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Sorting a DataFrame Based on Duplicates in Another Column with Pandas

Learn how to effectively `sort a DataFrame` column with respect to duplicates in another column using Pandas! --- This video is based on the question https://stackoverflow.com/q/74252660/ asked by the user 'Khaled DELLAL' ( https://stackoverflow.com/u/15852600/ ) and on the answer https://stackoverflow.com/a/74253050/ provided by the user 'Bushmaster' ( https://stackoverflow.com/u/15415267/ ) 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: Sort a DataFrame column based on duplicates in another 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. --- Sorting a DataFrame Based on Duplicates in Another Column with Pandas In the world of data analysis, we often encounter situations where we need to sort data based on multiple criteria. One common challenge occurs when we have duplicate values in one column and want to ensure that another column's values are sorted accordingly. If you find yourself facing this issue while working with Pandas in Python, you are in the right place! The Problem at Hand Let's take a look at a sample DataFrame to better understand the problem. Imagine you have a DataFrame structured like this: ABCD41af23z43bf14y42bf14x64cf51w65ch63v56cm79uWhen we want to sort this DataFrame based on column 'A' and column 'C', we find ourselves in a bind. If we simply sort using the below code: [[See Video to Reveal this Text or Code Snippet]] We receive the following output: ABCD41af23z43bf14y42bf14x56cm79u64cf51w65ch63vNotice how the rows for values bf14 under column 'C' are not properly sorted based on 'B'. The sorting algorithm ignores entries with duplicates in 'C', leaving the order of column 'B' intact. It's clear we want to sort 'B' as well when duplicates in 'C' occur. The expected output after the sort should look like this: ABCD41af23z42bf14x43bf14y56cm79u64cf51w65ch63vThe Solution To achieve the desired sorting, we need to include an additional column in our sorting criteria. The solution is straightforward! You simply need to adjust your sort function like this: [[See Video to Reveal this Text or Code Snippet]] This modification will allow the sorting algorithm to take into account the values in column 'B' when the values in 'C' are equal. Final Output Example After running the above code, your DataFrame will be sorted as follows: [[See Video to Reveal this Text or Code Snippet]] Conclusion Sorting a DataFrame where duplicates are involved might seem tricky at first, but with the correct approach, it's quite manageable. By tweaking the sorting function to include additional columns, we ensure that our data is presented in the desired order. This technique is powerful for data manipulation in Pandas, especially when preparing data for analysis. Now, you can confidently tackle sorting DataFrames with duplicates in one of their columns. Happy coding!