Combining two Series into a DataFrame in pandas

I have two Series s1 and s2 with the same (non-consecutive) indices. How do I combine s1 and s2 to being two columns in a DataFrame and keep one of the indices as a third column?

Solution 1:

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  s2
A   1   3
B   2   4

In [4]: pd.concat([s1, s2], axis=1).reset_index()
Out[4]:
  index  s1  s2
0     A   1   3
1     B   2   4

Note: This extends to more than 2 Series.

Solution 2:

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())

Solution 3:

Pandas will automatically align these passed in series and create the joint index They 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        s2
0      1 -0.176143  0.128635
1      2 -1.286470  0.908497
2      4 -0.995881  0.528050
3      5  0.402241  0.458870
4      6  0.380457  0.072251

Solution 4:

If I may answer this.

The fundamentals behind converting series to data frame is to understand that

1. At conceptual level, every column in data frame is a series.

2. And, every column name is a key name that maps to a series.

If you keep above two concepts in mind, you can think of many ways to convert series to data frame. One easy solution will be like this:

Create two series here

import pandas as pd

series_1 = pd.Series(list(range(10)))

series_2 = pd.Series(list(range(20,30)))

Create an empty data frame with just desired column names

df = pd.DataFrame(columns = ['Column_name#1', 'Column_name#1'])

Put series value inside data frame using mapping concept

df['Column_name#1'] = series_1

df['Column_name#2'] = series_2

Check results now

df.head(5)