How to Use Polars for Conditional Min/Max Calculations on DataFrames скачать в хорошем качестве

How to Use Polars for Conditional Min/Max Calculations on DataFrames

Learn how to effectively compute conditional minimum and maximum values in `Polars` DataFrames using specific filters. --- This video is based on the question https://stackoverflow.com/q/77398362/ asked by the user 'MPA' ( https://stackoverflow.com/u/10377244/ ) and on the answer https://stackoverflow.com/a/77398610/ provided by the user 'Wayoshi' ( https://stackoverflow.com/u/21191420/ ) 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: Polars - Windows function with condition 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. --- Understanding Conditional Min/Max Calculations with Polars In the world of data analysis, it’s crucial to summarize and interpret large datasets efficiently. One common requirement is to calculate minimum and maximum values under specific conditions. In this guide, we will explore how to achieve this using the Polars library in Python. Using a small example DataFrame, we’ll encounter a problem and walk through the solution step-by-step. The Problem Statement Suppose we have a DataFrame structured as follows: idactionscampaign_nameevent_timesession_id10None1session_111None2session_11223session_12010session_12121session_122None2session_1We want to calculate the minimum and maximum event_time for each combination of session_id and id, but only for those entries that have a campaign_name of either '1' or '2'. Here’s what we expect our output to look like: idactionscampaign_nameevent_timesession_idmin_actionmax_action10None1session_12211None2session_1221223session_1222010session_1012121session_10122None2session_101However, when we attempted to implement the calculations, we faced unexpected results. The initial code didn't filter the actions based on the campaign_name condition as intended. Let’s dive into the solution to rectify this issue. The Solution Understanding the Limitation The key limitation with the initial approach is the misunderstanding of the when and then statements in the context of Polars. The when clause sets conditions for the row, but it does not restrict the subsequent calculations — this is where our earlier implementation faltered. To specify conditions for calculations directly, we need to use a filter. Implementing the Correct Approach Here’s how to correctly compute the minimum and maximum values using a filter within your Polars DataFrame: [[See Video to Reveal this Text or Code Snippet]] What This Code Does Filtering the DataFrame: The method pl.col('actions').filter(pl.col('campaign_name').is_in(['1','2'])) ensures that only the actions related to the specified campaign names ('1' and '2') are considered. Aggregating with Minimum and Maximum: min().over(['session_id', 'id']) calculates the minimum action for each group defined by session_id and id. max().over(['session_id', 'id']) performs a similar function for maximum actions. Conclusion In summary, when working with the Polars library for data manipulation in Python, it’s vital to understand how filtering works in conjunction with group operations. By utilizing the filter method instead of conditions typically used in if statements, we can achieve our desired outcomes effectively. This approach not only simplifies the code but also ensures accuracy in our results. By following the steps outlined above, you can confidently calculate conditional min and max values in your DataFrames. Happy coding!