update pics display

COVID-19/Project/Project/Project.py

@@ -5,7 +5,7 @@ import numpy as np
 import datetime
 import math
 from scipy import interpolate
-plt.rcParams['font.sans-serif'] = ['SimHei']   
+ 
 plt.rcParams['axes.unicode_minus'] = False   
 
 #打开数据文件
@@ -20,10 +20,11 @@ dataset = dataset.fillna(0)
 
 dataset['Date'] = pd.to_datetime(dataset['Date'])
 
-#对China数据进行可视化操作
-China = dataset[dataset['Country/Region'] == 'China'].reset_index(drop=True) 
+#对China\US数据进行可视化操作
+China = dataset[dataset['Country/Region'] == 'China'].reset_index(drop=True)
+US = dataset[dataset['Country/Region'] == 'US'].reset_index(drop=True)
 
-#计算每日治愈率/确诊人数/治愈人数（分月份显示）
+#计算中\美每日治愈率/确诊人数/治愈人数（分月份显示）
 dfChinaJan = China[China['Date'].dt.month == 1]
 dfChinaFeb = China[China['Date'].dt.month == 2]
 dfChinaMar = China[China['Date'].dt.month == 3]
@@ -37,21 +38,68 @@ dfChinaOct = China[China['Date'].dt.month == 10]
 dfChinaNov = China[China['Date'].dt.month == 11]
 dfChinaDec = China[China['Date'].dt.month == 12]
 
-confirmed = [dfChinaJan['Confirmed'].sum(),dfChinaFeb['Confirmed'].sum(),dfChinaMar['Confirmed'].sum(),dfChinaApr['Confirmed'].sum(),
+dfUSJan = US[US['Date'].dt.month == 1]
+dfUSFeb = US[US['Date'].dt.month == 2]
+dfUSMar = US[US['Date'].dt.month == 3]
+dfUSApr = US[US['Date'].dt.month == 4]
+dfUSMay = US[US['Date'].dt.month == 5]
+dfUSJun = US[US['Date'].dt.month == 6]
+dfUSJul = US[US['Date'].dt.month == 7]
+dfUSAug = US[US['Date'].dt.month == 8]
+dfUSSept = US[US['Date'].dt.month == 9]
+dfUSOct = US[US['Date'].dt.month == 10]
+dfUSNov = US[US['Date'].dt.month == 11]
+dfUSDec = US[US['Date'].dt.month == 12]
+
+confirmedChina = [dfChinaJan['Confirmed'].sum(),dfChinaFeb['Confirmed'].sum(),dfChinaMar['Confirmed'].sum(),dfChinaApr['Confirmed'].sum(),
 			 dfChinaMay['Confirmed'].sum(),dfChinaJun['Confirmed'].sum(),dfChinaJul['Confirmed'].sum(),dfChinaAug['Confirmed'].sum(),
 			 dfChinaSept['Confirmed'].sum(),dfChinaOct['Confirmed'].sum(),dfChinaNov['Confirmed'].sum(),dfChinaDec['Confirmed'].sum()]
-recovered = [dfChinaJan['Recovered'].sum(),dfChinaFeb['Recovered'].sum(),dfChinaMar['Recovered'].sum(),dfChinaApr['Recovered'].sum(),
+recoveredChina = [dfChinaJan['Recovered'].sum(),dfChinaFeb['Recovered'].sum(),dfChinaMar['Recovered'].sum(),dfChinaApr['Recovered'].sum(),
 			 dfChinaMay['Recovered'].sum(),dfChinaJun['Recovered'].sum(),dfChinaJul['Recovered'].sum(),dfChinaAug['Recovered'].sum(),
 			 dfChinaSept['Recovered'].sum(),dfChinaOct['Recovered'].sum(),dfChinaNov['Recovered'].sum(),dfChinaDec['Recovered'].sum()]
-recoverPoss = []
-for i in range(len(confirmed)):
-	recoverPoss.append(recovered[i] / confirmed[i])
-date = pd.date_range(start='2020-01', periods=12, freq="m").strftime('%Y-%m')
+recoverPossChina = []
+
+confirmedUS = [dfUSJan['Confirmed'].sum(),dfUSFeb['Confirmed'].sum(),dfUSMar['Confirmed'].sum(),dfUSApr['Confirmed'].sum(),
+			 dfUSMay['Confirmed'].sum(),dfUSJun['Confirmed'].sum(),dfUSJul['Confirmed'].sum(),dfUSAug['Confirmed'].sum(),
+			 dfUSSept['Confirmed'].sum(),dfUSOct['Confirmed'].sum(),dfUSNov['Confirmed'].sum(),dfUSDec['Confirmed'].sum()]
+recoveredUS = [dfUSJan['Recovered'].sum(),dfUSFeb['Recovered'].sum(),dfUSMar['Recovered'].sum(),dfUSApr['Recovered'].sum(),
+			 dfUSMay['Recovered'].sum(),dfUSJun['Recovered'].sum(),dfUSJul['Recovered'].sum(),dfUSAug['Recovered'].sum(),
+			 dfUSSept['Recovered'].sum(),dfUSOct['Recovered'].sum(),dfUSNov['Recovered'].sum(),dfUSDec['Recovered'].sum()]
+recoverPossUS = []
+
+for i in range(len(confirmedChina)):
+	recoverPossChina.append(recoveredChina[i] / confirmedChina[i])
+	recoverPossUS.append(recoveredUS[i] / confirmedUS[i])
+
+#x轴为日期（月份）
+date = pd.date_range(start='2020-01', periods=12, freq="m").strftime('%m')
 
 fig = plt.figure()
-ax1 = fig.add_subplot(111)
-ax1.plot(date,confirmed,recovered)
+
+#中国
+ax1 = fig.add_subplot(221)
+ax1.set_title("Recovered/Confirmed/Recovery Possibility(month) of China",verticalalignment="bottom",fontsize="13")
+data1 = ax1.plot(date,confirmedChina,color="red",linewidth="1.7",label="confirmed")
+data2 = ax1.plot(date,recoveredChina,color="lime",linewidth="1.7",label="recovered")
+ax1.set_ylabel('recovered/confirmed')
 ax2 = ax1.twinx()
-ax2.plot(date,recoverPoss)
+ax2.set_ylabel('recovery possibility')
+data3 = ax2.plot(date,recoverPossChina,color="blue",linestyle="--",linewidth="1.0",label="recovery possibility")
+datasChina = data1 + data2 + data3
+labsChina = [d.get_label() for d in datasChina]
+ax1.legend(datasChina, labsChina, loc="lower right")
+
+#美国
+ax3 = fig.add_subplot(223)
+ax3.set_title("Recovered/Confirmed/Recovery Possibility(month) of US",verticalalignment="bottom",fontsize="13")
+data3 = ax3.plot(date,confirmedUS,color="red",linewidth="1.7",label="confirmed")
+data4 = ax3.plot(date,recoveredUS,color="lime",linewidth="1.7",label="recovered")
+ax3.set_ylabel('recovered/confirmed')
+ax4 = ax3.twinx()
+ax4.set_ylabel('recovery possibility')
+data5 = ax4.plot(date,recoverPossUS,color="blue",linestyle="--",linewidth="1.0",label="recovery possibility")
+datasUS = data3 + data4 + data5
+labsUS = [d.get_label() for d in datasUS]
+ax3.legend(datasUS, labsUS, loc="upper left")
 
 plt.show()