👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-02

numpy array conditional-replacement python

I'm stuck on something that should probably be simple. I'm working on a project and hit a roadblock... I'm working with a frustrating scenario with using `np.where` to conditionally replace values in a 2D numpy array. I have a 2D numpy array of shape (4, 5) where I want to replace all values greater than 10 with 0. However, when I execute my code, I get a shape mismatch behavior. Here's what I have: ```python import numpy as np arr = np.array([[5, 12, 3, 8, 20], [15, 6, 9, 11, 13], [7, 2, 14, 1, 4], [10, 0, 5, 6, 2]]) # Attempting to replace values greater than 10 with 0 new_arr = np.where(arr > 10, 0, arr) ``` When I run this code, I expect `new_arr` to have all values greater than 10 replaced with 0, but I get the following behavior: ``` ValueError: shape mismatch: value array of shape (4, 5) could not be broadcast to indexing result of shape (4, 5) ``` I've checked that both the condition and the array are indeed the same shape, so I am puzzled about why this is happening. I also tried using `np.clip` and `np.where` in a different way, like: ```python new_arr = arr.copy() new_arr[new_arr > 10] = 0 ``` This second approach works fine, but I really want to understand why the `np.where` method failed. I am using numpy version 1.23.0. Has anyone encountered this scenario, or can anyone explain what I'm missing here? Any insights would be greatly appreciated! I'd really appreciate any guidance on this. For context: I'm using Python on Ubuntu. Is there a better approach?