Скачать или смотреть How to Efficiently Merge Multiple Pandas DataFrames Grouped by a Specific Column

Discover a simple and effective method to `merge` multiple Pandas DataFrames by a grouping column value. Save time and improve performance with this step-by-step guide.
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Merging Multiple DataFrames in Pandas: A Step-by-Step Guide

When working with data in Python, particularly with the Pandas library, you may often find yourself needing to combine multiple DataFrames based on a specific grouping column — in this case, the date. If you're faced with merging a list of DataFrames that share the same columns, but are struggling with inefficient and slow methods, this post is for you!

Introduction to the Problem

Imagine you have three separate DataFrames, each containing data about various fruits and their associated values on specific dates:

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

You want to merge all these DataFrames into one, grouped by the date column, resulting in a comprehensive summary table. A common but ineffective approach is iterating through each date and DataFrame, which can be very slow and cumbersome. In this post, I will show you a more efficient way to accomplish this task.

Understanding the Current Iterative Approach

The current approach involves:

Creating an empty dictionary to store results for each date.

Looping through each date, checking each DataFrame for values related to that date, and merging them incrementally.

While this works, it is not efficient. For larger datasets, this method can take exponentially longer to execute due to multiple iterations.

Example of the Iterative Code:

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

An Efficient Pandas Approach

Using pandas.concat()

Instead of using a loop, you can leverage the function pandas.concat(), which is designed to concatenate multiple DataFrames efficiently. After concatenating the DataFrames, you can sort the combined DataFrame by the date column to achieve the desired output.

Here’s How to Do It:

Concatenate DataFrames: Utilize pandas.concat(dfs) to combine the list of DataFrames into a single DataFrame.

Sort Values: Call .sort_values('date') to order the rows based on the date column.

The Code:

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

Expected Output:

With this method, the output will be a single DataFrame that looks like this:

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

Conclusion

When merging multiple DataFrames in Pandas, using the pandas.concat() method followed by sorting is significantly more efficient than manually iterating through DataFrames. This approach leads to cleaner code and improved performance, allowing you to focus more on analysis rather than data manipulation.

If you have a lot of DataFrames to merge, try this method out and observe how it can simplify your workflow!