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-likeReturns : 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.
