Скачать или смотреть How to Group, Split, and Select Top Rows in a Pandas DataFrame

Learn how to effectively use Pandas to group, split, and select top rows in a DataFrame based on specific criteria including ordering and values.
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This video is based on the question https://stackoverflow.com/q/64864630/ asked by the user 'reservoirinvest' ( https://stackoverflow.com/u/7978112/ ) and on the answer https://stackoverflow.com/a/64865516/ provided by the user 'Quang Hoang' ( https://stackoverflow.com/u/4238408/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Working with data is a common task in data analysis, and often there comes a need to manipulate DataFrames in Pandas to extract meaningful insights. One common operation is grouping and sorting data based on specific criteria and then selecting the top rows.

In this guide, we will address a specific problem faced when trying to manipulate a DataFrame in Pandas. The requirement is straightforward but showcases various functionalities in a coordinated way. Let's explore the problem briefly and provide a step-by-step solution.

The Problem

We have a DataFrame called df that looks something like this:

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We want to achieve the following:

Group the data by thing.

Split it by order.

Sort it by value while maintaining the thing order.

Pick the top rows defined in the qty dictionary for each thing.

Expected Result

The result should resemble the following format, where you have the top values sorted correctly based on the criteria defined:

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

Solution Overview

To execute these operations using Pandas efficiently, we can utilize methods like groupby, sort_values, and set_index. However, there's an added complexity due to the different orders (ascending and descending). Let's break this down into clear steps.

Step 1: Adjust Value for Descending Order

To handle both ascending and descending orders, we can invert the values where the order is descending. This way, we can sort everything in one go, keeping our calculations simple.

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Step 2: Perform Grouping and Counting

Next, we will sort the values and use groupby to keep track of the cumulative count of entries for each grouping.

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

Step 3: Extract Top Rows

With s, we can now filter the DataFrame to provide us with the rows we want by using the mapped quantities from the qty dictionary:

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

Step 4: Restore Original Values for Descending Order

Finally, we need to convert descending values back to their original positive state for clear presentation:

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

Final Output

After executing the steps above, our output will be correctly transformed according to our requirements:

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

Conclusion

Using Pandas effectively allows you to manipulate data in powerful ways. By grouping, sorting, and filtering DataFrames, you can extract valuable insights from your datasets. The steps illustrated in this post should serve as a guide to tackle similar challenges you may encounter when working with data.

Now you’re equipped to work with groupings, orders, and selections in your DataFrame manipulations. Happy coding!