Скачать или смотреть How to Perform a Column-Wise Logical AND Operation on Numpy Arrays

Learn how to efficiently apply the logical AND operation on an n-dimensional boolean numpy array and count rows containing only ones.
---
This video is based on the question https://stackoverflow.com/q/69796732/ asked by the user 'Jim' ( https://stackoverflow.com/u/16956119/ ) and on the answer https://stackoverflow.com/a/69796802/ provided by the user 'Valdi_Bo' ( https://stackoverflow.com/u/7388477/ ) 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: Numpy: Folding an array column-wise

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.
---
Understanding Column-Wise Logical Operations in Numpy

When working with n-dimensional boolean arrays in Python, you might find yourself needing to perform column-wise operations. Specifically, you might want to determine how many rows in your array consist exclusively of ones. This is a common requirement in data analysis, especially when dealing with binary datasets.

The Problem

Let’s start by examining the challenge more closely. You have an n-dimensional boolean numpy array, and you wish to apply a logical AND operation across each column. For instance, consider the following numpy array:

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

Your goal is to find out how many rows consist solely of ones. In this example, the expected result is 2, since only the third and last rows contain exclusively ones.

Solutions to the Problem

Using Numpy for Logical AND Operations

While you could utilize the functools module alongside Numpy to achieve your objective, there exists an efficient method that uses Numpy alone. This method is not only straightforward but also leverages powerful functionalities built right into the Numpy library.

Here’s the recommended approach:

Compare Each Element: First, check if every element is equal to 1.

Aggregate Across Rows: Then, use the all() function along the specified axis to determine if all values in each row are True.

Sum the Results: Finally, sum the results to count how many rows meet the criterion.

Here’s how this looks in code:

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

Breaking Down the Code

np.equal(n, 1): This creates a boolean array where each element is True if it is equal to 1 and False otherwise.

.all(axis=1): This checks each row in the boolean array to see if all elements are True. It returns a boolean array indicating which rows meet the condition.

np.sum(...): Finally, this sums the boolean array where True counts as 1, giving the total count of rows filled entirely with ones.

Conclusion

Using Numpy to perform column-wise logical operations on boolean arrays is both efficient and straightforward. This method eliminates the need for additional modules, keeping your code clean and concise.

Now you can easily apply this technique in your data analysis projects whenever you find yourself needing to summarize boolean data in numpy arrays!