Скачать или смотреть How to Efficiently Iterate Through Rows in Pandas with Multiple Conditions

Learn how to effectively iterate through rows in a Pandas DataFrame based on multiple conditions using simple and efficient methods.
---
This video is based on the question https://stackoverflow.com/q/72388780/ asked by the user 'Tahnee' ( https://stackoverflow.com/u/18144711/ ) and on the answer https://stackoverflow.com/a/72388843/ provided by the user 'user2246849' ( https://stackoverflow.com/u/2246849/ ) 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 iterate through rows based on multiple conditions and then subtract from column?

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.
---
Mastering Row Iteration in Pandas: A Guide to Conditional Subtraction

When working with data, especially using Pandas in Python, it’s common to encounter scenarios where you need to make calculations based on multiple conditions. One such instance is when you want to adjust column values based on the values of other columns. In this guide, we will address a specific question regarding how to iterate through rows based on conditions and perform calculations efficiently, avoiding the pitfalls of common mistakes.

The Problem: Conditional Adjustments in a DataFrame

Imagine you have a DataFrame that looks like this:

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

In your case, you want to decrease the value of the 'Act' column by 1 for each row where:

The 'Act' value is equal to 4

The 'mean_bout_count' is less than 3

However, the initial approach taken involves a loop that was inefficient and incorrectly adjusted the entire column every time a condition was met. This is a typical scenario for many who are learning data manipulation with Pandas.

The Solution: A More Efficient Approach

Instead of using a for loop and iterrows(), which can be slow for large DataFrames, you can utilize boolean indexing, which is both faster and more pythonic. Here’s how you can do it:

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

Breaking It Down

Boolean Indexing: The expression (df['Act'] == 4) & (df['mean_bout_count'] < 3) creates a boolean Series. It checks each row to see if both conditions are satisfied.

.loc[] Accessor: The .loc method allows you to access a group of rows and columns by labels or a boolean array. Here it selects only the rows where both conditions are True.

In-place Modification: By using -= 1, you decrease the value of 'Act' directly where the conditions apply.

Example Demonstration

After running the above command on your DataFrame df, here’s how it looks:

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

Conclusion

In this post, we explored how to efficiently iterate through rows in a Pandas DataFrame using multiple conditions without resorting to slow looping methods. By employing boolean indexing along with the .loc accessor, you can greatly simplify and accelerate your data manipulation tasks. This will not only save time but also lead to cleaner and more maintainable code.

If you have further questions or need clarification on similar topics, feel free to ask in the comments below! Happy coding!