How to Merge Multiple CSV Files in Python with Pandas

Learn how to efficiently merge more than two CSV files in Python using Pandas. This guide offers step-by-step instructions and helpful tips. --- This video is based on the question https://stackoverflow.com/q/70244310/ asked by the user 'iamgroot' ( https://stackoverflow.com/u/17209577/ ) and on the answer https://stackoverflow.com/a/70244718/ 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: Merge more than 2 csv files 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 CSV Files in Python with Pandas Working with CSV files is a common task in data analysis and manipulation. Sometimes, you may find yourself needing to merge multiple CSV files into a single cohesive output. This can enhance your data processing and analysis workflows. In this guide, we’ll walk you through how to easily merge more than two CSV files using Python's Pandas library. Understanding the Problem Let’s say you have the following CSV files: File 1: data1.csv [[See Video to Reveal this Text or Code Snippet]] File 2: data2.csv [[See Video to Reveal this Text or Code Snippet]] File 3: data3.csv [[See Video to Reveal this Text or Code Snippet]] Desired Output After merging these files, the expected output should look like this: [[See Video to Reveal this Text or Code Snippet]] In this output, any missing data should be represented as nan (not a number). Solution Overview To achieve this result, we will utilize the powerful features of Pandas, specifically the merge() function along with reduce() from the functools module. This allows us to merge multiple dataframes seamlessly. Here’s how you can do it step-by-step. Step 1: Import Necessary Libraries First, you’ll need to import the Pandas library and the reduce function. Make sure you have Pandas installed, which you can do using pip: [[See Video to Reveal this Text or Code Snippet]] Then, import these libraries in your Python script: [[See Video to Reveal this Text or Code Snippet]] Step 2: Define the Filenames Create a list of CSV filenames that you want to merge: [[See Video to Reveal this Text or Code Snippet]] Step 3: Read the CSV Files Use a list comprehension to read each file into a Pandas DataFrame: [[See Video to Reveal this Text or Code Snippet]] Step 4: Merge the DataFrames Now, you can merge the DataFrames iteratively using the reduce function: [[See Video to Reveal this Text or Code Snippet]] Step 5: Export the Result Finally, export the merged DataFrame into a new CSV file: [[See Video to Reveal this Text or Code Snippet]] Complete Code Example Putting it all together, your code should look like this: [[See Video to Reveal this Text or Code Snippet]] Conclusion Merging multiple CSV files in Python using Pandas is straightforward and efficient. By following the steps outlined above, you can easily combine your data into a unified format. This not only makes data analysis easier but also helps in maintaining data consistency. Whether you’re handling just a few files or numerous datasets, using the powerful merge function is an invaluable skill in any data scientist's toolkit. Happy coding! If you have any questions or run into issues, feel free to leave a comment below!