I have the following pandas dataframe:
dfalph.head()
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)
dfalph.head()
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]