diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 6d59da9..48cc8db 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,10 +3,7 @@ - - - - + - - - - + - + + + + @@ -275,7 +272,8 @@ - + + diff --git a/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb b/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb new file mode 100644 index 0000000..7b87c81 --- /dev/null +++ b/GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb @@ -0,0 +1,7133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyecharts import options as opts\n", + "from pyecharts.charts import Pie, Bar" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "gdp = pd.read_csv('GDP_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "country = pd.read_csv('Country_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "country_data = country.dropna(subset=['Income_Group'])" + ] + }, + { + "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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217 rows × 5 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", + "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, + "metadata": {}, + "outputs": [], + "source": [ + "rank_cuntry = country_data.groupby('Income_Group').size()" + ] + }, + { + "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, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 各个收入等级分布\n", + "pie = Pie()\n", + "pie.add(\"\", [list(z) for z in zip(rank_cuntry.index.tolist(), rank_cuntry.values.tolist())],\n", + " radius=[\"30%\", \"75%\"], center=[\"45%\", \"50%\"], rosetype=\"radius\",\n", + " )\n", + "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"各个收入等级分布\"))\n", + "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}, {d}%\"))\n", + "pie.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "high = country_data[country_data['Income_Group'] == '高收入国家']" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# 高收入国家2018年的GDP\n", + "high_gdp = pd.merge(high, gdp, how='inner')\n", + "high_gdp['2018'] = high_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "high_gdp_top10 = high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(high_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"高收入国家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": 113, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_gdp_top20 = high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]\n", + "\n", + "bar = Bar()\n", + "bar.add_xaxis(high_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"高收入国家GDP Top20\", 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": 119, + "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": 117, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 117, + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "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.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", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "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": 122, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 122, + "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": 123, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(mid_low_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_low_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 Top20\", 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": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# 低等收入国家\n", + "low = country_data[country_data['Income_Group'] == '低收入国家']\n", + "low_gdp = pd.merge(low, gdp, how='inner')\n", + "low_gdp['2018'] = low_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "low_gdp_top10 = low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "low_gdp_top20 = low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(low_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"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": 129, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(low_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", low_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=\"低等收入国家GDP Top20\", 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": 91, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
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+09NaNNaNNaN
1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+09NaNNaN
2阿拉伯联合酋长国ARENaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.506660e+113.745906e+113.901076e+114.031371e+113.581351e+113.570451e+113.825751e+114.141789e+11NaNNaN
3安提瓜和巴布达ATGNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.142043e+091.211412e+091.192920e+091.275577e+091.359195e+091.464630e+091.510085e+091.623804e+09NaNNaN
4澳大利亚AUSNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.857767e+101.965394e+101.989249e+10...1.396650e+121.546152e+121.576184e+121.467484e+121.351520e+121.210028e+121.330803e+121.432195e+12NaNNaN
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74特立尼达和多巴哥TTONaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.356701e+085.849612e+086.193192e+08...2.543301e+102.576933e+102.711026e+102.747797e+102.512152e+102.174639e+102.225046e+102.341035e+10NaNNaN
75乌拉圭URYNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.242289e+091.547389e+091.710004e+09...4.796244e+105.126439e+105.753123e+105.723601e+105.327430e+105.268761e+105.648899e+105.959689e+10NaNNaN
76美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.433000e+115.633000e+116.051000e+11...1.554258e+131.619701e+131.678485e+131.752175e+131.821930e+131.870719e+131.948539e+132.049410e+13NaNNaN
77英屬維爾京群島VGBNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
78美属维京群岛VIRNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...4.239000e+094.095000e+093.762000e+093.622000e+093.748000e+093.863000e+093.855000e+09NaNNaNNaN
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" + ], + "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", + "\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", + "\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", + "\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", + "\n", + "[79 rows x 68 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameCountry CodeIndicator NameIndicator Code196019611962196319641965...201120122013201420152016201720182019Unnamed: 64
0阿鲁巴ABWGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...2.549721e+092.534637e+092.581564e+092.649721e+092.691620e+092.646927e+092.700559e+09NaNNaNNaN
1阿富汗AFGGDP（现价美元）NY.GDP.MKTP.CD5.377778e+085.488889e+085.466667e+087.511112e+088.000000e+081.006667e+09...1.780428e+102.000162e+102.056105e+102.048487e+101.990711e+101.936264e+102.019176e+101.936297e+10NaNNaN
2安哥拉AGOGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...1.117897e+111.280529e+111.367099e+111.457122e+111.161936e+111.011239e+111.221238e+111.057510e+11NaNNaN
3阿尔巴尼亚ALBGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...1.289087e+101.231978e+101.277628e+101.322825e+101.138693e+101.186135e+101.302506e+101.505888e+10NaNNaN
4安道尔共和国ANDGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...3.442063e+093.164615e+093.281585e+093.350736e+092.811489e+092.877312e+093.013387e+093.236544e+09NaNNaN
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259科索沃XKXGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...6.691827e+096.499936e+097.071960e+097.386891e+096.440612e+096.714712e+097.227765e+097.900269e+09NaNNaN
260也门共和国YEMGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...3.272642e+103.540134e+104.041523e+104.322859e+104.262833e+103.096824e+102.681870e+102.691440e+10NaNNaN
261南非ZAFGDP（现价美元）NY.GDP.MKTP.CD7.575397e+097.972997e+098.497997e+099.423396e+091.037400e+101.133440e+10...4.164170e+113.963294e+113.666449e+113.506376e+113.174156e+112.963409e+113.492681e+113.682882e+11NaNNaN
262赞比亚ZMBGDP（现价美元）NY.GDP.MKTP.CD7.130000e+086.962857e+086.931429e+087.187143e+088.394286e+081.082857e+09...2.346010e+102.550337e+102.804546e+102.715063e+102.115439e+102.095475e+102.586814e+102.672007e+10NaNNaN
263津巴布韦ZWEGDP（现价美元）NY.GDP.MKTP.CD1.052990e+091.096647e+091.117602e+091.159512e+091.217138e+091.311436e+09...1.410192e+101.711485e+101.909102e+101.949552e+101.996312e+102.054868e+102.281301e+103.100052e+10NaNNaN
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264 rows × 65 columns

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" + ], + "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
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+09NaNNaNNaN
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+10NaNNaN
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+11NaNNaN
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+10NaNNaN
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+09NaNNaN
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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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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TUR0.766509
33瑞士CHE0.705501
155波兰POL0.585783
181瑞典SWE0.551032
14比利时BEL0.531767
6阿根廷ARG0.518475
189泰国THA0.504993
11奥地利AUT0.455737
144挪威NOR0.434751
5阿拉伯联合酋长国ARE0.414179
141尼日利亚NGA0.397270
90爱尔兰IRL0.382487
93以色列ISR0.369690
213南非ZAF0.368288
169新加坡SGP0.364157
82中国香港特别行政区HKG0.362993
137马来西亚MYS0.354348
53丹麦DNK0.352058
152菲律宾PHL0.330910
41哥伦比亚COL0.330228
149巴基斯坦PAK0.312570
35智利CHL0.298231
17孟加拉国BGD0.274025
62芬兰FIN0.273961
57阿拉伯埃及共和国EGY0.250895
49捷克共和国CZE0.245226
208越南VNM0.244948
163罗马尼亚ROU0.239553
158葡萄牙PRT0.237979
91伊拉克IRQ0.225914
151秘鲁PER0.222238
76希腊GRC0.218032
147新西兰NZL0.205025
162卡塔尔QAT0.192009
55阿尔及利亚DZA0.180689
98哈萨克斯坦KAZ0.170539
86匈牙利HUN0.155703
105科威特KWT0.141678
200乌克兰UKR0.130832
119摩洛哥MAR0.118495
56厄瓜多尔ECU0.108398
179斯洛伐克共和国SVK0.106472
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" + ], + "text/plain": [ + " Country Name Country Code 2018\n", + "202 美国 USA 20.494100\n", + "36 中国 CHN 13.608152\n", + "97 日本 JPN 4.970916\n", + "50 德国 DEU 3.996759\n", + "68 英国 GBR 2.825208\n", + "64 法国 FRA 2.777535\n", + "89 印度 IND 2.726323\n", + "94 意大利 ITA 2.073902\n", + "26 巴西 BRA 1.868626\n", + "32 加拿大 CAN 1.712510\n", + "164 俄罗斯联邦 RUS 1.657554\n", + "104 大韩民国 KOR 1.619424\n", + "10 澳大利亚 AUS 1.432195\n", + "59 西班牙 ESP 1.426189\n", + "124 墨西哥 MEX 1.223809\n", + "87 印度尼西亚 IDN 1.042173\n", + "143 荷兰 NLD 0.913658\n", + "166 沙特阿拉伯 SAU 0.782483\n", + "196 土耳其 TUR 0.766509\n", + "33 瑞士 CHE 0.705501\n", + "155 波兰 POL 0.585783\n", + "181 瑞典 SWE 0.551032\n", + "14 比利时 BEL 0.531767\n", + "6 阿根廷 ARG 0.518475\n", + "189 泰国 THA 0.504993\n", + "11 奥地利 AUT 0.455737\n", + "144 挪威 NOR 0.434751\n", + "5 阿拉伯联合酋长国 ARE 0.414179\n", + "141 尼日利亚 NGA 0.397270\n", + "90 爱尔兰 IRL 0.382487\n", + "93 以色列 ISR 0.369690\n", + "213 南非 ZAF 0.368288\n", + "169 新加坡 SGP 0.364157\n", + "82 中国香港特别行政区 HKG 0.362993\n", + "137 马来西亚 MYS 0.354348\n", + "53 丹麦 DNK 0.352058\n", + "152 菲律宾 PHL 0.330910\n", + "41 哥伦比亚 COL 0.330228\n", + "149 巴基斯坦 PAK 0.312570\n", + "35 智利 CHL 0.298231\n", + "17 孟加拉国 BGD 0.274025\n", + "62 芬兰 FIN 0.273961\n", + "57 阿拉伯埃及共和国 EGY 0.250895\n", + "49 捷克共和国 CZE 0.245226\n", + "208 越南 VNM 0.244948\n", + "163 罗马尼亚 ROU 0.239553\n", + "158 葡萄牙 PRT 0.237979\n", + "91 伊拉克 IRQ 0.225914\n", + "151 秘鲁 PER 0.222238\n", + "76 希腊 GRC 0.218032\n", + "147 新西兰 NZL 0.205025\n", + "162 卡塔尔 QAT 0.192009\n", + "55 阿尔及利亚 DZA 0.180689\n", + "98 哈萨克斯坦 KAZ 0.170539\n", + "86 匈牙利 HUN 0.155703\n", + "105 科威特 KWT 0.141678\n", + "200 乌克兰 UKR 0.130832\n", + "119 摩洛哥 MAR 0.118495\n", + "56 厄瓜多尔 ECU 0.108398\n", + "179 斯洛伐克共和国 SVK 0.106472" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:60]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP分析" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_china = country_gdp[country_gdp['Country Name']=='中国']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: 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", + " \n" + ] + } + ], + "source": [ + "for i in range(1960, 2019):\n", + " df_china[str(i)] = df_china[str(i)].apply(lambda x: x/1000000000000)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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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\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": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\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": {}, + "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": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.pipeline as pl\n", + "import sklearn.linear_model as lm\n", + "import sklearn.preprocessing as sp\n", + "import matplotlib.pyplot as mp\n", + "import numpy as np\n", + "import sklearn.metrics as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1960],\n", + " [1961],\n", + " [1962],\n", + " [1963],\n", + " [1964],\n", + " [1965],\n", + " [1966],\n", + " [1967],\n", + " [1968],\n", + " [1969],\n", + " [1970],\n", + " [1971],\n", + " [1972],\n", + " [1973],\n", + " [1974],\n", + " [1975],\n", + " [1976],\n", + " [1977],\n", + " [1978],\n", + " [1979],\n", + " [1980],\n", + " [1981],\n", + " [1982],\n", + " [1983],\n", + " [1984],\n", + " [1985],\n", + " [1986],\n", + " [1987],\n", + " [1988],\n", + " [1989],\n", + " [1990],\n", + " [1991],\n", + " [1992],\n", + " [1993],\n", + " [1994],\n", + " [1995],\n", + " [1996],\n", + " [1997],\n", + " [1998],\n", + " [1999],\n", + " [2000],\n", + " [2001],\n", + " [2002],\n", + " [2003],\n", + " [2004],\n", + " [2005],\n", + " [2006],\n", + " [2007],\n", + " [2008],\n", + " [2009],\n", + " [2010],\n", + " [2011],\n", + " [2012],\n", + " [2013],\n", + " [2014],\n", + " [2015],\n", + " [2016],\n", + " [2017],\n", + " [2018]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year = [i for i in range(1960, 2019)]\n", + "X = np.array(year)\n", + "X = X.reshape(-1, 1)\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "y = usa_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "model = pl.make_pipeline(\n", + " sp.PolynomialFeatures(5), # 多项式特征拓展器\n", + " lm.LinearRegression() # 线性回归器\n", + ")\n", + "# 训练模型\n", + "model.fit(X, y)\n", + "# 求预测值y\n", + "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, 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制多项式回归线\n", + "px = np.linspace(X.min(), X.max(), 1000)\n", + "px = px.reshape(-1, 1)\n", + "pred_py = model.predict(px)\n", + "\n", + "# 绘制图像\n", + "mp.figure(\"美国历年GDP拟合结果\", facecolor='lightgray')\n", + "mp.title('USA GDP Regression', fontsize=16)\n", + "mp.tick_params(labelsize=10)\n", + "mp.grid(linestyle=':')\n", + "mp.xlabel('x')\n", + "mp.ylabel('y')\n", + "\n", + "mp.scatter(X, y, s=60, marker='o', c='dodgerblue', label='Points')\n", + "mp.plot(px, pred_py, c='orangered', label='PolyFit Line')\n", + "mp.tight_layout()\n", + "mp.legend()\n", + "mp.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# 中国\n", + "y = china_gdp\n", + "model = pl.make_pipeline(\n", + " sp.PolynomialFeatures(5), # 多项式特征拓展器\n", + " lm.LinearRegression() # 线性回归器\n", + ")\n", + "# 训练模型\n", + "model.fit(X, y)\n", + "# 求预测值y\n", + "pred_y = model.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制多项式回归线\n", + "px = np.linspace(X.min(), X.max(), 1000)\n", + "px = px.reshape(-1, 1)\n", + "pred_py = model.predict(px)\n", + "\n", + "# 绘制图像\n", + "mp.figure(\"中国历年GDP拟合结果\", facecolor='lightgray')\n", + "mp.title('CHN GDP Regression', fontsize=16)\n", + "mp.tick_params(labelsize=10)\n", + "mp.grid(linestyle=':')\n", + "mp.xlabel('x')\n", + "mp.ylabel('y')\n", + "\n", + "mp.scatter(X, y, s=60, marker='o', c='dodgerblue', label='Points')\n", + "mp.plot(px, pred_py, c='orangered', label='PolyFit Line')\n", + "mp.tight_layout()\n", + "mp.legend()\n", + "mp.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/GDP_analyse/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/GDP_analyse/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/GDP_analyse/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/GDP_analyse/API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.zip b/GDP_analyse/API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.zip new file mode 100644 index 0000000..bbd7c7a Binary files /dev/null and b/GDP_analyse/API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.zip differ diff --git a/GDP_analyse/Country_data.csv b/GDP_analyse/Country_data.csv new file mode 100644 index 0000000..3de18dc --- /dev/null +++ b/GDP_analyse/Country_data.csv @@ -0,0 +1,267 @@ +"Country Name","Country Code","Region","Income_Group", +"阿鲁巴","ABW","","高收入国家", +"阿富汗","AFG","南亚","低收入国家", +"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", +"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", +"安道尔共和国","AND","","高收入国家", +"阿拉伯联盟国家","ARB","","", +"阿拉伯联合酋长国","ARE","","高收入国家", +"阿根廷","ARG","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"亚美尼亚","ARM","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"美属萨摩亚","ASM","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"安提瓜和巴布达","ATG","","高收入国家", +"澳大利亚","AUS","","高收入国家", +"奥地利","AUT","","高收入国家", +"阿塞拜疆","AZE","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"布隆迪","BDI","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"比利时","BEL","","高收入国家", +"贝宁","BEN","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"布基纳法索","BFA","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"孟加拉国","BGD","南亚","中低等收入国家", +"保加利亚","BGR","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"巴林","BHR","","高收入国家", +"巴哈马","BHS","","高收入国家", +"波斯尼亚和黑塞哥维那","BIH","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"白俄罗斯","BLR","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"伯利兹","BLZ","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"百慕大","BMU","","高收入国家", +"玻利维亚","BOL","拉丁美洲与加勒比海地区（不包括高收入）","中低等收入国家", +"巴西","BRA","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"巴巴多斯","BRB","","高收入国家", +"文莱达鲁萨兰国","BRN","","高收入国家", +"不丹","BTN","南亚","中低等收入国家", +"博茨瓦纳","BWA","撒哈拉以南非洲地区（不包括高收入）","中高等收入国家", +"中非共和国","CAF","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"加拿大","CAN","","高收入国家", +"中歐和波羅的海 +","CEB","","", +"瑞士","CHE","","高收入国家", +"海峡群岛","CHI","","高收入国家", +"智利","CHL","","高收入国家", +"中国","CHN","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"科特迪瓦","CIV","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"喀麦隆","CMR","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"刚果（金）","COD","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"刚果（布）","COG","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"哥伦比亚","COL","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"科摩罗","COM","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"佛得角","CPV","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"哥斯达黎加","CRI","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"加勒比小国","CSS","","", +"古巴","CUB","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"库拉索","CUW","","高收入国家", +"开曼群岛","CYM","","高收入国家", +"塞浦路斯","CYP","","高收入国家", +"捷克共和国","CZE","","高收入国家", +"德国","DEU","","高收入国家", +"吉布提","DJI","中东与北非地区（不包括高收入）","中低等收入国家", +"多米尼克","DMA","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"丹麦","DNK","","高收入国家", +"多米尼加共和国","DOM","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"阿尔及利亚","DZA","中东与北非地区（不包括高收入）","中高等收入国家", +"东亚与太平洋地区（不包括高收入）","EAP","","", +"早人口紅利","EAR","","", +"东亚与太平洋地区","EAS","","", +"欧洲与中亚地区（不包括高收入）","ECA","","", +"欧洲与中亚地区","ECS","","", +"厄瓜多尔","ECU","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"阿拉伯埃及共和国","EGY","中东与北非地区（不包括高收入）","中低等收入国家", +"欧洲货币联盟","EMU","","", +"厄立特里亚","ERI","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"西班牙","ESP","","高收入国家", +"爱沙尼亚","EST","","高收入国家", +"埃塞俄比亚","ETH","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"欧洲联盟","EUU","","", +"脆弱和受衝突影響的情況下 +","FCS","","", +"芬兰","FIN","","高收入国家", +"斐济","FJI","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"法国","FRA","","高收入国家", +"法罗群岛","FRO","","高收入国家", +"密克罗尼西亚联邦","FSM","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"加蓬","GAB","撒哈拉以南非洲地区（不包括高收入）","中高等收入国家", +"英国","GBR","","高收入国家", +"格鲁吉亚","GEO","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"加纳","GHA","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"直布罗陀","GIB","","高收入国家", +"几内亚","GIN","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"冈比亚","GMB","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"几内亚比绍共和国","GNB","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"赤道几内亚","GNQ","撒哈拉以南非洲地区（不包括高收入）","中高等收入国家", +"希腊","GRC","","高收入国家", +"格林纳达","GRD","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"格陵兰","GRL","","高收入国家", +"危地马拉","GTM","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"关岛","GUM","","高收入国家", +"圭亚那","GUY","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"高收入国家","HIC","","", +"中国香港特别行政区","HKG","","高收入国家", +"洪都拉斯","HND","拉丁美洲与加勒比海地区（不包括高收入）","中低等收入国家", +"重债穷国 (HIPC)","HPC","","", +"克罗地亚","HRV","","高收入国家", +"海地","HTI","拉丁美洲与加勒比海地区（不包括高收入）","低收入国家", +"匈牙利","HUN","","高收入国家", +"只有IBRD","IBD","","", +"IBRD与IDA","IBT","","", +"IDA總","IDA","","", +"IDA混合","IDB","","", +"印度尼西亚","IDN","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"只有IDA","IDX","","", +"马恩岛","IMN","","高收入国家", +"印度","IND","南亚","中低等收入国家", +"未分类国家","INX","","", +"爱尔兰","IRL","","高收入国家", +"伊朗伊斯兰共和国 ","IRN","中东与北非地区（不包括高收入）","中高等收入国家", +"伊拉克","IRQ","中东与北非地区（不包括高收入）","中高等收入国家", +"冰岛","ISL","","高收入国家", 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+"卢森堡","LUX","","高收入国家", +"拉脱维亚","LVA","","高收入国家", +"中国澳门特别行政区","MAC","","高收入国家", +"圣马丁（法属）","MAF","","高收入国家", +"摩洛哥","MAR","中东与北非地区（不包括高收入）","中低等收入国家", +"摩纳哥","MCO","","高收入国家", +"摩尔多瓦","MDA","欧洲与中亚地区（不包括高收入）","中低等收入国家", +"马达加斯加","MDG","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"马尔代夫","MDV","南亚","中高等收入国家", +"中东与北非地区","MEA","","", +"墨西哥","MEX","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"马绍尔群岛","MHL","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"中等收入国家","MIC","","", +"北马其顿","MKD","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"马里","MLI","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"马耳他","MLT","","高收入国家", +"缅甸","MMR","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"中东与北非地区（不包括高收入）","MNA","","", +"黑山","MNE","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"蒙古","MNG","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"北马里亚纳群岛","MNP","","高收入国家", +"莫桑比克","MOZ","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"毛里塔尼亚","MRT","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"毛里求斯","MUS","撒哈拉以南非洲地区（不包括高收入）","中高等收入国家", +"马拉维","MWI","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"马来西亚","MYS","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"北美","NAC","","", +"纳米比亚","NAM","撒哈拉以南非洲地区（不包括高收入）","中高等收入国家", +"新喀里多尼亚","NCL","","高收入国家", +"尼日尔","NER","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"尼日利亚","NGA","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"尼加拉瓜","NIC","拉丁美洲与加勒比海地区（不包括高收入）","中低等收入国家", +"荷兰","NLD","","高收入国家", +"挪威","NOR","","高收入国家", +"尼泊尔","NPL","南亚","低收入国家", +"瑙魯","NRU","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"新西兰","NZL","","高收入国家", +"经合组织成员","OED","","", +"阿曼","OMN","","高收入国家", +"其他小国","OSS","","", +"巴基斯坦","PAK","南亚","中低等收入国家", +"巴拿马","PAN","","高收入国家", +"秘鲁","PER","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"菲律宾","PHL","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"帕劳","PLW","","高收入国家", +"巴布亚新几内亚","PNG","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"波兰","POL","","高收入国家", +"預人口紅利","PRE","","", +"波多黎各","PRI","","高收入国家", +"朝鲜民主主义人民共和国","PRK","东亚与太平洋地区（不包括高收入）","低收入国家", +"葡萄牙","PRT","","高收入国家", +"巴拉圭","PRY","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"约旦河西岸和加沙","PSE","中东与北非地区（不包括高收入）","中低等收入国家", +"太平洋岛国","PSS","","", +"人口紅利之後","PST","","", +"法属波利尼西亚","PYF","","高收入国家", +"卡塔尔","QAT","","高收入国家", +"罗马尼亚","ROU","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"俄罗斯联邦","RUS","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"卢旺达","RWA","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"南亚","SAS","","", +"沙特阿拉伯","SAU","","高收入国家", +"苏丹","SDN","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"塞内加尔","SEN","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"新加坡","SGP","","高收入国家", +"所罗门群岛","SLB","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"塞拉利昂","SLE","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"萨尔瓦多","SLV","拉丁美洲与加勒比海地区（不包括高收入）","中低等收入国家", +"圣马力诺","SMR","","高收入国家", +"索马里","SOM","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"塞尔维亚","SRB","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"撒哈拉以南非洲地区（不包括高收入）","SSA","","", +"南苏丹","SSD","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"撒哈拉以南非洲地区","SSF","","", +"小国","SST","","", +"圣多美和普林西比","STP","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"苏里南","SUR","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"斯洛伐克共和国","SVK","","高收入国家", +"斯洛文尼亚","SVN","","高收入国家", +"瑞典","SWE","","高收入国家", +"斯威士兰","SWZ","撒哈拉以南非洲地区（不包括高收入）","中低等收入国家", +"圣马丁(荷属)","SXM","","高收入国家", +"塞舌尔","SYC","","高收入国家", +"阿拉伯叙利亚共和国","SYR","中东与北非地区（不包括高收入）","低收入国家", +"特克斯科斯群岛","TCA","","高收入国家", +"乍得","TCD","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"东亚与太平洋地区 (IBRD与IDA)","TEA","","", +"欧洲与中亚地区 (IBRD与IDA)","TEC","","", +"多哥","TGO","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"泰国","THA","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"塔吉克斯坦","TJK","欧洲与中亚地区（不包括高收入）","低收入国家", +"土库曼斯坦","TKM","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"拉丁美洲与加勒比海地区 (IBRD与IDA)","TLA","","", +"东帝汶","TLS","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"中东与北非地区 (IBRD与IDA)","TMN","","", +"汤加","TON","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"南亚 (IBRD与IDA)","TSA","","", +"撒哈拉以南非洲地区 (IBRD与IDA)","TSS","","", +"特立尼达和多巴哥","TTO","","高收入国家", +"突尼斯","TUN","中东与北非地区（不包括高收入）","中低等收入国家", +"土耳其","TUR","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"图瓦卢","TUV","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"坦桑尼亚","TZA","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"乌干达","UGA","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"乌克兰","UKR","欧洲与中亚地区（不包括高收入）","中低等收入国家", +"中高等收入国家","UMC","","", +"乌拉圭","URY","","高收入国家", +"美国","USA","","高收入国家", +"乌兹别克斯坦","UZB","欧洲与中亚地区（不包括高收入）","中低等收入国家", +"圣文森特和格林纳丁斯","VCT","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"委内瑞拉玻利瓦尔共和国","VEN","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"英屬維爾京群島","VGB","","高收入国家", +"美属维京群岛","VIR","","高收入国家", +"越南","VNM","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"瓦努阿图","VUT","东亚与太平洋地区（不包括高收入）","中低等收入国家", +"世界","WLD","","", +"萨摩亚","WSM","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"科索沃","XKX","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"也门共和国","YEM","中东与北非地区（不包括高收入）","低收入国家", +"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", +"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", +"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", diff --git a/GDP_analyse/GDP_analyse.ipynb b/GDP_analyse/GDP_analyse.ipynb new file mode 100644 index 0000000..7b87c81 --- /dev/null +++ b/GDP_analyse/GDP_analyse.ipynb @@ -0,0 +1,7133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyecharts import options as opts\n", + "from pyecharts.charts import Pie, Bar" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "gdp = pd.read_csv('GDP_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "country = pd.read_csv('Country_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "country_data = country.dropna(subset=['Income_Group'])" + ] + }, + { + "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
..................
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, + "metadata": {}, + "outputs": [], + "source": [ + "rank_cuntry = country_data.groupby('Income_Group').size()" + ] + }, + { + "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, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 各个收入等级分布\n", + "pie = Pie()\n", + "pie.add(\"\", [list(z) for z in zip(rank_cuntry.index.tolist(), rank_cuntry.values.tolist())],\n", + " radius=[\"30%\", \"75%\"], center=[\"45%\", \"50%\"], rosetype=\"radius\",\n", + " )\n", + "pie.set_global_opts(title_opts=opts.TitleOpts(title=\"各个收入等级分布\"))\n", + "pie.set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}, {d}%\"))\n", + "pie.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "high = country_data[country_data['Income_Group'] == '高收入国家']" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# 高收入国家2018年的GDP\n", + "high_gdp = pd.merge(high, gdp, how='inner')\n", + "high_gdp['2018'] = high_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "high_gdp_top10 = high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(high_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"高收入国家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": 113, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_gdp_top20 = high_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]\n", + "\n", + "bar = Bar()\n", + "bar.add_xaxis(high_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"高收入国家GDP Top20\", 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": 119, + "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": 117, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 117, + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "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.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", + " xaxis_opts=opts.AxisOpts(\n", + " axislabel_opts=opts.LabelOpts(formatter=\"{value} /万亿\")\n", + " ),)\n", + "bar.render_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "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": 122, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 122, + "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": 123, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(mid_low_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", mid_low_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 Top20\", 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": 126, + "metadata": {}, + "outputs": [], + "source": [ + "# 低等收入国家\n", + "low = country_data[country_data['Income_Group'] == '低收入国家']\n", + "low_gdp = pd.merge(low, gdp, how='inner')\n", + "low_gdp['2018'] = low_gdp['2018'].apply(lambda x: x/1000000000000)\n", + "low_gdp_top10 = low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:10]\n", + "low_gdp_top20 = low_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(low_gdp_top10['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", 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=\"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": 129, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bar = Bar()\n", + "bar.add_xaxis(low_gdp_top20['Country Name'].values.tolist())\n", + "bar.add_yaxis(\"\", low_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=\"低等收入国家GDP Top20\", 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": 91, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameCountry CodeRegionIncome_GroupUnnamed: 4Indicator NameIndicator Code196019611962...201120122013201420152016201720182019Unnamed: 64
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+09NaNNaNNaN
1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+09NaNNaN
2阿拉伯联合酋长国ARENaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...3.506660e+113.745906e+113.901076e+114.031371e+113.581351e+113.570451e+113.825751e+114.141789e+11NaNNaN
3安提瓜和巴布达ATGNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...1.142043e+091.211412e+091.192920e+091.275577e+091.359195e+091.464630e+091.510085e+091.623804e+09NaNNaN
4澳大利亚AUSNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.857767e+101.965394e+101.989249e+10...1.396650e+121.546152e+121.576184e+121.467484e+121.351520e+121.210028e+121.330803e+121.432195e+12NaNNaN
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74特立尼达和多巴哥TTONaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.356701e+085.849612e+086.193192e+08...2.543301e+102.576933e+102.711026e+102.747797e+102.512152e+102.174639e+102.225046e+102.341035e+10NaNNaN
75乌拉圭URYNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD1.242289e+091.547389e+091.710004e+09...4.796244e+105.126439e+105.753123e+105.723601e+105.327430e+105.268761e+105.648899e+105.959689e+10NaNNaN
76美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD5.433000e+115.633000e+116.051000e+11...1.554258e+131.619701e+131.678485e+131.752175e+131.821930e+131.870719e+131.948539e+132.049410e+13NaNNaN
77英屬維爾京群島VGBNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
78美属维京群岛VIRNaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaN...4.239000e+094.095000e+093.762000e+093.622000e+093.748000e+093.863000e+093.855000e+09NaNNaNNaN
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79 rows × 68 columns

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" + ], + "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", + "\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", + "\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", + "\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", + "\n", + "[79 rows x 68 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "high_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Country NameCountry CodeIndicator NameIndicator Code196019611962196319641965...201120122013201420152016201720182019Unnamed: 64
0阿鲁巴ABWGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...2.549721e+092.534637e+092.581564e+092.649721e+092.691620e+092.646927e+092.700559e+09NaNNaNNaN
1阿富汗AFGGDP（现价美元）NY.GDP.MKTP.CD5.377778e+085.488889e+085.466667e+087.511112e+088.000000e+081.006667e+09...1.780428e+102.000162e+102.056105e+102.048487e+101.990711e+101.936264e+102.019176e+101.936297e+10NaNNaN
2安哥拉AGOGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...1.117897e+111.280529e+111.367099e+111.457122e+111.161936e+111.011239e+111.221238e+111.057510e+11NaNNaN
3阿尔巴尼亚ALBGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...1.289087e+101.231978e+101.277628e+101.322825e+101.138693e+101.186135e+101.302506e+101.505888e+10NaNNaN
4安道尔共和国ANDGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...3.442063e+093.164615e+093.281585e+093.350736e+092.811489e+092.877312e+093.013387e+093.236544e+09NaNNaN
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259科索沃XKXGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...6.691827e+096.499936e+097.071960e+097.386891e+096.440612e+096.714712e+097.227765e+097.900269e+09NaNNaN
260也门共和国YEMGDP（现价美元）NY.GDP.MKTP.CDNaNNaNNaNNaNNaNNaN...3.272642e+103.540134e+104.041523e+104.322859e+104.262833e+103.096824e+102.681870e+102.691440e+10NaNNaN
261南非ZAFGDP（现价美元）NY.GDP.MKTP.CD7.575397e+097.972997e+098.497997e+099.423396e+091.037400e+101.133440e+10...4.164170e+113.963294e+113.666449e+113.506376e+113.174156e+112.963409e+113.492681e+113.682882e+11NaNNaN
262赞比亚ZMBGDP（现价美元）NY.GDP.MKTP.CD7.130000e+086.962857e+086.931429e+087.187143e+088.394286e+081.082857e+09...2.346010e+102.550337e+102.804546e+102.715063e+102.115439e+102.095475e+102.586814e+102.672007e+10NaNNaN
263津巴布韦ZWEGDP（现价美元）NY.GDP.MKTP.CD1.052990e+091.096647e+091.117602e+091.159512e+091.217138e+091.311436e+09...1.410192e+101.711485e+101.909102e+101.949552e+101.996312e+102.054868e+102.281301e+103.100052e+10NaNNaN
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264 rows × 65 columns

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" + ], + "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
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+09NaNNaNNaN
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+10NaNNaN
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+11NaNNaN
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+10NaNNaN
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+09NaNNaN
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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TUR0.766509
33瑞士CHE0.705501
155波兰POL0.585783
181瑞典SWE0.551032
14比利时BEL0.531767
6阿根廷ARG0.518475
189泰国THA0.504993
11奥地利AUT0.455737
144挪威NOR0.434751
5阿拉伯联合酋长国ARE0.414179
141尼日利亚NGA0.397270
90爱尔兰IRL0.382487
93以色列ISR0.369690
213南非ZAF0.368288
169新加坡SGP0.364157
82中国香港特别行政区HKG0.362993
137马来西亚MYS0.354348
53丹麦DNK0.352058
152菲律宾PHL0.330910
41哥伦比亚COL0.330228
149巴基斯坦PAK0.312570
35智利CHL0.298231
17孟加拉国BGD0.274025
62芬兰FIN0.273961
57阿拉伯埃及共和国EGY0.250895
49捷克共和国CZE0.245226
208越南VNM0.244948
163罗马尼亚ROU0.239553
158葡萄牙PRT0.237979
91伊拉克IRQ0.225914
151秘鲁PER0.222238
76希腊GRC0.218032
147新西兰NZL0.205025
162卡塔尔QAT0.192009
55阿尔及利亚DZA0.180689
98哈萨克斯坦KAZ0.170539
86匈牙利HUN0.155703
105科威特KWT0.141678
200乌克兰UKR0.130832
119摩洛哥MAR0.118495
56厄瓜多尔ECU0.108398
179斯洛伐克共和国SVK0.106472
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" + ], + "text/plain": [ + " Country Name Country Code 2018\n", + "202 美国 USA 20.494100\n", + "36 中国 CHN 13.608152\n", + "97 日本 JPN 4.970916\n", + "50 德国 DEU 3.996759\n", + "68 英国 GBR 2.825208\n", + "64 法国 FRA 2.777535\n", + "89 印度 IND 2.726323\n", + "94 意大利 ITA 2.073902\n", + "26 巴西 BRA 1.868626\n", + "32 加拿大 CAN 1.712510\n", + "164 俄罗斯联邦 RUS 1.657554\n", + "104 大韩民国 KOR 1.619424\n", + "10 澳大利亚 AUS 1.432195\n", + "59 西班牙 ESP 1.426189\n", + "124 墨西哥 MEX 1.223809\n", + "87 印度尼西亚 IDN 1.042173\n", + "143 荷兰 NLD 0.913658\n", + "166 沙特阿拉伯 SAU 0.782483\n", + "196 土耳其 TUR 0.766509\n", + "33 瑞士 CHE 0.705501\n", + "155 波兰 POL 0.585783\n", + "181 瑞典 SWE 0.551032\n", + "14 比利时 BEL 0.531767\n", + "6 阿根廷 ARG 0.518475\n", + "189 泰国 THA 0.504993\n", + "11 奥地利 AUT 0.455737\n", + "144 挪威 NOR 0.434751\n", + "5 阿拉伯联合酋长国 ARE 0.414179\n", + "141 尼日利亚 NGA 0.397270\n", + "90 爱尔兰 IRL 0.382487\n", + "93 以色列 ISR 0.369690\n", + "213 南非 ZAF 0.368288\n", + "169 新加坡 SGP 0.364157\n", + "82 中国香港特别行政区 HKG 0.362993\n", + "137 马来西亚 MYS 0.354348\n", + "53 丹麦 DNK 0.352058\n", + "152 菲律宾 PHL 0.330910\n", + "41 哥伦比亚 COL 0.330228\n", + "149 巴基斯坦 PAK 0.312570\n", + "35 智利 CHL 0.298231\n", + "17 孟加拉国 BGD 0.274025\n", + "62 芬兰 FIN 0.273961\n", + "57 阿拉伯埃及共和国 EGY 0.250895\n", + "49 捷克共和国 CZE 0.245226\n", + "208 越南 VNM 0.244948\n", + "163 罗马尼亚 ROU 0.239553\n", + "158 葡萄牙 PRT 0.237979\n", + "91 伊拉克 IRQ 0.225914\n", + "151 秘鲁 PER 0.222238\n", + "76 希腊 GRC 0.218032\n", + "147 新西兰 NZL 0.205025\n", + "162 卡塔尔 QAT 0.192009\n", + "55 阿尔及利亚 DZA 0.180689\n", + "98 哈萨克斯坦 KAZ 0.170539\n", + "86 匈牙利 HUN 0.155703\n", + "105 科威特 KWT 0.141678\n", + "200 乌克兰 UKR 0.130832\n", + "119 摩洛哥 MAR 0.118495\n", + "56 厄瓜多尔 ECU 0.108398\n", + "179 斯洛伐克共和国 SVK 0.106472" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_gdp[['Country Name', 'Country Code', '2018']].sort_values(by='2018', ascending=False)[:60]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#############################\n", + "# 历年GDP分析" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_china = country_gdp[country_gdp['Country Name']=='中国']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\wei.zhou\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: 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", + " \n" + ] + } + ], + "source": [ + "for i in range(1960, 2019):\n", + " df_china[str(i)] = df_china[str(i)].apply(lambda x: x/1000000000000)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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
202美国USANaN高收入国家NaNGDP（现价美元）NY.GDP.MKTP.CD0.54330.56330.6051...15.54258116.19700716.78484917.52174718.21929818.70718819.48539420.4941NaNNaN
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\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": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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\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": {}, + "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": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn.pipeline as pl\n", + "import sklearn.linear_model as lm\n", + "import sklearn.preprocessing as sp\n", + "import matplotlib.pyplot as mp\n", + "import numpy as np\n", + "import sklearn.metrics as sm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1960],\n", + " [1961],\n", + " [1962],\n", + " [1963],\n", + " [1964],\n", + " [1965],\n", + " [1966],\n", + " [1967],\n", + " [1968],\n", + " [1969],\n", + " [1970],\n", + " [1971],\n", + " [1972],\n", + " [1973],\n", + " [1974],\n", + " [1975],\n", + " [1976],\n", + " [1977],\n", + " [1978],\n", + " [1979],\n", + " [1980],\n", + " [1981],\n", + " [1982],\n", + " [1983],\n", + " [1984],\n", + " [1985],\n", + " [1986],\n", + " [1987],\n", + " [1988],\n", + " [1989],\n", + " [1990],\n", + " [1991],\n", + " [1992],\n", + " [1993],\n", + " [1994],\n", + " [1995],\n", + " [1996],\n", + " [1997],\n", + " [1998],\n", + " [1999],\n", + " [2000],\n", + " [2001],\n", + " [2002],\n", + " [2003],\n", + " [2004],\n", + " [2005],\n", + " [2006],\n", + " [2007],\n", + " [2008],\n", + " [2009],\n", + " [2010],\n", + " [2011],\n", + " [2012],\n", + " [2013],\n", + " [2014],\n", + " [2015],\n", + " [2016],\n", + " [2017],\n", + " [2018]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year = [i for i in range(1960, 2019)]\n", + "X = np.array(year)\n", + "X = X.reshape(-1, 1)\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "y = usa_gdp" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "model = pl.make_pipeline(\n", + " sp.PolynomialFeatures(5), # 多项式特征拓展器\n", + " lm.LinearRegression() # 线性回归器\n", + ")\n", + "# 训练模型\n", + "model.fit(X, y)\n", + "# 求预测值y\n", + "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, 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制多项式回归线\n", + "px = np.linspace(X.min(), X.max(), 1000)\n", + "px = px.reshape(-1, 1)\n", + "pred_py = model.predict(px)\n", + "\n", + "# 绘制图像\n", + "mp.figure(\"美国历年GDP拟合结果\", facecolor='lightgray')\n", + "mp.title('USA GDP Regression', fontsize=16)\n", + "mp.tick_params(labelsize=10)\n", + "mp.grid(linestyle=':')\n", + "mp.xlabel('x')\n", + "mp.ylabel('y')\n", + "\n", + "mp.scatter(X, y, s=60, marker='o', c='dodgerblue', label='Points')\n", + "mp.plot(px, pred_py, c='orangered', label='PolyFit Line')\n", + "mp.tight_layout()\n", + "mp.legend()\n", + "mp.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# 中国\n", + "y = china_gdp\n", + "model = pl.make_pipeline(\n", + " sp.PolynomialFeatures(5), # 多项式特征拓展器\n", + " lm.LinearRegression() # 线性回归器\n", + ")\n", + "# 训练模型\n", + "model.fit(X, y)\n", + "# 求预测值y\n", + "pred_y = model.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 绘制多项式回归线\n", + "px = np.linspace(X.min(), X.max(), 1000)\n", + "px = px.reshape(-1, 1)\n", + "pred_py = model.predict(px)\n", + "\n", + "# 绘制图像\n", + "mp.figure(\"中国历年GDP拟合结果\", facecolor='lightgray')\n", + "mp.title('CHN GDP Regression', fontsize=16)\n", + "mp.tick_params(labelsize=10)\n", + "mp.grid(linestyle=':')\n", + "mp.xlabel('x')\n", + "mp.ylabel('y')\n", + "\n", + "mp.scatter(X, y, s=60, marker='o', c='dodgerblue', label='Points')\n", + "mp.plot(px, pred_py, c='orangered', label='PolyFit Line')\n", + "mp.tight_layout()\n", + "mp.legend()\n", + "mp.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/GDP_analyse/GDP_data.csv b/GDP_analyse/GDP_data.csv new file mode 100644 index 0000000..55aa887 --- /dev/null +++ b/GDP_analyse/GDP_data.csv @@ -0,0 +1,265 @@ +"Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019", 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b/GDP_regression/Country_data.csv new file mode 100644 index 0000000..3de18dc --- /dev/null +++ b/GDP_regression/Country_data.csv @@ -0,0 +1,267 @@ +"Country Name","Country Code","Region","Income_Group", +"阿鲁巴","ABW","","高收入国家", +"阿富汗","AFG","南亚","低收入国家", +"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", +"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", +"安道尔共和国","AND","","高收入国家", +"阿拉伯联盟国家","ARB","","", +"阿拉伯联合酋长国","ARE","","高收入国家", +"阿根廷","ARG","拉丁美洲与加勒比海地区（不包括高收入）","中高等收入国家", +"亚美尼亚","ARM","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"美属萨摩亚","ASM","东亚与太平洋地区（不包括高收入）","中高等收入国家", +"安提瓜和巴布达","ATG","","高收入国家", +"澳大利亚","AUS","","高收入国家", +"奥地利","AUT","","高收入国家", +"阿塞拜疆","AZE","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"布隆迪","BDI","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"比利时","BEL","","高收入国家", +"贝宁","BEN","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"布基纳法索","BFA","撒哈拉以南非洲地区（不包括高收入）","低收入国家", +"孟加拉国","BGD","南亚","中低等收入国家", +"保加利亚","BGR","欧洲与中亚地区（不包括高收入）","中高等收入国家", +"巴林","BHR","","高收入国家", +"巴哈马","BHS","","高收入国家", 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coding = utf-8 +""" +@author: zhou +@time:2019/11/13 15:00 +@File: main.py +""" + +import pandas as pd +from pyecharts.charts import Scatter +import sklearn.pipeline as pl +import sklearn.linear_model as lm +import sklearn.preprocessing as sp +import matplotlib.pyplot as mp +import numpy as np + + +gdp = pd.read_csv('GDP_data.csv') +country = pd.read_csv('Country_data.csv') +country_data = country.dropna(subset=['Income_Group']) +country_gdp = pd.merge(country_data, gdp, how='inner') + +# 美国GDP +df_usa = country_gdp[country_gdp['Country Name']=='美国'] +for i in range(1960, 2019): + df_usa[str(i)] = df_usa[str(i)].apply(lambda x: x/1000000000000) + +# 中国GDP +df_china = country_gdp[country_gdp['Country Name']=='中国'] +for i in range(1960, 2019): + df_china[str(i)] = df_china[str(i)].apply(lambda x: x/1000000000000) + +# 日本GDP +df_jpn = country_gdp[country_gdp['Country Name']=='日本'] +for i in range(1960, 2019): + df_jpn[str(i)] = df_jpn[str(i)].apply(lambda x: x/1000000000000) + + +# 德国GDP +df_de = country_gdp[country_gdp['Country Name']=='德国'] +for i in range(1960, 2019): + df_de[str(i)] = df_de[str(i)].apply(lambda x: x/1000000000000) + +# 英国GDP +df_uk = country_gdp[country_gdp['Country Name']=='英国'] +for i in range(1960, 2019): + df_uk[str(i)] = df_uk[str(i)].apply(lambda x: x/1000000000000) + +# 处理数据 +year_str = [str(i) for i in range(1960, 2019)] + +china_gdp = df_china[year_str].values.tolist()[0] +usa_gdp = df_usa[year_str].values.tolist()[0] +jpn_gdp = df_jpn[year_str].values.tolist()[0] +de_gdp = df_de[year_str].values.tolist()[0] +uk_gdp = df_uk[year_str].values.tolist()[0] + + +# 制作散点图 +def scatter_base(choose, values, country) -> Scatter: + c = ( + Scatter() + .add_xaxis(choose) + .add_yaxis("%s历年GDP" % country, values) + .set_global_opts(title_opts=opts.TitleOpts(title=""), + # datazoom_opts=opts.DataZoomOpts(), + yaxis_opts=opts.AxisOpts( + axislabel_opts=opts.LabelOpts(formatter="{value} /万亿") + ) + ) + .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) + ) + return c + + +# jupyter notebook +scatter_base(year_str, usa_gdp, '美国').render_notebook() +scatter_base(year_str, china_gdp, '中国').render_notebook() +scatter_base(year_str, jpn_gdp, '日本').render_notebook() +scatter_base(year_str, de_gdp, '德国').render_notebook() +scatter_base(year_str, uk_gdp, '英国').render_notebook() + +# 拟合数据-美国 +year = [i for i in range(1960, 2019)] +X = np.array(year) +X = X.reshape(-1, 1) +y = usa_gdp +model = pl.make_pipeline( + sp.PolynomialFeatures(5), # 多项式特征拓展器 + lm.LinearRegression() # 线性回归器 +) +# 训练模型 +model.fit(X, y) +# 求预测值y +pred_y = model.predict(X) + +# 绘制多项式回归线 +px = np.linspace(X.min(), X.max(), 1000) +px = px.reshape(-1, 1) +pred_py = model.predict(px) + +# 绘制图像 +mp.figure("美国历年GDP拟合结果", facecolor='lightgray') +mp.title('USA GDP Regression', fontsize=16) +mp.tick_params(labelsize=10) +mp.grid(linestyle=':') +mp.xlabel('x') +mp.ylabel('y') + +mp.scatter(X, y, s=60, marker='o', c='dodgerblue', label='Points') +mp.plot(px, pred_py, c='orangered', label='PolyFit Line') +mp.tight_layout() +mp.legend() +mp.show() + +# 预测2019年数据 +p_year = [i for i in range(1960, 2020)] +p_X = np.array(p_year) +p_X = p_X.reshape(-1, 1) +p_pred_y = model.predict(p_X) + diff --git a/football/yingchao/analyse.py b/football/yingchao/analyse.py index 6f7c4e4..963c942 100644 --- a/football/yingchao/analyse.py +++ b/football/yingchao/analyse.py @@ -67,10 +67,10 @@ # 前锋分析 df_vanguard = player_df[player_df['staff'] == '前锋'][['staff', 'club', 'shot', 'speed', 'power']] -df_vanguard = df_vanguard.groupby(by = 'club').mean()[['shot', 'speed', 'power']] +df_vanguard = df_vanguard.groupby(by='club').mean()[['shot', 'speed', 'power']] df_vanguard['size'] = ((df_vanguard['shot'] - df_vanguard['shot'].min()) / (df_vanguard['shot'].max() - df_vanguard['shot'].min()) + 1) * 10 -df_vanguard.reset_index(inplace = True) -df_vanguard.sort_values(by = 'shot', ascending = False, inplace = True) +df_vanguard.reset_index(inplace=True) +df_vanguard.sort_values(by='shot', ascending=False, inplace=True) vanguard_shot = df_vanguard['shot'].values.tolist() vanguard_speed = df_vanguard['speed'].values.tolist() vanguard_power = df_vanguard['power'].values.tolist()