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

Question

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)                 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.

Answer 1

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]