Import PANDAS AS PD pd.options.display.max_rows = 10 # The number of lines displayed DF1 = pd.read_csv (R & lt ' E: \ anacondatest \ PythonData \ of PM25 \ Beijing_2009_HourlyPM25_created20140709.csv ' , encoding = ' GBK ' ) # converts the data into a timestamp type pd.Timestamp (DF1 [ " a Date (the LST) " ] [0]) # establish datetimeindex objects df1idx = df1.set_index (pd.to_datetime (DF1 [ " a Date (the LST) " ])) # -based index Quick slicing Print (df1idx [ " 2018-11-1 " : "2018-11-5 " ]) # make the time series basic process Print (df1idx.index.hour) # directly fetches the corresponding level index df1idx.groupby (df1idx.index.month) .max () # directly groupby Summary # value processing sequence deletions using REINDEX df2idx = df1.set_index (pd.to_datetime (DF1 [ " a Date (the LST) " ])) # set the index IDX = pd.date_range (Start = ' 2009-2-1 00:00:00 ' , End = ' 2009-12-31 00:00:00 ' ) # custom a sequence index df2idx.reindex (IDX) # reset index custom index df2idx [df2idx.index.duplicated ()] #Re-check the data df2idx [~ df2idx.index.duplicated ()]. REINDEX (IDX, Method = ' bfill ' ) # the data as an index to re-re