Скачать или смотреть Finding Adjacent Row Values in Pandas DataFrames

This guide explores a solution to identify if adjacent rows contain values when other columns are empty in Pandas DataFrames.
---
This video is based on the question https://stackoverflow.com/q/73708914/ asked by the user 'ApacheOne' ( https://stackoverflow.com/u/11315276/ ) and on the answer https://stackoverflow.com/a/73708993/ provided by the user 'Umar.H' ( https://stackoverflow.com/u/9375102/ ) 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: Find if adjacent rows have 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.
---
Identifying Adjacent Row Values in Pandas DataFrames

Pandas is an incredible library for data manipulation in Python, but sometimes it can get a bit tricky, especially when you're dealing with conditions across rows. One common challenge users face is needing to check if adjacent rows contain specific values in one column while other columns are empty. In this guide, we’ll discuss a straightforward approach to address this issue using a combination of boolean conditions to filter your DataFrame efficiently.

The Problem

Let's say you have a DataFrame like this:

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

Your goal is to determine if any values in columns C1 or C2 exist when the adjacent rows in columns B1 and B2 are empty. In our example, row DD would return True because it has values in C2 while B1 and B2 are empty.

Solution Approach

Step 1: Clean Up Your DataFrame

First, it’s important to ensure that empty spaces are treated as NaN values, which can be done using:

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

Step 2: Create Boolean Conditions

Next, we'll set up the conditions we need to check. We'll create two boolean conditions:

One for checking if there are non-null values in columns C1 and C2.

The other for checking if any values in columns B1 and B2 are NaN.

Here's how you can create those conditions:

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

Step 3: Filter the DataFrame

After defining these conditions, you can easily filter the DataFrame to get the desired outcome. Here's how you would obtain the IDs of the rows meeting the criteria:

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

Example Output

Running the above code would yield:

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

This output confirms that row DD has values in C2 while both B1 and B2 are empty.

Conclusion

By employing boolean conditions to filter the DataFrame, we can efficiently identify which rows meet our specific criteria. This approach allows for both readability and flexibility when manipulating data, and is a fundamental technique to master when using Pandas.

Feel free to apply this method to your own DataFrames and modify the conditions as needed. Happy coding!