How to Merge Multiple DataFrames in Python Using Pandas

Learn how to efficiently merge multiple DataFrames in Python with Pandas, and transform your data into a single cohesive unit. --- This video is based on the question https://stackoverflow.com/q/63612196/ asked by the user 'Mediterráneo' ( https://stackoverflow.com/u/14126136/ ) and on the answer https://stackoverflow.com/a/63612230/ provided by the user 'Maciałek' ( https://stackoverflow.com/u/10815718/ ) 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 do I merge multiple dataframes in Python 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 Merge Multiple DataFrames in Python Using Pandas When working with data in Python, you may often find yourself needing to combine multiple DataFrames into one cohesive unit. This particularly comes into play when your DataFrames contain related information but are structured differently or have varying columns. If you're facing challenges in merging multiple DataFrames in Python, you're certainly not alone! Let's dive into a typical problem where three DataFrames need to be merged, and discover the solution step by step. The Initial Problem Imagine you have three DataFrames structured as follows: DataFrame 1 (df1) [[See Video to Reveal this Text or Code Snippet]] DataFrame 2 (df2) [[See Video to Reveal this Text or Code Snippet]] DataFrame 3 (df3) [[See Video to Reveal this Text or Code Snippet]] You want to combine these into a single DataFrame that looks like this: [[See Video to Reveal this Text or Code Snippet]] However, when trying to use the pd.concat() method as shown below: [[See Video to Reveal this Text or Code Snippet]] You encountered an output like this: [[See Video to Reveal this Text or Code Snippet]] Clearly, pd.concat() is not aligning the DataFrames as intended, leaving NaN (Not a Number) values in the merged DataFrame. The Solution: Using join Instead of concat To effectively merge your three DataFrames while preserving the data correctly, you can use the join() method. The join() function allows you to add columns from another DataFrame based on their index, which is perfect for your use case. Here’s how you can do it: Step-by-Step Instructions Using the Join Method: Replace the previous concatenation code with the following: [[See Video to Reveal this Text or Code Snippet]] Understanding the Join: The join() method takes a list of DataFrames to join with the first one (df1 in this case) on their index. It aligns them correctly to produce the desired output. Viewing the Result: After executing the join command, simply print the out DataFrame: [[See Video to Reveal this Text or Code Snippet]] Expected Output After successful execution, you can expect the following output: [[See Video to Reveal this Text or Code Snippet]] This output properly merges the DataFrames as required, combining all relevant information into one comprehensive table. Conclusion Merging multiple DataFrames in Python can seem daunting at first, especially when you encounter potential pitfalls with concatenation. However, by utilizing the join() method provided by Pandas, you can easily and effectively combine your data. Whether you're a beginner or an experienced data analyst, knowing how to manipulate DataFrames efficiently is crucial for any data analysis task. Now, with the ability to merge DataFrames like a pro, you’re one step closer to mastering data manipulation in Python!