Finding the intersection between two series in Pandas

Question

I have two series s1 and s2 in pandas and want to compute the intersection i.e. where all of the values of the series are common.

How would I use the concat function to do this? I have been trying to work it out but have been unable to (I don’t want to compute the intersection on the indices of s1 and s2, but on the values).

Asked By: user7289

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Source

Answer 1

Place both series in Python’s set container then use the set intersection method:

s1.intersection(s2)

and then transform back to list if needed.

Just noticed pandas in the tag. Can translate back to that:

pd.Series(list(set(s1).intersection(set(s2))))

From comments I have changed this to a more Pythonic expression, which is shorter and easier to read:

Series(list(set(s1) & set(s2)))

should do the trick, except if the index data is also important to you.

Have added the list(...) to translate the set before going to pd.Series as pandas does not accept a set as direct input for a Series.

Answered By: Joop

Answer 2

If you are using Pandas, I assume you are also using NumPy. Numpy has a function intersect1d that will work with a Pandas series.

Example:

pd.Series(np.intersect1d(pd.Series([1,2,3,5,42]), pd.Series([4,5,6,20,42])))

will return a Series with the values 5 and 42.

Answered By: jbn

Answer 3

Setup:

s1 = pd.Series([4,5,6,20,42])
s2 = pd.Series([1,2,3,5,42])

Timings:

%%timeit
pd.Series(list(set(s1).intersection(set(s2))))
10000 loops, best of 3: 57.7 µs per loop

%%timeit
pd.Series(np.intersect1d(s1,s2))
1000 loops, best of 3: 659 µs per loop

%%timeit
pd.Series(np.intersect1d(s1.values,s2.values))
10000 loops, best of 3: 64.7 µs per loop

So the numpy solution can be comparable to the set solution even for small series, if one uses the values explicitly.

Answered By: eldad-a

Answer 4

Python

s1 = pd.Series([4,5,6,20,42])
s2 = pd.Series([1,2,3,5,42])

s1[s1.isin(s2)]

R

s1  <- c(4,5,6,20,42)
s2 <- c(1,2,3,5,42)

s1[s1 %in% s2]

Edit: Doesn’t handle dupes.

Answered By: Glen Thompson

Answer 5

pd.merge can be used:

pd.merge(series1, series2, how='inner').drop_duplicates()

Note that the result is a dataframe.

Answered By: kvb

Answer 6

Here’s another solution by checking both left and right inclusions

import pandas as pd

def intersect(left, right):
    left, right = pd.Series(pd.unique(left)), pd.Series(pd.unique(right))
    right = right.loc[right.isin(left)]
    left  =  left.loc[left.isin(right)]
    return pd.Series(pd.unique(left))

left = pd.Series([1,2,pd.NA, pd.NA, pd.NA], index=[*"abcde"], dtype="Int32")
right = pd.Series([pd.NA, pd.NA, 1, 3], index=[*"efgh"], dtype="Int32")
intersect(left, right)

This has 2 major advantages:

It works with pandas Int32 and other nullable data types. If your columns contain pd.NA then np.intersect1d throws an error!
It keeps the pandas dtype intact

Answered By: Hyperplane

Finding the intersection between two series in Pandas

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