Скачать или смотреть How to Combine Multiple Columns into One Category Column in Pandas

Discover how to transform your DataFrame in Pandas by combining multiple columns into one category column with clear value labels.
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Transforming Dataframes in Pandas: A Guide to Combining Columns into One

When working with DataFrames in Pandas, you may find yourself in a scenario where you want to consolidate multiple columns into a single column based on some conditions. This article addresses a common problem: how to combine multiple boolean columns into a single category column while using the column names as value labels. Let’s take a closer look at how to achieve this transformation in a clean and efficient manner.

Understanding the Problem

Suppose you have the following DataFrame:

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Your goal is to transform this DataFrame into the following format:

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Key Requirements

You want to create a new column, group, which consists of the names of the columns originally containing True values.

Each row can only have one True value at most, which simplifies our process.

The Solution: Using Pandas Tools

While it might seem that you need to apply a complex operation, there’s a more straightforward approach using melt combined with filtering. Here’s how to do it step by step:

Step 1: Melt the DataFrame

The first thing you’ll do is to melt the DataFrame. The melt function is used to transform or reshape your data by specifying the identifier variable(s). In this case, we’ll keep the ID column and melt the other boolean columns.

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After this operation, you will get the following DataFrame:

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Step 2: Filter for True Values

Next, you will want to filter for only those rows where the value column is True. This will give you the result you are aiming for.

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Complete Code Example

Putting it all together, here’s the final code:

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Conclusion

By leveraging the melt function and filtering based on the boolean values, you can efficiently combine multiple columns into one category column in Pandas. This approach not only keeps the code clean and readable but also uses Pandas' powerful built-in functionalities.

Feel free to use this technique in your data manipulation tasks to keep your DataFrames organized and easy to work with!