1 概述

本文主要分析china,usa,king,japan,russia5個國家的gdp，時間從1800-2040，后面的數據為預測數據，不準確。

2 繪制折線圖

import pandas as pd import matplotlib.pyplot as pltdata = pd.read_csv("line_animation.csv") x = data.timechina = data.china usa = data.usa king= data.king japan= data.japan russia= data.russiaplt.plot(x, china,'k*-.',x, usa,'g*-.',x, king,'y*-.',x, japan,'r*-.',x, russia,'b*-.') plt.legend(["china","usa","king","japan","russia"]) plt.show()

3 計算增速

使用pct_change函數 import pandas as pd import matplotlib.pyplot as pltdata = pd.read_csv("line_animation.csv") # 1800 - 2020 中間還是220年 start_year = 1949-1800 end_year = 2020-1800 data = data.iloc[start_year :end_year]x = data.time china = data.china.pct_change(1).fillna(0).apply(lambda x: round(x * 100, 2)).values usa = data.usa.pct_change(1).fillna(0).apply(lambda x: round(x * 100, 2)).values king = data.king.pct_change(1).fillna(0).apply(lambda x: round(x * 100, 2)).values japan = data.japan.pct_change(1).fillna(0).apply(lambda x: round(x * 100, 2)).values russia = data.russia.pct_change(1).fillna(0).apply(lambda x: round(x * 100, 2)).valuesplt.plot(x, china, 'k*-.',x, usa, 'g*-.',x, king, 'y*-.',x, japan, 'r*-.',x, russia, 'b*-.') plt.legend(["china", "usa", "king", "japan", "russia"]) plt.show()

4 五國綜合分析

使用柱狀圖進行分析均值、方差、最大值、最小值

import pandas as pd import matplotlib.pyplot as plt data=pd.read_csv("line_animation.csv") plt.rcParams['font.sans-serif']=['SimHei']china_m=data.describe()["china"]["mean"] usa_m=data.describe()["usa"]["mean"] king_m=data.describe()["king"]["mean"] japan_m=data.describe()["japan"]["mean"] russia_m=data.describe()["russia"]["mean"] plt.subplot(221) plt.bar(["china","usa","king","japan","russia"],[china_m,usa_m,king_m,japan_m,russia_m],width=0.5,bottom=0,align='edge',color='g',edgecolor='r',linewidth=2) plt.text(0-0.3 ,china_m +0.05,str(round(china_m,2))) plt.text(0+0.7,usa_m,str(round(usa_m,2))) plt.text(0+1.7 ,king_m ,str(round(king_m,2))) plt.text(0+2.7 ,japan_m ,str(round(japan_m,2))) plt.text(0+3.7 ,russia_m ,str(round(russia_m,2))) plt.title("均值")china_a=data.describe()["china"]["max"] usa_a=data.describe()["usa"]["max"] king_a=data.describe()["king"]["max"] japan_a=data.describe()["japan"]["max"] russia_a=data.describe()["russia"]["max"] plt.subplot(222) plt.bar(["china","usa","king","japan","russia"],[china_a,usa_a,king_a,japan_a,russia_a],width=0.5,bottom=0,align='edge',color='g',edgecolor='r',linewidth=2) plt.title("最大值")china_i=data.describe()["china"]["min"] usa_i=data.describe()["usa"]["min"] king_i=data.describe()["king"]["min"] japan_i=data.describe()["japan"]["min"] russia_i=data.describe()["russia"]["min"] plt.subplot(223) plt.bar(["china","usa","king","japan","russia"],[china_i,usa_i,king_i,japan_i,russia_i],width=0.5,bottom=0,align='edge',color='g',edgecolor='r',linewidth=2) plt.title("最小值")china_s=data.describe()["china"]["std"] usa_s=data.describe()["usa"]["std"] king_s=data.describe()["king"]["std"] japan_s=data.describe()["japan"]["std"] russia_s=data.describe()["russia"]["std"] plt.subplot(224) plt.bar(["china","usa","king","japan","russia"],[china_s,usa_s,king_s,japan_s,russia_s],width=0.5,bottom=0,align='edge',color='g',edgecolor='r',linewidth=2) plt.title("方差") plt.show()

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