Скачать или смотреть How to Combine Values from Multiple Columns into a List in Each Row Using Python

Learn how to efficiently combine column values in a `Pandas` DataFrame into a list by using `Python`. This guide offers multiple solutions to suit different performance needs.
---
This video is based on the question https://stackoverflow.com/q/68030044/ asked by the user 'vikingd' ( https://stackoverflow.com/u/12979599/ ) and on the answer https://stackoverflow.com/a/68030135/ provided by the user 'jezrael' ( https://stackoverflow.com/u/2901002/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Combine values from multiple columns into a list in each row using python

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Combining Values from Multiple Columns into a List in Each Row Using Python

When dealing with large datasets, it is often necessary to manipulate and transform data to fit our needs. One common task is to combine the values from several columns in a Pandas DataFrame into a single list. If you've ever found yourself struggling with this type of data transformation, you're not alone. In this post, we'll take a look at how to effectively combine column values into lists for each row in a DataFrame.

The Problem Statement

Imagine you have the following DataFrame consisting of binary values (0s and 1s) representing different categories such as pets:

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

This gives you the following DataFrame:

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

You want to combine the column names corresponding to the values of 1 into a list for each row and create a new column at the end to store this result. Here’s what the desired outcome looks like:

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

The Solution

To achieve this, Pandas provides several methods that you can use. Let's break them down into clear steps.

Method 1: Using apply with a Lambda Function

This method involves applying a function to each row and creates a new column based on the condition:

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

Method 2: List Comprehension for Performance

If you are working with large DataFrames (with many rows and columns), performance becomes crucial. This approach is faster and should be considered when working with larger datasets:

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

Performance Note:
Testing shows that the second method is significantly more efficient:

The apply method takes around 526 ms.

The list comprehension method takes only 12.1 ms.

Method 3: Using dot and String Manipulation

An additional method worth mentioning is to use the dot product, which can also be a fast alternative:

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

Method 4: Using Masking and Stacking

For a more advanced option, you can mask the DataFrame and stack it:

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

Conclusion

Combining values from multiple columns in a Pandas DataFrame into a single list is a straightforward task once you know how to approach it. Whether you choose to use apply, list comprehension, or other methods, the key is to select the one that best fits your dataset's size and structure.

By leveraging these techniques, even with large datasets, you'll be able to enhance your data manipulation repertoire significantly. Happy coding!