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Learn how to effectively group your pandas DataFrame by multiple columns using the `mean` function based on shared prefixes. Perfect for data analysis in Python!
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How to Group By Multiple Columns in Pandas Based on Name

When working with data in Python, you may often find yourself needing to group similar columns together and perform calculations on them. A common scenario is when you have columns with similar prefixes, and you want to find their means. This guide will walk you through how to effectively group-by multiple columns in a pandas DataFrame based on their names.

The Problem

Consider the following situation:

You have a pandas DataFrame that contains multiple columns with names that share common prefixes, such as B6_i, B6_ii, and B6_iii. You want to create a new DataFrame that calculates the mean of these similar columns, grouping them together based on their prefix. This is what your initial DataFrame might look like:

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

Your goal is to transform this into a new DataFrame that looks like this:

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

In summary, the new DataFrame should reflect the average of the values from the original DataFrame for columns with similar prefixes.

The Solution

To achieve this grouping, you can make use of the powerful groupby functionality in pandas combined with some string manipulation. Below are two methods you can employ:

Method 1: Using MultiIndex with str.split

Split the Column Names: Use the str.split method to break the column names at the underscore (_) and create a multi-index for the columns.

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

Group and Calculate Mean: After creating the multi-index, you group the DataFrame by the first level of the index (i.e., the prefix) and calculate the mean.

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

Method 2: Using Lambda Function

If you prefer a more concise method without creating a multi-index:

Group Using a Lambda Function: You can pass a lambda function to groupby that splits the column names at the underscore and uses the first part (the prefix) for grouping.

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

Example Output

With either approach, printing df1 will yield:

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

Conclusion

Using pandas to group by multiple columns based on shared prefixes is a straightforward process that can greatly simplify your data analysis tasks. By using either the MultiIndex method or the lambda function, you can efficiently calculate means and reshape your DataFrame to better suit your analytical needs.

Feel free to implement these methods in your own data projects and enjoy the benefits of organized data analysis!