["python","pandas","group-by","multi-index"]
How to move pandas data from index to column after multiple groupby

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.
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]