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Merging Two DataFrames with Different Levels of Columns in Pandas

Learn how to effectively merge two Pandas DataFrames with different column levels using clear examples and step-by-step instructions. --- This video is based on the question https://stackoverflow.com/q/72070866/ asked by the user 'Yusuf Torun' ( https://stackoverflow.com/u/11554640/ ) and on the answer https://stackoverflow.com/a/72070987/ provided by the user 'Ynjxsjmh' ( https://stackoverflow.com/u/10315163/ ) 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 two dataframes with different level of columns 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 Two DataFrames with Different Levels of Columns in Pandas: A Simple Guide In the world of data manipulation, occasionally you might face a situation where you need to merge two DataFrames that have different levels of columns. This can be particularly challenging if the DataFrames are large, making it hard to visualize the merge operation. In this guide, we will tackle this problem head-on and provide a clear, practical solution for merging DataFrames in Pandas. Understanding the Problem Imagine you have two DataFrames, df1 and df2, which look like this: DataFrame Examples df1: ABCx yx yx y012df2: ABCzzz012Now, your desired output should look like this: df_merged: ABCx y zx y zx y z012As you can see, both DataFrames share the same number of level 0 columns, but merging them correctly becomes a bit tricky. Many people have attempted to solve this through methods like .concat() or .join(), but these often do not yield the desired results. A Solution to the Problem: Using concat The key to merging these two DataFrames properly lies in leveraging the concat function from the pandas library, along with sorting the columns afterward. Here are the steps you need to follow: Step 1: Import Pandas Before we dive into the merging process, ensure you have the pandas library imported in your Python environment. [[See Video to Reveal this Text or Code Snippet]] Step 2: Use the concat Function To combine df1 and df2, utilize the pd.concat() function along the columns (axis=1). After merging, you will want to sort the columns: [[See Video to Reveal this Text or Code Snippet]] Step 3: Verify Your Result Once you have executed the above code, check df_merged to ensure that the DataFrames have merged correctly. The result should align with your expectations, where columns from both df1 and df2 are merged side by side as specified. Conclusion Merging two DataFrames with different levels of columns in Pandas might seem daunting at first. However, by employing the concat method and sorting the resultant DataFrame’s columns, you can achieve the necessary structure and format. This approach not only simplifies the merging process but also preserves the integrity of your data. Now, you are equipped with a clear and efficient method to handle similar situations in your work. Happy coding!