How to Concatenate DataFrame Columns and Remove Duplicate Row Values in Pandas

Описание: Learn how to effectively combine columns in a Pandas DataFrame while removing duplicate values. This guide provides easy-to-follow solutions and code examples.
---
This video is based on the question https://stackoverflow.com/q/73940762/ asked by the user 'Boris' ( https://stackoverflow.com/u/16472597/ ) and on the answer https://stackoverflow.com/a/73940921/ provided by the user 'bitflip' ( https://stackoverflow.com/u/20027803/ ) 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: Pandas Concat and remove all duplicate row values

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 Columns in a Pandas DataFrame and Removing Duplicates

When working with data in Python using the Pandas library, it is common to encounter scenarios where you need to manipulate DataFrames to suit your analysis needs. One such task involves concatenating columns and removing duplicate values from the resulting rows. In this post, we'll walk through how to effectively achieve this with a practical example.

The Problem

Imagine you have a Pandas DataFrame structured like this:

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

The objective here is to concatenate these columns into a single column and eliminate any duplicate values from each row so that the output looks like this:

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

The Solution

There are a couple of ways to handle this task in Pandas, depending on whether you care about the order of your values or not. Let’s break down both approaches clearly.

1. Preserving Order of Values

If maintaining the order of your values is important, you can use the following method:

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

Explanation of the Code:

df.apply(): This function applies a function along an axis of the DataFrame.

lambda x: dict.fromkeys(x): This lambda function creates a dictionary from the row values, thus removing duplicates while retaining the original order.

axis=1: This specifies that the function is applied across the columns (i.e., it processes row-wise).

.explode(): Finally, this method transforms the lists in the DataFrame into separate rows.

Output:

Running the above code will give you the following output:

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

2. Ignoring the Order of Values

If you do not care about the order and simply want a faster solution, you can use:

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

Explanation of the Code:

map(set, df.values): Here, each row in the DataFrame is converted into a set, which automatically removes duplicate values.

list(): This converts the map object into a list of sets.

Output:

Using this method, you will get a list of unique values row-wise, but without maintaining the original order:

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

Conclusion

In this guide, we walked through two effective methods for concatenating columns in a Pandas DataFrame and removing duplicate values. Depending on whether you need to maintain the order of items, you can opt for either the apply method or the faster map method.

Feel free to choose the method that fits your requirements best and simplify your data manipulation tasks in Pandas!