Merging Duplicate Rows in CSV Files using Python скачать в хорошем качестве

Merging Duplicate Rows in CSV Files using Python

Discover how to efficiently merge rows in a CSV file with duplicate identifiers using Python and Pandas. Learn key methods to restructure your data for better analysis. --- This video is based on the question https://stackoverflow.com/q/68599522/ asked by the user 'Joseph Sanders' ( https://stackoverflow.com/u/10335144/ ) and on the answer https://stackoverflow.com/a/68599642/ provided by the user 'Anurag Dabas' ( https://stackoverflow.com/u/14289892/ ) 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: merging rows in csv where row[0] is a duplicate 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. --- Merging Duplicate Rows in CSV Files using Python: A Step-by-Step Guide Handling CSV files can sometimes be tricky, especially when it comes to managing duplicate rows. This is a common scenario, particularly for datasets that track relationships—like students and their guardians. In this post, we'll explore a practical solution for merging rows in a CSV file where a specific column (in this case, student_id) contains duplicate values. The Problem at Hand Imagine you have a CSV file containing columns for student_id, guardian_email, guardian_first_name, and guardian_last_name. In a household with both a mother and a father, a student's details might be represented in multiple rows. Here’s an example of what this CSV file might look like: [[See Video to Reveal this Text or Code Snippet]] The goal is to merge rows with the same student_id into a single row, resulting in an output that captures all guardian details while maintaining clean, accessible data. Desired Output For the student data above, the end result should consolidate the duplicate entries into one row, like this: [[See Video to Reveal this Text or Code Snippet]] The Solution Using Pandas Now that we understand the problem, let's dive into how to achieve this using Python’s Pandas library. Below are two effective methods to merge rows based on the duplicates in student_id. Method 1: Using pivot() Group Rows: We start by using the groupby() function to group rows by student_id. Count Duplicates: We track the position of each duplicate guardian using cumcount(). Pivot the DataFrame: We then utilize pivot() to reshape the DataFrame. Here’s the code implementation: [[See Video to Reveal this Text or Code Snippet]] Method 2: Using unstack() This method is very similar to the one above but uses unstack() instead of pivoting. Here’s how to implement it: [[See Video to Reveal this Text or Code Snippet]] Writing to a New CSV File Once you have transformed your DataFrame with either method, you can easily write the output back to a new CSV file: [[See Video to Reveal this Text or Code Snippet]] Conclusion By leveraging the powerful capabilities of the Pandas library in Python, merging duplicate rows in a CSV file becomes a straightforward task. Utilizing functions like groupby(), cumcount(), pivot(), and unstack(), you can creatively restructure your data to meet your analysis needs. Try implementing these solutions with your dataset and enjoy the simplicity of a well-organized CSV! Feel free to reach out for any questions regarding this process or share your experiences with data manipulation in Python!