Efficiently Remove Empty Strings from List in DataFrame Column using PySpark

Learn how to effectively remove empty strings from a list in a DataFrame column using PySpark, ensuring clean and organized data. --- This video is based on the question https://stackoverflow.com/q/64355465/ asked by the user 'pfnuesel' ( https://stackoverflow.com/u/1945981/ ) and on the answer https://stackoverflow.com/a/64356387/ provided by the user 'SCouto' ( https://stackoverflow.com/u/6378311/ ) 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: Remove empty strings from list in DataFrame column 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. --- Introduction Working with data can often be a tricky endeavor, especially when your datasets contain unwanted values that can skew results. One such common problem arises with empty strings found in list-type columns within a DataFrame. This can lead to difficulties in data manipulation and analysis. In this guide, we will address the issue of how to remove empty strings from a list in a DataFrame column using PySpark, ensuring your data remains clean and useful. The Problem Consider the following scenario: you have a DataFrame with a column named foo that contains lists, but some of these lists have empty strings. Here's an example of what your DataFrame might look like: [[See Video to Reveal this Text or Code Snippet]] As you can see, lists like [ ] are of no use in our analysis and can lead to misleading results. Your goal is to transform this list so that any empty strings are removed, achieving the following output: [[See Video to Reveal this Text or Code Snippet]] The Solution To tackle the problem of removing empty strings from list entries in a DataFrame column, we can utilize the filter method in PySpark. This method allows us to apply conditions to the elements of the lists and only keep those that meet specified criteria. Step-by-Step Implementation 1. Use filter with expr: The filter function will enable us to retain only the non-empty strings from the lists. Here's how to do that: [[See Video to Reveal this Text or Code Snippet]] 2. Overwrite original column (Optional): If you would like to keep your DataFrame neat, you might want to drop the original column instead of creating a new one. To achieve this, simply use the same column name: [[See Video to Reveal this Text or Code Snippet]] Resulting DataFrame After applying the methods above, your DataFrame should look cleaner and more organized: [[See Video to Reveal this Text or Code Snippet]] In this resulting DataFrame: The original column shows lists with empty strings replaced. The new column newColumn reflects the cleaned data without any empty strings. Conclusion Handling empty strings in DataFrame columns is a crucial step in preparing your data for analysis. By using the filter function in PySpark, you can seamlessly remove unwanted empty strings from lists, ensuring that your data is accurate and ready for further processing. By keeping your data clean, you can avoid pitfalls in your analyses and extract meaningful insights. Now, go ahead and implement this solution in your projects, and experience the clarity that comes with well-organized data!