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How to GroupBy and Sum Multiple Columns in Pandas Effortlessly

Discover easy methods to group and sum multiple columns in Pandas without listing them all. Learn efficient solutions for large dataframes! --- This video is based on the question https://stackoverflow.com/q/73104124/ asked by the user 'Bobby Heyer' ( https://stackoverflow.com/u/11116696/ ) and on the answer https://stackoverflow.com/a/73104181/ provided by the user 'Corralien' ( https://stackoverflow.com/u/15239951/ ) 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: how to groupby and sum multiple columns in pandas without listing them all 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. --- How to GroupBy and Sum Multiple Columns in Pandas Effortlessly Grouping and summing data are critical operations in data analysis, especially when you are working with large datasets. If you've ever found yourself tangled in wanting to group by and sum multiple columns in a Pandas DataFrame without having to manually list each column, you're not alone. In this guide, we'll dive into an effective solution for this scenario. Understanding the Problem Imagine you have a DataFrame containing a wealth of information, such as various metrics for different years and months. Here’s a simplified view of what your DataFrame might look like: [[See Video to Reveal this Text or Code Snippet]] Your objective is to group by the Year and get the sum of all numeric columns without having to specify each one. The expected result would look something like: [[See Video to Reveal this Text or Code Snippet]] Handling this can be a challenge, especially if you have many columns. Let’s explore a straightforward solution. The Solution Method 1: Using select_dtypes One of the most effective ways to group and sum only specific types of columns (specifically numeric columns) is by utilizing the select_dtypes function in Pandas. Here’s how you do it: Select Numeric Columns: Identify the numeric columns in your DataFrame. Group and Sum: Use groupby along with sum() to combine the data. Here’s the code that accomplishes this: [[See Video to Reveal this Text or Code Snippet]] This will yield: [[See Video to Reveal this Text or Code Snippet]] Method 2: Using sum(numeric_only=True) Another efficient method introduced in more recent versions of Pandas is to use the numeric_only=True parameter in the sum() method. This simplifies the process even further: [[See Video to Reveal this Text or Code Snippet]] This code provides the same result seamlessly without the need to filter columns manually. Conclusion By using the select_dtypes method or sum(numeric_only=True), you can quickly and effectively group by and sum multiple columns in a Pandas DataFrame. These methods save you from the hassle of explicitly listing each column, especially when dealing with large datasets or many columns. Now that you know how to handle this common challenge, you're better equipped for your data analysis tasks in Python's Pandas library! Happy coding!