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Transform Values in Array Columns with PySpark

Learn how to efficiently modify values in array columns of a PySpark DataFrame based on specific conditions, without exploding the arrays. --- This video is based on the question https://stackoverflow.com/q/75893221/ asked by the user '1131' ( https://stackoverflow.com/u/3203845/ ) and on the answer https://stackoverflow.com/a/75893360/ provided by the user 'notNull' ( https://stackoverflow.com/u/7632695/ ) 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: Modify values of array columns 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. --- Transform Values in Array Columns with PySpark: A Step-by-Step Guide When working with PySpark, you might encounter situations where you need to modify values within array columns of a DataFrame based on certain conditions. This challenge arises often, especially when dealing with aggregated data. In this post, we'll walk through a specific problem involving transforming array columns in a DataFrame and provide a clear solution to achieve the desired output. The Problem Imagine we have a DataFrame with two array columns obtained from aggregations. The data looks like this: idarrarr11["0", "1", "2"]["10"]2["0"]["20"]3["3"][null]We want to accomplish the following transformations: For the arr column, if it contains more than one element, we remove any occurrence of '0'. If '0' is the only element, we keep it as is. For the arr1 column, if it is empty, we want to replace it with a default value, say "default_value". The Solution To achieve these transformations without the need to explode the arrays (which can be inefficient), we can utilize PySpark's built-in array functions such as size and filter. Let’s break down the steps to perform the required modifications. Step 1: Set Up the DataFrame First, we’ll create the initial DataFrame as shown above: [[See Video to Reveal this Text or Code Snippet]] Step 2: Transform the arr Column Next, we transform the arr column. We use the when statement combined with size and filter to accomplish this conditionally: [[See Video to Reveal this Text or Code Snippet]] Explanation: If the size of the arr column is greater than 1, we filter out the '0' values. If not, we keep the column unchanged. Step 3: Handle the arr1 Column Now, for the arr1 column, we replace any null or empty entries with "default_value" using another when condition: [[See Video to Reveal this Text or Code Snippet]] Final Output After applying these transformations, our DataFrame will look like this: idarrarr11["1", "2"]["10"]2["0"]["20"]3["3"]["default_value"]Conclusion By following these steps, we efficiently transformed specific array values within a DataFrame in PySpark without exploding the columns. Utilizing built-in functions like size, filter, and conditional statements allows us to tailor our data to meet specific requirements. Now you can seamlessly adjust array column values in your PySpark workflows!