Combining two Series into a DataFrame in pandas Combining two Series into a DataFrame in pandas python python

Combining two Series into a DataFrame in pandas


I think concat is a nice way to do this. If they are present it uses the name attributes of the Series as the columns (otherwise it simply numbers them):

In [1]: s1 = pd.Series([1, 2], index=['A', 'B'], name='s1')In [2]: s2 = pd.Series([3, 4], index=['A', 'B'], name='s2')In [3]: pd.concat([s1, s2], axis=1)Out[3]:   s1  s2A   1   3B   2   4In [4]: pd.concat([s1, s2], axis=1).reset_index()Out[4]:  index  s1  s20     A   1   31     B   2   4

Note: This extends to more than 2 Series.


Why don't you just use .to_frame if both have the same indexes?

>= v0.23

a.to_frame().join(b)

< v0.23

a.to_frame().join(b.to_frame())


Pandas will automatically align these passed in series and create the joint indexThey happen to be the same here. reset_index moves the index to a column.

In [2]: s1 = Series(randn(5),index=[1,2,4,5,6])In [4]: s2 = Series(randn(5),index=[1,2,4,5,6])In [8]: DataFrame(dict(s1 = s1, s2 = s2)).reset_index()Out[8]:    index        s1        s20      1 -0.176143  0.1286351      2 -1.286470  0.9084972      4 -0.995881  0.5280503      5  0.402241  0.4588704      6  0.380457  0.072251


matomo