How to Map List of Multiple Substrings in PySpark DataFrames

Learn how to efficiently remove multiple substrings from a column in PySpark DataFrames with a simple regex solution. --- This video is based on the question https://stackoverflow.com/q/73695326/ asked by the user 'Jaol' ( https://stackoverflow.com/u/7959890/ ) and on the answer https://stackoverflow.com/a/73950876/ provided by the user 'Jaol' ( https://stackoverflow.com/u/7959890/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Map list of multiple substrings in PySpark Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Removing Substrings from PySpark DataFrame: A Quick Guide PySpark is a powerful tool for working with large datasets and transforming data efficiently. One common task in data preprocessing is the need to remove specific substrings from column values. This is often crucial before performing further analysis or transformations. In this guide, we will explore how to remove multiple substrings from a DataFrame column using regular expressions in PySpark. The Problem: Substrings in DataFrames Imagine you have a DataFrame with a column named Locations that contains entries formatted like this: LocationsGermany:city_BerlinFrance:town_MontpellierItaly:village_AmalfiIn this case, you want to clean up these entries by removing the unwanted substrings: city_, town_, and village_. After the cleanup, the output should look like this: LocationsGermany:BerlinFrance:MontpellierItaly:AmalfiThe Solution: Using regexp_replace To achieve this, we can use Pyspark’s regexp_replace function, which allows us to replace segments of strings based on a regular expression pattern. Here’s a quick breakdown of how to do it effectively. Step-by-step Breakdown Import Required Libraries: Make sure you’ve imported the necessary libraries to work with DataFrames. [[See Video to Reveal this Text or Code Snippet]] Initialize Spark Session: You need to initialize a Spark session to start working with DataFrames. [[See Video to Reveal this Text or Code Snippet]] Create the DataFrame: Next, you need to create the DataFrame with your data. [[See Video to Reveal this Text or Code Snippet]] Remove Substrings: Now you can apply the regexp_replace function to remove multiple substrings at once using a regular expression. Here’s how you can do it in a single line: [[See Video to Reveal this Text or Code Snippet]] How It Works The regexp_replace function takes three arguments: The column name you are modifying ('Locations' in this case). The regular expression pattern you want to match. The parentheses allow you to group the substrings that you want to remove. In our case, we grouped city_, town_, and village_. The replacement string, which is an empty string ('') here, effectively removing the matched substrings. This method is very efficient and clean, as you don’t need to create separate functions or write convoluted logic to remove each substring. Final Output After executing the code above, if you display your DataFrame, you’ll find it now looks like this: LocationsGermany:BerlinFrance:MontpellierItaly:AmalfiConclusion In summary, using PySpark’s regexp_replace function allows for easy and flexible removal of multiple substrings from a DataFrame column. This solution streamlines your data preprocessing efforts and enhances the quality of the data for subsequent analysis. Feel free to use this regex approach whenever you need to clean your DataFrame columns in PySpark!