Pandas Boolean indexing

by Online Tutorials Library July 14, 2022

Boolean indexing

Boolean indexing is defined as a very important feature of numpy, which is frequently used in pandas. Its main task is to use the actual values of the data in the DataFrame. We can filter the data in the boolean indexing in different ways, which are as follows:

Access the DataFrame with a boolean index.
Apply the boolean mask to the DataFrame.
Masking the data based on column value.
Masking the data based on the index value.

Example1

This example shows the working of how to access the DataFrame with a boolean index:

  # importing pandas as pd  import pandas as pd  # dictionary of lists   dict = {‘name’:[“Smith”, “William”, “Phill”, “Parker”],           ‘age’: [“28”, “39”, “34”, “36”]}   info = pd.DataFrame(dict, index = [True, True, False, True])   print(info)  

Output:

name      age  True     Smith     28  True     William   39  False    Phill     34  True     Parker    36

Example2

This example shows the working of how to access the DataFrame with a boolean index by using .loc[]

  # importing pandas as pd  import pandas as pd  # dictionary of lists   dict = {‘name’:[“Smith”, “William”, “Phill”, “Parker”],           ‘age’: [“28”, “39”, “34”, “36”]}   info = pd.DataFrame(dict, index = [True, True, False, True])   # accessing a dataframe using .loc[] function    print(info.loc[True])  

Output:

name    age  True   Smith     28  True   William   39  True   Parker    36

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Pandas Boolean indexing

Boolean indexing

Example1

Example2

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