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

group-by, multi-index, pandas, python

Solution

Method #1: `reset_index()`

>>> g
              uses  books
               sum    sum
token   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    sum
0  xanthos  1830     3      3
1  xanthos  1840     3      3
2  xanthos  1868     2      2
3  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  books
0  xanthos  1830     3      3
1  xanthos  1840     3      3
2  xanthos  1868     2      2
3  xanthos  1875     1      1

[4 rows x 4 columns]

Problem

I have the following pandas dataframe: ``` token year uses books 386 xanthos 1830 3 3 387 xanthos 1840 1 1 388 xanthos 1840 2 2 389 xanthos 1868 2 2 390 xanthos 1875 1 1 ``` I aggregate the rows with duplicate `token` and `years` like so: ``` dfalph = dfalph[['token','year','uses','books']].groupby(['token', 'year']).agg([np.sum]) dfalph.columns = dfalph.columns.droplevel(1) ``` which results in ``` uses books token year xanthos 1830 3 3 1840 3 3 1867 2 2 1868 2 2 1875 1 1 ``` Instead of having the 'token' and 'year' fields in the index, I would like to return them to columns and have an integer index.

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