plot confirmed/recovered/recovery possibility of each month

COVID-19/Project/Project/Project.py

@@ -1 +1,61 @@
+import pandas as pd                                                                                                                                                                                                                                                                                                                                                          
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+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
+dataset = pd.read_csv('E:\dase intro\COVID-19Analysis\COVID-19\covid-19-all.csv')
+
+#数据预处理
+def parse_ymd(s):
+	year_s, mon_s, day_s = s.split('-')
+	return datetime.datetime(int(year_s), int(mon_s), int(day_s)).strftime("%Y-%m-%d")
+
+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) 
+
+#计算每日治愈率/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]
+dfChinaApr = China[China['Date'].dt.month == 4]
+dfChinaMay = China[China['Date'].dt.month == 5]
+dfChinaJun = China[China['Date'].dt.month == 6]
+dfChinaJul = China[China['Date'].dt.month == 7]
+dfChinaAug = China[China['Date'].dt.month == 8]
+dfChinaSept = China[China['Date'].dt.month == 9]
+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(),
+			 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(),
+			 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')
+data_1={
+	'confirmed':confirmed,
+	'recovered':recovered,
+	'recoverPoss':recoverPoss
+	}
+data_1_df = pd.DataFrame(data_1, index=date)
+ax = data_1_df.plot(secondary_y=['recovery possibility'], x_compat=True, grid=True)
+ax.set_ylabel('confirmed/recovered')
+ax.grid(linestyle="--", alpha=0.3)
+
+plt.show()