Reindexing Multiindex dataframe - pandas

Question

I have Multiindex dataframe and I want to reindex it. However, I get 'duplicate axis error'.
Product Date col1
A September 2019 5
October 2019 7
B September 2019 2
October 2019 4
How can I achieve output like this?
Product Date col1
A January 2019 0
February 2019 0
March 2019 0
April 2019 0
May 2019 0
June 2019 0
July 2019 0
August 2019 0
September 2019 5
October 2019 7
B January 2019 0
February 2019 0
March 2019 0
April 2019 0
May 2019 0
June 2019 0
July 2019 0
August 2019 0
September 2019 2
October 2019 4
First I tried this:
nested_df = nested_df.reindex(annual_date_range, level = 1, fill_value = 0)
Secondly,
nested_df = nested_df.reset_index().set_index('Date')
nested_df = nested_df.reindex(annual_date_range, fill_value = 0)

Answer 1

You should do the following for each month:
df.loc[('A', 'January 2019'), :] = (0)
df.loc[('B', 'January 2019'), :] = (0)

Answer 2

Let df1 be your first data frame with non-zero values. The approach is to create another data frame df with zero values and merge both data frames to obtain the result.
dates = ['{month}-2019'.format(month=month) for month in range(1,9)]*2
length = int(len(dates)/2)
products = ['A']*length + ['B']*length
Col1 = [0]*len(dates)
df = pd.DataFrame({'Dates': dates, 'Products': products, 'Col1':Col1}).set_index(['Products','Dates'])
Now the MultiIndex is converted to datetime:
df.index.set_levels(pd.to_datetime(df.index.get_level_values(1)[:8]).strftime('%m-%Y'), level=1,inplace=True)
In df1 you have to do the same, i.e. change the datetime multiindex level to the same format:
df1.index.set_levels(pd.to_datetime(df1.index.get_level_values(1)[:2]).strftime('%m-%Y'), level=1,inplace=True)
I did it because otherwise (for example if datetimes are formatted like %B %y) the sorting of the MultiIndex by months goes wrong. Now it is sufficient to merge both data frames:
result = pd.concat([df1,df]).sort_values(['Products','Dates'])
The final move is to change the datetime format:
result.index.set_levels(levels = pd.to_datetime(result.index.get_level_values(1)[:10]).strftime('%B %Y'), level=1, inplace=True)