👀 Views: 160 💬 Answers: 1 📅 Created: 2025-06-21

numpy broadcasting np.where Python

I'm refactoring my project and Quick question that's been bugging me - Quick question that's been bugging me - I am trying to use `np.where` to conditionally replace values in a multi-dimensional NumPy array, but I am running into unexpected behavior related to broadcasting. I have a 3D array and a 2D mask, and I'm not sure how to properly use `np.where` to get the desired output. Here's a simplified version of my code: ```python import numpy as np # Create a 3D array (shape: 3, 3, 3) arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]) # Create a 2D mask (shape: 3, 3) mask = np.array([[True, False, True], [False, True, False], [True, True, False]]) # Use np.where to replace values in arr based on mask result = np.where(mask, arr, -1) print(result) ``` I expected this to replace entries in `arr` where the `mask` is `True` with their original values and entries where `mask` is `False` with `-1`. However, I received the following behavior: ``` ValueError: shape mismatch: value array of shape (3, 3, 3) could not be broadcast to indexing result of shape (3, 3) ``` It seems that the broadcasting rules are not handling the shapes as I expected. I tried reshaping the `mask` array to match the shape of `arr`, but that didn't help. I am using NumPy version 1.24.3. How can I use `np.where` in this scenario to achieve the intended output without working with shape mismatch errors? I'm working on a API that needs to handle this. What am I doing wrong? Hoping someone can shed some light on this.