Is it possible to use a groupby operation with Pandas datetimeindex? When grouping the index below, I would like to return the date intersection for each of the groupby objects. I know there is pandas.DatetimeIndex.intersection - however this seems best suited for comparisons of only two datetimeindexes. The data I am working with may have zero to many rows with various date ranges I am examining for a date intersection.
# example data
df = pd.DataFrame([['2020-01-01', '2020-12-31'],
['2020-01-01', '2020-02-29'],
['2020-01-01', '2020-12-31'],
['2020-06-01', '2020-12-31'],
['2020-08-01', '2020-10-31']], columns=['start', 'end'], index=list('AABBB'))
# apply period range for each row
df['period_range'] = df.apply(lambda x: pd.date_range(x['start'], x['end']), axis=1)
# example intersection I would like to see for index A
pd.date_range('2020-01-01', '2020-12-31').intersection(pd.date_range('2020-01-01', '2020-2-28'))
A - datetime index representing the months of Jan - Feb
B - datetime index representing the months of Aug - Oct
In your case do with Month after date_range , then we find the intersection of list
df['period_range'] = df.apply(lambda x: pd.date_range(x['start'], x['end'],freq='m').strftime('%B'), axis=1)
out = df.groupby(level=0)['period_range'].apply(lambda x : set.intersection(*map(set,x)))
A {January, February}
B {September, October, August}
Name: period_range, dtype: object