How to Remove Columns from a PySpark DataFrame that Match Another DataFrame

Learn how to efficiently `remove records` from a PySpark DataFrame based on matching values in a second DataFrame using PySpark functionalities.
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This video is based on the question https://stackoverflow.com/q/74270074/ asked by the user 'JP7' ( https://stackoverflow.com/u/20279077/ ) and on the answer https://stackoverflow.com/a/74271075/ provided by the user 'Jonathan' ( https://stackoverflow.com/u/10445333/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Remove Columns from a PySpark DataFrame that Match Another DataFrame

Working with large data sets can pose various challenges, especially when we need to filter out specific records based on conditions from other data frames. This guide will guide you on how to remove records from a PySpark DataFrame that match the values in another DataFrame based on the date, regardless of the time.

The Problem

Let's consider we have two PySpark DataFrames:

DataFrame 1 (fdcn_df):

idmyTimeStamp12022-06-01 05:0012022-06-06 05:0022022-06-01 05:0022022-06-02 05:0022022-06-03 05:0022022-06-04 08:0032022-06-02 05:0032022-06-04 10:00DataFrame 2 (holidays_df):

myTimeToRemove2022-06-01 05:002022-06-04 05:00Your goal is to remove any records from the first DataFrame that have matching dates in the holidays_df. The expected DataFrame after the removal should look like this:

idmyTimeStamp12022-06-06 05:0022022-06-02 05:0022022-06-03 05:0032022-06-02 05:00Finding a Solution

To achieve this, we can utilize some powerful features of PySpark. A left anti join will help us efficiently filter out the records we want to remove.

Step-by-Step Solution

Understand the Data Types: First, ensure that the data types for myTimeStamp and myTimeToRemove are of type timestamp. This is essential because we will perform date comparisons.

Use Left Anti Join: To perform the operation, use the leftanti join. This type of join will keep only the records from the first DataFrame that do not have matching records in the second DataFrame.

Implement the Join: Here’s the code you can use to achieve the expected output:

[[See Video to Reveal this Text or Code Snippet]]

Code Breakdown

alias('a') and alias('b'): These are used to reference the different DataFrames in the join condition, ensuring clarity in the join operation.

func.to_date(): This function converts the timestamp to a date only, allowing for the date comparison without considering the time component.

how='leftanti': This parameter specifies that you want records from DataFrame A (first DataFrame) that do not have a match in DataFrame B (second DataFrame).

Conclusion

By utilizing a leftanti join in PySpark, you can effectively clean your data by removing unwanted records based on criteria established in another DataFrame. This method streamlines the process of data cleaning and ensures you maintain the integrity of your analyses.
Whether you're just getting started with PySpark or are looking to enhance your data manipulation skills, mastering joins like this is a fundamental skill in data engineering. Happy coding!

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