我有一個使用 Pandas 的 Python 程式，它讀取兩個資料幀，在以下鏈接中獲得：
Casos-positivos-diarios-en-San-Nicolas-de-los-Garza-Promedio-movil-de-7-dias：https ://datamexico.org/es/profile/geo/san-nicolas-de-los-加爾薩#covid19-evolucion
Denuncias-segun-bien-afectado-en-San-Nicolas-de-los-GarzaClic-en-el-grafico-para-seleccionar：https ://datamexico.org/es/profile/geo/san-nicolas-de-洛斯加薩#seguridad-publica-denuncias
我目前想要做的是“covid”資料框中的一個 groupby 具有相同的日期，有這些的總和。無論如何，沒有任何方法可以解決，它會定期列印一個錯誤，表明我應該使用“PeriodIndex”的語法。有沒有人有建議或解決方案？提前致謝。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
#csv for the covid cases
covid = pd.read_csv('Casos-positivos-diarios-en-San-Nicolas-de-los-Garza-Promedio-movil-de-7-dias.csv')
#csv for complaints
comp = pd.read_csv('Denuncias-segun-bien-afectado-en-San-Nicolas-de-los-GarzaClic-en-el-grafico-para-seleccionar.csv')
#cleaning data in both dataframes
#keeping only the relevant columns
covid = covid[['Month','Daily Cases']]
comp = comp[['Month','Affected Legal Good', 'Value']]
#changing the labels from spanish to english
comp['Affected Legal Good'].replace({'Patrimonio': 'Heritage', 'Familia':'Family', 'Libertad y Seguridad Sexual':'Sexual Freedom and Safety', 'Sociedad':'Society', 'Vida e Integridad Corporal':'Life and Bodily Integrity', 'Libertad Personal':'Personal Freedom', 'Otros Bienes Jurídicos Afectados (Del Fuero Común)':'Other Affected Legal Assets (Common Jurisdiction)'}, inplace=True, regex=True)
#changing the month types to dates
covid['Month'] = pd.to_datetime(covid['Month'])
covid['Month'] = covid['Month'].dt.to_period('M')
covid
uj5u.com熱心網友回復：
您可以簡單地使用 group by statement.Timegrouper 默認將其轉換為日期時間
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
#csv for the covid cases
covid = pd.read_csv('Casos-positivos-diarios-en-San-Nicolas-de-los-Garza-Promedio-movil-de-7-dias.csv')
covid = covid.groupby(['Month'])['Daily Cases'].sum()
covid = covid.reset_index()
# #changing the month types to dates
covid['Month'] = pd.to_datetime(covid['Month'])
covid['Month'] = covid['Month'].dt.to_period('M')
covid
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