How to move pandas data from index to column after multiple groupby

Method #1: reset_index()

>>> g              uses  books               sum    sumtoken   year             xanthos 1830     3      3        1840     3      3        1868     2      2        1875     1      1[4 rows x 2 columns]>>> g = g.reset_index()>>> g     token  year  uses  books                   sum    sum0  xanthos  1830     3      31  xanthos  1840     3      32  xanthos  1868     2      23  xanthos  1875     1      1[4 rows x 4 columns]

Method #2: don't make the index in the first place, using as_index=False

>>> g = dfalph[['token', 'year', 'uses', 'books']].groupby(['token', 'year'], as_index=False).sum()>>> g     token  year  uses  books0  xanthos  1830     3      31  xanthos  1840     3      32  xanthos  1868     2      23  xanthos  1875     1      1[4 rows x 4 columns]

I defer form the accepted answer.While there are 2 ways to do this, these will not necessarily result in same output. Specially when you are using Grouper in groupby

• index=False
• reset_index()

example df

+---------+---------+-------------+------------+| column1 | column2 | column_date | column_sum |+---------+---------+-------------+------------+| A       | M       | 26-10-2018  |          2 || B       | M       | 28-10-2018  |          3 || A       | M       | 30-10-2018  |          6 || B       | M       | 01-11-2018  |          3 || C       | N       | 03-11-2018  |          4 |+---------+---------+-------------+------------+

They do not work the same way.

df = df.groupby(    by=[        'column1',        'column2',        pd.Grouper(key='column_date', freq='M')    ],    as_index=False).sum()

The above will give

+---------+---------+------------+| column1 | column2 | column_sum |+---------+---------+------------+| A       | M       |          8 || B       | M       |          3 || B       | M       |          3 || C       | N       |          4 |+---------+---------+------------+

While,

df = df.groupby(    by=[        'column1',        'column2',        pd.Grouper(key='column_date', freq='M')    ]).sum().reset_index()

Will give

+---------+---------+-------------+------------+| column1 | column2 | column_date | column_sum |+---------+---------+-------------+------------+| A       | M       | 31-10-2018  |          8 || B       | M       | 31-10-2018  |          3 || B       | M       | 30-11-2018  |          3 || C       | N       | 30-11-2018  |          4 |+---------+---------+-------------+------------+

You need to add drop=True:

df.reset_index(drop=True)df = df.groupby(    by=[        'column1',        'column2',        pd.Grouper(key='column_date', freq='M')    ]).sum().reset_index(drop=True)