Скачать или смотреть How to Merge DataFrames of Different Lengths in Pandas While Preserving All Values

This guide provides a simple yet effective solution for merging two DataFrames of different lengths in Pandas, ensuring all values from the "master" DataFrame are preserved.
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Merging Two DataFrames in Pandas: A Step-by-Step Guide

When working with data analysis in Pandas, you may frequently encounter the need to merge multiple DataFrames. This task can become tricky, especially when the DataFrames have different lengths or when the values require matching that isn't always exact. In this post, we'll tackle a common scenario using Fortune 500 company DataFrames to demonstrate how to merge them effectively.

The Problem

Imagine you have two DataFrames:

df1, containing Company names and their corresponding CIK (Central Index Key) values, with 117 rows.

df2, which lists Rank and Company names, but is longer at 225 rows.

Your goal is to create a new DataFrame that:

Matches company names between the two DataFrames (even if they don't match exactly)

Preserves the order of df2

Fills in the CIK values from df1 into df2, leaving NaN for companies that don't exist in df1

The Initial Attempt

You might have started with a straightforward merge using the following command:

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

This approach likely provided unexpected results, yielding only a few matches due to strict exact matching.

Why the Merge Failed

If your merge only filled a portion of the values, it could be due to:

Extra Characters in the company names causing mismatches, such as trailing spaces or special characters.

Case Sensitivity where "amazon" and "Amazon" are considered different strings.

A Better Solution

Let's improve upon the initial attempt. Here’s how to achieve the desired outcome step-by-step:

Step 1: Clean Company Names

Before merging, you need to ensure that company names in df2 are cleaned from any non-standard characters.

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

Step 2: Perform the Merge

With cleaned names, you can now proceed to perform a left join to keep all companies from df2.

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

Step 3: Inspect Your Result

After running the commands above, check the output of your new DataFrame to ensure it matches your expectations:

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

A successful output should look something like this:

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

In this result:

The CIK values successfully populate alongside the corresponding companies, and any company from df2 without a match in df1 displays NaN.

Conclusion

Merging DataFrames in Pandas when they have different lengths doesn't have to be complicated. By cleaning your data for consistency and using a left join, you can ensure that you correctly preserve all original values from your "master" DataFrame. This technique can be applied to a variety of datasets to enhance your data manipulation skills in Python.

Feel free to reach out if you have questions or need further explanation on this topic!