Скачать или смотреть How to Display the Top Values in a Multi-Indexed DataFrame for Each Group in Pandas

Learn how to efficiently group and analyze data in a multi-indexed dataframe using Pandas in Python, including how to show the top values and aggregate the rest.
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How to Display the Top Values in a Multi-Indexed DataFrame for Each Group in Pandas

Working with data can often be a complex task, especially when dealing with large datasets that require specific analyses. In this post, we'll tackle a common problem: displaying the top values in a multi-indexed DataFrame for each group using Python's Pandas library.

The Problem

Imagine you have a DataFrame containing information about various sectors in different countries, much like the example shown below:

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

From this DataFrame, you want to create a multi-index that counts the instances of each sector by country. Once that's done, the goal is to display only the top two sectors for each country while aggregating the rest under the label "Other."

The Initial Steps

First, you need to group the DataFrame by Country and Sector to get the counts:

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

This code will give you an output similar to this:

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

Implementing the Solution

Now that you have the counts, let's break down the steps to achieve your desired output, which will display the top two sectors and aggregate the others:

Count the Values: Use value_counts() and sort to get the counts in descending order.

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

Select Top Values: Group the counts by country and take the top two sectors per country.

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Handle Remaining Values: Drop the sectors you’ve already selected and sum the rest to categorize them as "Other."

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

Create "Other" Category: Modify the index of s2 to indicate that these values belong to the "Other" category.

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

Combine Results: Merge the top sectors and the "Other" categories, ensuring everything is sorted properly.

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After following these steps, you will get an output that looks like this:

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

Conclusion

With these steps, you can efficiently represent your multi-indexed DataFrame, displaying only the top two sectors while aggregating the rest under the label "Other." This approach not only keeps your data organized but also provides clear insights into the top sectors for each group.

Try implementing this in your own data analysis and see how it improves your data presentations! If you have any questions, feel free to leave a comment below.