Back ArrowExplore all kits

Boolean indexing in pandas Dataframes

share link

by vigneshchennai74 dot icon Updated: Feb 27, 2023

technology logo
technology logo

Solution Kit Solution Kit  

Boolean indexing is a technique used in pandas, a popular data analysis library for Python, to filter a DataFrame or a Series based on a set of Boolean conditions. It involves creating a Boolean array (or a Series with Boolean values) that specifies which rows of data meet certain criteria and then using that array to select or filter the desired rows of data. 


Boolean indexing allows for quick and efficient data manipulation, making it easier to perform data analysis tasks. Here are some ways Boolean indexing can help: 

  • Filtering data: Boolean indexing enables us to filter rows of a DataFrame or Series based on specific conditions. For instance, we can filter all rows where a specific column value exceeds a certain threshold, or multiple columns meet certain criteria. This makes it easier to analyze data subsets that meet specific conditions. 
  • Locating specific values: We can use Boolean indexing to locate specific values within a DataFrame or Series. This is useful when performing specific operations on a specific data point. For instance, we can search for all rows where a specific column value equals a particular value. 


Boolean indexing can be a powerful tool for selecting and filtering data based on complex conditions in pandas. Here is an example of how to create Boolean indexing: 

Preview of the output that you will get on running this code.

Code

In this code we have used Pandas Library

Boolean indexing in pandas dataframes

PythondotLines of Code : 46dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
capt=[True, False, True, True]
nm=pd.Series(['p1','p2', 'p3', 'p4'], index=capt)
ag=pd.Series([12,17,14, 19], index=capt)
sc=pd.Series([120, 130, 150, 100], index=capt)

Cricket=pd.DataFrame({"Name":nm,"Age":ag ,"Score":sc})
print(Cricket) 
      Name  Age  Score
True    p1   12    120
False   p2   17    130
True    p3   14    150
True    p4   19    100

print(pd.Series(['p1','p2', 'p3', 'p4'])) 
0    p1
1    p2
2    p3
3    p4
dtype: object

print(pd.Series(['p1','p2', 'p3', 'p4'], index=capt)) 
True     p1
False    p2
True     p3
True     p4
dtype: object

capt=[True, False, True, True]
nm=pd.Series(['p1','p2', 'p3', 'p4'])
ag=pd.Series([12,17,14, 19])
sc=pd.Series([120, 130, 150, 100])

Cricket=pd.DataFrame({"Name":nm,"Age":ag ,"Score":sc})
print(Cricket) 
  Name  Age  Score
0   p1   12    120
1   p2   17    130
2   p3   14    150
3   p4   19    100

print (Cricket[capt])
  Name  Age  Score
0   p1   12    120
2   p3   14    150
3   p4   19    100
  1. Copy this code using "Copy" button above and paste it in your Python ide
  2. Import Pandas library of python.
  3. Run the code to get a Boolean indexing in a dataframe


I hope you have found this useful. I have added the dependent library and version information in the following section.


I found this code snippet by searching "Boolean indexing in pandas dataframes" in kandi. you can try any use case.

Dependent Library


pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Support
    Quality
      Security
        License
          Reuse

            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have Pandas that is required to run this code you can install it by clicking on th above link and copying the pip install command from the pandas page in Kandi. You can search for any dependent library in Kandi like Pandas.

                      Environment Tested

                      In this solution we have used the following versions. Be mindful to change when working with other versions.


                      • This solution is created and tested using Vscode version 1.75.1
                      • This solution is created using Python version 3.7.15
                      • This solution is Tested using Pandas 1.5.2


                      Using this solution we can able to Indexing a Dataframe with Boolean using Pandas library in python with simple Steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to index a Dataframe in Python.

                      Support


                      1. For any support on kandi solution kits, please use the chat
                      2. For further learning resources, visit the Open Weaver Community learning page.

                      See similar Kits and Libraries

                      Open Weaver – Develop Applications Faster with Open Source

                      kandi

                      Community and Support

                      Company

                      Follow

                      • © 2023 Open Weaver Inc.