Скачать или смотреть How to Find All Possible Combinations of Rows in a numpy.ndarray

Discover how to compute all possible combinations of row sums from a `numpy.ndarray` without using for loops, making your code cleaner and faster.
---
This video is based on the question https://stackoverflow.com/q/63066203/ asked by the user 'lost_and_found' ( https://stackoverflow.com/u/9678791/ ) and on the answer https://stackoverflow.com/a/63066352/ provided by the user 'Quang Hoang' ( https://stackoverflow.com/u/4238408/ ) 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: Combination of rows in numpy.ndarray

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.
---
Introduction to the Problem

If you're working with a numpy array (specifically a numpy.ndarray), you may find yourself needing to compute all possible combinations of sums of rows from different slices of the array. Suppose you have a 3D array with an unspecified shape. You may want to sum the elements of rows in one slice of your array with the rows of another slice. This scenario is particularly common in data analysis and machine learning tasks, where such combinations can reveal insights or features in data.

In this post, I will guide you through a specific challenge: determining all possible combinations of the sums of each row from two slices of a numpy.ndarray, excluding specific elements. Let's go through the solution!

Understanding the Data Structure

Let’s take a look at the specific numpy array we are dealing with:

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

Breakdown of the Array:

Shape: The array S has a shape of (2, 3, 3), which indicates it has 2 blocks of 3 rows each with 3 columns.

Goal: We aim to find all combinations of the sums of individual rows from S[0,:,:] with the rows of S[1,:,:].

Step-by-Step Solution

1. Slicing the Numpy Array

First, we need to isolate the slices we want to work with:

First Slice: S[0,:,:] contains the first set of rows.

Second Slice: S[1,:,:] contains the second set of rows.

2. Summing the Rows of Interest

We only want to sum the elements of each row excluding the last column. To achieve this, we use slicing:

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

3. Using Broadcasting

Numpy's broadcasting is a powerful feature that allows you to perform operations on arrays of different shapes. Here’s how to leverage it:

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

Explanation of the Above Line:

s1[:, None, :] adds a new axis to s1, allowing it to align with s2.

s2[None, :, :] does the same for s2.

When you add them together, numpy will automatically combine each row from s1 with each row from s2.

Finally, .reshape(-1, 2) flattens the output into a 2D array with pairs of combinations.

4. Complete Code

The complete code to obtain the desired output looks like this:

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

Final Output

Executing the above code will yield the following output:

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

Conclusion

In this guide, we explored how to effectively calculate combinations of sums from different slices of a numpy.ndarray using broadcasting. This method not only simplifies your code by avoiding explicit loops but also takes advantage of numpy's performance optimizations under the hood. Now, you can easily adapt this approach to work with arrays of varying shapes. Happy coding!