Скачать или смотреть The Best Way to Join Two DataFrames in Pandas Based on a Common Column

Discover the most efficient methods to merge two Pandas DataFrames based on a common column, `Person_id`, ensuring cleaner code and streamlined data manipulation.
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This video is based on the question https://stackoverflow.com/q/65286754/ asked by the user 'Mayank Porwal' ( https://stackoverflow.com/u/5820814/ ) and on the answer https://stackoverflow.com/a/65286781/ provided by the user 'jezrael' ( https://stackoverflow.com/u/2901002/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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The Best Way to Join Two DataFrames in Pandas Based on a Common Column

Merging data from different sources is a common task when working with DataFrames in Pandas. If you're a data enthusiast or professional, you might find yourself grappling with how to join two DataFrames effectively, especially when looking to combine information based on a common column. Let's dive into a practical example to clarify this process.

The Problem: Merging Two DataFrames

Suppose you have two DataFrames, m1 and m3, which contain individual records associated with Person_id. Here are the two DataFrames:

DataFrame m1

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

DataFrame m3

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Expected Output

When you join these DataFrames, the expected output is:

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

The Solution: Cleaner Methods to Merge

After trying a more verbose way of merging the DataFrames using the merge() function followed by manual adjustments, there are indeed cleaner alternatives you can use. Here’s how to do it effectively.

Method 1: Using combine_first

If the column names match between both DataFrames, you can make use of the combine_first() method after setting the Person_id as the index:

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

Explanation:

set_index('Person_id'): This sets Person_id as the index, which is crucial for aligning the two DataFrames on this column.

combine_first(): This method combines the two DataFrames, filling in NaN values from the first DataFrame (m1) with corresponding values from the second DataFrame (m3).

reset_index(): This converts the index back to a column for a tidy DataFrame structure.

Method 2: Direct Combine with Matching Index Values

If the index values and the Person_id values are the same in both DataFrames, simplifying the code becomes possible:

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

Explanation:

This method assumes that the DataFrames are aligned correctly by both index and Person_id, allowing a straightforward combination of values without needing to reset indices.

Conclusion

In conclusion, merging two DataFrames in Pandas can be achieved more efficiently using the combine_first() method, offering a cleaner approach than manually handling multiple columns. For beginners or those looking to simplify their data merging processes, these methods will help streamline your workflow in data manipulation tasks.

By adopting this knowledge, you should find that combining DataFrames becomes not only easier but also a more enjoyable experience. Happy coding!