CODEWITHSUNDEEP
pythonpandasseries
Dsto4
Dsto4

Reputation: 123

Add two pandas dataframes with different indices

I have two dataframes, each one having a lot of columns and rows. The elements in each row are the same, but their indexing is different. I want to add the elements of one of the columns of the two dataframes.

As a basic example consider the following two Series:

Sr1 = pd.Series([1,2,3,4], index = [0, 1, 2, 3])
Sr2 = pd.Series([3,4,-3,6], index = [1, 2, 3, 4])

Say that each row contains the same element, only in different indexing. I want to add the two columns and get in the end a new column that contains [4,6,0,10]. Instead, due to the indices, I get [nan, 5, 7, 1].

Is there an easy way to solve this without changing the indices?

I want output as a series.

Upvotes: 0

Views: 1829

Answers (4)

neehari
neehari

Reputation: 2612

You could use reset_index(drop=True):

Sr1 = pd.Series([1,2,3,4], index = [0, 1, 2, 3])
Sr2 = pd.Series([3,4,-3,6], index = [1, 2, 3, 4])

Sr1 + Sr2.reset_index(drop=True)

0     4
1     6
2     0
3    10
dtype: int64

Also,

pd.Series(Sr1.values + Sr2.values, index=Sr1.index)

Upvotes: 2

jpp
jpp

Reputation: 164623

One way is to use reset_index on one or more series:

Sr1 = pd.Series([1,2,3,4], index = [0, 1, 2, 3])
Sr2 = pd.Series([3,4,-3,6], index = [1, 2, 3, 4])

res = Sr1 + Sr2.reset_index(drop=True)

0     4
1     6
2     0
3    10
dtype: int64

Upvotes: 1

root
root

Reputation: 103

You could use the .values, which gives you a numpy representation, and then you can add them like this:

Sr1.values + Sr2.values

Upvotes: 0

Rakesh
Rakesh

Reputation: 82765

Using zip

Ex:

import pandas as pd

Sr1 = pd.Series([1,2,3,4], index = [0, 1, 2, 3])
Sr2 = pd.Series([3,4,-3,6], index = [1, 2, 3, 4])

sr3 = [sum(i) for i in zip(Sr1, Sr2)]
print(sr3)

Output:

[4, 6, 0, 10]

Upvotes: 0

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