGA" + }, + { + "id": 131, + "name": "Malawi", + "iso3": "MWI", + "iso2": "MW", + "phone_code": "265", + "capital": "Lilongwe", + "currency": "MWK" + }, + { + "id": 132, + "name": "Malaysia", + "iso3": "MYS", + "iso2": "MY", + "phone_code": "60", + "capital": "Kuala Lumpur", + "currency": "MYR" + }, + { + "id": 133, + "name": "Maldives", + "iso3": "MDV", + "iso2": "MV", + "phone_code": "960", + "capital": "Male", + "currency": "MVR" + }, + { + "id": 134, + "name": "Mali", + "iso3": "MLI", + "iso2": "ML", + "phone_code": "223", + "capital": "Bamako", + "currency": "XOF" + }, + { + "id": 135, + "name": "Malta", + "iso3": "MLT", + "iso2": "MT", + "phone_code": "356", + "capital": "Valletta", + "currency": "EUR" + }, + { + "id": 136, + "name": "Man (Isle of)", + "iso3": "IMN", + "iso2": "IM", + "phone_code": "+44-1624", + "capital": "Douglas, Isle of Man", + "currency": "GBP" + }, + { + "id": 137, + "name": "Marshall Islands", + "iso3": "MHL", + "iso2": "MH", + "phone_code": "692", + "capital": "Majuro", + "currency": "USD" + }, + { + "id": 138, + "name": "Martinique", + "iso3": "MTQ", + "iso2": "MQ", + "phone_code": "596", + "capital": "Fort-de-France", + "currency": "EUR" + }, + { + "id": 139, + "name": "Mauritania", + "iso3": "MRT", + "iso2": "MR", + "phone_code": "222", + "capital": "Nouakchott", + "currency": "MRO" + }, + { + "id": 140, + "name": "Mauritius", + "iso3": "MUS", + "iso2": "MU", + "phone_code": "230", + "capital": "Port Louis", + "currency": "MUR" + }, + { + "id": 141, + "name": "Mayotte", + "iso3": "MYT", + "iso2": "YT", + "phone_code": "262", + "capital": "Mamoudzou", + "currency": "EUR" + }, + { + "id": 142, + "name": "Mexico", + "iso3": "MEX", + "iso2": "MX", + "phone_code": "52", + "capital": "Mexico City", + "currency": "MXN" + }, + { + "id": 143, + "name": "Micronesia", + "iso3": "FSM", + "iso2": "FM", + "phone_code": "691", + "capital": "Palikir", + "currency": "USD" + }, + { + "id": 144, + "name": "Moldova", + "iso3": "MDA", + "iso2": "MD", + "phone_code": "373", + "capital": "Chisinau", + "currency": "MDL" + }, + { + "id": 145, + "name": "Monaco", + "iso3": "MCO", + "iso2": "MC", + "phone_code": "377", + "capital": "Monaco", + "currency": "EUR" + }, + { + "id": 146, + "name": "Mongolia", + "iso3": "MNG", + "iso2": "MN", + "phone_code": "976", + "capital": "Ulan Bator", + "currency": "MNT" + }, + { + "id": 147, + "name": "Montenegro", + "iso3": "MNE", + "iso2": "ME", + "phone_code": "382", + "capital": "Podgorica", + "currency": "EUR" + }, + { + "id": 148, + "name": "Montserrat", + "iso3": "MSR", + "iso2": "MS", + "phone_code": "+1-664", + "capital": "Plymouth", + "currency": "XCD" + }, + { + "id": 149, + "name": "Morocco", + "iso3": "MAR", + "iso2": "MA", + "phone_code": "212", + "capital": "Rabat", + "currency": "MAD" + }, + { + "id": 150, + "name": "Mozambique", + "iso3": "MOZ", + "iso2": "MZ", + "phone_code": "258", + "capital": "Maputo", + "currency": "MZN" + }, + { + "id": 151, + "name": "Myanmar", + "iso3": "MMR", + "iso2": "MM", + "phone_code": "95", + "capital": "Nay Pyi Taw", + "currency": "MMK" + }, + { + "id": 152, + "name": "Namibia", + "iso3": "NAM", + "iso2": "NA", + "phone_code": "264", + "capital": "Windhoek", + "currency": "NAD" + }, + { + "id": 153, + "name": "Nauru", + "iso3": "NRU", + "iso2": "NR", + "phone_code": "674", + "capital": "Yaren", + "currency": "AUD" + }, + { + "id": 154, + "name": "Nepal", + "iso3": "NPL", + "iso2": "NP", + "phone_code": "977", + "capital": "Kathmandu", + "currency": "NPR" + }, + { + "id": 155, + "name": "Netherlands Antilles", + "iso3": "ANT", + "iso2": "AN", + "phone_code": "", + "capital": "", + "currency": "" + }, + { + "id": 156, + "name": "Netherlands The", + "iso3": "NLD", + "iso2": "NL", + "phone_code": "31", + "capital": "Amsterdam", + "currency": "EUR" + }, + { + "id": 157, + "name": "New Caledonia", + "iso3": "NCL", + "iso2": "NC", + "phone_code": "687", + "capital": "Noumea", + "currency": "XPF" + }, + { + "id": 158, + "name": "New Zealand", + "iso3": "NZL", + "iso2": "NZ", + "phone_code": "64", + "capital": "Wellington", + "currency": "NZD" + }, + { + "id": 159, + "name": "Nicaragua", + "iso3": "NIC", + "iso2": "NI", + "phone_code": "505", + "capital": "Managua", + "currency": "NIO" + }, + { + "id": 160, + "name": "Niger", + "iso3": "NER", + "iso2": "NE", + "phone_code": "227", + "capital": "Niamey", + "currency": "XOF" + }, + { + "id": 161, + "name": "Nigeria", + "iso3": "NGA", + "iso2": "NG", + "phone_code": "234", + "capital": "Abuja", + "currency": "NGN" + }, + { + "id": 162, + "name": "Niue", + "iso3": "NIU", + "iso2": "NU", + "phone_code": "683", + "capital": "Alofi", + "currency": "NZD" + }, + { + "id": 163, + "name": "Norfolk Island", + "iso3": "NFK", + "iso2": "NF", + "phone_code": "672", + "capital": "Kingston", + "currency": "AUD" + }, + { + "id": 164, + "name": "Northern Mariana Islands", + "iso3": "MNP", + "iso2": "MP", + "phone_code": "+1-670", + "capital": "Saipan", + "currency": "USD" + }, + { + "id": 165, + "name": "Norway", + "iso3": "NOR", + "iso2": "NO", + "phone_code": "47", + "capital": "Oslo", + "currency": "NOK" + }, + { + "id": 166, + "name": "Oman", + "iso3": "OMN", + "iso2": "OM", + "phone_code": "968", + "capital": "Muscat", + "currency": "OMR" + }, + { + "id": 167, + "name": "Pakistan", + "iso3": "PAK", + "iso2": "PK", + "phone_code": "92", + "capital": "Islamabad", + "currency": "PKR" + }, + { + "id": 168, + "name": "Palau", + "iso3": "PLW", + "iso2": "PW", + "phone_code": "680", + "capital": "Melekeok", + "currency": "USD" + }, + { + "id": 169, + "name": "Palestinian Territory Occupied", + "iso3": "PSE", + "iso2": "PS", + "phone_code": "970", + "capital": "East Jerusalem", + "currency": "ILS" + }, + { + "id": 170, + "name": "Panama", + "iso3": "PAN", + "iso2": "PA", + "phone_code": "507", + "capital": "Panama City", + "currency": "PAB" + }, + { + "id": 171, + "name": "Papua new Guinea", + "iso3": "PNG", + "iso2": "PG", + "phone_code": "675", + "capital": "Port Moresby", + "currency": "PGK" + }, + { + "id": 172, + "name": "Paraguay", + "iso3": "PRY", + "iso2": "PY", + "phone_code": "595", + "capital": "Asuncion", + "currency": "PYG" + }, + { + "id": 173, + "name": "Peru", + "iso3": "PER", + "iso2": "PE", + "phone_code": "51", + "capital": "Lima", + "currency": "PEN" + }, + { + "id": 174, + "name": "Philippines", + "iso3": "PHL", + "iso2": "PH", + "phone_code": "63", + "capital": "Manila", + "currency": "PHP" + }, + { + "id": 175, + "name": "Pitcairn Island", + "iso3": "PCN", + "iso2": "PN", + "phone_code": "870", + "capital": "Adamstown", + "currency": "NZD" + }, + { + "id": 176, + "name": "Poland", + "iso3": "POL", + "iso2": "PL", + "phone_code": "48", + "capital": "Warsaw", + "currency": "PLN" + }, + { + "id": 177, + "name": "Portugal", + "iso3": "PRT", + "iso2": "PT", + "phone_code": "351", + "capital": "Lisbon", + "currency": "EUR" + }, + { + "id": 178, + "name": "Puerto Rico", + "iso3": "PRI", + "iso2": "PR", + "phone_code": "+1-787 and 1-939", + "capital": "San Juan", + "currency": "USD" + }, + { + "id": 179, + "name": "Qatar", + "iso3": "QAT", + "iso2": "QA", + "phone_code": "974", + "capital": "Doha", + "currency": "QAR" + }, + { + "id": 180, + "name": "Reunion", + "iso3": "REU", + "iso2": "RE", + "phone_code": "262", + "capital": "Saint-Denis", + "currency": "EUR" + }, + { + "id": 181, + "name": "Romania", + "iso3": "ROU", + "iso2": "RO", + "phone_code": "40", + "capital": "Bucharest", + "currency": "RON" + }, + { + "id": 182, + "name": "Russia", + "iso3": "RUS", + "iso2": "RU", + "phone_code": "7", + "capital": "Moscow", + "currency": "RUB" + }, + { + "id": 183, + "name": "Rwanda", + "iso3": "RWA", + "iso2": "RW", + "phone_code": "250", + "capital": "Kigali", + "currency": "RWF" + }, + { + "id": 184, + "name": "Saint Helena", + "iso3": "SHN", + "iso2": "SH", + "phone_code": "290", + "capital": "Jamestown", + "currency": "SHP" + }, + { + "id": 185, + "name": "Saint Kitts And Nevis", + "iso3": "KNA", + "iso2": "KN", + "phone_code": "+1-869", + "capital": "Basseterre", + "currency": "XCD" + }, + { + "id": 186, + "name": "Saint Lucia", + "iso3": "LCA", + "iso2": "LC", + "phone_code": "+1-758", + "capital": "Castries", + "currency": "XCD" + }, + { + "id": 187, + "name": "Saint Pierre and Miquelon", + "iso3": "SPM", + "iso2": "PM", + "phone_code": "508", + "capital": "Saint-Pierre", + "currency": "EUR" + }, + { + "id": 188, + "name": "Saint Vincent And The Grenadines", + "iso3": "VCT", + "iso2": "VC", + "phone_code": "+1-784", + "capital": "Kingstown", + "currency": "XCD" + }, + { + "id": 189, + "name": "Saint-Barthelemy", + "iso3": "BLM", + "iso2": "BL", + "phone_code": "590", + "capital": "Gustavia", + "currency": "EUR" + }, + { + "id": 190, + "name": "Saint-Martin (French part)", + "iso3": "MAF", + "iso2": "MF", + "phone_code": "590", + "capital": "Marigot", + "currency": "EUR" + }, + { + "id": 191, + "name": "Samoa", + "iso3": "WSM", + "iso2": "WS", + "phone_code": "685", + "capital": "Apia", + "currency": "WST" + }, + { + "id": 192, + "name": "San Marino", + "iso3": "SMR", + "iso2": "SM", + "phone_code": "378", + "capital": "San Marino", + "currency": "EUR" + }, + { + "id": 193, + "name": "Sao Tome and Principe", + "iso3": "STP", + "iso2": "ST", + "phone_code": "239", + "capital": "Sao Tome", + "currency": "STD" + }, + { + "id": 194, + "name": "Saudi Arabia", + "iso3": "SAU", + "iso2": "SA", + "phone_code": "966", + "capital": "Riyadh", + "currency": "SAR" + }, + { + "id": 195, + "name": "Senegal", + "iso3": "SEN", + "iso2": "SN", + "phone_code": "221", + "capital": "Dakar", + "currency": "XOF" + }, + { + "id": 196, + "name": "Serbia", + "iso3": "SRB", + "iso2": "RS", + "phone_code": "381", + "capital": "Belgrade", + "currency": "RSD" + }, + { + "id": 197, + "name": "Seychelles", + "iso3": "SYC", + "iso2": "SC", + "phone_code": "248", + "capital": "Victoria", + "currency": "SCR" + }, + { + "id": 198, + "name": "Sierra Leone", + "iso3": "SLE", + "iso2": "SL", + "phone_code": "232", + "capital": "Freetown", + "currency": "SLL" + }, + { + "id": 199, + "name": "Singapore", + "iso3": "SGP", + "iso2": "SG", + "phone_code": "65", + "capital": "Singapur", + "currency": "SGD" + }, + { + "id": 200, + "name": "Slovakia", + "iso3": "SVK", + "iso2": "SK", + "phone_code": "421", + "capital": "Bratislava", + "currency": "EUR" + }, + { + "id": 201, + "name": "Slovenia", + "iso3": "SVN", + "iso2": "SI", + "phone_code": "386", + "capital": "Ljubljana", + "currency": "EUR" + }, + { + "id": 202, + "name": "Solomon Islands", + "iso3": "SLB", + "iso2": "SB", + "phone_code": "677", + "capital": "Honiara", + "currency": "SBD" + }, + { + "id": 203, + "name": "Somalia", + "iso3": "SOM", + "iso2": "SO", + "phone_code": "252", + "capital": "Mogadishu", + "currency": "SOS" + }, + { + "id": 204, + "name": "South Africa", + "iso3": "ZAF", + "iso2": "ZA", + "phone_code": "27", + "capital": "Pretoria", + "currency": "ZAR" + }, + { + "id": 205, + "name": "South Georgia", + "iso3": "SGS", + "iso2": "GS", + "phone_code": "", + "capital": "Grytviken", + "currency": "GBP" + }, + { + "id": 206, + "name": "South Sudan", + "iso3": "SSD", + "iso2": "SS", + "phone_code": "211", + "capital": "Juba", + "currency": "SSP" + }, + { + "id": 207, + "name": "Spain", + "iso3": "ESP", + "iso2": "ES", + "phone_code": "34", + "capital": "Madrid", + "currency": "EUR" + }, + { + "id": 208, + "name": "Sri Lanka", + "iso3": "LKA", + "iso2": "LK", + "phone_code": "94", + "capital": "Colombo", + "currency": "LKR" + }, + { + "id": 209, + "name": "Sudan", + "iso3": "SDN", + "iso2": "SD", + "phone_code": "249", + "capital": "Khartoum", + "currency": "SDG" + }, + { + "id": 210, + "name": "Suriname", + "iso3": "SUR", + "iso2": "SR", + "phone_code": "597", + "capital": "Paramaribo", + "currency": "SRD" + }, + { + "id": 211, + "name": "Svalbard And Jan Mayen Islands", + "iso3": "SJM", + "iso2": "SJ", + "phone_code": "47", + "capital": "Longyearbyen", + "currency": "NOK" + }, + { + "id": 212, + "name": "Swaziland", + "iso3": "SWZ", + "iso2": "SZ", + "phone_code": "268", + "capital": "Mbabane", + "currency": "SZL" + }, + { + "id": 213, + "name": "Sweden", + "iso3": "SWE", + "iso2": "SE", + "phone_code": "46", + "capital": "Stockholm", + "currency": "SEK" + }, + { + "id": 214, + "name": "Switzerland", + "iso3": "CHE", + "iso2": "CH", + "phone_code": "41", + "capital": "Berne", + "currency": "CHF" + }, + { + "id": 215, + "name": "Syria", + "iso3": "SYR", + "iso2": "SY", + "phone_code": "963", + "capital": "Damascus", + "currency": "SYP" + }, + { + "id": 216, + "name": "Taiwan", + "iso3": "TWN", + "iso2": "TW", + "phone_code": "886", + "capital": "Taipei", + "currency": "TWD" + }, + { + "id": 217, + "name": "Tajikistan", + "iso3": "TJK", + "iso2": "TJ", + "phone_code": "992", + "capital": "Dushanbe", + "currency": "TJS" + }, + { + "id": 218, + "name": "Tanzania", + "iso3": "TZA", + "iso2": "TZ", + "phone_code": "255", + "capital": "Dodoma", + "currency": "TZS" + }, + { + "id": 219, + "name": "Thailand", + "iso3": "THA", + "iso2": "TH", + "phone_code": "66", + "capital": "Bangkok", + "currency": "THB" + }, + { + "id": 220, + "name": "Togo", + "iso3": "TGO", + "iso2": "TG", + "phone_code": "228", + "capital": "Lome", + "currency": "XOF" + }, + { + "id": 221, + "name": "Tokelau", + "iso3": "TKL", + "iso2": "TK", + "phone_code": "690", + "capital": "", + "currency": "NZD" + }, + { + "id": 222, + "name": "Tonga", + "iso3": "TON", + "iso2": "TO", + "phone_code": "676", + "capital": "Nuku'alofa", + "currency": "TOP" + }, + { + "id": 223, + "name": "Trinidad And Tobago", + "iso3": "TTO", + "iso2": "TT", + "phone_code": "+1-868", + "capital": "Port of Spain", + "currency": "TTD" + }, + { + "id": 224, + "name": "Tunisia", + "iso3": "TUN", + "iso2": "TN", + "phone_code": "216", + "capital": "Tunis", + "currency": "TND" + }, + { + "id": 225, + "name": "Turkey", + "iso3": "TUR", + "iso2": "TR", + "phone_code": "90", + "capital": "Ankara", + "currency": "TRY" + }, + { + "id": 226, + "name": "Turkmenistan", + "iso3": "TKM", + "iso2": "TM", + "phone_code": "993", + "capital": "Ashgabat", + "currency": "TMT" + }, + { + "id": 227, + "name": "Turks And Caicos Islands", + "iso3": "TCA", + "iso2": "TC", + "phone_code": "+1-649", + "capital": "Cockburn Town", + "currency": "USD" + }, + { + "id": 228, + "name": "Tuvalu", + "iso3": "TUV", + "iso2": "TV", + "phone_code": "688", + "capital": "Funafuti", + "currency": "AUD" + }, + { + "id": 229, + "name": "Uganda", + "iso3": "UGA", + "iso2": "UG", + "phone_code": "256", + "capital": "Kampala", + "currency": "UGX" + }, + { + "id": 230, + "name": "Ukraine", + "iso3": "UKR", + "iso2": "UA", + "phone_code": "380", + "capital": "Kiev", + "currency": "UAH" + }, + { + "id": 231, + "name": "United Arab Emirates", + "iso3": "ARE", + "iso2": "AE", + "phone_code": "971", + "capital": "Abu Dhabi", + "currency": "AED" + }, + { + "id": 232, + "name": "United Kingdom", + "iso3": "GBR", + "iso2": "GB", + "phone_code": "44", + "capital": "London", + "currency": "GBP" + }, + { + "id": 233, + "name": "United States", + "iso3": "USA", + "iso2": "US", + "phone_code": "1", + "capital": "Washington", + "currency": "USD" + }, + { + "id": 234, + "name": "United States Minor Outlying Islands", + "iso3": "UMI", + "iso2": "UM", + "phone_code": "1", + "capital": "", + "currency": "USD" + }, + { + "id": 235, + "name": "Uruguay", + "iso3": "URY", + "iso2": "UY", + "phone_code": "598", + "capital": "Montevideo", + "currency": "UYU" + }, + { + "id": 236, + "name": "Uzbekistan", + "iso3": "UZB", + "iso2": "UZ", + "phone_code": "998", + "capital": "Tashkent", + "currency": "UZS" + }, + { + "id": 237, + "name": "Vanuatu", + "iso3": "VUT", + "iso2": "VU", + "phone_code": "678", + "capital": "Port Vila", + "currency": "VUV" + }, + { + "id": 238, + "name": "Vatican City State (Holy See)", + "iso3": "VAT", + "iso2": "VA", + "phone_code": "379", + "capital": "Vatican City", + "currency": "EUR" + }, + { + "id": 239, + "name": "Venezuela", + "iso3": "VEN", + "iso2": "VE", + "phone_code": "58", + "capital": "Caracas", + "currency": "VEF" + }, + { + "id": 240, + "name": "Vietnam", + "iso3": "VNM", + "iso2": "VN", + "phone_code": "84", + "capital": "Hanoi", + "currency": "VND" + }, + { + "id": 241, + "name": "Virgin Islands (British)", + "iso3": "VGB", + "iso2": "VG", + "phone_code": "+1-284", + "capital": "Road Town", + "currency": "USD" + }, + { + "id": 242, + "name": "Virgin Islands (US)", + "iso3": "VIR", + "iso2": "VI", + "phone_code": "+1-340", + "capital": "Charlotte Amalie", + "currency": "USD" + }, + { + "id": 243, + "name": "Wallis And Futuna Islands", + "iso3": "WLF", + "iso2": "WF", + "phone_code": "681", + "capital": "Mata Utu", + "currency": "XPF" + }, + { + "id": 244, + "name": "Western Sahara", + "iso3": "ESH", + "iso2": "EH", + "phone_code": "212", + "capital": "El-Aaiun", + "currency": "MAD" + }, + { + "id": 245, + "name": "Yemen", + "iso3": "YEM", + "iso2": "YE", + "phone_code": "967", + "capital": "Sanaa", + "currency": "YER" + }, + { + "id": 246, + "name": "Zambia", + "iso3": "ZMB", + "iso2": "ZM", + "phone_code": "260", + "capital": "Lusaka", + "currency": "ZMK" + }, + { + "id": 247, + "name": "Zimbabwe", + "iso3": "ZWE", + "iso2": "ZW", + "phone_code": "263", + "capital": "Harare", + "currency": "ZWL" + } +] \ No newline at end of file diff --git a/GDP_analyse/f51403fc-f4b4-47f6-8f9e-5aff034c24b5_Series - Metadata.csv b/GDP_analyse/f51403fc-f4b4-47f6-8f9e-5aff034c24b5_Series - Metadata.csv new file mode 100644 index 0000000..53f69f5 --- /dev/null +++ b/GDP_analyse/f51403fc-f4b4-47f6-8f9e-5aff034c24b5_Series - Metadata.csv @@ -0,0 +1,14 @@ +Code,License Type,Indicator Name,Long definition,Source,Topic,Periodicity,Aggregation method,Statistical concept and methodology,Development relevance,Limitations and exceptions,License URL +NY.GDP.MKTP.KD.ZG,CC BY-4.0,GDP growth (annual %),Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.,"World Bank national accounts data, and OECD National Accounts data files.",Economic Policy & Debt: National accounts: Growth rates,Annual,Weighted average,"Gross domestic product (GDP) represents the sum of value added by all its producers. Value added is the value of the gross output of producers less the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production. The United Nations System of National Accounts calls for value added to be valued at either basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers but excluding sales or value added taxes). Both valuations exclude transport charges that are invoiced separately by producers. Total GDP is measured at purchaser prices. Value added by industry is normally measured at basic prices. When value added is measured at producer prices. + +Growth rates of GDP and its components are calculated using the least squares method and constant price data in the local currency. Constant price U.S. dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year.","An economy's growth is measured by the change in the volume of its output or in the real incomes of its residents. The 2008 United Nations System of National Accounts (2008 SNA) offers three plausible indicators for calculating growth: the volume of gross domestic product (GDP), real gross domestic income, and real gross national income. The volume of GDP is the sum of value added, measured at constant prices, by households, government, and industries operating in the economy. GDP accounts for all domestic production, regardless of whether the income accrues to domestic or foreign institutions.","Each industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs. + +In many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult. + +Moreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation. + +Informal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians. + +Rebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful. + +To obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate.",https://datacatalog.worldbank.org/public-licenses#cc-by diff --git a/GDP_analyse/growth_Data.csv b/GDP_analyse/growth_Data.csv new file mode 100644 index 0000000..88d2e00 --- /dev/null +++ b/GDP_analyse/growth_Data.csv @@ -0,0 +1,270 @@ +Series Name,Series Code,Country Name,Country Code,1990 [YR1990],2000 [YR2000],2010 [YR2010],2011 [YR2011],2012 [YR2012],2013 [YR2013],2014 [YR2014],2015 [YR2015],2016 [YR2016],2017 [YR2017],2018 [YR2018],2019 [YR2019] +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿拉伯联盟国家,ARB,13.1108930579909,5.48019245374824,4.77207865022406,3.62908644937873,6.6566784518331,3.16615143797144,2.45520052829529,3.30761814833609,3.24732515694024,0.999881844632398,2.09674677213106,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,加勒比小国,CSS,0.978716321457739,3.81991851870573,1.46228304677069,1.11817959628434,1.32390135121392,1.22014468892218,0.330079320674102,1.16825123657954,-2.05051393637426,0.294576117146079,1.63864383756487,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中歐和波羅的海,CEB,..,4.04258027503064,1.55604813828722,3.20692185780575,0.743004149761632,1.3377205874275,3.04859220439504,3.83007928428599,3.14122989289736,4.72069022443516,4.31113883375609,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,早人口紅利,EAR,5.87703686458907,4.47662845353429,6.16796544601219,4.9716490552657,4.23238037670288,4.29850909173919,4.01238351476674,4.6557374195757,4.78963282660179,4.69925741870382,4.10660551686681,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,东亚与太平洋地区,EAS,5.09710989547662,4.88873433522114,7.0614511696273,4.61468213676409,4.68439892244321,4.75022654118906,4.12660292358014,4.13402311344049,4.04300698578956,4.58520868869084,4.20686184970319,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,东亚与太平洋地区（不包括高收入）,EAP,5.40792354806743,7.50467999261552,9.7766019579125,8.46853573799895,7.5091460179766,7.20088214461674,6.75932058387755,6.50970836126803,6.36845598030584,6.47242799808781,6.32392330453717,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,东亚与太平洋地区 (IBRD与IDA),TEA,5.4079235480674,7.50467999261552,9.77711191413245,8.46906353478703,7.50986460914898,7.20127980251914,6.75959915521018,6.51005712801096,6.36877957759461,6.47276926448824,6.32390035105041,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,欧洲货币联盟,EMU,3.58269963425053,3.86689259667359,2.07687496364026,1.57558790196843,-0.857964535844587,-0.24817289410467,1.39328438254789,2.09958164289772,1.89722355121495,2.44098615942872,1.91043697561828,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,欧洲与中亚地区,ECS,2.32521921487667,4.2812963471941,2.56070594711267,2.33351497503801,0.314504711962641,0.894560514981151,1.86136575900007,2.03400361742123,1.90827543259343,2.66780953839729,2.1167629435537,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,欧洲与中亚地区（不包括高收入）,ECA,-1.4067696717879,8.01130501907399,5.07979129338054,6.04202283679467,3.70054416143404,4.03565995508355,2.22952487508228,0.818800416417261,1.74327387081252,4.02785344368954,2.76757903865624,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,欧洲与中亚地区 (IBRD与IDA),TEC,-1.40676967178788,7.41695283132519,4.76898325366744,5.80205366241265,3.3339549497065,3.62945248304914,2.33389350412135,1.2240602318024,1.94005731211517,4.11579546642042,3.08147140121537,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,欧洲联盟,EUU,2.97912891192291,3.84199381520374,2.084944736588,1.71133442132974,-0.39614857365676,0.25701452154911,1.77818631136206,2.33886610781113,2.00292032535658,2.50181059299311,2.01191681668011,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,脆弱和受衝突影響的情況下,FCS,14.779819681054,1.81956984197826,6.56347553258713,-5.42948508075212,12.2842676497925,3.85517887786071,1.0839632077128,2.20762963854129,5.21189864424663,2.98009593034632,2.34960825452231,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,重债穷国 (HIPC),HPC,-0.52610465066229,2.73584494189507,5.86402237692639,4.52293595339206,5.7158651453225,5.77322759293423,5.56853068345798,4.92154470737884,4.63462025891279,5.16897374919054,4.10331580217355,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,高收入国家,HIC,3.1411195372836,4.0456184155979,2.93518071815552,1.83114471094392,1.29003489242297,1.43603107407874,2.035733491654,2.32351692740312,1.6939283114054,2.26303484938583,2.20145835942691,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,只有IBRD,IBD,2.02224862383189,5.6296404657606,7.31217018846051,5.99836681265586,5.03034730540463,4.96681544751294,4.27422626529173,3.83840307797836,4.26791760789924,4.86139050745189,4.62090396952649,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,IBRD与IDA,IBT,2.16127625288046,5.51486956369824,7.24110003137022,5.90351820904654,5.00722761211294,5.03035555561119,4.36554650267335,3.85992056093288,4.18766016662279,4.81951012206456,4.59913627497453,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,IDA混合,IDB,6.60534974287941,3.73538826249626,6.27541713074335,4.99939744568185,4.54767958964028,5.83120442986402,5.78317530038757,3.87309412704127,1.53252326856284,2.71739305754697,3.52804847781452,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,只有IDA,IDX,1.05067479681895,3.98337573207223,6.1927756976564,4.07856171939545,4.80105926202252,6.08723652649145,5.58068659459703,4.46201139414811,4.66195597217242,5.75816612843596,5.05188246713537,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,IDA總,IDA,4.05404966215421,3.85449577676604,6.23479132400442,4.54690167684274,4.67142767734352,5.95640279656276,5.68403711026045,4.16114523936741,3.06761213856326,4.22581063038558,4.29508114215231,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,後期人口紅利,LTE,0.213950448775762,6.78077239501016,7.61888411875469,6.70000673889587,5.3791743452972,5.21025087795157,4.59179673688534,3.86532065468398,3.7712238392654,4.70940022790083,4.83882883653301,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,拉丁美洲与加勒比海地区,LCN,0.346959861250667,3.79181916994436,5.84983265709349,4.39108221486633,2.78598691284483,2.75998374119834,1.00406026911244,0.0873921114982181,-0.354517170291743,1.656188213951,1.4576739589516,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,拉丁美洲与加勒比海地区（不包括高收入）,LAC,0.314983792515349,3.78314699284428,6.00904575151111,4.40771970750696,2.69739479708835,2.73050173133464,0.971010317169856,-0.0640610830211301,-0.482336544600784,1.73426969923018,1.41425918045942,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,拉丁美洲与加勒比海地区 (IBRD与IDA),TLA,0.48337001038999,3.7790300539625,6.04516522498938,4.52329520702135,2.849989171072,2.82730923438967,1.04520576126991,0.0479124592174429,-0.350490596171994,1.73413956778458,1.56572555451046,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,最不发达国家：联合国分类,LDC,0.413905813095838,4.20196493354663,6.22626552949687,3.79028080488931,4.87039334440351,5.74794123080871,5.45836328308506,4.04727151887232,3.67912797067351,4.84149529517725,4.225377689502,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中低收入国家,LMY,2.11881612519662,5.56737042216496,7.36232476740628,5.93394049881468,5.09649490014593,5.12874260501096,4.42038447745882,3.88691146529688,4.24181359412543,4.85902297936636,4.60573519685981,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,低收入国家,LIC,0.496149015432607,2.65492806341976,6.93095744673995,3.47837101810849,3.29108983337755,6.12012999612574,5.82880569025963,2.87442585269397,3.07430589473159,4.95784123279401,4.94831990352868,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中低等收入国家,LMC,3.86008187233078,4.31948196722045,6.84501533920341,4.99249861761353,5.17860437773523,5.64066372936487,5.65615431357296,5.58512871174324,5.56701749889955,5.55764948231226,5.54594860536911,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中东与北非地区,MEA,13.1178054651632,5.76291908423772,5.04685619543174,3.69417240936025,3.87979182175864,2.68889674338217,2.92114646239445,2.50997564402449,4.82319030071088,1.60430524642059,2.36820853281843,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中东与北非地区（不包括高收入）,MNA,12.9490452499578,4.44554421824184,5.27163871136086,-0.929361354001458,2.80480700162407,1.6627073415103,2.09288342347691,0.984977951265776,7.50267813808019,3.04290343380032,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中东与北非地区 (IBRD与IDA),TMN,12.9490452499578,4.55516762871393,5.25295889292865,-1.01989682053113,2.77799406413509,1.658286803161,2.11111086180087,0.965836221620364,7.52509911578947,3.04214289063832,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中等收入国家,MIC,2.14211952399435,5.60703958237082,7.36855045460399,5.96923594269374,5.12164221714313,5.1151741276811,4.40092403819131,3.90133726459443,4.25828372558432,4.857672766634,4.60093964821043,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,北美,NAC,1.70849759691708,4.22931386117091,2.61290888794352,1.70417631143123,2.1992154232877,1.88862674727903,2.4930154338023,2.66420558043698,1.52258181175779,2.29058241493749,2.76182883548563,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,未分类国家,INX,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,经合组织成员,OED,3.07895359602044,4.00474530237094,2.8863858909292,1.78796676500077,1.2712134535757,1.47332223252015,2.05239517695136,2.41949492711832,1.73298022110717,2.42666048130511,2.2102212783317,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,其他小国,OSS,..,..,8.27793100055612,7.65589927201829,3.6599840360395,3.01349990092797,2.91829554373631,2.79341260174752,2.39398481120277,2.17053607248413,2.14341323618797,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,太平洋岛国,PSS,4.16284066359924,-0.635678841796476,2.90044751473953,4.10282051504379,1.75944962362176,2.74523313384987,3.94549921460916,3.67848460255458,3.3508071796135,4.04238395834378,3.57192122674502,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,人口紅利之後,PST,2.83891638087761,3.9373588086774,2.79684827996545,1.56518203597982,1.13223844596277,1.33315568510433,1.88529359308356,2.10942802434681,1.62795091858732,2.26702511234996,2.12211491438003,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,預人口紅利,PRE,9.89127038865647,2.98754437425836,6.30406290036075,4.56702277009664,5.64162490846923,5.96283779788784,4.80065579928208,3.07087705437885,2.74634431892393,1.71313073342152,2.20516102688315,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,小国,SST,..,4.51760155271552,7.02960475493988,6.54577740024016,3.27287903694349,2.74054404642278,2.55231761288532,2.57277191558001,1.77622356145785,1.94392944005233,2.11398424866456,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,南亚,SAS,5.42836904237693,4.06288070274789,7.70266678267187,5.13725334149187,5.50173728109253,6.08731105227758,6.99230192812428,7.48002056896395,7.70780544007212,6.93576819348706,6.77063352702724,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,南亚 (IBRD与IDA),TSA,5.42836904237691,4.06288070274788,7.70266678267187,5.13725334149187,5.50173728109253,6.08731105227756,6.99230192812431,7.48002056896395,7.70780544007215,6.93576819348706,6.77063352702724,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,撒哈拉以南非洲地区,SSF,2.38263287298443,3.50380248969486,5.58408809597037,4.44641239678741,4.00371220707147,5.01946129370931,4.67035953339017,2.81584391594731,1.19162863362692,2.54586476616237,2.41197337469244,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,撒哈拉以南非洲地区（不包括高收入）,SSA,2.37906805982722,3.50582987087056,5.58382596807722,4.44397190005621,4.00572502587735,5.01874781685692,4.67047895627503,2.81431614517803,1.18919477445422,2.54450266453958,2.41103057587746,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,撒哈拉以南非洲地区 (IBRD与IDA),TSS,2.38263287298446,3.50380248969486,5.58408809597042,4.44641239678738,4.00371220707143,5.0194612937093,4.67035953339018,2.8158439159473,1.19162863362692,2.54586476616234,2.41197337469252,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中高等收入国家,UMC,1.6562504171206,5.97557223606162,7.51676070362049,6.24401795447349,5.10580602533955,4.96898027113812,4.04947808681253,3.41195755797234,3.86991794068872,4.64656135554857,4.30456822629077,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,世界,WLD,2.91246320175344,4.39462666013884,4.27958636573598,3.11388632188054,2.51258721769445,2.65194436277218,2.83994006432333,2.8529393604575,2.56540895781401,3.16540231041313,3.03877529212566,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿富汗,AFG,..,..,14.3624414596815,0.426354792856571,12.7522870825788,5.60074466131904,2.72454336495028,1.45131465458005,2.2603142045464,2.66529204636834,1.03066005804328,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿尔巴尼亚,ALB,-9.57564016948645,6.95003613422918,3.70688068400311,2.5454053935363,1.41752599216282,1.00198792581364,1.77000029658963,2.23000017922044,3.34999968813095,3.83661965642376,4.00441323438758,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿尔及利亚,DZA,0.800000579981415,3.81967849559879,3.63414535334246,2.89186599464848,3.37476865068784,2.76763886677112,3.78912121166823,3.76346695783126,3.29999999975597,1.60000000052275,2.10000000000001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,美属萨摩亚,ASM,..,..,0.442477876106182,0.293685756240819,-4.39238653001463,-2.75650842266462,0.944881889763778,1.24804992199688,-2.61941448382126,-5.37974683544303,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,安道尔共和国,AND,3.7813875896292,3.52836129711109,-5.35882577533336,-4.64654305163337,-1.61521818298665,0.351645001850741,2.27768315306425,0.842203518374134,1.8891243866394,1.72402223678981,1.62934547096762,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,安哥拉,AGO,-3.45009868360484,3.05462423430785,4.85921958436937,3.47198137230222,8.54214733707519,4.95459047755875,4.8226255492722,0.94357561305911,-2.58009724033977,-0.147207426247036,-2.13349325676356,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,安提瓜和巴布达,ATG,3.01164666223812,6.69174238194239,-7.20002446135504,-2.07912704683282,3.50660922205834,-0.103864693282389,4.65812743059392,4.02573680753291,5.5897408043279,3.03260611189997,4.93065090850533,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿根廷,ARG,-2.46721377637459,-0.78899893905691,10.1253981561002,6.00395169280579,-1.02642045443208,2.40532378079436,-2.51261532081396,2.73115982828946,-2.08032784377811,2.66859037935303,-2.51458960248539,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,亚美尼亚,ARM,..,5.90000000103537,2.19999999752649,4.69999999879775,7.20000000365837,3.3,3.60000000058993,3.19999999896625,0.199999999825991,7.5000000006513,5.2,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿鲁巴,ABW,3.96140172676485,7.6165897041888,-3.68502958088573,3.44605475040258,-1.36986301369863,4.19823232323233,0.848227809754619,-0.450585761489947,-0.211225105612556,1.33051103719384,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,澳大利亚,AUS,3.56894443347744,3.93310233186104,2.06752009293855,2.46272464735648,3.90090002283101,2.61572247679898,2.56870704342289,2.33607548109676,2.84675495108566,2.34258228676258,2.83494813296824,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,奥地利,AUT,4.34564156052724,3.37572197324998,1.83709459152502,2.92279763636382,0.68044437424868,0.0255041006158336,0.661274652949004,1.14298010071532,2.03957498344849,2.55088075977935,2.72908309897117,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿塞拜疆,AZE,..,11.0999992535407,4.7888327097976,-1.57299762108386,2.20293902267818,5.84267326989973,2.79728938165775,1.05075098242399,-3.06420003538679,-0.282011196847733,1.41287352692132,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴哈马,BHS,-1.5962490864956,4.14924301720671,1.53877740677106,0.6128974521461,3.0866868599452,-0.410428618068195,-0.147988573034226,1.04495952423387,-1.68954985564559,1.43719133908084,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴林,BHR,4.43799717642636,5.29999479407712,4.33440708785902,1.98382325562152,3.73225152129817,5.41650371529137,4.34984232980895,2.86196782508222,3.47092370171953,3.80352226237173,1.77769375269004,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,孟加拉国,BGD,5.62225816160702,5.2932947184604,5.57180227396866,6.46438388047517,6.52143507837333,6.01361036536019,6.06105935903958,6.55265279631962,7.1134894741232,7.28418409195113,7.86370889257542,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴巴多斯,BRB,-3.29996700033,4.45305951383068,0.26544622425628,0.657294139127245,0.281153636858349,0.00904404449670437,0.018086453246525,0.904159132007237,2.00716845878137,1,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,白俄罗斯,BLR,..,5.80000344007456,7.79826682269731,5.37870744981015,1.68713553642841,1.00347084085645,1.72638485417005,-3.82957055828382,-2.52644643552361,2.53218350023035,3.04990554989585,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,比利时,BEL,3.13740245214575,3.63363580186243,2.74423108261561,1.79830621979427,0.234778700910667,0.200661827638712,1.25467698401434,1.73958044796446,1.45255222408282,1.73438965580127,1.43783467602618,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,伯利兹,BLZ,11.4433887289319,13.0195773748259,3.38347170641828,2.16260682157193,2.93781262796161,0.851934627513558,3.6923714654517,3.43195048353593,-0.588023649731511,1.43728894631943,3.04964379717087,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,贝宁,BEN,8.97613436064626,5.85999210110583,2.11019929660023,2.96275035081007,4.81647824066904,7.18971631205676,6.35183194111322,2.0958083832335,3.96485991037314,5.83782330470896,6.85800227090556,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,百慕大,BMU,0.0192122958719381,9.31715557653585,-2.09411353414293,-3.33949441020565,-4.8345401709793,-2.51115443098975,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,不丹,BTN,10.8764346909591,6.93302423977509,11.7308543603364,7.89091389329108,5.07170981496216,2.14249655359879,5.74545516756977,6.64456425820161,8.02210122067797,4.62910139072935,2.29064819273836,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,玻利维亚,BOL,4.6357859096007,2.50780755336397,4.12671936920815,5.20409594656597,5.12227466098376,6.79601170559745,5.46056715368503,4.85718801458208,4.2639195041269,4.19520936722277,4.22362493214148,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,波斯尼亚和黑塞哥维那,BIH,..,5.50000043583101,0.872151022572723,0.958953087750629,-0.821729266233604,2.35103401437631,1.14803801103687,3.08822376132143,3.14565809237735,3.16355948711239,3.06613484784417,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,博茨瓦纳,BWA,6.77282194932553,1.98769585438754,8.56363174773267,6.04831636706808,4.45616721309787,11.3434242527752,4.1492898931352,-1.69796561588223,4.30377335257066,2.90753491024356,4.45355960085716,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴西,BRA,-3.10235594875007,4.38794944267383,7.52822583005563,3.97442307944702,1.92117598576537,3.00482266944432,0.503955740242247,-3.54576339269425,-3.30545431266852,1.06386125924512,1.11757918028799,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,英屬維爾京群島,VGB,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,文莱达鲁萨兰国,BRN,1.08942053548755,2.84942185782728,2.59896574643226,3.74531835205994,0.912841670964411,-2.12602851740175,-2.349746749517,-0.566814608844453,-2.4655149233665,1.32875142237043,0.0523466726926927,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,保加利亚,BGR,-9.11737709653305,4.76577106392688,1.32408780093829,1.91490585291416,0.0309698686798754,0.493921421234305,1.8375211479416,3.47118604090714,3.9367581489791,3.81124815844305,3.08115723374425,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,布基纳法索,BFA,-0.60292848001005,1.82024189276156,5.37337312603061,6.62686519230525,6.45269875497425,5.79258931687122,4.32683735691617,3.89454805671508,5.93413510489553,6.30209605892456,6.5098030396815,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,布隆迪,BDI,3.49982214003701,-0.856864058424151,5.12416330338039,4.03260249625296,4.44670822214543,4.9241952612883,4.24065164368079,-3.9000030859375,-0.600020000546493,0.500009998779461,1.59997661445593,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,佛得角,CPV,0.692171874341184,14.2848688321225,1.46679009706938,3.96888634471986,1.08191827877127,0.802797600574223,0.611212666207877,1.00686370684559,4.70579150927118,4.01325724418756,5.52056447484614,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,柬埔寨,KHM,..,10.7119948019697,5.96307857538696,7.0695699458918,7.31334550530008,7.35666514887974,7.14257110074294,7.03608717929632,7.03096677590116,7.01503010998164,7.52044442610649,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,喀麦隆,CMR,-6.10569764608267,3.55337436416127,3.4225076411544,4.12927716056967,4.54326501566736,5.40426570971735,5.88405932668046,5.6514637436283,4.64848155096477,3.54908731821561,3.85989699559069,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,加拿大,CAN,0.16352306970289,5.17876294444093,3.08949463821517,3.14688136494657,1.7622782464354,2.33017476954971,2.86845396944616,0.689906521218902,1.10709938326161,2.97856596293016,1.87856366177211,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,开曼群岛,CYM,..,0.999997811389036,-2.71563682344265,1.16906269526342,1.22975368307725,1.27933059281749,2.65452932151373,2.83312242670047,3.09628240719829,3.03934746728503,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中非共和国,CAF,-2.14752806574087,-2.48943244028874,4.63081843223307,4.19461533741989,5.05376125356918,-36.0374260757551,0.117267102913047,4.5628084403442,5.00628582386908,4.45728316184176,4.34060998344907,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,乍得,TCD,-4.17847237277677,-0.879681025444839,13.550100859549,0.0828697984381677,8.88257607170316,5.70000136285864,6.89998504532167,2.7676756848447,-6.25552708545281,-2.98869598553931,2.6413300339928,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,海峡群岛,CHI,..,5.82664146877514,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,智利,CHL,3.33356547272084,5.32693841912362,5.84417729578996,6.11091882913644,5.31862800041417,4.04500429817161,1.76673978360202,2.30376703614562,1.67054017325985,1.27918334145851,4.02465296526672,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中国,CHN,3.90711389605592,8.49150849150864,10.6361404632299,9.55091409001014,7.8596274932851,7.76861528412806,7.29951892117124,6.90531667019702,6.73667525262536,6.75700761091511,6.60000000000001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,哥伦比亚,COL,4.28199833207572,2.92486148314592,4.34755326848266,7.36253091447171,3.90305421927597,4.56686977279675,4.72831224597601,2.95594590662741,2.08738250162794,1.35132667631581,2.65812100000001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,科摩罗,COM,5.09123641339696,10.847878616072,3.79993345468068,4.09984695496956,3.2002729031888,4.49987421530828,2.10000643830286,1.09996463224388,2.1881974557357,2.70765882914921,2.79999999999998,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,刚果（金）,COD,-6.56831069464246,-6.91092731652098,7.10797657621967,6.87467088970635,7.08689894672231,8.48195663552301,9.47028809759604,6.91618781038352,2.39937909800997,3.72694765326061,5.75793273800032,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,刚果（布）,COG,1.00000356694369,7.57598039215684,8.75160145933471,3.42066568060184,3.79997131252208,3.4407052491938,6.77991615766737,2.64648587686472,-2.80001878768313,-3.10000000000001,1.02555420747001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,哥斯达黎加,CRI,3.55306336479583,3.83316666149418,4.95186384413803,4.30710165986632,4.79691991819773,2.2690274007773,3.51533856385215,3.63173144148803,4.24598987825375,3.3971817012296,2.66404984384756,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,科特迪瓦,CIV,-1.09590841115427,-2.06840005678232,2.01763859205302,-4.38725478828525,10.7065041030832,8.88942130172924,8.79407739034323,8.84285994957199,7.97175118624882,7.70208942212352,7.43307162464457,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,克罗地亚,HRV,..,3.77260555708246,-1.4729162961615,-0.340190377704701,-2.30064285837889,-0.491748791173023,-0.087513347134788,2.40059187897236,3.53612955655616,2.92136337409232,2.62982608916515,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,古巴,CUB,-2.94856494522189,5.9147601740486,2.39035229650723,2.80230083647626,3.01490028898772,2.74760256257211,1.04757663187654,4.43814195922121,0.513761467889907,1.78349762687111,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,库拉索,CUW,..,..,0.0823052183296795,0.612609896110897,-0.138918185253203,-0.79410697934297,-1.13883792915853,0.278695606616793,-0.98657059813128,-1.73976478403257,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塞浦路斯,CYP,7.40399950603849,5.96531774387263,1.33540349841795,0.403429931640773,-2.87809955633817,-5.79930466368432,-1.31146542300257,1.9623040096572,4.81218762402544,4.46887904445133,3.87598486671344,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,捷克共和国,CZE,..,4.26674135714225,2.27342005165725,1.77783318662328,-0.799844281176618,-0.483671040702603,2.71511613157801,5.30923851909814,2.45054238739621,4.35260351822929,2.95778447327679,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,丹麦,DNK,1.47524426112957,3.7468626151995,1.87099111020392,1.33677777743229,0.226499797192986,0.93334095714377,1.61939381227008,2.34259043214833,2.3996720926861,2.26236900792341,1.48965908418744,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,吉布提,DJI,..,..,..,..,..,..,8.92058462492513,9.68379811648174,8.72226006105292,4.09026362770027,5.95536790031441,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,多米尼克,DMA,5.41865187107055,2.33950740701536,0.672543297656986,-0.223681461951699,-1.05878293792446,-0.607692270036239,4.38889610196991,-2.55050301979611,2.52205087356081,-9.53040952427523,0.532740631610153,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,多米尼加共和国,DOM,-5.45431239470214,4.6617622800146,8.33965105962218,3.13342300326755,2.71736789670562,4.87520509332271,7.63603182423664,7.03337106918114,6.61220822292972,4.55181513818401,6.95074308631736,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,厄瓜多尔,ECU,3.6799140527842,1.09179888475457,3.52529866894027,7.86814091910996,5.641962066712,4.94651126690626,3.78886854920833,0.0988726083462836,-1.22638398463877,2.36838652633651,1.37712702988119,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿拉伯埃及共和国,EGY,5.66702915073417,6.37000383315308,5.14723485755344,1.76457194899807,2.22619979863512,2.18546605362178,2.91591187985725,4.37201907790137,4.3466434555546,4.18122099994775,5.31412103746398,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,萨尔瓦多,SLV,4.8315603853172,1.126057050299,2.5457559611846,3.81525788753869,2.8144874318595,2.23563783909665,1.7112697645963,2.39502935805318,2.50745853177364,2.30793850388811,2.53763190900662,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,赤道几内亚,GNQ,-1.75856408404934,18.2137799569674,-8.92417588851806,6.52392362989873,8.31287184445641,-4.13319850131583,0.415066301596866,-9.11004114881537,-8.81641723219757,-4.68568386750484,-2.94973018881664,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,厄立特里亚,ERI,..,-3.14198577404406,2.19419054483319,8.67979993646826,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,爱沙尼亚,EST,..,10.5680749511053,2.25907589383118,7.5973002092456,4.30725898765326,1.93654344434879,2.8885638447165,1.90028658970603,3.48909711147971,4.85668685644394,3.86575358097339,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,斯威士兰,SWZ,21.0180005361395,1.76017376022224,3.79375496828149,2.247229761068,4.71792061123591,6.42104380697586,1.93101002705092,0.391128860553607,3.22247241618905,1.87305434188787,0.624044070312181,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,埃塞俄比亚,ETH,2.72645178301019,6.07321747957957,12.5505383459308,11.1782962271642,8.64781163337416,10.5822700482672,10.2574929610051,10.3924630202334,9.43348265784402,9.5038585710959,6.81016939097316,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,法罗群岛,FRO,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,斐济,FJI,5.80000271926605,-1.69999854029385,2.95467207375195,2.7051429713903,1.41131366068838,4.73422041499947,5.60351451718321,4.66380783801587,2.64528958386489,5.17974352639772,4.99999999999963,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,芬兰,FIN,0.675783493968225,5.63484745219101,2.99233750220186,2.57081774452163,-1.42618935959564,-0.758036294820087,-0.631728090593853,0.500662010259816,2.7719255201694,3.04332482198832,1.65753369462296,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,法国,FRA,2.92393513681762,3.92366920574179,1.94943763160794,2.19270065138433,0.313134744426151,0.576326679224579,0.956183042120372,1.11291234211977,1.09546439403398,2.2602027222695,1.72488062812486,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,法属波利尼西亚,PYF,2.20000012757878,4.0000003434361,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,加蓬,GAB,5.19223727652398,-1.88296639760897,7.08988731455126,7.09175334258654,5.25107691750407,5.63869900338693,4.31496444107471,3.8788993950789,2.09144220829562,0.479762270647498,1.22110357753027,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,冈比亚,GMB,3.55887936867576,5.50000003313032,6.52629733804085,-4.2951217683525,5.59976132327449,4.7889218906391,-0.94023552932785,5.86824752625856,0.40559658862658,4.55773190885563,6.59999512038003,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,格鲁吉亚,GEO,-14.7882259000602,1.83834176781448,6.24948778888705,7.22160890504406,6.3507978400543,3.38677083597563,4.62361326359952,2.88052654618222,2.8466032990662,4.83268629095114,4.71737338003256,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,德国,DEU,5.25500608609781,2.96204536784524,4.07993330487069,3.66000015503516,0.491992829138056,0.489584482494635,2.17806656078447,1.73896761850342,2.2422350985948,2.15710947029994,1.4253034667189,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,加纳,GHA,3.3288178832203,3.70000003991538,7.89971188935303,14.047123630319,9.29278940459687,7.31252500733115,2.89743883021139,2.17820680001356,3.44779297388334,8.14344649746531,6.26348077175622,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,直布罗陀,GIB,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,希腊,GRC,0,3.91977077200825,-5.4790371077553,-9.13249415322949,-7.30049393532073,-3.24142502506591,0.739777123496509,-0.437833917631266,-0.19095227668727,1.50509930751895,1.93437082147373,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,格陵兰,GRL,-11.7195329842899,7.10159688181619,1.73582505889665,-0.496519391959083,1.39419335955624,-1.29828069335511,4.70646979099794,-2.52949832411367,5.95561768303337,0.987493073695873,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,格林纳达,GRD,4.01261190237577,4.88795213650562,-0.511262754149129,0.764976824032317,-1.15514373468322,2.35141877347161,7.34407652185287,6.44314373327768,3.73561413158529,5.05921615957674,4.82698393466603,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,关岛,GUM,..,..,2.0916126333403,0.122925629993858,2.02578268876611,1.82511030886484,1.73330707110497,0.561471442400773,0.250288794763193,0.192049164586123,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,危地马拉,GTM,3.10256318046595,3.60886874513247,2.86948775555717,4.16204894002361,2.96985740113649,3.69758560131321,4.17416913658175,4.14004435825474,3.09248255769093,2.76033848366622,3.14500045766512,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,几内亚,GIN,4.32406275371827,2.50306056145635,4.82071541392241,5.61210427912495,5.91691173154778,3.93478402501792,3.7074516130114,3.81494988965694,10.8288068402291,13.3601054667296,8.65939320585427,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,几内亚比绍共和国,GNB,6.09999998640174,5.42698736686771,4.61097090010318,8.08477973398718,-1.71268301181073,3.25590426517692,0.96456075098132,6.13408294113292,6.26280563780463,5.9191766258605,3.79999999834297,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圭亚那,GUY,-3.06748466257669,-1.36380390711416,4.13844312103164,5.19629812869242,5.27630614305671,5.00056499105213,3.84692391244501,3.16240778720626,3.31613165474852,2.08017739394283,3.44265406311395,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,海地,HTI,1.03812280103124,0.870059256805945,-5.49779243532787,5.52373814577498,2.88509537768327,4.23405320591242,2.81014823784587,1.21121875159815,1.45270665825504,1.17327950545629,1.48388303510178,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,洪都拉斯,HND,2.79174894892149,7.29128815869618,3.73114034433009,3.83569066207508,4.12868774866939,2.79155975746801,3.0580805621437,3.84007997093951,3.89297219726413,4.78802694524671,3.74558474629505,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中国香港特别行政区,HKG,3.83059827340216,7.66348594940034,6.76768541390523,4.81467724176471,1.70027739772063,3.10151622363742,2.76239445216129,2.38780805294535,2.176392352043,3.83840630654977,3.02140152972467,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,匈牙利,HUN,..,4.20917993135265,0.65529336957259,1.65765742187791,-1.63140333682364,2.09364216402103,4.22465375476067,3.53642547600026,2.28079775023321,4.13706393670374,4.94041868527559,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,冰岛,ISL,1.16937033279427,4.89159350570363,-3.4358780790073,1.88161948432872,1.29643609531233,4.13385110009837,2.08028719850364,4.46786050817248,7.35147530184462,4.60378137949624,4.61147602886756,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,印度,IND,5.53345456306442,3.84099115674177,8.49758687599316,5.24131422481111,5.45638755166588,6.38610640094825,7.41022760508852,7.99625378571471,8.16952650547138,7.16788886086535,6.98233355585374,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,印度尼西亚,IDN,7.24213163856462,4.9200677470169,6.22385418062366,6.1697842077098,6.03005065305615,5.55726368891037,5.00666842575535,4.87632230022051,5.03306918280238,5.06740636558547,5.17127032830882,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,伊朗伊斯兰共和国,IRN,13.8337800132853,5.85806659996007,5.79793830169417,2.64571791806476,-7.4445570297602,-0.194073471015827,4.60341887988824,-1.32064511973337,13.3962444619863,3.75518736694957,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,伊拉克,IRQ,57.8178290288542,1.40647473483672,6.40256484471192,7.546471200426,13.9364301737537,7.60000000000001,0.699999999994745,2.47766461711252,13.5722188353836,-1.6712871559571,0.632455398016305,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,爱尔兰,IRL,8.46652797902536,9.44575062392686,1.80997976855282,0.343440832538349,0.225714814318877,1.35163398769838,8.55668798449921,25.1625330914968,3.67781579353812,8.14529613547501,8.1698709708408,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马恩岛,IMN,4.18697681556888,5.33836223919168,3.40000000000002,2,3.20000000000005,4.50000000000014,4.99999999999989,-0.899999999999963,7.40000000000001,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,以色列,ISR,7.31519916554592,7.71942885368134,5.50756015225488,4.96820704281595,2.11619351702845,4.14457522766341,3.89789590938017,2.56735213530706,4.00788872389455,3.44461203002118,3.30883147025702,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,意大利,ITA,1.98577490336878,3.71010658689261,1.68652340308921,0.576623022104201,-2.81901377925493,-1.72816080249231,0.113673237878274,0.923992487509764,1.11890061799522,1.68418260951518,0.858260215893509,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,牙买加,JAM,4.2005351147029,0.87870354796236,-1.45784841518038,1.72576401073788,-0.614918541099811,0.503817450382684,0.688117552821055,0.904845989688582,1.38065838326224,0.978556222849122,1.85626889162546,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,日本,JPN,4.89271306574963,2.7796328252636,4.1917392586067,-0.115421339715752,1.49508958593792,2.00026784110196,0.374719476337631,1.2229210410667,0.609093181240581,1.92875725128059,0.787965413355508,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,约旦,JOR,0.973756490238898,4.24524741941383,2.3113924735689,2.58678433077786,2.65117055537524,2.82876680398884,3.09633027522955,2.39169597043328,2.00373364209898,2.1157328887228,1.94029220505026,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,哈萨克斯坦,KAZ,..,9.80000000214159,7.29999999999953,7.40000000000069,4.79999999999939,6.0000000000007,4.19999999999938,1.20000000000067,1.09999999999941,4.10000000000042,4.09999999999972,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,肯尼亚,KEN,4.19205097421585,0.599695391613636,8.40569922421719,6.1082637197965,4.5632091307112,5.87868056675418,5.3571256433749,5.71850713375994,5.87894929918309,4.86253822126818,6.31978136452209,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,基里巴斯,KIR,-0.869565217391298,6.25,-0.923884656867074,1.59456110202041,4.71331913631832,4.21477391203615,-0.697828087271759,10.4054079265676,5.12975052525447,0.328439777111029,2.00103603146545,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,朝鲜民主主义人民共和国,PRK,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,大韩民国,KOR,9.81122968350864,8.92442603420267,6.49679358555511,3.68168856910729,2.29239784625679,2.89620493507104,3.34144776129996,2.79023616714657,2.9293047947001,3.06276846243263,2.66831140171185,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,科索沃,XKX,..,..,3.30957483925633,4.81256332320163,2.89534856958733,3.4312300087235,1.19807097597369,4.09445453574099,4.05657859621029,4.22790853670567,4.14537194775694,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,科威特,KWT,..,4.69458198753053,-2.36706194393776,9.62843608199742,6.62638808077409,1.14903884697843,0.500876982158658,0.593019617221231,2.92612113180444,-3.48154918987453,1.24005549827521,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,吉尔吉斯斯坦,KGZ,5.70279663523441,5.42667384921542,-0.471566847508598,5.95627437653896,-0.199999999999974,10.9,3.99999999999996,3.90000000000006,4.29999999999986,4.70000000000006,3.50000000000006,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,老挝,LAO,6.70457868738686,5.79878232615873,8.52690551722873,8.03865268080929,8.02609843404085,8.02630022637751,7.61196344074382,7.26959177501747,7.02309187410415,6.85143125934344,6.50086840737087,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,拉脱维亚,LVA,..,5.40685848447123,-3.94067030557116,6.38102125886553,4.03462837497035,2.42985120848557,1.85824365165655,2.97170383161259,2.06438129607109,4.63647962142689,4.76971668163448,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,黎巴嫩,LBN,26.533160093229,1.34187161939805,8.0372493541868,0.917698798466859,2.72015156573167,2.61916087876214,1.88428566230098,0.415030764688112,1.60552459997925,0.551994777421314,0.199990636430059,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,莱索托,LSO,6.04547332985949,3.8755468142558,6.07203831611325,6.90133439056955,5.99807178165932,1.84376429938331,3.12171157408265,2.76665968689002,3.17898642686478,-2.28629910692287,1.47332698754288,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,利比里亚,LBR,..,..,6.09982760205581,8.2007658404608,7.99381569313657,8.70402806598014,0.701143911750762,0,-1.59958407502003,2.46862609505123,1.22255131299607,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,利比亚,LBY,..,3.67921329264158,5.02128973900766,-62.0759195849001,123.139555198582,-13.5999999741829,-24.000000034478,-8.8620393625491,-2.79546888990185,26.6758701163536,7.83824218716293,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,列支敦士登,LIE,2.25287097057456,3.22019099847837,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,立陶宛,LTU,..,3.83166714045696,1.63981964913999,6.04313071521365,3.82695401007858,3.49858086982306,3.53758582436556,2.02062799496009,2.3531813074454,4.13994847283978,3.49368880814318,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,卢森堡,LUX,5.31993218169593,8.2397991119304,4.86496856032889,2.53923483936856,-0.352519360092558,3.65437038505787,4.29678642481963,3.91596276667379,2.41143078605366,1.54566060969455,2.60267394560714,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,中国澳门特别行政区,MAC,7.97934515934284,5.74711532886218,25.2638807141456,21.6724356665152,9.23739962566393,11.2000678763425,-1.20108600047053,-21.5944876822351,-0.858021516202811,9.70399275850269,4.70820262679268,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马达加斯加,MDG,3.13002942786873,4.76006509891198,0.263110855703317,1.45439216770913,3.02750809231563,2.25520404025168,3.31585416456267,3.11661471423099,4.17999023566718,4.30687977972366,5.18771873764004,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马拉维,MWI,5.6922944937575,1.5760778373467,6.87406563500952,4.85405510898322,1.88579950731867,5.19999999835549,5.70000000377209,2.79999999900333,2.48404062641868,4.00003051653042,3.49999999999999,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马来西亚,MYS,9.00852713975688,8.85886817695642,7.42484738592763,5.2939128402282,5.47345419229524,4.6937225201998,6.00672194999372,5.09151572079188,4.22341019438007,5.89700929269976,4.72363366654488,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马尔代夫,MDV,..,3.84581039203469,7.26512906840138,8.56673353050461,2.51738394219683,7.28107397899866,7.32960638587076,2.88456752643582,7.28918633003957,6.91169446497798,6.05323536780354,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马里,MLI,-2.50239510347758,-0.0608349718131365,5.41345222224766,3.24025291319951,-0.836178865643376,2.30358480929436,7.04335620365953,5.96258159051517,5.79999999505212,5.40000010323682,4.89999574546111,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马耳他,MLT,6.29139095831171,6.77019391099778,3.54268321383185,1.33706698913296,2.68297041565376,4.54000506297962,8.53247004456111,10.6610613756818,5.7046301280572,6.70551040742215,6.55273167887003,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,马绍尔群岛,MHL,2.67624646362781,5.89069431160706,6.45363768538634,1.20652077369282,3.45786676333924,2.86007921644884,-0.756942818844394,-0.364546611531892,1.91049498750886,3.59997104158995,2.49999999999999,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,毛里塔尼亚,MRT,-1.7713045336383,-0.430406256338173,4.77369717489576,4.70406653145888,5.79507817187705,6.09025873236142,5.57954385588333,1.3999999969575,2.00000000023441,3.0294801603543,3.60000000579959,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,毛里求斯,MUS,7.18673677131321,8.20279180352924,4.37720322681258,4.07753807617581,3.49611835979293,3.36040609817579,3.74457577745544,3.5530717035885,3.83793265409193,3.81415210844426,3.7738189089938,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,墨西哥,MEX,5.17576838619527,4.94245371467422,5.11811814321162,3.66300792950094,3.64232267941347,1.35409196151679,2.80434012838097,3.28799159933094,2.92161516530298,2.06971518465136,1.99420681652698,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,密克罗尼西亚联邦,FSM,3.74092685650476,4.56320950034359,2.04189024308035,3.34706715645454,-1.98947930678645,-3.85852509447598,-2.15605893134472,4.93150055725128,0.700002436442929,3.19998162815548,1.40000000000001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,摩尔多瓦,MDA,..,2.10771605209766,7.10000009243468,5.81816640453508,-0.589734134738677,9.04386552833462,4.99962558416271,-0.338235296100407,4.40888674328883,4.69079342838687,3.99999999999996,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,摩纳哥,MCO,2.64345417269378,3.91022922578345,2.05429385224207,7.07370084963446,0.984960324783145,9.57079878480552,7.17963684879821,4.94232976095542,3.21384883419209,-3.5,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,蒙古,MNG,-3.18446017802542,1.14606213683845,6.36516168485748,17.290777583689,12.3198198484838,11.6489161898863,7.8852254815194,2.37983580688481,1.1683934561722,5.30166847047178,6.94883868471059,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,黑山,MNE,..,3.09999741693463,2.73434154076678,3.22845038714465,-2.72376865415211,3.54901391824323,1.78368204613477,3.39042087590376,2.94933868513803,4.71646496209853,4.85444364455017,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,摩洛哥,MAR,3.41181222631381,1.91287298063767,3.8157179167666,5.24569729729485,3.00996126221978,4.53542420003856,2.66949392694423,4.53637816806425,1.12597745123138,4.08750681721838,2.95006201908701,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,莫桑比克,MOZ,0.999992833134456,1.6785029847975,6.68722160123971,7.11760666756443,7.19818582788976,7.14168332863071,7.44406944137428,6.59398595679544,3.76327559260542,3.73695824073801,3.26486587112763,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,缅甸,MMR,2.81693327329167,13.7459305559899,9.63443945214466,5.5914823781961,7.33267044715451,8.42600102485187,7.99091559728875,6.99284029025135,5.86247291517364,6.75862882426273,6.20005107150712,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,纳米比亚,NAM,2.04745993950704,3.49218342924829,6.03924954600518,5.09133819752299,5.06168201326828,5.61471970064058,6.35167796144982,6.08637611380499,1.12316886739141,-0.867877108197661,-0.0746768253348904,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,瑙魯,NRU,..,..,13.550826776623,11.6845220726166,10.0865515410902,34.2149825107977,36.5240980933161,2.80819122591865,10.4000039995401,4.15468510415921,-3.47826086956522,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,尼泊尔,NPL,4.63503634705982,6.19999998759771,4.81641465022444,3.42182824087472,4.78119225754814,4.12887767631092,5.98898466088025,3.32290543937557,0.588678498822517,7.90574161030806,6.28976217341481,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,荷兰,NLD,4.18322281189747,4.19564246630577,1.34273944403994,1.5511892450879,-1.03035399675242,-0.130175239488068,1.4233954009121,1.95916969909209,2.19171372514313,2.91090256981492,2.59745362333229,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,新喀里多尼亚,NCL,3.60000127425515,2.10010884233,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,新西兰,NZL,0.153016760489223,2.90332378118615,1.5392455997316,2.33420028117229,2.2259655019993,2.57538601580582,3.71553302036169,3.58470455734231,3.6457989497049,3.1284608180374,2.78107911797703,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,尼加拉瓜,NIC,-0.0524997208627127,4.10159015455254,4.41009911716546,6.31668553491842,6.49613654320945,4.92709406341379,4.78546020044223,4.79226764841731,4.56314737482455,4.67547537697217,-3.81571016878904,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,尼日尔,NER,-1.28435538100815,-1.40950935004449,8.3642195301652,2.28300848699708,11.8498191816674,5.26841489730204,7.52904306849105,4.33707236874471,4.92593093265921,4.89343669249749,5.1726982626509,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,尼日利亚,NGA,11.7768859323494,5.01593475720539,8.00565591528179,5.30792420366642,4.23006117510556,6.67133539288376,6.30971865572383,2.65269329541835,-1.61686894991816,0.805886619542704,1.93726806769712,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,北马其顿,MKD,..,4.54913578288874,3.35875085773812,2.33988604520331,-0.4559226963529,2.92472656750948,3.62964130783041,3.85561030191197,2.84844146117298,0.240521014001672,2.66459188911705,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,北马里亚纳群岛,MNP,..,..,1.38364779874213,-7.69230769230769,0.537634408602145,2.40641711229948,3.78590078328982,3.39622641509433,28.2238442822384,25.1423149905123,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,挪威,NOR,1.93243886932204,3.20528507503268,0.691663072400317,0.971934878567197,2.72162679227745,1.04438907843544,1.97511744594576,1.97005293385466,1.1873547638491,1.98043774938603,1.44602964100082,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿曼,OMN,-0.130425740072312,5.40137271684384,4.80471128505226,-1.10874578676601,9.33267557628487,4.37261577587267,2.75103163686383,4.74125071715432,4.98017097200618,-0.928051651319905,2.12513728907484,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴基斯坦,PAK,4.45858681509816,4.26008801156799,1.60669195949077,2.74840254954,3.50703342009689,4.39645663349772,4.67470798143725,4.73114747532901,5.52673584474448,5.7006212410121,5.43001078627286,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,帕劳,PLW,..,..,0.416866485798579,5.38659954740059,3.59721647870866,-2.10395135721033,4.77485179983563,10.4309732952978,0.526568960576412,-3.57056682532007,5,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴拿马,PAN,8.09899300563276,2.71537415482821,5.82783939246903,11.3137317481848,9.77890394560946,6.90327256581698,5.06674321802782,5.7327636321358,4.96642044408036,5.32123018419239,3.6770136293445,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴布亚新几内亚,PNG,-3.01216922049173,-2.49484199260023,10.1246862187585,1.10731101085722,4.64842068485383,3.82993127351973,13.5,9.49999999999997,4.10000000000004,1.54982084674984,0.427556312957719,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,巴拉圭,PRY,4.12328287958159,-2.31414056819048,11.1437357938518,4.24911104462905,-0.538512942503061,8.41749528470305,4.86089037055322,3.08037400793089,4.31290497002365,4.95805142159847,3.63782677195871,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,秘鲁,PER,-4.98256353646605,2.69437139806912,8.33245910749577,6.32719240161117,6.13972470560435,5.85251821084928,2.38193828255939,3.25588959720268,3.95588180435296,2.51908853491756,3.9765015187284,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,菲律宾,PHL,3.03696629398218,4.4112221600475,7.63226477978098,3.65975160085374,6.68381888126677,7.06402426383154,6.14529878578456,6.066548904721,6.88405503672969,6.67755356557139,6.24373774240088,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,波兰,POL,..,4.55963142810978,3.60692826143993,5.01730438982133,1.60790558520713,1.39189141872289,3.31844541480248,3.8389451674761,3.06259854136425,4.81412954395472,5.14946407917856,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,葡萄牙,PRT,3.95052333179655,3.78749400060012,1.89869117560959,-1.82685235062658,-4.0282567482528,-1.13015582288244,0.893187724532879,1.82206564350729,1.92573629650256,2.79502424788114,2.14461216154047,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,波多黎各,PRI,-2.84054072620243,3.2719645286232,-0.413254118484915,-0.358510628287519,0.0292751104899196,-0.306826657099961,-1.19036345385327,-1.04944365110553,-1.26300292505275,-2.65739341978775,-4.90562615259037,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,卡塔尔,QAT,..,..,19.592331533546,13.3751764061915,4.68725917732711,4.41027461916184,3.97888214728583,3.65751026057268,2.13131091589808,1.57985025330348,1.43164807667107,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,罗马尼亚,ROU,..,2.4612634592269,-3.9012362801862,2.00715624364216,2.07716500653777,3.51455180408476,3.41080910017932,3.8715223230984,4.80079990634931,6.99139354329581,4.09567394177144,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,俄罗斯联邦,RUS,-2.99999564223592,10.0000668155913,4.49999999999997,4.30002918571843,3.70005705515455,1.80000014017688,0.699999429699687,-2.30773350532326,0.32928233248073,1.63019594439062,2.2548400528962,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,卢旺达,RWA,-2.39929109691023,8.37091044494798,7.33819481720899,7.78392027484294,8.82032111668411,4.7135507135074,7.62457574959272,8.86797737443902,5.98131491296596,6.05783131918818,8.67168339449135,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,萨摩亚,WSM,-4.42145094868916,6.91879029910028,0.47915871283746,5.77854429724889,0.402103619462892,-1.93385875571262,1.39675800802897,1.45106458049524,7.16942148640129,2.70479622366904,0.724091003967558,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣马力诺,SMR,..,2.21075902726602,-4.65549348230913,-9.44010416666666,-7.54852624011502,-3.03265940902023,-0.900000000000006,0.599999999999994,2.2,1.50000000000001,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣多美和普林西比,STP,..,..,6.67198532008364,4.39820168502787,3.14197585150298,4.81479438630852,6.54993309994882,3.79839893574065,4.17237773370091,3.87118594735394,2.65769375666697,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,沙特阿拉伯,SAU,15.1934258909531,5.62541614258343,5.03949367491363,9.996857793684,5.41144490216436,2.69925472255672,3.65248169757899,4.10640887013658,1.67064234738177,-0.741529926677842,2.21496872383027,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塞内加尔,SEN,-0.67563815073639,3.19898803557805,3.56274538962795,1.45838867102761,5.11739423750484,2.82210564888692,6.61307474209912,6.36726342710998,6.35639679150557,7.08292229081955,6.76629382361236,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塞尔维亚,SRB,..,7.7592152412588,0.73104644308539,2.03627572593629,-0.68154213304129,2.89263416214173,-1.58950618376664,1.77632097282286,3.34032906859376,2.04931052528016,4.30178771501495,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塞舌尔,SYC,6.99554469003365,1.51433881472376,5.95496246872393,7.88727959697732,1.26082883408726,6.01807682463131,4.50478524378195,4.93738561888499,4.50303266611134,4.332388098528,3.62735158274192,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塞拉利昂,SLE,3.34999798387723,6.65272788470479,5.34646605237529,6.31504503637647,15.1817690830225,20.7157682858728,4.55677236615293,-20.5987707154032,6.05547402877262,4.21118264884808,3.72795555462642,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,新加坡,SGP,9.82097169443168,9.03919573987264,14.5256466619866,6.26226776242771,4.44931580454463,4.81504503767849,3.90057322758308,2.89249922790063,2.96232725878104,3.69978157650705,3.13946454303462,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣马丁(荷属),SXM,..,..,3.20000000000012,4.60000000000009,1.39054666984961,1.30672788274767,1.5803846377317,0.509065949779796,0.432506259959027,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,斯洛伐克共和国,SVK,..,1.21017337643214,5.04171666503819,2.81909951757757,1.65714868719411,1.49064643782009,2.75033501688633,4.1748731972655,3.12541010471965,3.1883410545346,4.10904958250215,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,斯洛文尼亚,SVN,..,4.15543396003217,1.23775590870558,0.649366927703497,-2.66958205232561,-1.13205498110763,2.95056515351175,2.30489583453499,3.06808705949028,4.88053750601732,4.48722055054185,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,所罗门群岛,SLB,..,-14.2674830306195,6.8090449603589,13.1956326599746,4.55539429942904,3.01784944350992,2.25009071057131,2.54224020069339,3.45660623275934,3.53433914692091,3.38552284233624,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,索马里,SOM,-1.48407568424224,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,南非,ZAF,-0.317785675763318,4.2000034755179,3.03973081360169,3.28416814231139,2.21335480848441,2.4852005003106,1.84699160365716,1.19373280124428,0.399087929556359,1.41451262585058,0.787055570495099,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,南苏丹,SSD,..,..,5.49341639529945,-4.64031673600859,-46.0821223743007,13.1297311406943,3.37448084910015,-10.7918084928296,-11.184087070745,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,西班牙,ESP,3.78139346477435,5.28909959505918,0.0140638777627089,-0.998764958114961,-2.92775050717711,-1.70570500034654,1.37999723824269,3.64468015224406,3.1725896604465,2.97918240061863,2.58174653442416,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,斯里兰卡,LKA,6.3999953069393,6.00003316014399,8.01596737088065,8.404733021011,9.14457224642567,3.39573264983426,4.9607005916973,5.00768330472781,4.48663453096836,3.41981918897194,3.20913112799197,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣基茨和尼维斯,KNA,4.87753040532819,10.4071934060667,-1.46605086302448,1.78389830895847,-0.655672569716828,5.46597400837716,6.05557759878681,2.14665652390131,2.31750850479571,1.17056267600921,3.001453554872,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣卢西亚,LCA,9.89070152928586,0.0487484244816301,0.303818185809106,4.13937588904392,-0.313727783670373,-1.99580164075252,0.00470477149868032,0.272500389647632,3.89027988629434,3.67186143477542,0.59763357725582,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣马丁（法属）,MAF,..,..,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,圣文森特和格林纳丁斯,VCT,4.14991262400642,1.6391788812376,-3.35352588952401,-0.41935148189593,1.38242041064788,1.83316943694409,1.21367169640408,1.34163899206334,1.97545593919382,0.859946222072466,2.55689713658356,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,苏丹,SDN,-5.47005377615478,6.34586776869979,3.46933509264973,-1.96772897878996,0.521559266641702,4.39471113218659,2.67941181278037,4.90604517452817,4.70000004201418,4.28307100401813,-2.32082732082732,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,苏里南,SUR,-4.49999999999986,2.09999865984885,5.16523777230078,5.84936778449699,2.69035005713098,2.93344123002225,0.255503144654085,-3.41109586355617,-5.56119342399026,1.68708360197722,1.99725245693753,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,瑞典,SWE,0.75467475016579,4.74561672037481,5.99196804099729,2.67922810276563,-0.298512381188857,1.23834939778263,2.60190266972866,4.45656910952738,2.68396634344812,2.10471614023258,2.35893955161824,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,瑞士,CHE,3.67462563846016,3.93682345267324,3.00269910775171,1.69280849515039,1.00602409742663,1.85203975735246,2.44921809651646,1.33336908591109,1.60122355611645,1.61669215479019,2.54042671344739,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿拉伯叙利亚共和国,SYR,7.64077232347231,2.74285739723466,..,..,..,..,..,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,塔吉克斯坦,TJK,-0.59999963133663,8.3243244947386,6.5206807512775,7.40064054338714,7.48617522370516,7.40008125648346,6.70596816770588,6.0082878718954,6.87296379689192,7.61749269091885,7.30000028747864,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,坦桑尼亚,TZA,7.04507164384178,4.52078463488321,6.33652342747493,7.67215543499384,4.50015355982127,6.78158560065316,6.73246186832482,6.16062877406685,6.86711619644551,6.78568011405196,5.20000000000019,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,泰国,THA,11.1671634446292,4.45567603093122,7.5135906579751,0.839959472432227,7.24278660542524,2.68737991886854,0.984414063833114,3.13389696194272,3.35648887223402,4.02408578077439,4.12922610260051,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,东帝汶,TLS,..,..,-1.17206244491041,11.4579084002533,5.02312233019191,-11.086219848141,-25.9071720800131,20.6256011239109,0.706714167515997,-9.15398647807237,2.81398142870853,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,多哥,TGO,-0.243656135667436,-0.783479607131241,6.09925916082048,6.39819905238801,6.54350703065616,6.11234307771727,5.92058857118545,5.74286845333449,4.9183564426411,4.4494073919864,4.88413046423071,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,汤加,TON,-2.04409143450822,3.3698396935288,3.58565737051792,2.78514588859416,0.890322580645162,-3.12060365775675,2.07260726072607,3.71184687015003,3.37947374984412,2.69999999999999,0.299999999999983,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,特立尼达和多巴哥,TTO,1.50916784203096,6.90135959072803,3.32322471027371,-0.294354409043763,1.293978927249,2.00600253440354,-0.96968415547687,1.77788585127057,-6.50920697902158,-1.90000000000001,0.700000004544691,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,突尼斯,TUN,7.94981943592961,4.70985994539875,3.5106086386031,-1.91717767962636,3.99767400230937,2.87552384230733,2.97139834510902,1.19454178071759,1.26252855507596,1.82467531936202,2.50533296534539,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,土耳其,TUR,9.26614667149322,6.64006112058992,8.48737213928641,11.1134955725645,4.78994028093425,8.49130929868582,5.16669071840813,6.08588661619443,3.18383154709569,7.44119329129511,2.56662303733567,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,土库曼斯坦,TKM,35.3845581084463,5.46906413252853,9.19999998805483,14.6999999278723,11.0999999615545,10.200000101399,10.2999999705459,6.49999990951666,6.1999999664301,6.50000004682975,6.19999994137119,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,特克斯科斯群岛,TCA,..,..,..,..,-2.53000000000006,1.3653208503998,6.70368436002458,5.9412708281095,4.39970171513791,4.27546583850929,5.28208549380973,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,图瓦卢,TUV,..,-0.970599813962579,-2.72942791190968,7.51595420581725,-3.83574882108887,4.57682370419998,1.34801268229315,9.14289393283154,3.03472315323769,4.0940177168018,2.50000000000001,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,乌干达,UGA,6.47414015085921,3.14190733820372,5.63759980250855,9.39166774905111,3.83745560597488,3.58690582619572,5.10630732428807,5.1878598626103,4.7810002914413,3.86302375685661,6.09014455290458,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,乌克兰,UKR,-6.3452351284915,5.89999999959699,3.83438769855928,5.46553190543162,0.23868130555249,-0.0267296493157545,-6.55261889108043,-9.77297394655352,2.44099473810617,2.46605345179482,3.33548930910057,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,阿拉伯联合酋长国,ARE,18.3279855336405,10.8527042125985,1.60280996068229,6.93027162943835,4.4846260849087,5.05334582526655,4.39869668246446,5.06470152944831,2.98844182081464,0.790400387794406,1.4238403611281,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,英国,GBR,0.739318852179167,3.45346055355552,1.7112064453315,1.64477001712162,1.44706044309314,2.04635515272859,2.94756056386132,2.34912144328266,1.78923605462249,1.82292777386984,1.39760759249872,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,美国,USA,1.88596032242019,4.12748401255413,2.56376655847168,1.55083550620974,2.24954585216848,1.8420810704697,2.4519730360895,2.88091046576689,1.56721516988685,2.21701033035224,2.8569878160516,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,乌拉圭,URY,0.297348378311099,-1.92993063972195,7.80340966844247,5.16213302579712,3.53817870685688,4.63753864311411,3.23879121621601,0.370741266957879,1.68979816237868,2.59133868710855,1.62008362892945,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,乌兹别克斯坦,UZB,1.60000000555954,3.83500000000051,8.47906186371669,8.2813033220434,8.17975142411682,8.02604897087058,7.17938898924697,7.44847030833367,6.09432524285347,4.46162769687355,5.13298639160256,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,瓦努阿图,VUT,11.7064847304093,5.92204453891594,1.62908159635768,1.22322553548928,1.75474992652525,1.96914608039688,2.33100621490578,-0.801094177413049,4.00057448624516,4.50068030474749,3.2,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,委内瑞拉玻利瓦尔共和国,VEN,6.46794085305808,3.68694416687683,-1.48879125078348,4.17642535923927,5.62595697508641,1.34309403607476,-3.89438647450662,..,..,..,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,越南,VNM,5.10091814039046,6.78731640822197,6.42323821717494,6.24030274887527,5.2473671560487,5.42188299130713,5.98365463697851,6.67928878891431,6.21081166789989,6.8122456596398,7.07578861674985,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,美属维京群岛,VIR,..,..,0.904116107542023,-8.15845319500117,-15.0192554557123,-5.8308157099698,-0.86621751684325,0.194174757281715,0.90439276485796,-1.6965428937263,..,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,约旦河西岸和加沙,PSE,..,-8.55618329065622,8.09908926205796,12.413635701795,6.28423577159163,2.21742245691443,-0.181890996723581,3.42873225371518,4.70843525605618,3.14023678003272,0.905278232405877,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,也门共和国,YEM,..,6.1819155928913,7.70230703961865,-12.7148968954117,2.3929902093532,4.82351906650533,-0.188690232450909,-16.6784631311462,-13.621458438417,-5.94231950439713,-2.7014748649632,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,赞比亚,ZMB,-0.481072027781664,3.89732294341896,10.2982058517493,5.56462471673873,7.59761696886545,5.05937637822657,4.69582637269048,2.91988111018713,3.75717780476374,3.40316885119505,3.79490091669091,.. +GDP 增长率（年百分比）,NY.GDP.MKTP.KD.ZG,津巴布韦,ZWE,6.98855293318542,-3.05918976180379,19.6753231424645,14.1939129572162,16.6654287684678,1.98949276207208,2.37692932698003,1.77987270340296,0.75586925093063,4.70403534833559,6.1591897984876,.. +,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,, +,,,,,,,,,,,,,,, +Data from Database: 世界发展指标,,,,,,,,,,,,,,, +Last Updated: 10/28/2019,,,,,,,,,,,,,,, diff --git a/GDP_analyse/test.txt b/GDP_analyse/test.txt deleted file mode 100644 index ca789c3..0000000 --- a/GDP_analyse/test.txt +++ /dev/null @@ -1,3 +0,0 @@ -2009,52000000 -2010,936000000 -2011,3360000000 \ No newline at end of file diff --git a/boss_spider/analyse.py b/boss_spider/analyse.py index 1b3af3a..df060d2 100644 --- a/boss_spider/analyse.py +++ b/boss_spider/analyse.py @@ -14,13 +14,6 @@ from PIL import Image import numpy as np -import pymysql - -db = pymysql.connect() -cursor = db.cursor() -cursor.execute() - - job_conn = MongoClient("mongodb://%s:%s@ds151612.mlab.com:51612/boss" % ('boss', 'boss123')) job_db = job_conn.boss