Python | Pandas Index.intersection()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas Index.intersection() function form the intersection of two Index objects. This returns a new Index with elements common to the index and other, preserving the order of the calling index.

Syntax: Index.intersection(other)

Parameters :
other : Index or array-like

Returns : intersection : Index

Example #1: Use Index.intersection() function to find the set intersection of two Indexes.

# importing pandas as pd
import pandas as pd

# Creating the first Index
idx1 = pd.Index(['Labrador', 'Beagle', 'Mastiff',
                     'Lhasa', 'Husky', 'Beagle'])

# Creating the second Index
idx2 = pd.Index(['Labrador', 'Great_Dane', 'Pug',
           'German_sepherd', 'Husky', 'Pitbull'])

# Print the first and second Index
print(idx1, '\n', idx2)

Output :

Now we find the set intersection of the two Indexes.

# Find the elements common to both the Indexes
idx2.intersection(idx1)

Output :

As we can see in the output, the Index.intersection() function has returned the intersection of the two indexes. The ordering of the labels has been maintained based on the calling Index.

Example #2: Use Index.intersection() function to find the set intersection of two Indexes. The Index contains NaN values.

# importing pandas as pd
import pandas as pd

# Creating the first Index
idx1 = pd.Index(['2015-10-31', '2015-12-02', None, '2016-01-03',
                      '2016-02-08', '2017-05-05', '2014-02-11'])

# Creating the second Index
idx2 = pd.Index(['2015-10-31', '2015-10-02', '2018-01-03',
           '2016-02-08', '2017-06-05', '2014-07-11', None])

# Print the first and second Index
print(idx1, '\n', idx2)

Output :

Now we find the intersection of idx1 and idx2.

# find intersection and maintain
# ordering of labels based on idx1
idx1.intersection(idx2)

Output :

Note : The missing values in both the indexes are considered common to each other.