From a8ce91f7901f9f91dab1fdaa09903208de466535 Mon Sep 17 00:00:00 2001 From: zhouwei713 <736552883@qq.com> Date: Wed, 13 Nov 2019 15:07:23 +0800 Subject: [PATCH] add gdp regression code --- .idea/workspace.xml | 20 +- .../GDP_analyse-checkpoint.ipynb | 7133 +++++++++++++++++ .../Untitled-checkpoint.ipynb | 6 + ...PI_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.zip | Bin 0 -> 115590 bytes GDP_analyse/Country_data.csv | 267 + GDP_analyse/GDP_analyse.ipynb | 7133 +++++++++++++++++ GDP_analyse/GDP_data.csv | 265 + ...PI_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.csv | 2 + GDP_analyse/Untitled.ipynb | 6 + GDP_analyse/test.txt | 3 + GDP_regression/Country_data.csv | 267 + GDP_regression/GDP_data.csv | 265 + GDP_regression/main.py | 120 + football/yingchao/analyse.py | 6 +- 14 files changed, 15479 insertions(+), 14 deletions(-) create mode 100644 GDP_analyse/.ipynb_checkpoints/GDP_analyse-checkpoint.ipynb create mode 100644 GDP_analyse/.ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 GDP_analyse/API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.zip create mode 100644 GDP_analyse/Country_data.csv create mode 100644 GDP_analyse/GDP_analyse.ipynb create mode 100644 GDP_analyse/GDP_data.csv create mode 100644 GDP_analyse/Metadata_Indicator_API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.csv create mode 100644 GDP_analyse/Untitled.ipynb create mode 100644 GDP_analyse/test.txt create mode 100644 GDP_regression/Country_data.csv create mode 100644 GDP_regression/GDP_data.csv create mode 100644 GDP_regression/main.py 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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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
..................................................................
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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\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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xHDhwoMjXCRWg6tevnyujaVGJj48XOi+N6H4gvqPi59iI7ge2HYu7u4ryiM8CW3uXiYTofiC+o+Ln2IjuB9I6KgHKgqysLHtXQVZE9wPxHRU/x0Z0P5DWUQlQFhRm+xdHRnQ/EN9R8XNsRPcDaR2FGoOyRXZ2NklJSdy/f7/Ac3NycswpBESkNPzc3d2pXbu21U7mpYW97ltaKH6Ojeh+IK2j8AEqKSmJihUrUr9+/QI39tRqteZkeCIit5/BYODWrVskJSXRoEED2e7zKDIzM23mNhIFxc+xcRQ/vQEiLsDq05ByF2pUhPGtYVATcC5gf2QpHYUPUPfv3y9UcALrRHqiIbefk5MTVatWNed9sgeO8J+/JCh+jo0j+OkNELYLDl6DrGzje6kaeOe/sPsSLAt+dJCS0lH8B6LYTsZmC9EzcJaGn5zpJwpDUlKSXe8vN4qfY+MIfhEXcgcnE1k6OHAVfrnw6OuldPxXBKjC4u7ubu8qyIrofgCNGjWydxVkRfFzbBzBb/Vp6+BkIksHq04/+nopHZUAZYFGo5GlXBcXF/z9/WnRogUjRowocBpmQbl6/v77b7y8vPD398ff359evXoBsGzZMtavXw/AunXrSE5OznWdyS80NJRt27blOpacnGwzH5Kjcf78eXtXQVYUP8fGEfxS7pbsuJSOSoCyIL9srCXFw8ODM2fOcO7cOcqVK8eyZctKXGaXLl04c+YMZ86cMafxnjx5sjkLra0A9Si/mjVrWgUtR6RVq1b2roKsKH6OjSP41ahYsuNSOsoWoK5fv864ceMYOHAggwcP5scffwQgPT2diRMnEhwczMSJE0lPT7d5fUREBMHBwQQHBxMRESFXNXNx79492e/RpUsXLl26BMDChQtp0aIFLVq0YNGiRVbnmtJemxg9ejS//PJLvmV/+OGHfPnll2zbto2TJ08yevRo/P39zT2nR/klJCTQokULwBjchg4dSp8+fWjcuDGzZ882n7dnzx4CAwMJCAhgxIgRZGZmFu0HIDOnTp2ydxVkRfFzbBzBb3xr8MxnPpWnK0xo/ejrpXSUbVqXi4sLs2bNolmzZty7d4+QkBACAwOJiIigQ4cOTJgwgVWrVrF69WpmzpyZ69r09HSWLl3K5s2bAQgJCaF79+54eXmVrFJLZ0D8mXwPFyuB9xP+MMU6uNhCp9Oxe/du+vTpw6lTp1i7di3Hjh3DYDDQoUMHunXrRuvWD1t/woQJfP311wwaNIj09HQOHz7MDz/8wMGDBzlw4AD+/v4AjBgxgnfffdd83fDhw/n+++/58ssvadu27UO/IqQoP3PmDKdPn+axxx6jSZMmTJs2DQ8PD+bOncvevXspX7488+fPZ+HChbz//vuFLldu2rRpY+8qyIri59g4gt+gJvBbHBxMhKoZV5gWN5f3W3yH82OedKkHA5s8+nopHWXrQVWrVo1mzZoBxj+MDRo04MaNG+zbt49BgwYBMGjQIPbt22d17aFDhwgMDMTLywsvLy8CAwM5dOiQXFU1k5OTI0u5Go0Gf39/2rZtS926dRk/fjwHDx5kyJAhlC9fngoVKjB06FCr3X67devGpUuXUKvVbNq0iWHDhpmnils+4rMMTo+iKD3EoKAgvLy8cHd3p1mzZly9epWjR48SExNDp06d8Pf354cffuDq1auF/0GUAo7wCbUkKH6OjSP4OTvB8v7wVXs1W04+S58b4XTzTOKzoIKnmIO0jqUyBqVSqYiNjaVly5bcunWLatWqAcYgduvWLavz1Wo11atXN3/v6+uLWq22WfbWrVsJCQkhJCSElJQUUlNTSUlJQaVSkZaWRnZ2Nnq9Ho1Gg2Hy19z76Ff48u9cr4YF+9B8shunr/7h/tzf0X2+lwfz9vBg3h6yP/sT7ad/kDP/v2g+2Y1hwT6yPv7tYRlTFpn/8GdlZaHX67l//z45OTlotVqys7Px8PDg2LFjnDp1igULFuDq6opWqwUeBg3TFHDTBAqNRkNOTg6jRo1i/fr1rFmzhhdeeAGdTseDBw8wGAxGJ4PBqgzT9/fv3zfXR6fT4ebmxoMHD9Dr9WRnZ5OTk2MuI+9jQNN2JVlZWTg7O5OVlYVOp6Nnz56cOHGCEydOcPr0aVasWGG+j6nupnqYflGjoqLQ6XTExsaSmZlJQkKCVTvFx8ej0WiIiYlBr9cTGRmZq4zIyEj0ej0xMTFoNBri4+NJS0tDpVKZ2z0hIYEmTZoQGxuLTqcjKioqVxmm1+joaLRaLXFxcWRkZJCYmIharUatVpOYmEhGRgZxcXFotVqio6NtllGaTpmZmWYn0wcUkZws26lNmzbCOVm+litXziGc0pISCFrxDDW0KtKmLmbJhIY00Ebh7FTw7161atWsnIqL7Bl1s7KyCA0NZdKkSfTq1YuOHTty+PBh8/G83wOsXbuWBw8eEBYWBhhnp3l4ePDSSy898l6hoaFW6Tb+7//+j6eeeqrQdZVjokSFChWsxmoiIyMJDQ3l6NGj5kd8//nPf2jdunWu82/cuEH79u2pXr06x44dA4yz+L788kt27dqVq8wPP/yQChUqMGvWLAYMGMDMmTPp0aOHlV9oaCj9+/fPNWsvISGB/v37c+7cOdatW8fJkyf5/vvvAejfvz+zZs2iefPmtGnThr/++otGjRqRlZVFUlISTz75ZK56FOVnLjXR0dH4+fnZ5d6lgeLn2JRFv7y7RtTxfMCKY8FUi9uH04c74On+RSrPlmPz5s3NQzZFQdYeVHZ2Nq+99hrBwcHmqdCWOw3cvHmTqlWrWl3n6+vL9evXzd/fuHHD3OuSk9JcJxQQEEBoaCjt27c3j8lZjj+Z8PX15amnnmLs2LFFKj80NJTJkyfnmiRh6RcWFkbt2rWpXbs2gYGBhSqzWrVqrFu3jlGjRtGyZUuefvppYmNji1QvuckbLEVD8XNsypqfadeId/6CaDXcytIzdu9L+FzYy7qeq9B3KFpwAmkdZetBGQwG3n33Xby8vHjzzTfN73/11Vd4eXmZJ0lkZGTYnCQREhKSa5LE5s2bC5wkUdIe1P3798vcYtasrCz8/PyIjIws8SSR0vKzZw8qLi6Oxo0b2+XepYHi59iUNb/wWGNwysoGDAY+Oj+d0Kvf81nTz1nf5E0+C4LBTYtWpi3HMteDOn36NDt37uTYsWMMHz6c4cOHs3//fsaPH8/Ro0cJDg7m6NGjjB8/HjAu7vrggw8A8PLyIiwsjFGjRjFq1CjCwsJKPoOvEJS1nYb37t1L06ZNmTZtmiT+Zc1PDnx9fe1dBVlR/BybsuZnuWvEq3EfE3r1e5Y3nMWyhrMLtWuELaR0lG2aeUBAgHmQLy+rVq2yeq958+Z89NFH5u+HDBnCkCFD5KqeTXQ6HS4uLqV6z0fRq1cvEhMTJSuvrPnJwZ07d3j88cftXQ3ZUPwcm7LmZ9oV4qWE75kZ9yFbao9lXtMv4H97aha0a4QtpHQUe/vuIiJ6MjHR/UD8/QYVP8emrPnVqAidLm3k4/PT+MN3EG/5rTAHJ9PxoiKloxKgFBQUFP6lvFPuN9pFvcSRKt2Y1voncpwfhoTC7BohN0qAskCv19u7CrIiuh9QqMzJjozi59iUKb/zhwhcN5xE75ZMa/cLWqeHPR9PVwq1a4QtpHRUApQFSsJCx6dSpUr2roKsKH6OjT39LNc7lb92ltX7+4NXbWp9t5v31I+zyiJ77oTWxuBU0K4RtpDSUfxBiSKQnZ1PEpQSUtR0Gx07diywzEWLFhVYTl7k8itL3Lhxw95VkBXFz7Gxl5/leqc7CZf57kBv7jqXZ5D/n7x82IeBTWDXKDg1yfg6uGnxghNI66gEKAtc3coRHgv9N0GbFcbX8Fhj45aEoqbbyLuzhi2KE6DKlStXpPMdkbp169q7CrKi+Dk29vIzZcktf/c6G449g5v+AWPa7+FSuXqFypJbFKR0VALU/9AbYNJOvXlFdarG+PrOf2HyryUPUiYKk27DlLDw77//pnv37gwfPpymTZsyevRoDAYD3377LcnJyfTo0YMePXqQk5NDaGgoLVq0wM/Pj6+//trmvcvU82+ZuHjxor2rICuKn2NjL7/Vp8E16w7rj/fGW3uD0Ha/EVfRuJl3cdc75YeUjuIPShSSiAtwJNnVKtVxlg7zJ4yirqjOS1HTbYBxwfP58+epWbMmnTp14tChQ0yfPp2FCxeyb98+vL29OXXqFCqVinPnzgHGdQi2kCshY1mirO1zJjWKn2NjL7+0tCxWnxhAo8z/Y1y7XZyp3CHX8eKsd8oPKR2VHtT/sFxRnZeSfsIobroNgPbt21O7dm2cnZ3x9/cnISHB6pyGDRty+fJlpk2bxu+//57vIrnSSMhobxwhnUFJUPwcm9Lw0xvINVQxaEM2XxwfSdu0Q8zw/5ED1Z61uqY4653ywyESFjoaBX2CKMknDNMYlCUGQ+GeGT722GPmr11cXNDpdFbnVK5cmaioKP744w8WL17Mli1bWLNmjdV5RUlY6Kg4QkK4kqD4OTZy+5kmQxy8ZvzA7WTQ897hsXRK/pV3/Zbxa82RVtdIvd7JIRIWOhoFfYKQ8hMGQNeuXdmxYwdZWVncu3eP8PBwunTpUujrK1asyN27xqiZmpqKXq9n2LBhfPLJJ+bcL3lRelCOj+Ln2MjtZ5oMYdr89ZNzrzAkeQNfNPmUjfXCKJdnp7OSrHfKD6UHJQPjWxsnRGRZd1BkWVFtmW4DyDfdRn5MmjSJvn37UqNGDRYtWsTYsWPNC3E/++wzm9coPSjHR/FzbOT2Mw9VGAy8HfsmYxKXseSJN1n8xNtgAN/yUMldmvVO+SGlo+wJC0uTkqTb0BtgQoSOIyrXXEHK9AmjMKmOyzpyJWTMiz3TbURFRdGqVSu73Ls0UPwcG7n92qwwzkCeFjeXWRfnsL7ey8xp/r15fz1vD+NaJzmx5VjcdBtKD+p/ODvByoEu7LyIZCuqyxoeHh72roLsNG/e3N5VkBXFz7GR269GRRgQ8w2zLs7h51pjeL/5dyXe/LWoSOmojEFZ8EB7n8FNpVtRXdb4N6yDMq0xExXFz7GR2+/j+2v4MGYGv/sO4Y2WazA4PfwTX1qbv0rp+K/oQRkMBpycCo4you+0UBp+hZ2dKBe1a9e26/3lRvFzbKT2s9xfr0XMFj49PpFTtXrzRutN5Bhy70wu9WSI/JDSUbYANWfOHPbv30+VKlUIDw8HYNasWeZ1PHfv3qVixYps27bN6trevXvj6emJi4sLLi4uxXp2acLd3Z1bt25RtWrVAoOU6An95PYzGAzcunXLrjlvUlNTzTtxiIji59hI6Wc5pfzppF/55NRoTlbuRFir7dSrZlyeYo+hCikdZQtQgwYNYtSoUbz77rvm97788kvz1wsWLHikxJo1a6hcuXKJ61G7dm2SkpK4efNmgefm5OQIHaBKw8/d3d2un4JF/uMGip+jI6WfaUp5q5R9LI0cxv893orxbXdy18kT7W34LKjku98UBykdZQtQbdu2RaVS2TxmMBj4448/WL16tVy3N+Pm5kaDBg0KdW5KSgo1atSQuUb2Q3Q/EH/HdsXPsZHSb/VpeFJ9jFUnB5Lo+QQvtv+du25ewMPdb+wRoKR0tMskiVOnTlG1alXq1atn87iTkxNhYWGMHDmSrVu3PrKsrVu3EhISQkhICCkpKaSmppKSkoJKpSItLY34+Hg0Gg0xMTHo9XrzIlbTYrLIyEj0ej0xMTHcv3+f+Ph40tLSUKlU5vISEhLIzMwkNjYWnU5HVFRUrjJMr9HR0Wi1WuLi4sjIyCAxMRG1Wo1arSYxMZGMjAzi4uLQarVER0fbLCMqKgqdTkdsbCyZmZkkJCSUyEmj0ZidTOWI5JS3nbKysoRzsmynK1euCOdk2U56vV44J8vXhIQEyZy6pkaw/kRfMj2q8k6XzdTyyqa1p4q65dLoWjGee/dKxylvO5l8LJ2Ki6zroFQqFVOnTjWPQZn45JNPqFu3Li+99JLN69RqNT4+Pty6dYtJkybx9ttv07Zt2wLvZ2sdVFFIS0uT5LFiWUV0PxDfUfFzbCTzS7pI2stduI8bwwMPkuRZ3+oUPx/jTOTSxpZjcddBlXoPSqfLaYnpAAAgAElEQVTTsXfvXnr37p3vOT4+PgBUrVqVoKAg8y7dcnP79u1SuY+9EN0PxHdU/BwbSfyuJ8CbvfB0NTC+416bwam0ppTbQso2LPUAdfToURo0aED16tVtHjftTWf6+vDhwzRq1KhU6lazZs1SuY+9EN0PxHdU/Byb4vqZdigPXXmNpCk9yUy/y4Epe6jZvCmeeWYSlOaUcltI2YayBajZs2fzwgsvkJCQQFBQENu3bwdg9+7d9OvXL9e5arWaKVOmAHDr1i1efPFFhg0bxvPPP0/Xrl3p3LmzXNXMhen5vqiI7gfiOyp+jk1x/EzTyb/+LYX3fwvice0tRrXbw7SL/jgB84KMj/O8PYyvnwXZd2s2KdtQ+L34ioJer8fZWdzNNUT3A/EdFT/Hpjh+4bHw5W416w50p6YmkTHt93CqSkfA2Fuy13Ty/LDl6DBjUGWZvDmbREN0PxDfUfFzbIrjt+XILVYd7EXtrARC2/1mDk4gfbp2KZCyDf8VWx0VloCAAHtXQVZE9wPxHRU/x6bIfnfT+GD3M9S/d5Fx7XZxvGpXq1OkTNcuBVK2odKDskBJlub4iO6o+Dk2RfK7lwHv9OGJjPOEtQnnkHcvm6eVxg7lRUHKNlQClAVKsjTHR3RHxc+xKYyf3gC/RGUSE9YX3cVI3uu0lcM1+to8157TyfNDSfkuE/mlShcF0f1AfEfFz7EpyE9vgGnhWfjOH8CTN48xrfUmfnp8IDo9uOSZlWfv6eT5IWUbKmNQFvj7+9u7CrIiuh+I76j4OTYF+e08d5/nfx5Mu9R/mOH/I7/VGA4YA1c5Z6hZETTZZTuZqpRtqPSgLIiNjbV3FWRFdD8Q31Hxc2we6fdAS82vh9Hp5p+80XINEbWez31YD5Xcy34yVSnbUAlQFhR213NHRXQ/EN9R8XNs8vXTZcO852iX9Btv+S1nW51Qm6eVtRl7tpCyDZUAZUFycrK9qyArovuB+I6Kn2Nj0+9/wYnDO1jW4Vs21Z2U7/VlbcaeLaRsQyVAWVClShV7V0FWRPcD8R0VP8fG0k9vgB3nszk05Tk4uJ0V7Rdxrcc0PPKZGVAWZ+zZQso2VAKUBVlZWfaugqyI7gfiOyp+jo3JT2+AlyOy8fziOTpd3c6HzRbxabVX+TkGKpbDKkiV1Rl7tpCyDZVZfBaIvAcYiO8H4jsqfo6Nye+XmGyGhj/HsynG4LS2wasAaHIALQxvBmduGMecyvKMPVtI2YZKgLLAzc3N3lWQFdH9QHxHxc+xcXNzA1021RY9R6c8wcmEJscYnOyRbFAKpGxDsT+uFJHMzEx7V0FWRPcD8R0VP8cm804azHv4WC9vcDLhCLP18kPKNlR6UBZ4e3vbuwqyIrofiO+o+Dkwumxqr58FR39hRftFrK1mOziBY8zWyw8p21DpQVmQlJRk7yrIiuh+IL6j4ud4WM7Wc/1fcLra81WHn62XH1K2oWwBas6cOXTr1o0hQ4aY31uyZAlBQUEMHz6c4cOHs3//fpvXHjx4kAEDBtCvXz9WrVolVxWtKK3U8vZCdD8Q31Hxcyzyztab2+wrYWbr5YeUbShbgBo0aBBLly61en/MmDFs27aNbdu20bWrdW6TnJwcPv30U5YsWUJERAS7d+8mPj5ermrm4vz586VyH3shuh+I76j4ORZ5Z+ultwkCjBMh7mph+FNlK127FEjZhrKNQbVt2xaVSlXk66Kjo6lbty516tQBoG/fvuzbt48nnnhC6ipa0apVK9nvYU9E9wPxHRW/soHeABEXYPXph1PBx7eGQZZTwbMfUG3RqNyz9W4/LMPRZ+vlh5RtWOpjUJs2bWLo0KHMmTOH9PR0q+NqtZrq1aubv/f19eXGjRv5lrd161ZCQkIICQkhJSWF1NRUUlJSUKlUpKWlER8fj0ajISYmBr1eb94K3pRUKzIyEr1eT0xMDMeOHSM+Pp60tDRUKpW5vISEBDIzM4mNjUWn0xEVFZWrDNNrdHQ0Wq2WuLg4MjIySExMRK1Wo1arSUxMJCMjg7i4OLRaLdHR0TbLiIqKQqfTERsbS2ZmJgkJCSVy0mg0ZqcDBw4I55S3nY4ePSqck2U77d27Vzgny3Yy/SvLTpfi43lzZxo/HVFhuJuClz6VilkJfPVPJl/tjOVBto6zp07AJ8PNs/V07ToD8E7N/1LBWUvQ43HUcMugWnbZcJLy797+/futnIqLU3R0tKHYVxeASqVi6tSphIeHA5CamkrlypVxcnLi+++/5+bNm3zyySe5rvnjjz84fPgwH330EQA7d+4kOjqad955p8D7hYaGcvLkSelFFBQU/pXY6im18oXt/wdZOuvzPVzgucYaBvw0hDaqP/iw5RLW1pmSb/l+PuL1oGzRvHlzNm/eXOTrSrUH5e3tjYuLC87OzgwbNoxz585ZnePr68v169fN39+4cQMfH59SqZ+SbtrxEd1R8Ss99AYI2wXv/AXRakjVGF83RNsOTgBo7/HMuv60Vu1htt8qq+A0xvuhn6PP1ssPh035fvPmTfPX//3vf23O9mjRogVXr14lKSmJ7Oxsdu/eTffu3Uulfkq6acdHdEfFr/SIuAAHr0FWdu7383vkVCE7g/XH+/D0rb+Z2Wo9m+uOtzrnP6lGPxFm6+WHQ6R8nz17Ni+88AIJCQkEBQWxfft2Fi5cyJAhQxg6dCgnTpxg9uzZgHHcacoU4ycNV1dX3nnnHSZPnszAgQPp3bt3qU09NT3zFRXR/UB8R8Wv9Fh92jo45cfj2Xf4z/FnCbhzhGmtNxFe+wWrc9xd4flq0cLM1ssPKdtQ1jGo0qakY1BarZbHHntMwhqVLUT3A/EdFb/So80K42O9gqj04BY/HnuWJnejeSVgC3uqD7Z5nrcHHH6p7PjJha02dIgxqLJOYmKivasgK6L7gfiOil/pUdB2Q05AVa2aTUd70jjzPJPa7sg3OJnKK0t+ciGloxKgLPD19bV3FWRFdD8Q31HxKz3GtzaOFdnCwwUmN0gh/GQPGmbF8ckzu6gd1K/A7YvKkp9cSOmobBZrwZ07d3j88cftXQ3ZEN0PxHdU/OQj75Ty6hWgYRWIvw0ai1l7nq4woEoSb4b3xOl+Mszfzactu6E3wI17cDAx9yw/ywkRSdfEbj+Qtg2VAGWBu7u7vasgK6L7gfiOip88mKaUW87aS9UYe0qNqhq/N62Dml43gWdWBOGUngqf7YHmHQHjhIfl/eGXC7DqtO1kg6K3H0jrqAQoBQWFfz35TSnX5Bh7UJ8FweCmQGIsvPUMaO/B/L3QpF2u852djOcNblp6dRcZZQzKgvv379u7CrIiuh+I76j4ycOjppRn6Yw9Ii6dhte7Qk42LPjbKjgVBtHbD6R1VHpQFlSqVMneVZAV0f1AfEfFTx4KymBb7cph2NIPynsZe061GhfrPqK3H0jrqPSgLHjUprQiILofiO+o+MnDo6aUd775J0sPPgOVfGDhgWIHJxC//UBaRyVAWVC3bl17V0FWRPcD8R0VP3nIb0p57+vhrD3ZH61PI2Nw8ilZ/URvP5DWUQlQFly8eNHeVZAV0f1AfEfFr+ToDRAeC/03GXeL6L/J+F6nurmD1NCk9SyJHME1nwAqfvs3VC75+h7R2w+kdVS2OlJQUPjXYGs6ORgDU+e60K+xccJE59OLeevMVNRPBuH9xQ6cPSvYr9ICoGx1JAFlaat/ORDdD8R3VPxKRn7TybN0xgW2TgYDu5jHW2emQuBAfBbukjQ4id5+IK2jMovPgrK01b8ciO4H4jsqfiXjkdPJsw3oVr4F576AHs/DG+vA1U3S+4vefuAg6TYcEdE/3YjuB+I7Kn4lI7/p5M6GHOaee5nh576A/pPhzf9IHpxA/PYDB05YWNYR/dON6H4gvqPiVzJsTScvl6Plu9OjGJO4jG0tZsO0JeAsz59G0dsPHKQHNWfOHLp168aQIUPM73311VcMGDCAoUOH8uqrr5KRkWHz2t69ezNkyBCGDx9OSEiIXFW0IioqqtTuZQ9E9wPxHRW/kpF3Onl53V3Wngimf8pWvmi+ANdJ88FJviyCorcfSOsoW4AaNGgQS5cuzfVeYGAg4eHhbN++nXr16rFq1ap8r1+zZg3btm0r1syP4tK8efNSu5c9EN0PxHdU/ErGoCbG2XqersZcTj8d7cHTt//mrdbruBQ0S/YU7KK3H0jrKFuAatu2LV5eXrne69ixI66uxo8vrVq1KnOrqi9dumTvKsiK6H4gvqPiVzTyrnka+BP0aQSL/BPYebwzT2bG8GnQDp4e+1KppGAXvf1AWke7zeILDw+nd+/eNo85OTkRFhYGwIgRIxgxYkSp1Kl27dqlch97IbofiO+o+BWe/FJo/LAjmvXHe+PlpMHpy7188L90GaWB6O0H0jraZZLEihUrcHFxoX///jaPr1+/ni1btrB06VJ++umnRy6+3bp1KyEhIYSEhJCSkkJqaiopKSmoVCrS0tKIj49Ho9EQExODXq8nMjISeDjTJDIyEr1eT0xMDMnJycTHx5OWloZKpTKXl5CQQGZmJrGxseh0OvMzVlMZptfo6Gi0Wi1xcXFkZGSQmJiIWq1GrVaTmJhIRkYGcXFxaLVaoqOjbZYRFRWFTqcjNjaWzMxMEhISSuSk0WjMTnFxccI55W0nlUolnJNlO505c0Y4J8t2Sk1Nlcwp4gJ4Zxid+njFUs01k7GarWw42IUcvYG/xm4jreZTsjvl/RmL0E6P+t27ePGilVNxkXUnCZVKxdSpUwkPDze/FxERwZYtW1i1ahUeHh4FlrFkyRI8PT0JDQ0t8NyS7iSRmpqKt7d3sa8v64juB+I7Kn6Fp/8miFY//D7oxk6WRI5E5VGXMe33UKV+PXaNkuRWhUb09gPbjg6xk8TBgwdZs2YN3333Xb7BKSsri3v37pm/Pnz4MI0aNSqV+mVn57OCTxBE9wPxHRW/wmO55mn4tXWsODWE2Ip+DA88iMqzXoEpNuRA9PYDaR1lG4OaPXs2J06c4M6dOwQFBfHKK6+watUqHjx4wKRJkwBo2bIl77//Pmq1mg8++IClS5dy69YtZsyYAUBOTg79+vWjc+fOclUzF3q9vlTuYy9E9wPxHRW/wlOjIqRmGQi7/CXvxM5mv/czhLXZTpZrBfPx0kb09gNpHWULUF988YXVe0OHDrV5ro+Pj3lKep06dfj555/lqtYj8fT0tMt9SwvR/UB8R8Wv8IxvlcPdb1/nxcvf8EuNEGb6ryfbuZzxPq4wobVktyo0orcfSOuo7CRhwe3bt+1dBVkR3Q/Ed1T8ColWw+BtIbx4+RvWPfEa01tvzBWcutRD9jVPthC9/UBaR2WzWAtq1qxp7yrIiuh+IL6j4mcbvcG4U/nq05CVeovvjg2i2c3D5ExaSKUWr9HitHFMqkZFY89pYBP51zzZQvT2A2kdlR6UBVeuXLF3FWRFdD8Q31Hxs8a03umdv+BOwhVW/NWJJ1JP8lqbzUxxf42BTWDXKDg1yfg6uKl9ghOI334graMSoCxo2rSpvasgK6L7gfiOip81phxPDVNPsf1wIN4P1LzQ4U/CfUdw4Cr8ckGGihYT0dsPpHVUApQFpkWQoiK6H4jvqPhZs/o0tFftZsuRbmid3RkWeIgTVboAxkSEq05LXcviI3r7gbSOSoCyICAgwN5VkBXR/UB8R8XPmvZnV7P65ACulH+SoR2PcKniU7mO22O9U36I3n4graMSoCwQPZmY6H4gvuO/3S/X5q/LDWyc/SHvn5rAQe9ejAz8B7V7Datr7LHeKT9Ebz9QUr7LhujJxET3A/Ed/81+lpu/Zmsf8Gn0FEKS1rCtzlje8VuO1sk6A6691jvlh+jtBw6SsNARMW2oKCqi+4H4jv9mP9NkCNesNH443peQpDUsavw+r/utJtvZjXIuuc+353qn/BC9/UBaR6UHZYG/v7+9qyArovuB+I7/Jj/LtU0pdyEzG3zS41l7Ipi6WZd5rdUPbK/9ovlc3/JQyb1srHfKD9HbD6R1VHpQFsTGxtq7CrIiuh+I7/hv8bNc2xStNuZxaqE+xI7DT1P1wU1Gd9hrDk4mNNllZ71TfojefiCtoxKgLGjQoIG9qyArovuB+I7/Fj/T4zxTosFBqo1sPNaTdLfKDO50lONVu1pdW5YmQ+SH6O0H0joqAcqC5ORke1dBVkT3A/Ed/y1+q0//LzgZDEyP+5hvz4zmdKWnGdLxCAnlG1tdV9YmQ+SH6O0H0joWOAa1ceNGgoOD8fLykuymZZUqVarYuwqyIrofiO8oop/lWJOTpgqG43DlDpTL0TI/egJDVT+yrdaLvO23ggcuj1ldXxYnQ+SHiO2XFykdC+xBpaamMmrUKF5//XUOHjyIwSBbAl67k5WVZe8qyIrofiC+o2h+eceaXHKyiFZDucxUNhzrxVDVjyx4ci6vt1qXKzi5u4K3B/j5wGdBsCy47I032UK09rOFlI4F9qCmT5/OtGnTOHz4MDt27GDevHn07t2boUOHUqdOHckqUhZwdhb7iafofiC+o2h+eceasg3ONMy8wNoTwVS/n8TU1pvYWfO5XNd4uhqD0mAH3NZOtPazhZSOhSrJyckJb29vvL29cXFxISMjg5kzZ7Jw4cJHXjdnzhy6devGkCFDzO+lp6czceJEgoODmThxIunp6TavjYiIIDg4mODgYCIiIoqgVHzc3KwX+omE6H4gvqNofuaxpv/RPOUgOw49TUVdOs93+ItdNoKTozzOs4Vo7WcLKR0LDFAbNmxg5MiRLFy4EH9/f7Zv386cOXPYvHkzf/755yOvHTRokDlTronVq1fToUMHfv31Vzp06MDq1autrktPT2fp0qVs3LiRjRs3snTp0nwDmZRkZmbKfg97IrofiO8omp95nzyDgbFXvuHzw8+R4lGbQZ2Oc6pKR8q7GR/jOeLjPFuI1n62kNKxwEd8aWlpLFq0yCoJlbOzM4sXL37ktW3btkWlUuV6b9++faxZswYwBrBx48Yxc+bMXOccOnSIwMBA88SMwMBADh06RL9+/Qo2KgHe3t6ylm9vRPcD8R1F86tRETIytXxy/hWeu7aav6sH83KrTdxzNc4Zb1DZuKZJFERrP1tI6VhgD2rq1Kn5Zkhs2LBhkW9469YtqlWrBkC1atW4deuW1TlqtZrq1aubv/f19UWtVtssb+vWrYSEhBASEkJKSgqpqamkpKSgUqlIS0sjPj4ejUZDTEwMer3evA2HaUPDyMhI9Ho9MTExXLlyhfj4eNLS0lCpVObyEhISyMzMJDY2Fp1OR1RUVK4yTK/R0dFotVri4uLIyMggMTERtVqNWq0mMTGRjIwM4uLi0Gq1REdH2ywjKioKnU5HbGwsmZmZJCQklMhJo9GYnWJiYoRzyttOly9fFs7Jsp1OnjzpsE7JKSmER6Yya0sCz6zO5P2tsXQun8yfkU8bg5PfOH7q8QVDq18EYGTVaCb4lW2nov5/ioyMLPPtVNK/EefPn7dyKi5O0dHRsk7LU6lUTJ06lfDwcAA6duzI4cOHzcfzfg+wdu1aHjx4QFhYGADLli3Dw8ODl1566ZH3Cg0NNf8HLg46nQ5XV3F3fxLdD8R3dFQ/y41eTWNOT2VEsfrkQKpq1bwdsI7tviGUc9LxwOBqHmty5Md5tnDU9isKthybN2/O5s2bi1xWqU8pqVq1Kjdv3gTg5s2bVK1a1eocX19frl+/bv7+xo0b5l6XnJw/f172e9gT0f1AfEdH9cs7W69Pyna2H+6IsyGH0Z0PUr5XCH4+8Fy180KMNeWHo7ZfUZDSsdQDVPfu3c2z8iIiIujRo4fVOZ06deLIkSOkp6eTnp7OkSNH6NSpk+x1a9Wqlez3sCei+4H4jo7ql3dniOWRw4it6MfATic4WbENZ24Yx5o+GdWqzO6jJwWO2n5FQUpHWQPU7NmzeeGFF0hISCAoKIjt27czfvx4jh49SnBwMEePHmX8+PGAMep+8MEHAHh5eREWFsaoUaMYNWoUYWFhpbKThejJxET3A/EdHdUv5S546O6x+HQIr1/8gG21XuS5p/82Jxg0zeZzVL/CIrofSOso+xhUaVLSMSgFBQVpyJsqo/KdeL47NoQn755nftPPWd5wFjg97CL5+Yg1W08hNw4zBlWWEf3Tjeh+IL6jI/jl3b6oxdXd/Px3W2rcT+Kl9rtZ/sQbuYKT5UavjuBXEkT3A2kdlQBlgejpmEX3A/EdHcHPNCFC80DPtLi5rD0RTJJnPfp3PsWBas/mOjfvzhCO4FcSRPcDJeW7bJjWHYiK6H4gvqMj+K0+Dc5ZGSw/NZRZF+cQUfN5hnY8zDVPY56gR2306gh+JUF0P5DWUewJ+UXkySeftHcVZEV0PxDf0RH8HlPFEnFoMPWzLvFhs0WsrT891yO9Cm7GrLe2cAS/kiC6H0jrqPSgLEhMTLR3FWRFdD8Q37Gs+ekNEB4L/TdBmxUw9/MdrP+rPZWybzO6w17WNng1V3CCR2e+LWt+UiO6H0jrqPSgLPD19bV3FWRFdD8Q37Es+VnuDnH/QQ6vX3ifqfHziKrUniltfkblXtvqmoIy35YlPzkQ3Q+kdVR6UBbcuXPH3lWQFdH9QHzHsuRnmgzx2L1U1p4IZmr8PDbVmcCIp/8hxaM25Vxyn1+YVBllyU8ORPcDaR2VHpQF7u7u9q6CrIjuB+I7liW/1aehqfoIiyNHUvWBmrf8lrOp7v8GlwzgWx4quRvXQdWoaOw5DWzy6B0iypKfHIjuB9I6KgFKQUGh6BgMdI/8hlfPvkGKex2GdjzCOa+AXKdosuHgWDvVT0EIlABlwf379+1dBVkR3Q/Ed7Snn2l3iE3H0pn093hmqX7md9/BvNFqLRlulazOf9RkiPxQ2s/xkdJRCVAWVKpk/Z9MJET3A/Ed7eVnmhCRGn2GhcdHUEdzhblPfcnKBjOtZulBwZMh8kNpP8dHSkdlkoQFN27csHcVZEV0PxDf0V5+EbEGfA+sZtP+p/HIySLk6b9Z2fD1fINTQZMh8kNpP8dHSkelB2VB3bp17V0FWRHdD8R3tIvf/Swqfvcycy/9wAHvXrzqv4Fbj/nkOsXd1bgAt7CTIfJDaT/HR0pHpQdlwcWLF+1dBVkR3Q/Edyx1v4TzMK09PS+tZ1Hj93mx/e9WwQke7g5R0lxOSvs5PlI6Kj0oC/z8/OxdBVkR3Q/EdywNP73B+EgvafNKJh59FU25x/m4++9sL/9svtcUZ0KELZT2c3ykdFR6UBaIvhW+6H4gvqPcfnoDzPj5Du7zQ5h2OIzjVbrwTKcodlV8Fpd8ekXFnRBhC6X9HB+HTlh45coV3njjDfP3SUlJvPLKK4wZM8b83okTJ5g+fTq1atUCICgoiClTphRYtpKwUEGhZPy9+yiNlz2Hz30VXzaZy/KGb2BwMn6OdXYCV2d4kPPwfNOECMsdyRUU8lLchIWl/oivQYMGbNu2DYCcnByCgoIICgqyOi8gIIDFixeXat1OnToldL4W0f1AfEcp/Syz3l7P0DM14QteiHyPFPc6jAg8wOnKT1udX5zdIYqC0n6Oj5SOdh2DOnbsGHXq1KFmzZr2rIYZ0X9xRPcD8R2lDE6mjV7L373O11Fj6JK6l101RvC23wqbC29B/t0hlPZzfIRJWLh792769u1r81hUVBTDhg1j8uTJXLp0Kd8ytm7dSkhICCEhIaSkpJCamkpKSgoqlYq0tDTi4+PRaDTExMSg1+uJjIwEHj4njYyMRK/XExMTw6lTp4iPjyctLQ2VSmUuLyEhgczMTGJjY9HpdERFReUqw/QaHR2NVqslLi6OjIwMEhMTUavVqNVqEhMTycjIIC4uDq1Wa07qlbeMqKgodDodsbGxZGZmkpCQUCInjUZjdjp8+LBwTnnb6eTJk8I5WbbTX3/9JYlTxAWolXmKtsl/8Peh5rS9fYitT8/htYANdPS+TjXXTDpWSOCJx1Jp4ZFCa08Vdcul0auy9E6W7RQVFSVEO+X3u7dv3z7hnPK206FDh6ycikupj0GZyM7OpmfPnoSHh+Pt7Z3rWGZmJs7Oznh6erJ//37mz5/Pr7/+WmCZJR2D0ul0uLqKO7FRdD8Q31Eqv6H/0dD/wFuMS/iWCxWa80rAZuIqNn/kNZ6uxgy4g5uW+Pb5orSf42PLsbhjUHbrQR04cICnnnrKKjgBVKhQAU9PTwC6du2KTqcjLS1N9jo9qqcmAqL7gfiOxfWzTCw46oszfB7elnEJ37K2/jQGdD5RqOBU3N0hioLSfo6PlI52C+WPeryXmppK1apVcXJyIjo6Gr1eXyp7WNWubZ1gTSRE9wPxHYvjZxpvOpyYw+gLC5l14V3Synkzpv3v7K/W2+Y1dR6XdzJEfijt5/hI6WiXAKXRaDhy5Ajvv/+++b0tW7YAMHLkSPbs2cOWLVtwcXHB3d2dBQsW4GRjzy+pSU1NpUKFCrLfx16I7gfiOxbHL+ICxF9IZNXJFwm8/Q+7qw/lLb8V3ClX1eb5nq4wK1DeR3n5obSf4yOlo10ClIeHBwcPHsz13siRI81fP//88zz//POlXS3hf3FE9wPxHYvjd2nbRsIPv4yLIYfXW65lW+2XbG7yCqX3KC8/lPZzfKR0FHu0rohkZ2fbuwqyIrofiO9YkJ/l2qbMW2l8eO4V3ri8iZOVOzLD/z9c82xodY2LE1R2L91Hefnxb28/EZDSUQlQFuj1entXQVZE9wPxHR/lZ7m2qZ3qd1ZGT8Bbe4MFT85l6RNvkuNs+797s2rGTV7LAv/m9hMFKR2VAGWBaeagqIjuB+I7Psov4gKcuZzOB2df57lrq7lYoRkT2+wgulLb/MuTcB89Kfg3t58oSOmoBCgLbt++TeXKle1dDdkQ3Q/EdzT5WT7KM820a3TlT3YcHU/1+yqWPPEmixp/iNbF3XytE5uaSPUAACAASURBVGC56NHe4022+Le0n8hI6agEKAvKypZLciG6H4jvWLNmzVyP8rKyobzuLs8df4MXEpdzqXwThnY8zJnKHayuLe8GDSqX/tTxovBvaD/RkdJRCVAWXLlyhWbNmtm7GrIhuh+I6WjZW3rKcIXD2mao7xl3Fe+U+l/mnx1PLU0iyxvO4qsnP0br4mGznAaVy85YU36I2H6WiO4H0joqAcqCpk3tsPCjFBHdD8RzzNtbOk9T9ICnLpM5sW/y4tUlXC7fmBGBBzhZpVO+5ZS1sab8EK398iK6H0jrqCQstODMmTP2roKsiO4H4jlGXHgYnABGeZ+hm/p39uxvwQtXl7KqwQz6djlTYHAqa2NN+SFa++VFdD+Q1lHpQVkQEBBg7yrIiuh+4NiOtiY+3Ln/MDhVfpBK271fM0/1I5fKN2FE4H5OVulsVY69timSAkduv8Iguh9I66j0oCwQPR2z6H7guI6mR3nv/AXRakjVGF+vZQAGA4NVG/jvP08xKHkT3zSaQ78uZ2wGJ9M2RbtGwalJxtfBTR0jOIHjtl9hEd0PpHVUelAWiJ5MTHQ/cFzHvI/yTNTKusq8c5PpfvN3Iit1YJTfSi487mezDEd6lJcfjtp+hUV0PxAoYWFZw5TUS1RE9wPHdVx9OndwcjbkMPbKN/y5vzntbh/gg2bfMKzjIdo2fHhSOWfj4zxvD/DzMeZqWhbsOL0lWzhq+xUW0f1AWkelB2WBv7+/vasgK6L7geM45h1vSrv/8Fjz9NN8em4yre8cZ1+1vrzbYikqz3oAbEo1+pl6S44ekPLiKO1XXET3A2kdlR6UBbGxsfaugqyI7geO4WhrvCnHABWz0/ng/KvsPNiW2lkJTPffQGi7X1F51qPO48Ze0vBqscL0lmzhCO1XEkT3A2kdlR6UBQ0aNLB3FWRFdD9wDEer8SaDgYHJPzHn/2birb3Bj/WmsKDJXDLcjNvFWOZn0mga4GF7Ha4QOEL7lQTR/UBaR7v1oHr37s2QIUMYPnw4ISEhVscNBgOfffYZ/fr1Y+jQocTExMhep+TkZNnvYU9E9wPHcLQcb2qYeYENx57huzPPk+Jem4GdjjOnxeJcwcly4oMj+JUExc/xkdLRrj2oNWvW5Lup4IEDB7h69Sq//vorZ8+eZe7cuWzcuFHW+lSpUkXW8u2N6H5Q9hxtrW26cgfcc7KYemkeYfFfoHHx5N0WS9hYdxJ6J5dH5mcqa35So/g5PlI6ltlHfPv27WPgwIE4OTnRqlUr7t69y82bN6lWrZps98zKyhJ6p2HR/aBsOebdpgggNcvAM+qdfHD+VepoEvi51hjmPbWA1Md8zdc9Kj9TWfKTA8XP8ZHS0W4BysnJibCwMABGjBjBiBEjch1Xq9VUr17d/L2vry9qtVrWAOXsLPacEdH9oGw55h1ranw3hvdjZtA19U8uVmjGyKf/5ljVbrmuKWjPvLLkJweKn+MjpaPdflrr169ny5YtLF26lJ9++omTJ0/mOm4wGPK5Mjdbt24lJCSEkJAQUlJSSE1NJSUlBZVKRVpaGvHx8Wg0GmJiYtDr9eY5+qbVzpGRkej1evPx+Ph40tLSUKlU5vISEhLIzMwkNjYWnU5HVFRUrjJMr9HR0Wi1WuLi4sjIyCAxMRG1Wo1arSYxMZGMjAzi4uLQarVER0fbLCMqKgqdTkdsbCyZmZkkJCSUyEmj0Zid7ty5I5xT3nYy3ae0na5cSSA8MpWXt6Qw5gcVYzensS8ynnJ6DSM9jvDh+en8caAlre6cYFf7WfTtcoZGTSrijJ4BlWKo5KKhV6V4+tdNo02F/H/3EhMThWin/H733NzchHOyfL127ZpwTnnbKS0tzcqpuDhFR0cXLhLIyJIlS/D09CQ0NNT83kcffUS7du3o168fAAMGDGDNmjWP7EGFhoZaBbqikJCQQP369Yt9fVlHdD+wj6OtR3kALnodo66t5PULc/DKTmNj3TC+avIxaeW8AahQjPxMoreh4uf42HJs3rw5mzdvLnJZdnnEl5WVhcFgoHz58mRlZXH48GEmT56c65wePXqwceNG+vbty9mzZ6lQoYKsj/cAvL29ZS3f3ojuB6XjmHfig7sb5vxMJgJT9/F+zAya3T3LkSrd+bD5N8Q+3jJXOcXJzyR6Gyp+jo+UjnYJULdu3WLGjBkA5OTk0K9fPzp37syWLVsAGDlyJF26dGH//v3069cPd3d35s6dK3u9kpKShM7XIrofyO9os7ekeXi8QeZF3rzwNn2vb+eaR33CAn7m9+pDwCl3t6i4+ZlEb0PFz/GR0rFMPOKTipI+4tPpdLi6ltmJjSVGdD+Q3zE81rgDRN5NXatq1bwa9zHPJy5H6+zO0ifeYmXDmTaz25ZkmyLR21Dxc3xsORb3EZ/4U0qKwPnz5+1dBVkR3Q+kddQbjAGp/yZos8L4+tWR3MHJPSeLqXGf8s/fjRiduIxNdSfRrcclvm/8rjk4mbYpkmJTV9HbUPFzfKR0VHpQCgo2yG/igwlnQw7Dk37g9QtzqK5N5nffwcxv+jmXK+TOdeHpagxIg8V+qqOg8EiUHpQEiJ5MTHQ/kM4xv/xMGAz0UP/G7gP+LDg7nmSPugwLPEBY23CbwUnq/Eyit6Hi5/goCQtlQvRkYqL7gXSOefMzATx9629mXXiPdmmHSPB8gikBW/mt+jDzBIhyzuBbATTZ8qVaF70NFT/HR0lYKBOif7oR3Q+K52hrrOnKnYfHW6cdZcPRXmw+2oM6WVd4p8VSenWL4bcaw83BydMVejSA/aHyploXvQ0VP8dHSkdlDErhX01+Y01OwFPpZ3j94hx6qXeRWq4aixu9w4a6YWhdPKjzOFRyL9oiWwWFfyvKGJQEmLYWERXR/aDojrbGmp7IjOX7yJHsPtiadrcPMr/JPLr0uMyaBjPQuniY8zPtGiVvb8kWoreh4uf4SOmojEFZ8OSTT9q7CrIiuh8U7Jh3F4jMbLiv+9+1d88x9dKnDEjeTJZLeb5pNIfVDWeS7lbJfL0cEx+KguhtqPg5PlI6KgHKgsTERBo3bmzvasiG6H7w0NFWHqax/rD7EhzK02NqkX6KaXGf0udGOJkuFVj2xGxWNnid249Vo4Ib+BVxv7zS8BMVxc/xkdJRCVAW+Pr6FnySAyO6HxgdbeZh0sBbeyHHYPwHEJB2hGlxc+l58zfSXSvxdeMPWFt/OunlHiZcK85+eXIiehsqfo6PlI5KgLLgzv+3d+5xUZb5Av/OBeUyAoZczEvhJT2uoWuaa+mmq1aCEZAr7uoe2y3NdsuP9vFsq7lxci0z67TbabN1M0u7uGmSqLmpxFlFzUgMUcIQBXTkqlwcZhhmmDl/zDINwgiDA/O+r8/38+Ez8M47zzxfnvczv3ku7/OrqSE4ONjX1egylO4HDsf0S8Ft3sPUaAPsdn5y5V88XbCaiZfTudyjD2uHvcSW237LVb+QFud3dr+8rkTpbSj85I83HUWAcsHf39/XVehSlO4HDseNh1sHJ43NyoNlO1h47lVG12ZR0TOS1f/xKh8MXIRJG9SqHF/PNblD6W0o/OSPNx1FgBLIHtf5pgAz5LnkRwuw1pN84V0eO/86A03nORc0lOUj3+bT/v/ZaiNXf60jR5MU5poEAoEIUC1oaGjwdRW6FCX6XTvfdHdQA0126GMuZ37Rm/yq+C16W67wTe97+NOI/2F/ZDx2Veu7K+SyZ54S29AV4Sd/vOkoApQLoaGh7Z8kY+Tu19bKvFGRkFkCxn8vFe9ZeZGXz/6JRP0Wetga2ReZwIZByzh+yz2Ao0fkp26ZXFCqw3ltIfc2bA/hJ3+86SgClAvl5eWKnsCUs5+7lXmnKkBjsxBb/hmPFv0v468cwqQOYHv/R3kn+hnO6364JyNQCxMHQuzQlkFOTsN5cm7DjiD85I83Hbs9QJWVlbFixQqqqqpQq9XMmjWLefPmtTgnKyuLxYsX069fPwCmTp3Kk08+2eV1GzhwYJe/hy+Rs19bOz6EmSv4ZckG5pa8Td8GPSUB0bwy4mW29F9InV9vADQq6O3fOhAlSnwozx1ybsOOIPzkjzcduz1AaTQali1bxogRI6ivryc5OZkJEyYwePDgFueNGTOGv/71r91at++//54777yzW9+zO5GLX1tDeTUN/w5Odjtjar7iV8VvEVf6CT1tjfyrz/08N3I9GRGxPHxLHnXVvZ1ljQiX1n1MN4pc2rCzCD/5403Hbg9Q4eHhhIeHAxAUFER0dDTl5eWtApQvUPqFIwc/d0N5IY1X+LV+C3MuvMPwq6cwaHR8PGAhm2//HYW6H7pDqdU/OErxPqYbRQ5teCMIP/njTUefbhar1+vJz88nJiam1XM5OTk88sgjLFq0iLNnz7otY9u2bSQnJ5OcnExpaSlVVVWUlpai1+uprq6msLAQk8lEXl4eNpuN7Oxs4Ict4bOzs7HZbOTl5XHs2DEKCwuprq5Gr9c7yysqKsJgMJCfn4/VaiUnJ6dFGc2Pubm5mM1mCgoKqKuro6SkhIqKCioqKigpKaGuro6CggLMZrNzQ8Vry8jJycFqtZKfn4/BYKCoqOiGnEwmk9Pp0KFDkndKy8zjyAUbiSHZYLfzvPVv/OXEXI6n9+W/85bg3wNeGPUmSxMy2ThmFcERvRgZUMqQnlXcoyvi8fCveDAkn1A/K08OyCF+mO+dPG2n6117Bw4ckEQ7edPJ9dpr/lGSk+tjenq64pyubaeDBw+2cuosPku3YTQaefTRR1m4cCHTpk1r8ZzBYECtVhMYGMjBgwdZu3Yte/bsabdMkW5DfrS1eWuIQU+i/gPmlLxDtPEstdoQPus3j48HLuC74FGtygjQwKwR8G25PBc+CARKR1bpNiwWC0uXLiUuLq5VcALQ6XQEBgYC8NOf/hSr1Up1dXWX10vpycSk5tc8nLfiSzh36Sr3FbzPxsxpfJU+gOX5f6DCvy9LRm1m3LRSnh/5ZpvBKVALP70dVk1xzDVtuOt4t6a/6G6k1obeRvjJH1mnfLfb7aSkpDBo0CDmz5/f5jlVVVWEhYWhUqnIzc3FZrN1y/0DSk/H7Eu/thY+/DjcClkHWFOyhQfKUgmwmSgOHMQbQ/9Iar95FAW13hG5vUSBog3ljfCTP9507PYAdeLECXbt2sXQoUOZNWsWAIsXL6asrAyA2bNns2/fPj755BM0Gg3+/v6sW7cOlarrvw7n5OQwalTrb+lKoTv82ktzYWq0Mbb6MHGl25hZ+gnh5nJq/Hqzvf98Uvv9iuO9JzjTqF9Lc6LA6+32INpQ3gg/+eNNR5Hy3QWr1YpWq9x7l7vaz136dH9VE6MvH2bGpW3MKPuUSHMpDeqe/F94LDv6/4qM8FgaNT2vW3bzbg9vx11/6E60obwRfvKnLcfOzkEp+z/lIWfPnmX4cJnewdkBvO13bW/J3w8q6h3bCGlsVsZVZxJbup0ZZZ8SYS6jQe1PRkQsn0fNIj1yJvXaXtctvzObt4o2lDfCT/5401EEKBf69+/v6yp0KZ3162h22uC6Gh6o/CfTyncxuXIvoZZqTOoAMiJi2dP353wZEYdRq+vQe3Z281bRhvJG+MkfbzqKAOVCVVUVOl3HPkDlSHt+HQ1Ertlp+xvOMq18F9MqdjHuyiH87FYu9+jD/sh40iMe4l/hD7QblFSA6zjzjWzeerO3odwRfvLHm44iQLmg9AunveDUkTTpwZYaJlzOYFLlfu69fIBB9QUA5Pcayd8G/RdfRszkRO/x2FSaDtXJ2/cw3cxtqASEn/zxpqMIUC5YLJb2T5Ixrn7Xmz9yxW5tZGz1USZWHWBi1X5G1WShwUa9Joivwibz/m1Pkx45kwuB0dd9b7UKtG7SXKya4r17lm6mNlQiwk/+eNNRBCgXbDabr6twQ7gbolMB734LvRttVPdoe9gOk+Mh0GpgTPVR7r5yiLuvHGJ0zTECbCaaUJMTejdvDnmOQ+HT+TZ0PBZ1jw7VqzvTXMi9DdtD+MkbpfuBdx1FgHKhefcKqePJXNGy/Y4A1WSHgT0CKalxGbaz2enbcJHRNV8ztjqTcVcy+VHdCbT2JppQkxc8mo8HLuBo2BS+CptMnV/HbpbuoYZIHZgs3Z/mQi5t2FmEn7xRuh9411EEKBeuXLlC79692z+xC2iv99PRRQtN9tblNnOnvYDBFVXE1GQRU/sNMbVZRJjLAWhQ+3MidDzrB/+Br2+ZRHboBAx+1086dr1hu/buV+oqfNmG3YHwkzdK9wPvOooA5cKtt97qtbLaCjiP/RgeugN2fe9576f5mLtA1OjSq/azNRJd/z3D63IZdvUUw67mMvzqSQaYih11Q0WhbjgH+zzAydBx5ISMIy94tNubZd0FIilmp/VmG0oR4SdvlO4H3nUUAcqF8+fPM2LECLfPdzToROkAFZy7Akar47VVJlh+AF46BIbGlsc70vtpxjUQhTZe5vb6s0TXF3Cb8SyD6s8w7OopBhvy8bM73sCi0nIuaBgnQn/CN8N/zsc9Z3IqZEy7N8k205FAJKXstO21odwRfvJG6X7gXcebfqsj16BTdtVGVC91h4MOOJZJ9+rZMuh4Bbud3pbL3Gq6QFTDRfo2XKSv6SIDTOe5rf4stxvPEmr5YYd3Gyr0AQP5vtdI8nvdyZled3Km10jOBQ1z9ozU2LC1s4H99eaP5IDNZkOt9mmasy5F+MkbpftB245iq6NO4Hrvz6jSDJ7WnWT3xR+z+VwvtgToqLT3opJemDSBVJna/oQ2NYHJ6P491PYmAq31BDYZCLIaCGoyEGg1oLPWcUtjFbc0VtHbUkWYuZLeFsfffczlRDVcxN9mblGWVaWh1H8ARUFD2NV3DkVBQzgfNJTiwCFcCIzGrPG/ru8v+nzLh1VjHPWS4PyRN/j2228ZM2aMr6vRZQg/eaN0P/Cu403dg0rNd+QiMlogLXMco2rbfq0NFY3qnljUfjSptFhVWqwqP6xqLU0qLWq7DY3ditZudTzarKhpws9mwd/W0G49GlV+XOkRzpUefaju0YeqnhGU+fen1L8/pf4DKA1w/F7VM/K6N8C2FXTUqpbzWCDd+SOBQKBMRA+qE2w88cOihKd+vJVfBmZy6HJ/gqxX/93buUqQ9So661V62MwuAciC1m5Fa7egtVlpUmloUmmdj82By6rSUq/VYdTo2ny80qMPV3qEU6/RuU0x0RaeLFr4zWhH0RtPwHjVcY7Z75Ls/JE3OH78uKJz7gg/eaN0P/Cu403dg7prg2ORghQQvR+BQKBUZJXyXSr0vWYh29w+2V36fgEaiAiEgGv6rYFamBYNr0yDOyOgT4Dj8bXp8Nr9LY+tmQp/m+no+ez+BRxfSIdTnGdnd62fFFC6o/CTN0r3A+86+qQHlZmZydq1a2lqaiIpKYnHH3+8xfONjY2sWLGCvLw8QkNDWbduHf369Wu33E7NQaX/sPquI6vcrqV5Fd/VRjBds7pvSJjjd9dezsw7YPf38I4Pej836woiJSH85I3S/UDmq/iampp48cUX2bBhA1FRUcyZM4cpU6YwePBg5zk7duwgODiYzz//nL179/L666/z6quver0uDw+Dzwsgs8QRpOJC89lVM6LLg07CcM/zHHmD/Px8xd+DoXRH4SdvlO4H3nXs9gCVm5vLwIEDGTBgAAAzZswgIyOjRYDKyMjgySefBGD69Om89NJL2O12VB4sJOgIapVjuCztjCO4nK6P5s4IeQUdT4iOvv6O40pA6Y7CT94o3Q+869jtfc2KigqioqKcf0dGRlJeXu72HK1Wi06no6amps3ytm3bRnJyMsnJyZSWllJVVUVpaSl6vZ7q6moKCwsxmUzk5eVhs9mc46PHjx8H4NsT2cTfYeOVUXm8dW8Rf7m7kPsiqykv1TM+pJT3plfx6f1FbH3IwHDysTVZycnJaVFG82Nubi5ms5mCggLq6uooKSmhoqKCiooKSkpKqKuro6CgALPZTG5ubptl5OTkYLVayc/Px2AwUFRU5LFTdnY2NpuNvLw8TCYThYWFVFdX89133zn/R0VFRRgMBvLz87Fa5euk1+tbOJ0/f15xTq7t1DyErSQn13a6dOmS4pyufVSa07XtdPr06VZOnaXb56C++OILjhw5wgsvvADArl27yM3NZcWKFc5zEhISePvtt51BasaMGXz88ceEhl5/N+3O7CThSnV1taI3clS6HyjfUfjJG6X7QduOslnFFxkZSVlZmfPv8vJyIiIi3J5jtVoxGAyEhIR0ed2MxutsCaEAlO4HyncUfvJG6X7gXcduD1AjR46kuLiYixcvYrFY2Lt3L5MnT25xzuTJk0lLSwNg//793H333V6ff2oLpa+uUbofKN9R+MkbpfuBdx27/b+l1WpZsWIFixYtIj4+ngceeIAhQ4bw5ptvkpGRAUBSUhI1NTXExsayefNmlixZ0i118/Pz65b38RVK9wPlOwo/eaN0P/Cuo6J2kpg0adIN5SJR+viw0v1A+Y7CT94o3Q/adrx06RKHDh3yuCxFBagbJTk5uVMTeXJB6X6gfEfhJ2+U7gfedVT+gKhAIBAIZIkIUAKBQCCQJJrf/va3/+3rSkiJH/3oR76uQpeidD9QvqPwkzdK9wPvOYo5KIFAIBBIEjHEJxAIBAJJIgKUQCAQCCSJogPUH//4R+677z4SExOdx86cOcPcuXNJTEzkqaeewmAwtHouISGBxMREzGYzAKdPnyYxMZHY2FjWrFmD3S6dUVFPHHfv3s2sWbOcPzExMeTn5wPSdfTEz2Kx8Nxzz5GYmEh8fDzvvPOO8zWZmZk89NBDxMbGtjjuazz1W7lyJYmJiTzyyCNkZWU5XyPV9isrK+M3v/kN8fHxJCQk8MEHHwBQW1vLggULiIuLY8GCBdTW1gJgt9tZs2YNsbGxJCUlkZeX5yxr586dxMXFERcXx86dO33icy2e+p07d465c+cyZswY3nvvvRZlSfUa9dRx9+7dJCUlkZSUxLx58zhz5oyzLE8dFR2gHn74YdavX9/iWEpKCkuWLCE1NZWpU6eyadMmwLHn3/Lly3n++ef57LPP2LRpE1qtIxvJ6tWrSUlJYc+ePRQXF5OZmdntLu7wxHHmzJls376d7du389JLL3HrrbcyfLgjR4hUHT3x27dvH42NjaSmpvKPf/yDbdu2odfrnTnI3nrrLXbu3MnevXspLCz0hU4rPPHbvn07AKmpqWzYsIF169Zhs9kA6bafRqNh2bJlpKWl8eGHH7J161YKCwvZuHEj48ePZ8+ePYwfP56NGzcCcOjQIYqLi9mzZw8pKSmsXr0acHwYrl+/no8++oiPPvqI9evXOz8QfYmnfiEhISxfvpxHH320RTlSvkY9dezfvz+bNm1ix44dPPHEE86NwTvjqOgANXbs2FabzBYVFTF27FgAJkyYwIEDBwA4cuQId9xxB8OGDQMgNDQUjUZDZWUlBoOB0aNHo1KpiI+P58svv+xekevgiaMre/fuJTY2FkDSjp74qVQqTCYTVqsVs9mMn58fOp2uRQ4yPz8/Zw4yKeCJX2FhIePHjwcgLCyM4OBgTp8+Len2Cw8PdyavCwoKIjo6mvLycjIyMnj44YcBR5Bubo+MjAzi4+NRqVSMGjWKq1evUllZyeHDh5kwYQIhISGEhIQwYcIEDh8+7DOvZjz1CwsLY+TIkc4vv81I+Rr11HH06NHOazomJsaZTqkzjooOUG0xZMgQ5z/liy++cO6aXlxcjEql4oknnmD27Nm8++67gCM3VWRkpPP1kZGRVFRUdH/FPcCdoyv//Oc/mTFjBiA/R3d+06dPJyAggJ/97Gfcf//9zJ8/n5CQkA7lIJMS7vyGDRtGRkYGVquVixcvkpeXR1lZmWzaT6/Xk5+fT0xMDJcvXyY8PBxwfABevnwZaDtfXHNuobaOS4mO+LlDLteop46pqalMnDgR6JzjTRegVq1axdatW5k9ezZGo9G5sWFTUxMnTpzg5Zdf5v333yc9PZ2vvvqqzbH87thZ/UZw59jMyZMn8ff3Z+jQoQCyc3Tnd+rUKdRqNenp6ezdu5fNmzdz4cIFxfglJiYSGRnJnDlzWLt2LaNGjUKj0cjCz2g0snTpUp599ll0Op3b89zNnUndsaN+7pC6H3ju+PXXX7Njxw6WLl0KdM6x21O++5pBgwaxYcMGwDGUcvDgQcARze+66y7nJoeTJk3iu+++Y+bMmS2ifHl5ufNbg1Rx59iM6/AetP4mI3VHd3579uxh4sSJ+Pn5ERYWxujRozl9+jRRUVHt5iCTEu78tFotzz77rPO8efPmcdtttxEcHCzp9rNYLCxdupS4uDimTZsGOIa6KisrCQ8Pp7KykrCwMMB9vrjIyMgWi0LKy8sZN25c94q4wRM/d3QkT54v8dTxzJkzpKSksH79emei2c443nQ9qOZuqM1mY8OGDcyePRuAe+65h4KCAuccxjfffMPgwYMJDw8nKCiInJwc7HY7aWlpTJkyxZcK7eLOsfnYvn37ePDBB53H5Obozq9v374cO3YMu92O0Wjk5MmTREdHdygHmZRw52cymZzJ4I4cOYJGo5H8NWq320lJSWHQoEHMnz/feXzy5MnOlXg7d+501nfKlCmkpaVht9vJyclBp9MRHh7Ovffey9GjR6mtraW2tpajR49y7733+sTJFU/93CHla9RTx9LSUpYuXcqaNWu4/fbbned3xlHRO0n8/ve/Jysri5qaGm655RZ+97vfYTQa2bp1KwBTp05lyZIlzm7mrl272LhxIyqVikmTJvHMM88AjiW8K1eupKGhgYkTJ7JixQrJdL89dczKyuLPf/4zH374YYtypOroiZ/RaGTlypWcO3cOu91OQkICv/71rwE4ePAgr7zyCk1NTSQmJrJw4UJfajnxxE+v17No0SJUKhURERGsWrXKTMYhuAAAAbtJREFUmV5Gqu2XnZ3N/PnzGTp0qDOR3eLFi4mJiWHZsmWUlpbSt29fXnvtNUJCQrDb7bz44oscPnwYf39/Vq9e7dw2JzU1lb///e8ALFiwoMXSfF/hqV9VVRXJycnU19ejVqsJCAhg586d6HQ6yV6jnjqmpKSwf/9+57Wp0Wicu5t76qjoACUQCAQC+XLTDfEJBAKBQB6IACUQCAQCSSIClEAgEAgkiQhQAoFAIJAkIkAJBAKBQJKIACUQCAQCSSIClEAgEAgkiQhQAoGEOHXqFElJSZjNZoxGIwkJCRQUFPi6WgKBTxA36goEEuONN96gsbGRhoYGoqKiePzxx31dJYHAJ4gAJRBIDIvFwpw5c+jZsydbtmxBo9H4ukoCgU8QQ3wCgcSora3FaDRSX1+P2Wz2dXUEAp8helACgcR4+umnefDBB9Hr9VRWVvLcc8/5ukoCgU8QPSiBQEKkpaWh0WiIi4vjscce4/Tp0xw7dszX1RIIfILoQQkEAoFAkogelEAgEAgkiQhQAoFAIJAkIkAJBAKBQJKIACUQCAQCSSIClEAgEAgkiQhQAoFAIJAkIkAJBAKBQJL8Pwl6CiPyyGHLAAAAAElFTkSuQmCC\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": 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z`vxoCn(km)3U|VbhA%b?wd~zt1q-&r24zMibt%@+*Xu)Bikdqlwjw+s<2@tifZm|a z@6gQ1#~aNNKlmv~0|L1pNP!*b361VE>5+{Cm*< ME|cKE?+M_40q?`|_y7O^ literal 0 HcmV?d00001 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", +"以色列","ISR","","高收入国家", +"意大利","ITA","","高收入国家", +"牙买加","JAM","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","JOR","中东与北非地区(不包括高收入)","中高等收入国家", +"日本","JPN","","高收入国家", +"哈萨克斯坦","KAZ","欧洲与中亚地区(不包括高收入)","中高等收入国家", +"肯尼亚","KEN","撒哈拉以南非洲地区(不包括高收入)","中低等收入国家", +"吉尔吉斯斯坦","KGZ","欧洲与中亚地区(不包括高收入)","中低等收入国家", +"柬埔寨","KHM","东亚与太平洋地区(不包括高收入)","中低等收入国家", +"基里巴斯","KIR","东亚与太平洋地区(不包括高收入)","中低等收入国家", +"圣基茨和尼维斯","KNA","","高收入国家", +"大韩民国","KOR","","高收入国家", +"科威特","KWT","","高收入国家", +"拉丁美洲与加勒比海地区(不包括高收入)","LAC","","", +"老挝","LAO","东亚与太平洋地区(不包括高收入)","中低等收入国家", +"黎巴嫩","LBN","中东与北非地区(不包括高收入)","中高等收入国家", +"利比里亚","LBR","撒哈拉以南非洲地区(不包括高收入)","低收入国家", +"利比亚","LBY","中东与北非地区(不包括高收入)","中高等收入国家", +"圣卢西亚","LCA","拉丁美洲与加勒比海地区(不包括高收入)","中高等收入国家", +"拉丁美洲与加勒比海地区","LCN","","", +"最不发达国家:联合国分类","LDC","","", +"低收入国家","LIC","","", +"列支敦士登","LIE","","高收入国家", +"斯里兰卡","LKA","南亚","中高等收入国家", +"中低等收入国家","LMC","","", +"中低收入国家","LMY","","", +"莱索托","LSO","撒哈拉以南非洲地区(不包括高收入)","中低等收入国家", +"後期人口紅利","LTE","","", +"立陶宛","LTU","","高收入国家", +"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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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
..................................................................
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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xHDhwoMjXCRWg6tevnyujaVGJj48XOi+N6H4gvqPi59iI7ge2HYu7u4ryiM8CW3uXiYTofiC+o+Ln2IjuB9I6KgHKgqysLHtXQVZE9wPxHRU/x0Z0P5DWUQlQFhRm+xdHRnQ/EN9R8XNsRPcDaR2FGoOyRXZ2NklJSdy/f7/Ac3NycswpBESkNPzc3d2pXbu21U7mpYW97ltaKH6Ojeh+IK2j8AEqKSmJihUrUr9+/QI39tRqteZkeCIit5/BYODWrVskJSXRoEED2e7zKDIzM23mNhIFxc+xcRQ/vQEiLsDq05ByF2pUhPGtYVATcC5gf2QpHYUPUPfv3y9UcALrRHqiIbefk5MTVatWNed9sgeO8J+/JCh+jo0j+OkNELYLDl6DrGzje6kaeOe/sPsSLAt+dJCS0lH8B6LYTsZmC9EzcJaGn5zpJwpDUlKSXe8vN4qfY+MIfhEXcgcnE1k6OHAVfrnw6OuldPxXBKjC4u7ubu8qyIrofgCNGjWydxVkRfFzbBzBb/Vp6+BkIksHq04/+nopHZUAZYFGo5GlXBcXF/z9/WnRogUjRowocBpmQbl6/v77b7y8vPD398ff359evXoBsGzZMtavXw/AunXrSE5OznWdyS80NJRt27blOpacnGwzH5Kjcf78eXtXQVYUP8fGEfxS7pbsuJSOSoCyIL9srCXFw8ODM2fOcO7cOcqVK8eyZctKXGaXLl04c+YMZ86cMafxnjx5sjkLra0A9Si/mjVrWgUtR6RVq1b2roKsKH6OjSP41ahYsuNSOsoWoK5fv864ceMYOHAggwcP5scffwQgPT2diRMnEhwczMSJE0lPT7d5fUREBMHBwQQHBxMRESFXNXNx79492e/RpUsXLl26BMDChQtp0aIFLVq0YNGiRVbnmtJemxg9ejS//PJLvmV/+OGHfPnll2zbto2TJ08yevRo/P39zT2nR/klJCTQokULwBjchg4dSp8+fWjcuDGzZ882n7dnzx4CAwMJCAhgxIgRZGZmFu0HIDOnTp2ydxVkRfFzbBzBb3xr8MxnPpWnK0xo/ejrpXSUbVqXi4sLs2bNolmzZty7d4+QkBACAwOJiIigQ4cOTJgwgVWrVrF69WpmzpyZ69r09HSWLl3K5s2bAQgJCaF79+54eXmVrFJLZ0D8mXwPFyuB9xP+MMU6uNhCp9Oxe/du+vTpw6lTp1i7di3Hjh3DYDDQoUMHunXrRuvWD1t/woQJfP311wwaNIj09HQOHz7MDz/8wMGDBzlw4AD+/v4AjBgxgnfffdd83fDhw/n+++/58ssvadu27UO/IqQoP3PmDKdPn+axxx6jSZMmTJs2DQ8PD+bOncvevXspX7488+fPZ+HChbz//vuFLldu2rRpY+8qyIri59g4gt+gJvBbHBxMhKoZV5gWN5f3W3yH82OedKkHA5s8+nopHWXrQVWrVo1mzZoBxj+MDRo04MaNG+zbt49BgwYBMGjQIPbt22d17aFDhwgMDMTLywsvLy8CAwM5dOiQXFU1k5OTI0u5Go0Gf39/2rZtS926dRk/fjwHDx5kyJAhlC9fngoVKjB06FCr3X67devGpUuXUKvVbNq0iWHDhpmnils+4rMMTo+iKD3EoKAgvLy8cHd3p1mzZly9epWjR48SExNDp06d8Pf354cffuDq1auF/0GUAo7wCbUkKH6OjSP4OTvB8v7wVXs1W04+S58b4XTzTOKzoIKnmIO0jqUyBqVSqYiNjaVly5bcunWLatWqAcYgduvWLavz1Wo11atXN3/v6+uLWq22WfbWrVsJCQkhJCSElJQUUlNTSUlJQaVSkZaWRnZ2Nnq9Ho1Gg2Hy19z76Ff48u9cr4YF+9B8shunr/7h/tzf0X2+lwfz9vBg3h6yP/sT7ad/kDP/v2g+2Y1hwT6yPv7tYRlTFpn/8GdlZaHX67l//z45OTlotVqys7Px8PDg2LFjnDp1igULFuDq6opWqwUeBg3TFHDTBAqNRkNOTg6jRo1i/fr1rFmzhhdeeAGdTseDBw8wGAxGJ4PBqgzT9/fv3zfXR6fT4ebmxoMHD9Dr9WRnZ5OTk2MuI+9jQNN2JVlZWTg7O5OVlYVOp6Nnz56cOHGCEydOcPr0aVasWGG+j6nupnqYflGjoqLQ6XTExsaSmZlJQkKCVTvFx8ej0WiIiYlBr9cTGRmZq4zIyEj0ej0xMTFoNBri4+NJS0tDpVKZ2z0hIYEmTZoQGxuLTqcjKioqVxmm1+joaLRaLXFxcWRkZJCYmIharUatVpOYmEhGRgZxcXFotVqio6NtllGaTpmZmWYn0wcUkZws26lNmzbCOVm+litXziGc0pISCFrxDDW0KtKmLmbJhIY00Ebh7FTw7161atWsnIqL7Bl1s7KyCA0NZdKkSfTq1YuOHTty+PBh8/G83wOsXbuWBw8eEBYWBhhnp3l4ePDSSy898l6hoaFW6Tb+7//+j6eeeqrQdZVjokSFChWsxmoiIyMJDQ3l6NGj5kd8//nPf2jdunWu82/cuEH79u2pXr06x44dA4yz+L788kt27dqVq8wPP/yQChUqMGvWLAYMGMDMmTPp0aOHlV9oaCj9+/fPNWsvISGB/v37c+7cOdatW8fJkyf5/vvvAejfvz+zZs2iefPmtGnThr/++otGjRqRlZVFUlISTz75ZK56FOVnLjXR0dH4+fnZ5d6lgeLn2JRFv7y7RtTxfMCKY8FUi9uH04c74On+RSrPlmPz5s3NQzZFQdYeVHZ2Nq+99hrBwcHmqdCWOw3cvHmTqlWrWl3n6+vL9evXzd/fuHHD3OuSk9JcJxQQEEBoaCjt27c3j8lZjj+Z8PX15amnnmLs2LFFKj80NJTJkyfnmiRh6RcWFkbt2rWpXbs2gYGBhSqzWrVqrFu3jlGjRtGyZUuefvppYmNji1QvuckbLEVD8XNsypqfadeId/6CaDXcytIzdu9L+FzYy7qeq9B3KFpwAmkdZetBGQwG3n33Xby8vHjzzTfN73/11Vd4eXmZJ0lkZGTYnCQREhKSa5LE5s2bC5wkUdIe1P3798vcYtasrCz8/PyIjIws8SSR0vKzZw8qLi6Oxo0b2+XepYHi59iUNb/wWGNwysoGDAY+Oj+d0Kvf81nTz1nf5E0+C4LBTYtWpi3HMteDOn36NDt37uTYsWMMHz6c4cOHs3//fsaPH8/Ro0cJDg7m6NGjjB8/HjAu7vrggw8A8PLyIiwsjFGjRjFq1CjCwsJKPoOvEJS1nYb37t1L06ZNmTZtmiT+Zc1PDnx9fe1dBVlR/BybsuZnuWvEq3EfE3r1e5Y3nMWyhrMLtWuELaR0lG2aeUBAgHmQLy+rVq2yeq958+Z89NFH5u+HDBnCkCFD5KqeTXQ6HS4uLqV6z0fRq1cvEhMTJSuvrPnJwZ07d3j88cftXQ3ZUPwcm7LmZ9oV4qWE75kZ9yFbao9lXtMv4H97aha0a4QtpHQUe/vuIiJ6MjHR/UD8/QYVP8emrPnVqAidLm3k4/PT+MN3EG/5rTAHJ9PxoiKloxKgFBQUFP6lvFPuN9pFvcSRKt2Y1voncpwfhoTC7BohN0qAskCv19u7CrIiuh9QqMzJjozi59iUKb/zhwhcN5xE75ZMa/cLWqeHPR9PVwq1a4QtpHRUApQFSsJCx6dSpUr2roKsKH6OjT39LNc7lb92ltX7+4NXbWp9t5v31I+zyiJ77oTWxuBU0K4RtpDSUfxBiSKQnZ1PEpQSUtR0Gx07diywzEWLFhVYTl7k8itL3Lhxw95VkBXFz7Gxl5/leqc7CZf57kBv7jqXZ5D/n7x82IeBTWDXKDg1yfg6uGnxghNI66gEKAtc3coRHgv9N0GbFcbX8Fhj45aEoqbbyLuzhi2KE6DKlStXpPMdkbp169q7CrKi+Dk29vIzZcktf/c6G449g5v+AWPa7+FSuXqFypJbFKR0VALU/9AbYNJOvXlFdarG+PrOf2HyryUPUiYKk27DlLDw77//pnv37gwfPpymTZsyevRoDAYD3377LcnJyfTo0YMePXqQk5NDaGgoLVq0wM/Pj6+//trmvcvU82+ZuHjxor2rICuKn2NjL7/Vp8E16w7rj/fGW3uD0Ha/EVfRuJl3cdc75YeUjuIPShSSiAtwJNnVKtVxlg7zJ4yirqjOS1HTbYBxwfP58+epWbMmnTp14tChQ0yfPp2FCxeyb98+vL29OXXqFCqVinPnzgHGdQi2kCshY1mirO1zJjWKn2NjL7+0tCxWnxhAo8z/Y1y7XZyp3CHX8eKsd8oPKR2VHtT/sFxRnZeSfsIobroNgPbt21O7dm2cnZ3x9/cnISHB6pyGDRty+fJlpk2bxu+//57vIrnSSMhobxwhnUFJUPwcm9Lw0xvINVQxaEM2XxwfSdu0Q8zw/5ED1Z61uqY4653ywyESFjoaBX2CKMknDNMYlCUGQ+GeGT722GPmr11cXNDpdFbnVK5cmaioKP744w8WL17Mli1bWLNmjdV5RUlY6Kg4QkK4kqD4OTZy+5kmQxy8ZvzA7WTQ897hsXRK/pV3/Zbxa82RVtdIvd7JIRIWOhoFfYKQ8hMGQNeuXdmxYwdZWVncu3eP8PBwunTpUujrK1asyN27xqiZmpqKXq9n2LBhfPLJJ+bcL3lRelCOj+Ln2MjtZ5oMYdr89ZNzrzAkeQNfNPmUjfXCKJdnp7OSrHfKD6UHJQPjWxsnRGRZd1BkWVFtmW4DyDfdRn5MmjSJvn37UqNGDRYtWsTYsWPNC3E/++wzm9coPSjHR/FzbOT2Mw9VGAy8HfsmYxKXseSJN1n8xNtgAN/yUMldmvVO+SGlo+wJC0uTkqTb0BtgQoSOIyrXXEHK9AmjMKmOyzpyJWTMiz3TbURFRdGqVSu73Ls0UPwcG7n92qwwzkCeFjeXWRfnsL7ey8xp/r15fz1vD+NaJzmx5VjcdBtKD+p/ODvByoEu7LyIZCuqyxoeHh72roLsNG/e3N5VkBXFz7GR269GRRgQ8w2zLs7h51pjeL/5dyXe/LWoSOmojEFZ8EB7n8FNpVtRXdb4N6yDMq0xExXFz7GR2+/j+2v4MGYGv/sO4Y2WazA4PfwTX1qbv0rp+K/oQRkMBpycCo4you+0UBp+hZ2dKBe1a9e26/3lRvFzbKT2s9xfr0XMFj49PpFTtXrzRutN5Bhy70wu9WSI/JDSUbYANWfOHPbv30+VKlUIDw8HYNasWeZ1PHfv3qVixYps27bN6trevXvj6emJi4sLLi4uxXp2acLd3Z1bt25RtWrVAoOU6An95PYzGAzcunXLrjlvUlNTzTtxiIji59hI6Wc5pfzppF/55NRoTlbuRFir7dSrZlyeYo+hCikdZQtQgwYNYtSoUbz77rvm97788kvz1wsWLHikxJo1a6hcuXKJ61G7dm2SkpK4efNmgefm5OQIHaBKw8/d3d2un4JF/uMGip+jI6WfaUp5q5R9LI0cxv893orxbXdy18kT7W34LKjku98UBykdZQtQbdu2RaVS2TxmMBj4448/WL16tVy3N+Pm5kaDBg0KdW5KSgo1atSQuUb2Q3Q/EH/HdsXPsZHSb/VpeFJ9jFUnB5Lo+QQvtv+du25ewMPdb+wRoKR0tMskiVOnTlG1alXq1atn87iTkxNhYWGMHDmSrVu3PrKsrVu3EhISQkhICCkpKaSmppKSkoJKpSItLY34+Hg0Gg0xMTHo9XrzIlbTYrLIyEj0ej0xMTHcv3+f+Ph40tLSUKlU5vISEhLIzMwkNjYWnU5HVFRUrjJMr9HR0Wi1WuLi4sjIyCAxMRG1Wo1arSYxMZGMjAzi4uLQarVER0fbLCMqKgqdTkdsbCyZmZkkJCSUyEmj0ZidTOWI5JS3nbKysoRzsmynK1euCOdk2U56vV44J8vXhIQEyZy6pkaw/kRfMj2q8k6XzdTyyqa1p4q65dLoWjGee/dKxylvO5l8LJ2Ki6zroFQqFVOnTjWPQZn45JNPqFu3Li+99JLN69RqNT4+Pty6dYtJkybx9ttv07Zt2wLvZ2sdVFFIS0uT5LFiWUV0PxDfUfFzbCTzS7pI2stduI8bwwMPkuRZ3+oUPx/jTOTSxpZjcddBlXoPSqfLaYnpAAAgAElEQVTTsXfvXnr37p3vOT4+PgBUrVqVoKAg8y7dcnP79u1SuY+9EN0PxHdU/BwbSfyuJ8CbvfB0NTC+416bwam0ppTbQso2LPUAdfToURo0aED16tVtHjftTWf6+vDhwzRq1KhU6lazZs1SuY+9EN0PxHdU/Byb4vqZdigPXXmNpCk9yUy/y4Epe6jZvCmeeWYSlOaUcltI2YayBajZs2fzwgsvkJCQQFBQENu3bwdg9+7d9OvXL9e5arWaKVOmAHDr1i1efPFFhg0bxvPPP0/Xrl3p3LmzXNXMhen5vqiI7gfiOyp+jk1x/EzTyb/+LYX3fwvice0tRrXbw7SL/jgB84KMj/O8PYyvnwXZd2s2KdtQ+L34ioJer8fZWdzNNUT3A/EdFT/Hpjh+4bHw5W416w50p6YmkTHt93CqSkfA2Fuy13Ty/LDl6DBjUGWZvDmbREN0PxDfUfFzbIrjt+XILVYd7EXtrARC2/1mDk4gfbp2KZCyDf8VWx0VloCAAHtXQVZE9wPxHRU/x6bIfnfT+GD3M9S/d5Fx7XZxvGpXq1OkTNcuBVK2odKDskBJlub4iO6o+Dk2RfK7lwHv9OGJjPOEtQnnkHcvm6eVxg7lRUHKNlQClAVKsjTHR3RHxc+xKYyf3gC/RGUSE9YX3cVI3uu0lcM1+to8157TyfNDSfkuE/mlShcF0f1AfEfFz7EpyE9vgGnhWfjOH8CTN48xrfUmfnp8IDo9uOSZlWfv6eT5IWUbKmNQFvj7+9u7CrIiuh+I76j4OTYF+e08d5/nfx5Mu9R/mOH/I7/VGA4YA1c5Z6hZETTZZTuZqpRtqPSgLIiNjbV3FWRFdD8Q31Hxc2we6fdAS82vh9Hp5p+80XINEbWez31YD5Xcy34yVSnbUAlQFhR213NHRXQ/EN9R8XNs8vXTZcO852iX9Btv+S1nW51Qm6eVtRl7tpCyDZUAZUFycrK9qyArovuB+I6Kn2Nj0+9/wYnDO1jW4Vs21Z2U7/VlbcaeLaRsQyVAWVClShV7V0FWRPcD8R0VP8fG0k9vgB3nszk05Tk4uJ0V7Rdxrcc0PPKZGVAWZ+zZQso2VAKUBVlZWfaugqyI7gfiOyp+jo3JT2+AlyOy8fziOTpd3c6HzRbxabVX+TkGKpbDKkiV1Rl7tpCyDZVZfBaIvAcYiO8H4jsqfo6Nye+XmGyGhj/HsynG4LS2wasAaHIALQxvBmduGMecyvKMPVtI2YZKgLLAzc3N3lWQFdH9QHxHxc+xcXNzA1021RY9R6c8wcmEJscYnOyRbFAKpGxDsT+uFJHMzEx7V0FWRPcD8R0VP8cm804azHv4WC9vcDLhCLP18kPKNlR6UBZ4e3vbuwqyIrofiO+o+Dkwumxqr58FR39hRftFrK1mOziBY8zWyw8p21DpQVmQlJRk7yrIiuh+IL6j4ud4WM7Wc/1fcLra81WHn62XH1K2oWwBas6cOXTr1o0hQ4aY31uyZAlBQUEMHz6c4cOHs3//fpvXHjx4kAEDBtCvXz9WrVolVxWtKK3U8vZCdD8Q31Hxcyzyztab2+wrYWbr5YeUbShbgBo0aBBLly61en/MmDFs27aNbdu20bWrdW6TnJwcPv30U5YsWUJERAS7d+8mPj5ermrm4vz586VyH3shuh+I76j4ORZ5Z+ultwkCjBMh7mph+FNlK127FEjZhrKNQbVt2xaVSlXk66Kjo6lbty516tQBoG/fvuzbt48nnnhC6ipa0apVK9nvYU9E9wPxHRW/soHeABEXYPXph1PBx7eGQZZTwbMfUG3RqNyz9W4/LMPRZ+vlh5RtWOpjUJs2bWLo0KHMmTOH9PR0q+NqtZrq1aubv/f19eXGjRv5lrd161ZCQkIICQkhJSWF1NRUUlJSUKlUpKWlER8fj0ajISYmBr1eb94K3pRUKzIyEr1eT0xMDMeOHSM+Pp60tDRUKpW5vISEBDIzM4mNjUWn0xEVFZWrDNNrdHQ0Wq2WuLg4MjIySExMRK1Wo1arSUxMJCMjg7i4OLRaLdHR0TbLiIqKQqfTERsbS2ZmJgkJCSVy0mg0ZqcDBw4I55S3nY4ePSqck2U77d27Vzgny3Yy/SvLTpfi43lzZxo/HVFhuJuClz6VilkJfPVPJl/tjOVBto6zp07AJ8PNs/V07ToD8E7N/1LBWUvQ43HUcMugWnbZcJLy797+/futnIqLU3R0tKHYVxeASqVi6tSphIeHA5CamkrlypVxcnLi+++/5+bNm3zyySe5rvnjjz84fPgwH330EQA7d+4kOjqad955p8D7hYaGcvLkSelFFBQU/pXY6im18oXt/wdZOuvzPVzgucYaBvw0hDaqP/iw5RLW1pmSb/l+PuL1oGzRvHlzNm/eXOTrSrUH5e3tjYuLC87OzgwbNoxz585ZnePr68v169fN39+4cQMfH59SqZ+SbtrxEd1R8Ss99AYI2wXv/AXRakjVGF83RNsOTgBo7/HMuv60Vu1htt8qq+A0xvuhn6PP1ssPh035fvPmTfPX//3vf23O9mjRogVXr14lKSmJ7Oxsdu/eTffu3Uulfkq6acdHdEfFr/SIuAAHr0FWdu7383vkVCE7g/XH+/D0rb+Z2Wo9m+uOtzrnP6lGPxFm6+WHQ6R8nz17Ni+88AIJCQkEBQWxfft2Fi5cyJAhQxg6dCgnTpxg9uzZgHHcacoU4ycNV1dX3nnnHSZPnszAgQPp3bt3qU09NT3zFRXR/UB8R8Wv9Fh92jo45cfj2Xf4z/FnCbhzhGmtNxFe+wWrc9xd4flq0cLM1ssPKdtQ1jGo0qakY1BarZbHHntMwhqVLUT3A/EdFb/So80K42O9gqj04BY/HnuWJnejeSVgC3uqD7Z5nrcHHH6p7PjJha02dIgxqLJOYmKivasgK6L7gfiOil/pUdB2Q05AVa2aTUd70jjzPJPa7sg3OJnKK0t+ciGloxKgLPD19bV3FWRFdD8Q31HxKz3GtzaOFdnCwwUmN0gh/GQPGmbF8ckzu6gd1K/A7YvKkp9cSOmobBZrwZ07d3j88cftXQ3ZEN0PxHdU/OQj75Ty6hWgYRWIvw0ai1l7nq4woEoSb4b3xOl+Mszfzactu6E3wI17cDAx9yw/ywkRSdfEbj+Qtg2VAGWBu7u7vasgK6L7gfiOip88mKaUW87aS9UYe0qNqhq/N62Dml43gWdWBOGUngqf7YHmHQHjhIfl/eGXC7DqtO1kg6K3H0jrqAQoBQWFfz35TSnX5Bh7UJ8FweCmQGIsvPUMaO/B/L3QpF2u852djOcNblp6dRcZZQzKgvv379u7CrIiuh+I76j4ycOjppRn6Yw9Ii6dhte7Qk42LPjbKjgVBtHbD6R1VHpQFlSqVMneVZAV0f1AfEfFTx4KymBb7cph2NIPynsZe061GhfrPqK3H0jrqPSgLHjUprQiILofiO+o+MnDo6aUd775J0sPPgOVfGDhgWIHJxC//UBaRyVAWVC3bl17V0FWRPcD8R0VP3nIb0p57+vhrD3ZH61PI2Nw8ilZ/URvP5DWUQlQFly8eNHeVZAV0f1AfEfFr+ToDRAeC/03GXeL6L/J+F6nurmD1NCk9SyJHME1nwAqfvs3VC75+h7R2w+kdVS2OlJQUPjXYGs6ORgDU+e60K+xccJE59OLeevMVNRPBuH9xQ6cPSvYr9ICoGx1JAFlaat/ORDdD8R3VPxKRn7TybN0xgW2TgYDu5jHW2emQuBAfBbukjQ4id5+IK2jMovPgrK01b8ciO4H4jsqfiXjkdPJsw3oVr4F576AHs/DG+vA1U3S+4vefuAg6TYcEdE/3YjuB+I7Kn4lI7/p5M6GHOaee5nh576A/pPhzf9IHpxA/PYDB05YWNYR/dON6H4gvqPiVzJsTScvl6Plu9OjGJO4jG0tZsO0JeAsz59G0dsPHKQHNWfOHLp168aQIUPM73311VcMGDCAoUOH8uqrr5KRkWHz2t69ezNkyBCGDx9OSEiIXFW0IioqqtTuZQ9E9wPxHRW/kpF3Onl53V3Wngimf8pWvmi+ANdJ88FJviyCorcfSOsoW4AaNGgQS5cuzfVeYGAg4eHhbN++nXr16rFq1ap8r1+zZg3btm0r1syP4tK8efNSu5c9EN0PxHdU/ErGoCbG2XqersZcTj8d7cHTt//mrdbruBQ0S/YU7KK3H0jrKFuAatu2LV5eXrne69ixI66uxo8vrVq1KnOrqi9dumTvKsiK6H4gvqPiVzTyrnka+BP0aQSL/BPYebwzT2bG8GnQDp4e+1KppGAXvf1AWke7zeILDw+nd+/eNo85OTkRFhYGwIgRIxgxYkSp1Kl27dqlch97IbofiO+o+BWe/FJo/LAjmvXHe+PlpMHpy7188L90GaWB6O0H0jraZZLEihUrcHFxoX///jaPr1+/ni1btrB06VJ++umnRy6+3bp1KyEhIYSEhJCSkkJqaiopKSmoVCrS0tKIj49Ho9EQExODXq8nMjISeDjTJDIyEr1eT0xMDMnJycTHx5OWloZKpTKXl5CQQGZmJrGxseh0OvMzVlMZptfo6Gi0Wi1xcXFkZGSQmJiIWq1GrVaTmJhIRkYGcXFxaLVaoqOjbZYRFRWFTqcjNjaWzMxMEhISSuSk0WjMTnFxccI55W0nlUolnJNlO505c0Y4J8t2Sk1Nlcwp4gJ4Zxid+njFUs01k7GarWw42IUcvYG/xm4jreZTsjvl/RmL0E6P+t27ePGilVNxkXUnCZVKxdSpUwkPDze/FxERwZYtW1i1ahUeHh4FlrFkyRI8PT0JDQ0t8NyS7iSRmpqKt7d3sa8v64juB+I7Kn6Fp/8miFY//D7oxk6WRI5E5VGXMe33UKV+PXaNkuRWhUb09gPbjg6xk8TBgwdZs2YN3333Xb7BKSsri3v37pm/Pnz4MI0aNSqV+mVn57OCTxBE9wPxHRW/wmO55mn4tXWsODWE2Ip+DA88iMqzXoEpNuRA9PYDaR1lG4OaPXs2J06c4M6dOwQFBfHKK6+watUqHjx4wKRJkwBo2bIl77//Pmq1mg8++IClS5dy69YtZsyYAUBOTg79+vWjc+fOclUzF3q9vlTuYy9E9wPxHRW/wlOjIqRmGQi7/CXvxM5mv/czhLXZTpZrBfPx0kb09gNpHWULUF988YXVe0OHDrV5ro+Pj3lKep06dfj555/lqtYj8fT0tMt9SwvR/UB8R8Wv8IxvlcPdb1/nxcvf8EuNEGb6ryfbuZzxPq4wobVktyo0orcfSOuo7CRhwe3bt+1dBVkR3Q/Ed1T8ColWw+BtIbx4+RvWPfEa01tvzBWcutRD9jVPthC9/UBaR2WzWAtq1qxp7yrIiuh+IL6j4mcbvcG4U/nq05CVeovvjg2i2c3D5ExaSKUWr9HitHFMqkZFY89pYBP51zzZQvT2A2kdlR6UBVeuXLF3FWRFdD8Q31Hxs8a03umdv+BOwhVW/NWJJ1JP8lqbzUxxf42BTWDXKDg1yfg6uKl9ghOI334graMSoCxo2rSpvasgK6L7gfiOip81phxPDVNPsf1wIN4P1LzQ4U/CfUdw4Cr8ckGGihYT0dsPpHVUApQFpkWQoiK6H4jvqPhZs/o0tFftZsuRbmid3RkWeIgTVboAxkSEq05LXcviI3r7gbSOSoCyICAgwN5VkBXR/UB8R8XPmvZnV7P65ACulH+SoR2PcKniU7mO22O9U36I3n4graMSoCwQPZmY6H4gvuO/3S/X5q/LDWyc/SHvn5rAQe9ejAz8B7V7Datr7LHeKT9Ebz9QUr7LhujJxET3A/Ed/81+lpu/Zmsf8Gn0FEKS1rCtzlje8VuO1sk6A6691jvlh+jtBw6SsNARMW2oKCqi+4H4jv9mP9NkCNesNH443peQpDUsavw+r/utJtvZjXIuuc+353qn/BC9/UBaR6UHZYG/v7+9qyArovuB+I7/Jj/LtU0pdyEzG3zS41l7Ipi6WZd5rdUPbK/9ovlc3/JQyb1srHfKD9HbD6R1VHpQFsTGxtq7CrIiuh+I7/hv8bNc2xStNuZxaqE+xI7DT1P1wU1Gd9hrDk4mNNllZ71TfojefiCtoxKgLGjQoIG9qyArovuB+I7/Fj/T4zxTosFBqo1sPNaTdLfKDO50lONVu1pdW5YmQ+SH6O0H0joqAcqC5ORke1dBVkT3A/Ed/y1+q0//LzgZDEyP+5hvz4zmdKWnGdLxCAnlG1tdV9YmQ+SH6O0H0joWOAa1ceNGgoOD8fLykuymZZUqVarYuwqyIrofiO8oop/lWJOTpgqG43DlDpTL0TI/egJDVT+yrdaLvO23ggcuj1ldXxYnQ+SHiO2XFykdC+xBpaamMmrUKF5//XUOHjyIwSBbAl67k5WVZe8qyIrofiC+o2h+eceaXHKyiFZDucxUNhzrxVDVjyx4ci6vt1qXKzi5u4K3B/j5wGdBsCy47I032UK09rOFlI4F9qCmT5/OtGnTOHz4MDt27GDevHn07t2boUOHUqdOHckqUhZwdhb7iafofiC+o2h+eceasg3ONMy8wNoTwVS/n8TU1pvYWfO5XNd4uhqD0mAH3NZOtPazhZSOhSrJyckJb29vvL29cXFxISMjg5kzZ7Jw4cJHXjdnzhy6devGkCFDzO+lp6czceJEgoODmThxIunp6TavjYiIIDg4mODgYCIiIoqgVHzc3KwX+omE6H4gvqNofuaxpv/RPOUgOw49TUVdOs93+ItdNoKTozzOs4Vo7WcLKR0LDFAbNmxg5MiRLFy4EH9/f7Zv386cOXPYvHkzf/755yOvHTRokDlTronVq1fToUMHfv31Vzp06MDq1autrktPT2fp0qVs3LiRjRs3snTp0nwDmZRkZmbKfg97IrofiO8omp95nzyDgbFXvuHzw8+R4lGbQZ2Oc6pKR8q7GR/jOeLjPFuI1n62kNKxwEd8aWlpLFq0yCoJlbOzM4sXL37ktW3btkWlUuV6b9++faxZswYwBrBx48Yxc+bMXOccOnSIwMBA88SMwMBADh06RL9+/Qo2KgHe3t6ylm9vRPcD8R1F86tRETIytXxy/hWeu7aav6sH83KrTdxzNc4Zb1DZuKZJFERrP1tI6VhgD2rq1Kn5Zkhs2LBhkW9469YtqlWrBkC1atW4deuW1TlqtZrq1aubv/f19UWtVtssb+vWrYSEhBASEkJKSgqpqamkpKSgUqlIS0sjPj4ejUZDTEwMer3evA2HaUPDyMhI9Ho9MTExXLlyhfj4eNLS0lCpVObyEhISyMzMJDY2Fp1OR1RUVK4yTK/R0dFotVri4uLIyMggMTERtVqNWq0mMTGRjIwM4uLi0Gq1REdH2ywjKioKnU5HbGwsmZmZJCQklMhJo9GYnWJiYoRzyttOly9fFs7Jsp1OnjzpsE7JKSmER6Yya0sCz6zO5P2tsXQun8yfkU8bg5PfOH7q8QVDq18EYGTVaCb4lW2nov5/ioyMLPPtVNK/EefPn7dyKi5O0dHRsk7LU6lUTJ06lfDwcAA6duzI4cOHzcfzfg+wdu1aHjx4QFhYGADLli3Dw8ODl1566ZH3Cg0NNf8HLg46nQ5XV3F3fxLdD8R3dFQ/y41eTWNOT2VEsfrkQKpq1bwdsI7tviGUc9LxwOBqHmty5Md5tnDU9isKthybN2/O5s2bi1xWqU8pqVq1Kjdv3gTg5s2bVK1a1eocX19frl+/bv7+xo0b5l6XnJw/f172e9gT0f1AfEdH9cs7W69Pyna2H+6IsyGH0Z0PUr5XCH4+8Fy180KMNeWHo7ZfUZDSsdQDVPfu3c2z8iIiIujRo4fVOZ06deLIkSOkp6eTnp7OkSNH6NSpk+x1a9Wqlez3sCei+4H4jo7ql3dniOWRw4it6MfATic4WbENZ24Yx5o+GdWqzO6jJwWO2n5FQUpHWQPU7NmzeeGFF0hISCAoKIjt27czfvx4jh49SnBwMEePHmX8+PGAMep+8MEHAHh5eREWFsaoUaMYNWoUYWFhpbKThejJxET3A/EdHdUv5S546O6x+HQIr1/8gG21XuS5p/82Jxg0zeZzVL/CIrofSOso+xhUaVLSMSgFBQVpyJsqo/KdeL47NoQn755nftPPWd5wFjg97CL5+Yg1W08hNw4zBlWWEf3Tjeh+IL6jI/jl3b6oxdXd/Px3W2rcT+Kl9rtZ/sQbuYKT5UavjuBXEkT3A2kdlQBlgejpmEX3A/EdHcHPNCFC80DPtLi5rD0RTJJnPfp3PsWBas/mOjfvzhCO4FcSRPcDJeW7bJjWHYiK6H4gvqMj+K0+Dc5ZGSw/NZRZF+cQUfN5hnY8zDVPY56gR2306gh+JUF0P5DWUewJ+UXkySeftHcVZEV0PxDf0RH8HlPFEnFoMPWzLvFhs0WsrT891yO9Cm7GrLe2cAS/kiC6H0jrqPSgLEhMTLR3FWRFdD8Q37Gs+ekNEB4L/TdBmxUw9/MdrP+rPZWybzO6w17WNng1V3CCR2e+LWt+UiO6H0jrqPSgLPD19bV3FWRFdD8Q37Es+VnuDnH/QQ6vX3ifqfHziKrUniltfkblXtvqmoIy35YlPzkQ3Q+kdVR6UBbcuXPH3lWQFdH9QHzHsuRnmgzx2L1U1p4IZmr8PDbVmcCIp/8hxaM25Vxyn1+YVBllyU8ORPcDaR2VHpQF7u7u9q6CrIjuB+I7liW/1aehqfoIiyNHUvWBmrf8lrOp7v8GlwzgWx4quRvXQdWoaOw5DWzy6B0iypKfHIjuB9I6KgFKQUGh6BgMdI/8hlfPvkGKex2GdjzCOa+AXKdosuHgWDvVT0EIlABlwf379+1dBVkR3Q/Ed7Snn2l3iE3H0pn093hmqX7md9/BvNFqLRlulazOf9RkiPxQ2s/xkdJRCVAWVKpk/Z9MJET3A/Ed7eVnmhCRGn2GhcdHUEdzhblPfcnKBjOtZulBwZMh8kNpP8dHSkdlkoQFN27csHcVZEV0PxDf0V5+EbEGfA+sZtP+p/HIySLk6b9Z2fD1fINTQZMh8kNpP8dHSkelB2VB3bp17V0FWRHdD8R3tIvf/Swqfvcycy/9wAHvXrzqv4Fbj/nkOsXd1bgAt7CTIfJDaT/HR0pHpQdlwcWLF+1dBVkR3Q/Edyx1v4TzMK09PS+tZ1Hj93mx/e9WwQke7g5R0lxOSvs5PlI6Kj0oC/z8/OxdBVkR3Q/EdywNP73B+EgvafNKJh59FU25x/m4++9sL/9svtcUZ0KELZT2c3ykdFR6UBaIvhW+6H4gvqPcfnoDzPj5Du7zQ5h2OIzjVbrwTKcodlV8Fpd8ekXFnRBhC6X9HB+HTlh45coV3njjDfP3SUlJvPLKK4wZM8b83okTJ5g+fTq1atUCICgoiClTphRYtpKwUEGhZPy9+yiNlz2Hz30VXzaZy/KGb2BwMn6OdXYCV2d4kPPwfNOECMsdyRUU8lLchIWl/oivQYMGbNu2DYCcnByCgoIICgqyOi8gIIDFixeXat1OnToldL4W0f1AfEcp/Syz3l7P0DM14QteiHyPFPc6jAg8wOnKT1udX5zdIYqC0n6Oj5SOdh2DOnbsGHXq1KFmzZr2rIYZ0X9xRPcD8R2lDE6mjV7L373O11Fj6JK6l101RvC23wqbC29B/t0hlPZzfIRJWLh792769u1r81hUVBTDhg1j8uTJXLp0Kd8ytm7dSkhICCEhIaSkpJCamkpKSgoqlYq0tDTi4+PRaDTExMSg1+uJjIwEHj4njYyMRK/XExMTw6lTp4iPjyctLQ2VSmUuLyEhgczMTGJjY9HpdERFReUqw/QaHR2NVqslLi6OjIwMEhMTUavVqNVqEhMTycjIIC4uDq1Wa07qlbeMqKgodDodsbGxZGZmkpCQUCInjUZjdjp8+LBwTnnb6eTJk8I5WbbTX3/9JYlTxAWolXmKtsl/8Peh5rS9fYitT8/htYANdPS+TjXXTDpWSOCJx1Jp4ZFCa08Vdcul0auy9E6W7RQVFSVEO+X3u7dv3z7hnPK206FDh6ycikupj0GZyM7OpmfPnoSHh+Pt7Z3rWGZmJs7Oznh6erJ//37mz5/Pr7/+WmCZJR2D0ul0uLqKO7FRdD8Q31Eqv6H/0dD/wFuMS/iWCxWa80rAZuIqNn/kNZ6uxgy4g5uW+Pb5orSf42PLsbhjUHbrQR04cICnnnrKKjgBVKhQAU9PTwC6du2KTqcjLS1N9jo9qqcmAqL7gfiOxfWzTCw46oszfB7elnEJ37K2/jQGdD5RqOBU3N0hioLSfo6PlI52C+WPeryXmppK1apVcXJyIjo6Gr1eXyp7WNWubZ1gTSRE9wPxHYvjZxpvOpyYw+gLC5l14V3Synkzpv3v7K/W2+Y1dR6XdzJEfijt5/hI6WiXAKXRaDhy5Ajvv/+++b0tW7YAMHLkSPbs2cOWLVtwcXHB3d2dBQsW4GRjzy+pSU1NpUKFCrLfx16I7gfiOxbHL+ICxF9IZNXJFwm8/Q+7qw/lLb8V3ClX1eb5nq4wK1DeR3n5obSf4yOlo10ClIeHBwcPHsz13siRI81fP//88zz//POlXS3hf3FE9wPxHYvjd2nbRsIPv4yLIYfXW65lW+2XbG7yCqX3KC8/lPZzfKR0FHu0rohkZ2fbuwqyIrofiO9YkJ/l2qbMW2l8eO4V3ri8iZOVOzLD/z9c82xodY2LE1R2L91Hefnxb28/EZDSUQlQFuj1entXQVZE9wPxHR/lZ7m2qZ3qd1ZGT8Bbe4MFT85l6RNvkuNs+797s2rGTV7LAv/m9hMFKR2VAGWBaeagqIjuB+I7Psov4gKcuZzOB2df57lrq7lYoRkT2+wgulLb/MuTcB89Kfg3t58oSOmoBCgLbt++TeXKle1dDdkQ3Q/EdzT5WT7KM820a3TlT3YcHU/1+yqWPPEmixp/iNbF3XytE5uaSPUAACAASURBVGC56NHe4022+Le0n8hI6agEKAvKypZLciG6H4jvWLNmzVyP8rKyobzuLs8df4MXEpdzqXwThnY8zJnKHayuLe8GDSqX/tTxovBvaD/RkdJRCVAWXLlyhWbNmtm7GrIhuh+I6WjZW3rKcIXD2mao7xl3Fe+U+l/mnx1PLU0iyxvO4qsnP0br4mGznAaVy85YU36I2H6WiO4H0joqAcqCpk3tsPCjFBHdD8RzzNtbOk9T9ICnLpM5sW/y4tUlXC7fmBGBBzhZpVO+5ZS1sab8EK398iK6H0jrqCQstODMmTP2roKsiO4H4jlGXHgYnABGeZ+hm/p39uxvwQtXl7KqwQz6djlTYHAqa2NN+SFa++VFdD+Q1lHpQVkQEBBg7yrIiuh+4NiOtiY+3Ln/MDhVfpBK271fM0/1I5fKN2FE4H5OVulsVY69timSAkduv8Iguh9I66j0oCwQPR2z6H7guI6mR3nv/AXRakjVGF+vZQAGA4NVG/jvP08xKHkT3zSaQ78uZ2wGJ9M2RbtGwalJxtfBTR0jOIHjtl9hEd0PpHVUelAWiJ5MTHQ/cFzHvI/yTNTKusq8c5PpfvN3Iit1YJTfSi487mezDEd6lJcfjtp+hUV0PxAoYWFZw5TUS1RE9wPHdVx9OndwcjbkMPbKN/y5vzntbh/gg2bfMKzjIdo2fHhSOWfj4zxvD/DzMeZqWhbsOL0lWzhq+xUW0f1AWkelB2WBv7+/vasgK6L7geM45h1vSrv/8Fjz9NN8em4yre8cZ1+1vrzbYikqz3oAbEo1+pl6S44ekPLiKO1XXET3A2kdlR6UBbGxsfaugqyI7geO4WhrvCnHABWz0/ng/KvsPNiW2lkJTPffQGi7X1F51qPO48Ze0vBqscL0lmzhCO1XEkT3A2kdlR6UBQ0aNLB3FWRFdD9wDEer8SaDgYHJPzHn/2birb3Bj/WmsKDJXDLcjNvFWOZn0mga4GF7Ha4QOEL7lQTR/UBaR7v1oHr37s2QIUMYPnw4ISEhVscNBgOfffYZ/fr1Y+jQocTExMhep+TkZNnvYU9E9wPHcLQcb2qYeYENx57huzPPk+Jem4GdjjOnxeJcwcly4oMj+JUExc/xkdLRrj2oNWvW5Lup4IEDB7h69Sq//vorZ8+eZe7cuWzcuFHW+lSpUkXW8u2N6H5Q9hxtrW26cgfcc7KYemkeYfFfoHHx5N0WS9hYdxJ6J5dH5mcqa35So/g5PlI6ltlHfPv27WPgwIE4OTnRqlUr7t69y82bN6lWrZps98zKyhJ6p2HR/aBsOebdpgggNcvAM+qdfHD+VepoEvi51hjmPbWA1Md8zdc9Kj9TWfKTA8XP8ZHS0W4BysnJibCwMABGjBjBiBEjch1Xq9VUr17d/L2vry9qtVrWAOXsLPacEdH9oGw55h1ranw3hvdjZtA19U8uVmjGyKf/5ljVbrmuKWjPvLLkJweKn+MjpaPdflrr169ny5YtLF26lJ9++omTJ0/mOm4wGPK5Mjdbt24lJCSEkJAQUlJSSE1NJSUlBZVKRVpaGvHx8Wg0GmJiYtDr9eY5+qbVzpGRkej1evPx+Ph40tLSUKlU5vISEhLIzMwkNjYWnU5HVFRUrjJMr9HR0Wi1WuLi4sjIyCAxMRG1Wo1arSYxMZGMjAzi4uLQarVER0fbLCMqKgqdTkdsbCyZmZkkJCSUyEmj0Zid7ty5I5xT3nYy3ae0na5cSSA8MpWXt6Qw5gcVYzensS8ynnJ6DSM9jvDh+en8caAlre6cYFf7WfTtcoZGTSrijJ4BlWKo5KKhV6V4+tdNo02F/H/3EhMThWin/H733NzchHOyfL127ZpwTnnbKS0tzcqpuDhFR0cXLhLIyJIlS/D09CQ0NNT83kcffUS7du3o168fAAMGDGDNmjWP7EGFhoZaBbqikJCQQP369Yt9fVlHdD+wj6OtR3kALnodo66t5PULc/DKTmNj3TC+avIxaeW8AahQjPxMoreh4uf42HJs3rw5mzdvLnJZdnnEl5WVhcFgoHz58mRlZXH48GEmT56c65wePXqwceNG+vbty9mzZ6lQoYKsj/cAvL29ZS3f3ojuB6XjmHfig7sb5vxMJgJT9/F+zAya3T3LkSrd+bD5N8Q+3jJXOcXJzyR6Gyp+jo+UjnYJULdu3WLGjBkA5OTk0K9fPzp37syWLVsAGDlyJF26dGH//v3069cPd3d35s6dK3u9kpKShM7XIrofyO9os7ekeXi8QeZF3rzwNn2vb+eaR33CAn7m9+pDwCl3t6i4+ZlEb0PFz/GR0rFMPOKTipI+4tPpdLi6ltmJjSVGdD+Q3zE81rgDRN5NXatq1bwa9zHPJy5H6+zO0ifeYmXDmTaz25ZkmyLR21Dxc3xsORb3EZ/4U0qKwPnz5+1dBVkR3Q+kddQbjAGp/yZos8L4+tWR3MHJPSeLqXGf8s/fjRiduIxNdSfRrcclvm/8rjk4mbYpkmJTV9HbUPFzfKR0VHpQCgo2yG/igwlnQw7Dk37g9QtzqK5N5nffwcxv+jmXK+TOdeHpagxIg8V+qqOg8EiUHpQEiJ5MTHQ/kM4xv/xMGAz0UP/G7gP+LDg7nmSPugwLPEBY23CbwUnq/Eyit6Hi5/goCQtlQvRkYqL7gXSOefMzATx9629mXXiPdmmHSPB8gikBW/mt+jDzBIhyzuBbATTZ8qVaF70NFT/HR0lYKBOif7oR3Q+K52hrrOnKnYfHW6cdZcPRXmw+2oM6WVd4p8VSenWL4bcaw83BydMVejSA/aHyploXvQ0VP8dHSkdlDErhX01+Y01OwFPpZ3j94hx6qXeRWq4aixu9w4a6YWhdPKjzOFRyL9oiWwWFfyvKGJQEmLYWERXR/aDojrbGmp7IjOX7yJHsPtiadrcPMr/JPLr0uMyaBjPQuniY8zPtGiVvb8kWoreh4uf4SOmojEFZ8OSTT9q7CrIiuh8U7Jh3F4jMbLiv+9+1d88x9dKnDEjeTJZLeb5pNIfVDWeS7lbJfL0cEx+KguhtqPg5PlI6KgHKgsTERBo3bmzvasiG6H7w0NFWHqax/rD7EhzK02NqkX6KaXGf0udGOJkuFVj2xGxWNnid249Vo4Ib+BVxv7zS8BMVxc/xkdJRCVAW+Pr6FnySAyO6HxgdbeZh0sBbeyHHYPwHEJB2hGlxc+l58zfSXSvxdeMPWFt/OunlHiZcK85+eXIiehsqfo6PlI5KgLLgzv+3d+5xUZb5Av/OBeUyAoZczEvhJT2uoWuaa+mmq1aCEZAr7uoe2y3NdsuP9vFsq7lxci0z67TbabN1M0u7uGmSqLmpxFlFzUgMUcIQBXTkqlwcZhhmmDl/zDINwgiDA/O+r8/38+Ez8M47zzxfnvczv3ku7/OrqSE4ONjX1egylO4HDsf0S8Ft3sPUaAPsdn5y5V88XbCaiZfTudyjD2uHvcSW237LVb+QFud3dr+8rkTpbSj85I83HUWAcsHf39/XVehSlO4HDseNh1sHJ43NyoNlO1h47lVG12ZR0TOS1f/xKh8MXIRJG9SqHF/PNblD6W0o/OSPNx1FgBLIHtf5pgAz5LnkRwuw1pN84V0eO/86A03nORc0lOUj3+bT/v/ZaiNXf60jR5MU5poEAoEIUC1oaGjwdRW6FCX6XTvfdHdQA0126GMuZ37Rm/yq+C16W67wTe97+NOI/2F/ZDx2Veu7K+SyZ54S29AV4Sd/vOkoApQLoaGh7Z8kY+Tu19bKvFGRkFkCxn8vFe9ZeZGXz/6JRP0Wetga2ReZwIZByzh+yz2Ao0fkp26ZXFCqw3ltIfc2bA/hJ3+86SgClAvl5eWKnsCUs5+7lXmnKkBjsxBb/hmPFv0v468cwqQOYHv/R3kn+hnO6364JyNQCxMHQuzQlkFOTsN5cm7DjiD85I83Hbs9QJWVlbFixQqqqqpQq9XMmjWLefPmtTgnKyuLxYsX069fPwCmTp3Kk08+2eV1GzhwYJe/hy+Rs19bOz6EmSv4ZckG5pa8Td8GPSUB0bwy4mW29F9InV9vADQq6O3fOhAlSnwozx1ybsOOIPzkjzcduz1AaTQali1bxogRI6ivryc5OZkJEyYwePDgFueNGTOGv/71r91at++//54777yzW9+zO5GLX1tDeTUN/w5Odjtjar7iV8VvEVf6CT1tjfyrz/08N3I9GRGxPHxLHnXVvZ1ljQiX1n1MN4pc2rCzCD/5403Hbg9Q4eHhhIeHAxAUFER0dDTl5eWtApQvUPqFIwc/d0N5IY1X+LV+C3MuvMPwq6cwaHR8PGAhm2//HYW6H7pDqdU/OErxPqYbRQ5teCMIP/njTUefbhar1+vJz88nJiam1XM5OTk88sgjLFq0iLNnz7otY9u2bSQnJ5OcnExpaSlVVVWUlpai1+uprq6msLAQk8lEXl4eNpuN7Oxs4Ict4bOzs7HZbOTl5XHs2DEKCwuprq5Gr9c7yysqKsJgMJCfn4/VaiUnJ6dFGc2Pubm5mM1mCgoKqKuro6SkhIqKCioqKigpKaGuro6CggLMZrNzQ8Vry8jJycFqtZKfn4/BYKCoqOiGnEwmk9Pp0KFDkndKy8zjyAUbiSHZYLfzvPVv/OXEXI6n9+W/85bg3wNeGPUmSxMy2ThmFcERvRgZUMqQnlXcoyvi8fCveDAkn1A/K08OyCF+mO+dPG2n6117Bw4ckEQ7edPJ9dpr/lGSk+tjenq64pyubaeDBw+2cuosPku3YTQaefTRR1m4cCHTpk1r8ZzBYECtVhMYGMjBgwdZu3Yte/bsabdMkW5DfrS1eWuIQU+i/gPmlLxDtPEstdoQPus3j48HLuC74FGtygjQwKwR8G25PBc+CARKR1bpNiwWC0uXLiUuLq5VcALQ6XQEBgYC8NOf/hSr1Up1dXWX10vpycSk5tc8nLfiSzh36Sr3FbzPxsxpfJU+gOX5f6DCvy9LRm1m3LRSnh/5ZpvBKVALP70dVk1xzDVtuOt4t6a/6G6k1obeRvjJH1mnfLfb7aSkpDBo0CDmz5/f5jlVVVWEhYWhUqnIzc3FZrN1y/0DSk/H7Eu/thY+/DjcClkHWFOyhQfKUgmwmSgOHMQbQ/9Iar95FAW13hG5vUSBog3ljfCTP9507PYAdeLECXbt2sXQoUOZNWsWAIsXL6asrAyA2bNns2/fPj755BM0Gg3+/v6sW7cOlarrvw7n5OQwalTrb+lKoTv82ktzYWq0Mbb6MHGl25hZ+gnh5nJq/Hqzvf98Uvv9iuO9JzjTqF9Lc6LA6+32INpQ3gg/+eNNR5Hy3QWr1YpWq9x7l7vaz136dH9VE6MvH2bGpW3MKPuUSHMpDeqe/F94LDv6/4qM8FgaNT2vW3bzbg9vx11/6E60obwRfvKnLcfOzkEp+z/lIWfPnmX4cJnewdkBvO13bW/J3w8q6h3bCGlsVsZVZxJbup0ZZZ8SYS6jQe1PRkQsn0fNIj1yJvXaXtctvzObt4o2lDfCT/5401EEKBf69+/v6yp0KZ3162h22uC6Gh6o/CfTyncxuXIvoZZqTOoAMiJi2dP353wZEYdRq+vQe3Z281bRhvJG+MkfbzqKAOVCVVUVOl3HPkDlSHt+HQ1Ertlp+xvOMq18F9MqdjHuyiH87FYu9+jD/sh40iMe4l/hD7QblFSA6zjzjWzeerO3odwRfvLHm44iQLmg9AunveDUkTTpwZYaJlzOYFLlfu69fIBB9QUA5Pcayd8G/RdfRszkRO/x2FSaDtXJ2/cw3cxtqASEn/zxpqMIUC5YLJb2T5Ixrn7Xmz9yxW5tZGz1USZWHWBi1X5G1WShwUa9Joivwibz/m1Pkx45kwuB0dd9b7UKtG7SXKya4r17lm6mNlQiwk/+eNNRBCgXbDabr6twQ7gbolMB734LvRttVPdoe9gOk+Mh0GpgTPVR7r5yiLuvHGJ0zTECbCaaUJMTejdvDnmOQ+HT+TZ0PBZ1jw7VqzvTXMi9DdtD+MkbpfuBdx1FgHKhefcKqePJXNGy/Y4A1WSHgT0CKalxGbaz2enbcJHRNV8ztjqTcVcy+VHdCbT2JppQkxc8mo8HLuBo2BS+CptMnV/HbpbuoYZIHZgs3Z/mQi5t2FmEn7xRuh9411EEKBeuXLlC79692z+xC2iv99PRRQtN9tblNnOnvYDBFVXE1GQRU/sNMbVZRJjLAWhQ+3MidDzrB/+Br2+ZRHboBAx+1086dr1hu/buV+oqfNmG3YHwkzdK9wPvOooA5cKtt97qtbLaCjiP/RgeugN2fe9576f5mLtA1OjSq/azNRJd/z3D63IZdvUUw67mMvzqSQaYih11Q0WhbjgH+zzAydBx5ISMIy94tNubZd0FIilmp/VmG0oR4SdvlO4H3nUUAcqF8+fPM2LECLfPdzToROkAFZy7Akar47VVJlh+AF46BIbGlsc70vtpxjUQhTZe5vb6s0TXF3Cb8SyD6s8w7OopBhvy8bM73sCi0nIuaBgnQn/CN8N/zsc9Z3IqZEy7N8k205FAJKXstO21odwRfvJG6X7gXcebfqsj16BTdtVGVC91h4MOOJZJ9+rZMuh4Bbud3pbL3Gq6QFTDRfo2XKSv6SIDTOe5rf4stxvPEmr5YYd3Gyr0AQP5vtdI8nvdyZled3Km10jOBQ1z9ozU2LC1s4H99eaP5IDNZkOt9mmasy5F+MkbpftB245iq6NO4Hrvz6jSDJ7WnWT3xR+z+VwvtgToqLT3opJemDSBVJna/oQ2NYHJ6P491PYmAq31BDYZCLIaCGoyEGg1oLPWcUtjFbc0VtHbUkWYuZLeFsfffczlRDVcxN9mblGWVaWh1H8ARUFD2NV3DkVBQzgfNJTiwCFcCIzGrPG/ru8v+nzLh1VjHPWS4PyRN/j2228ZM2aMr6vRZQg/eaN0P/Cu403dg0rNd+QiMlogLXMco2rbfq0NFY3qnljUfjSptFhVWqwqP6xqLU0qLWq7DY3ditZudTzarKhpws9mwd/W0G49GlV+XOkRzpUefaju0YeqnhGU+fen1L8/pf4DKA1w/F7VM/K6N8C2FXTUqpbzWCDd+SOBQKBMRA+qE2w88cOihKd+vJVfBmZy6HJ/gqxX/93buUqQ9So661V62MwuAciC1m5Fa7egtVlpUmloUmmdj82By6rSUq/VYdTo2ny80qMPV3qEU6/RuU0x0RaeLFr4zWhH0RtPwHjVcY7Z75Ls/JE3OH78uKJz7gg/eaN0P/Cu403dg7prg2ORghQQvR+BQKBUZJXyXSr0vWYh29w+2V36fgEaiAiEgGv6rYFamBYNr0yDOyOgT4Dj8bXp8Nr9LY+tmQp/m+no+ez+BRxfSIdTnGdnd62fFFC6o/CTN0r3A+86+qQHlZmZydq1a2lqaiIpKYnHH3+8xfONjY2sWLGCvLw8QkNDWbduHf369Wu33E7NQaX/sPquI6vcrqV5Fd/VRjBds7pvSJjjd9dezsw7YPf38I4Pej836woiJSH85I3S/UDmq/iampp48cUX2bBhA1FRUcyZM4cpU6YwePBg5zk7duwgODiYzz//nL179/L666/z6quver0uDw+Dzwsgs8QRpOJC89lVM6LLg07CcM/zHHmD/Px8xd+DoXRH4SdvlO4H3nXs9gCVm5vLwIEDGTBgAAAzZswgIyOjRYDKyMjgySefBGD69Om89NJL2O12VB4sJOgIapVjuCztjCO4nK6P5s4IeQUdT4iOvv6O40pA6Y7CT94o3Q+869jtfc2KigqioqKcf0dGRlJeXu72HK1Wi06no6amps3ytm3bRnJyMsnJyZSWllJVVUVpaSl6vZ7q6moKCwsxmUzk5eVhs9mc46PHjx8H4NsT2cTfYeOVUXm8dW8Rf7m7kPsiqykv1TM+pJT3plfx6f1FbH3IwHDysTVZycnJaVFG82Nubi5ms5mCggLq6uooKSmhoqKCiooKSkpKqKuro6CgALPZTG5ubptl5OTkYLVayc/Px2AwUFRU5LFTdnY2NpuNvLw8TCYThYWFVFdX89133zn/R0VFRRgMBvLz87Fa5euk1+tbOJ0/f15xTq7t1DyErSQn13a6dOmS4pyufVSa07XtdPr06VZOnaXb56C++OILjhw5wgsvvADArl27yM3NZcWKFc5zEhISePvtt51BasaMGXz88ceEhl5/N+3O7CThSnV1taI3clS6HyjfUfjJG6X7QduOslnFFxkZSVlZmfPv8vJyIiIi3J5jtVoxGAyEhIR0ed2MxutsCaEAlO4HyncUfvJG6X7gXcduD1AjR46kuLiYixcvYrFY2Lt3L5MnT25xzuTJk0lLSwNg//793H333V6ff2oLpa+uUbofKN9R+MkbpfuBdx27/b+l1WpZsWIFixYtIj4+ngceeIAhQ4bw5ptvkpGRAUBSUhI1NTXExsayefNmlixZ0i118/Pz65b38RVK9wPlOwo/eaN0P/Cuo6J2kpg0adIN5SJR+viw0v1A+Y7CT94o3Q/adrx06RKHDh3yuCxFBagbJTk5uVMTeXJB6X6gfEfhJ2+U7gfedVT+gKhAIBAIZIkIUAKBQCCQJJrf/va3/+3rSkiJH/3oR76uQpeidD9QvqPwkzdK9wPvOYo5KIFAIBBIEjHEJxAIBAJJIgKUQCAQCCSJogPUH//4R+677z4SExOdx86cOcPcuXNJTEzkqaeewmAwtHouISGBxMREzGYzAKdPnyYxMZHY2FjWrFmD3S6dUVFPHHfv3s2sWbOcPzExMeTn5wPSdfTEz2Kx8Nxzz5GYmEh8fDzvvPOO8zWZmZk89NBDxMbGtjjuazz1W7lyJYmJiTzyyCNkZWU5XyPV9isrK+M3v/kN8fHxJCQk8MEHHwBQW1vLggULiIuLY8GCBdTW1gJgt9tZs2YNsbGxJCUlkZeX5yxr586dxMXFERcXx86dO33icy2e+p07d465c+cyZswY3nvvvRZlSfUa9dRx9+7dJCUlkZSUxLx58zhz5oyzLE8dFR2gHn74YdavX9/iWEpKCkuWLCE1NZWpU6eyadMmwLHn3/Lly3n++ef57LPP2LRpE1qtIxvJ6tWrSUlJYc+ePRQXF5OZmdntLu7wxHHmzJls376d7du389JLL3HrrbcyfLgjR4hUHT3x27dvH42NjaSmpvKPf/yDbdu2odfrnTnI3nrrLXbu3MnevXspLCz0hU4rPPHbvn07AKmpqWzYsIF169Zhs9kA6bafRqNh2bJlpKWl8eGHH7J161YKCwvZuHEj48ePZ8+ePYwfP56NGzcCcOjQIYqLi9mzZw8pKSmsXr0acHwYrl+/no8++oiPPvqI9evXOz8QfYmnfiEhISxfvpxHH320RTlSvkY9dezfvz+bNm1ix44dPPHEE86NwTvjqOgANXbs2FabzBYVFTF27FgAJkyYwIEDBwA4cuQId9xxB8OGDQMgNDQUjUZDZWUlBoOB0aNHo1KpiI+P58svv+xekevgiaMre/fuJTY2FkDSjp74qVQqTCYTVqsVs9mMn58fOp2uRQ4yPz8/Zw4yKeCJX2FhIePHjwcgLCyM4OBgTp8+Len2Cw8PdyavCwoKIjo6mvLycjIyMnj44YcBR5Bubo+MjAzi4+NRqVSMGjWKq1evUllZyeHDh5kwYQIhISGEhIQwYcIEDh8+7DOvZjz1CwsLY+TIkc4vv81I+Rr11HH06NHOazomJsaZTqkzjooOUG0xZMgQ5z/liy++cO6aXlxcjEql4oknnmD27Nm8++67gCM3VWRkpPP1kZGRVFRUdH/FPcCdoyv//Oc/mTFjBiA/R3d+06dPJyAggJ/97Gfcf//9zJ8/n5CQkA7lIJMS7vyGDRtGRkYGVquVixcvkpeXR1lZmWzaT6/Xk5+fT0xMDJcvXyY8PBxwfABevnwZaDtfXHNuobaOS4mO+LlDLteop46pqalMnDgR6JzjTRegVq1axdatW5k9ezZGo9G5sWFTUxMnTpzg5Zdf5v333yc9PZ2vvvqqzbH87thZ/UZw59jMyZMn8ff3Z+jQoQCyc3Tnd+rUKdRqNenp6ezdu5fNmzdz4cIFxfglJiYSGRnJnDlzWLt2LaNGjUKj0cjCz2g0snTpUp599ll0Op3b89zNnUndsaN+7pC6H3ju+PXXX7Njxw6WLl0KdM6x21O++5pBgwaxYcMGwDGUcvDgQcARze+66y7nJoeTJk3iu+++Y+bMmS2ifHl5ufNbg1Rx59iM6/AetP4mI3VHd3579uxh4sSJ+Pn5ERYWxujRozl9+jRRUVHt5iCTEu78tFotzz77rPO8efPmcdtttxEcHCzp9rNYLCxdupS4uDimTZsGOIa6KisrCQ8Pp7KykrCwMMB9vrjIyMgWi0LKy8sZN25c94q4wRM/d3QkT54v8dTxzJkzpKSksH79emei2c443nQ9qOZuqM1mY8OGDcyePRuAe+65h4KCAuccxjfffMPgwYMJDw8nKCiInJwc7HY7aWlpTJkyxZcK7eLOsfnYvn37ePDBB53H5Obozq9v374cO3YMu92O0Wjk5MmTREdHdygHmZRw52cymZzJ4I4cOYJGo5H8NWq320lJSWHQoEHMnz/feXzy5MnOlXg7d+501nfKlCmkpaVht9vJyclBp9MRHh7Ovffey9GjR6mtraW2tpajR49y7733+sTJFU/93CHla9RTx9LSUpYuXcqaNWu4/fbbned3xlHRO0n8/ve/Jysri5qaGm655RZ+97vfYTQa2bp1KwBTp05lyZIlzm7mrl272LhxIyqVikmTJvHMM88AjiW8K1eupKGhgYkTJ7JixQrJdL89dczKyuLPf/4zH374YYtypOroiZ/RaGTlypWcO3cOu91OQkICv/71rwE4ePAgr7zyCk1NTSQmJrJw4UJfajnxxE+v17No0SJUKhURERGsWrXKTMYhuAAAAbtJREFUmV5Gqu2XnZ3N/PnzGTp0qDOR3eLFi4mJiWHZsmWUlpbSt29fXnvtNUJCQrDb7bz44oscPnwYf39/Vq9e7dw2JzU1lb///e8ALFiwoMXSfF/hqV9VVRXJycnU19ejVqsJCAhg586d6HQ6yV6jnjqmpKSwf/9+57Wp0Wicu5t76qjoACUQCAQC+XLTDfEJBAKBQB6IACUQCAQCSSIClEAgEAgkiQhQAoFAIJAkIkAJBAKBQJKIACUQCAQCSSIClEAgEAgkiQhQAoGEOHXqFElJSZjNZoxGIwkJCRQUFPi6WgKBTxA36goEEuONN96gsbGRhoYGoqKiePzxx31dJYHAJ4gAJRBIDIvFwpw5c+jZsydbtmxBo9H4ukoCgU8QQ3wCgcSora3FaDRSX1+P2Wz2dXUEAp8helACgcR4+umnefDBB9Hr9VRWVvLcc8/5ukoCgU8QPSiBQEKkpaWh0WiIi4vjscce4/Tp0xw7dszX1RIIfILoQQkEAoFAkogelEAgEAgkiQhQAoFAIJAkIkAJBAKBQJKIACUQCAQCSSIClEAgEAgkiQhQAoFAIJAkIkAJBAKBQJL8Pwl6CiPyyGHLAAAAAElFTkSuQmCC\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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file mode 100644 index 0000000..c11da52 --- /dev/null +++ b/GDP_analyse/Metadata_Indicator_API_NY.GDP.MKTP.CD_DS2_zh_csv_v2_424460.csv @@ -0,0 +1,2 @@ +"INDICATOR_CODE","INDICATOR_NAME","SOURCE_NOTE","SOURCE_ORGANIZATION", +"NY.GDP.MKTP.CD","GDP(现价美元)","以购买者价格计算的GDP是一个经济体内所有居民生产者创造的增加值的总和加上任何产品税并减去不包括在产品价值中的补贴。计算时未扣除资产折旧或自然资源损耗和退化。数据为现价美元。GDP的美元数据采用单一年份官方汇率从各国货币换算得出。对于官方汇率不反映实际外汇交易中所采用的有效汇率的少数国家,采用的是替代换算因子。","世界银行国民经济核算数据,以及经济合作与发展组织 国民经济核算数据文件。", diff --git a/GDP_analyse/Untitled.ipynb b/GDP_analyse/Untitled.ipynb new file mode 100644 index 0000000..2fd6442 --- /dev/null +++ b/GDP_analyse/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/GDP_analyse/test.txt b/GDP_analyse/test.txt new file mode 100644 index 0000000..ca789c3 --- /dev/null +++ b/GDP_analyse/test.txt @@ -0,0 +1,3 @@ +2009,52000000 +2010,936000000 +2011,3360000000 \ No newline at end of file diff --git a/GDP_regression/Country_data.csv 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()