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Learn how to easily append multiple DataFrames in Python using `pandas.concat`, solving the common issue of handling CSV files.
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This video is based on the question https://stackoverflow.com/q/73289200/ asked by the user 'Azmina Hamizah' ( https://stackoverflow.com/u/14952399/ ) and on the answer https://stackoverflow.com/a/73289316/ provided by the user 'Brandt' ( https://stackoverflow.com/u/687896/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Efficiently Append Multiple DataFrames in Python with pandas.concat

Appending data from multiple CSV files into a single DataFrame is a common task when working with data. If you're handling logs or datasets that span several files, it's crucial to ensure that all data is incorporated correctly. In this post, we'll break down a solution to a common problem: appending DataFrames from multiple files and how to avoid the pitfalls that can lead to only partial data being included.

The Problem: Incomplete Data Appending

Suppose you have three CSV files containing logs:

log1 with 5,441 rows

log2 with 1,003 rows

log3 with 2,137 rows

You expect that when appended together, the total number of rows should be 8,581. However, when running your code, you find that only the rows from log1 are included. This frustrating situation might arise due to improper handling of the files or how they are being appended into the DataFrame.

Initial Code Issues

Here’s a snippet of the code that might be causing the issue:

[[See Video to Reveal this Text or Code Snippet]]

In this code, a few issues can lead to incomplete data appending. Specifically, the method of appending DataFrames is not effective for your needs.

Solution: Using pandas.concat

Instead of the append method, which is less efficient and can lead to errors when concatenating multiple DataFrames, you can use pandas.concat. This function is simpler, more efficient, and eliminates many common issues associated with appending.

Here’s an improved version of your code using pandas.concat:

[[See Video to Reveal this Text or Code Snippet]]

Key Improvements Made

Use of pandas.concat: This method concatenates a list of DataFrames efficiently, ensuring that all rows from each DataFrame are included.

Dynamic File Count: The total number of files is determined directly from the length of paths, removing the need for a separate counter.

Code Clarity: The loop variable file has been renamed from files for better clarity, making it easier to read and understand the purpose of each variable.

Conclusion

By making these changes and utilizing pandas.concat, you should be able to successfully append rows from multiple CSV files into a single DataFrame without any loss of data. This method is not only more efficient but also reduces potential errors in your data handling process.

Now you can confidently append your DataFrames knowing that all the data from your CSV files will be included. Happy coding!