Скачать или смотреть How to Merge DataFrames with Different Structures in Python

Learn how to effectively combine two DataFrames with different structures in Python using the `merge` function to preserve necessary data.
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How to Merge DataFrames with Different Structures in Python

When working with data in Python, especially with libraries like pandas, you may encounter situations where you have multiple DataFrames that don't share an identical structure. This can happen in various data processing scenarios, such as when integrating datasets from different sources. In this guide, we'll explore a common problem: how to merge two DataFrames with different structures and create a new DataFrame that retains the necessary information.

The Problem

Imagine you have two DataFrames:

DataFrame 1 (df1):

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

DataFrame 2 (df2):

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

Your goal is to create a new DataFrame (df3) that includes the rows from df1 corresponding to the values present in df2, but in the structure of df1. This means df3 should look like this:

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

The Solution

The good news is that merging DataFrames in pandas is straightforward once you know the right function to use. Here’s how you can achieve the desired df3:

Step-by-Step Instructions

Import the pandas library:
First, ensure you have pandas installed and imported in your Python environment.

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

Create the DataFrames:
You can create the DataFrames df1 and df2 using the following code:

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

Merge the DataFrames:
Use the merge() function to combine df1 and df2. Here’s the code:

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

In this statement:

on=['a', 'b', 'c'] specifies that the merge should be performed based on these three columns.

This function performs an inner join, meaning it will only keep the rows that have matching values across all specified columns in both DataFrames.

Check the Result:
You can now check the newly created DataFrame df3:

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

Additional Notes

The merge() function will include all columns from df1 in the final DataFrame. If you have any additional columns in either DataFrame, they will be present in the result, preserving the integrity of your data.

If your DataFrames have more complex structures or additional columns, you may need to adjust the merging conditions or clean up the resulting DataFrame afterward.

Conclusion

Merging DataFrames with different structures in Python is easily achievable with pandas using the merge() function. By applying the steps outlined in this post, you can maintain the necessary data across DataFrames while ensuring the output retains the desired structure. The merge() operation is a powerful tool that can significantly streamline your data processing tasks.

Now that you're equipped with this knowledge, give it a try in your own projects, and see how it can simplify your data manipulation tasks!