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How to Filter Rows with Only NULL Values in PySpark DataFrames

Learn how to effectively filter out rows containing only NULL values in PySpark DataFrames using straightforward methods. --- This video is based on the question https://stackoverflow.com/q/76413439/ asked by the user 'the_economist' ( https://stackoverflow.com/u/2971574/ ) and on the answer https://stackoverflow.com/a/76414484/ provided by the user 'samkart' ( https://stackoverflow.com/u/8279585/ ) 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: PySpark: Filter all rows that contain only NULL values 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. --- Filtering Rows Containing Only NULL Values in PySpark When working with data in PySpark, encountering rows filled entirely with NULL values is not uncommon. These rows can skew your analysis and lead to incorrect insights if left unaccounted for. Thus, knowing how to effectively filter these rows is imperative for maintaining the integrity of your data. Understanding the Problem Imagine you have a DataFrame, df, that includes several rows. Some of these rows are entirely composed of NULL values. In this case, you’re tasked with filtering out these rows to focus on meaningful data points. The challenge lies in constructing a filter condition that checks all columns of the DataFrame and ensures that every single value in those rows is NULL. You want to avoid a situation where the filter only checks a few columns and misses others. The Solution: Using Python's reduce A clean and efficient way to accomplish this task involves utilizing Python's reduce function from the functools module. This approach allows you to combine multiple conditions logically and verify that every column in the DataFrame meets your NULL condition. Step-by-Step Guide to Filter Rows Import Necessary Libraries: First, ensure that you have imported the necessary libraries to work with Spark DataFrames: [[See Video to Reveal this Text or Code Snippet]] Create a Spark DataFrame: If you haven’t done so yet, create your Spark DataFrame (for demonstration purposes): [[See Video to Reveal this Text or Code Snippet]] Construct the Filter Condition: Use the reduce function to combine the conditions for all columns: [[See Video to Reveal this Text or Code Snippet]] Here’s how this works: func.col(c).isNull() checks if a particular column c is NULL. The reduce function applies the logical AND operator between all conditions created for each column. Filter the DataFrame: Apply the condition to filter out rows where all specified columns contain NULL values: [[See Video to Reveal this Text or Code Snippet]] Complete Example Putting it all together, here's the complete example code: [[See Video to Reveal this Text or Code Snippet]] Conclusion Filtering out rows with only NULL values in a PySpark DataFrame not only improves the performance of your data processing tasks but also enhances the reliability of your analyses. By employing Python's reduce function alongside PySpark's functionalities, you can efficiently manage DataFrames containing unwanted NULL rows. Understanding this procedure is crucial for anyone looking to leverage PySpark for big data analytics effectively.