Скачать или смотреть Combining Two Sets of Data Frames with Different Columns in Pandas

Learn how to easily combine two pandas DataFrames with different column structures into one cohesive table using Python, with clear examples and code snippets.
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Combining Two Sets of Data Frames with Different Columns in Pandas

Working with data can be challenging, especially when you're faced with the need to merge two data sets that have different columns. In this guide, we will tackle the problem of combining two pandas DataFrames into one single DataFrame in Python. We'll go through the issue step-by-step and provide you with clear solutions so that you can effectively work with your data.

The Problem: Different DataFrame Structures

You have two sets of DataFrames:

DataFrame df contains a label and one column of percentage values.

DataFrame df1 contains a different label and another percentage value.

Here's a summary of the DataFrames you are dealing with:

DataFrame df

Labelcol1adidas26%Jordan1.6%Asics1.7%DataFrame df1

Answer Code Labelcol2adidas11%Jordan16%Asics1%You want to create a new DataFrame that combines these two DataFrames into one table like this:

Labelcol1col2adidas26%11%Jordan1.6%16%Asics1.7%1%The Solution: Merging DataFrames

To achieve the desired result, you can use the merge() function from the pandas library. There are two main approaches you can take here, depending on how you want to handle the column names.

Approach 1: Merging with Different Column Names

If you want to keep the original column names, you should specify which columns to join on using the left_on and right_on parameters. Here's how you can do it:

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

Approach 2: Renaming Columns for Simplicity

If you prefer a cleaner approach, you can rename the columns in the second DataFrame (df2) to match those in the first DataFrame (df1). This way, you can use the on parameter to directly join them:

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

Result Interpretation

Running either of these scripts will yield the desired combined DataFrame:

Labelcol1col2adidas26%11%Jordan1.6%16%Asics1.7%1%This new DataFrame now contains both columns from the original DataFrames in a neatly organized format.

Conclusion

Combining two DataFrames in pandas with different column names is straightforward when you understand how to use the merge() function effectively. By either specifying the join keys with left_on and right_on or by renaming columns to match, you can create a unified DataFrame that meets your needs.

With these techniques in hand, you can now handle similar situations in your data analysis projects with ease. Happy coding!