Efficiently Remove Multiple Values from Numpy ndarray at Random

Learn how to effortlessly remove multiple values from a Numpy ndarray and reshape it into a 3D array with a specified second dimension.
---

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: Removing multiple values from ndarray at random

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Removing Multiple Values from Numpy ndarray at Random

If you're working with Numpy arrays in Python, you may encounter situations where you need to manipulate your data by removing specific values. One such scenario involves wanting to remove multiple elements from an ndarray and then reshape the remaining data into a new format. In this post, we'll walk through how to effectively remove 11 samples from a Numpy array with a shape of (5891, 10), ensuring that the final shape of the array is adjusted accordingly.

The Challenge: What We're Trying to Achieve

You have a Numpy array with dimensions (5891, 10) and you need to:

Remove 11 rows randomly from the array.

Reshape the resulting slimmer array into a 3D structure with dimensions that include a second dimension of size 6 (resulting in a shape of (-1, 6, 10)).

The Solution: Step-by-Step Breakdown

Let's explore the steps needed to accomplish this task using Python and Numpy.

Step 1: Create the Numpy Array

First, we'll create a sample Numpy array. Here's how you might typically initiate your array:

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

Step 2: Set Up Random Seed for Reproducibility

To ensure that the random selections can be replicated, you can set a seed for the random number generator:

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

Step 3: Remove Rows from the Array

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

Step 4: Reshape the Array

After removing the specified rows, the next step is to reshape the remaining array into the desired 3D format:

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

Conclusion

Whether you're preparing datasets for training a model or just cleaning your data, understanding how to manipulate your Numpy arrays effectively is a valuable skill in your programming toolkit. Happy coding!