Скачать или смотреть How to Merge Multiple DataFrames in Python Using Pandas

Learn how to efficiently merge multiple dataframes on a key column in Python with Pandas. Streamline your data analysis by combining dataframes effortlessly.
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This video is based on the question https://stackoverflow.com/q/65035279/ asked by the user 'Mayank Goyal' ( https://stackoverflow.com/u/4859840/ ) and on the answer https://stackoverflow.com/a/65036571/ provided by the user 'Richard Taujenis' ( https://stackoverflow.com/u/11008368/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Merging Multiple DataFrames in Python: A Complete Guide

Working with data often involves merging multiple dataframes, especially when they share a common key or column. In this guide, we will tackle a common scenario in data analysis: merging three dataframes that all contain a column named Date_Final. If you've ever found yourself tangled in messy merge code, fear not! We will guide you through the optimal way to combine your data seamlessly.

The Problem

You have been provided with three dataframes, each containing a common column named Date_Final. The challenge lies in merging these dataframes into a single, cohesive dataframe for further analysis. Here's the original approach you might have taken:

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

This code, while functional, can be improved for efficiency and clarity. Let's explore a more streamlined solution.

An Optimized Approach

Step 1: Gather Your DataFrames

Instead of individually merging each dataframe, you can consolidate them into a list. This approach allows for cleaner and more readable code:

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

Here, you simply create a list called dfs containing all three dataframes that you wish to merge.

Step 2: Performing the Merge

Next, we will use the reduce function from the functools library together with Pandas’ merge function. The reduce function will iterate through our list of dataframes and merge them consecutively on the Date_Final column:

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

Explanation of the Code

from functools import reduce: This allows us to apply a function (in this case, merging) cumulatively to the items of the dfs list.

pd.merge(left, right, on='Date_final'): This is a clever way to merge two dataframes at a time on the specified key—Date_Final.

df_final: The result is a new dataframe which includes merged data from all initial dataframes based on the Date_Final key.

Why This Method is Better

Efficiency: Merging multiple dataframes at once is computationally more efficient, especially with larger datasets.

Clarity: Keeping related data structures bundled in a list makes the code easier to read and maintain.

Scalability: If you need to add more dataframes in the future, you can simply append them to the dfs list without having to change your merge logic.

Conclusion

Merging dataframes in Python using Pandas doesn't have to be tedious or convoluted. By using a list to store your dataframes and the reduce function for merges, you can create a more elegant and effective solution.

Transform your data analysis workflow today by adopting this streamlined approach to merging dataframes. Happy coding!