I got the following dataframe,df, with the report_date as the index:

And I want to extract 2021-06 and 2021-08 only. How could I achieve this goal?

I can just extract two individual dataframe by df['2021-06'] and df['2021-08']

CodePudding user response：

For match values is possible convert DatetimeIndex to months periods and test membership by Index.isin:

#if necessary
#df.index = pd.to_datetime(df.index)

df3 = df[df.index.to_period('m').isin(pd.to_datetime(['2021-06','2021-08']).to_period('m'))]
print (df3)
              sales
report_date        
2021-06-30   130000
2021-06-30   140000
2021-08-31   110000
2021-08-31   110000

Or:

df3 = df[df.index.to_period('m').isin(pd.PeriodIndex(['2021-06','2021-08'], freq='m'))]
print (df3)
              sales
report_date        
2021-06-30   130000
2021-06-30   140000
2021-08-31   110000
2021-08-31   110000

Or convert values to strings YYYY-MM and test by strings in list:

df3 = df[df.index.strftime('%Y-%m').isin(['2021-06','2021-08'])]
print (df3)
              sales
report_date        
2021-06-30   130000
2021-06-30   140000
2021-08-31   110000
2021-08-31   110000

CodePudding user response：

Just do

df = df.reset_index()
new_df = df[(df["report_date"] == "2021-06") | (df["report_date"] == "2021-07")]

or using numpy

new_df = df.iloc[np.where((df.index == "2021-06") | (df.index == "2021-07"))[0], :]

Output -