👀 Views: 2 💬 Answers: 1 📅 Created: 2025-06-06

numpy broadcasting python

I'm encountering unexpected behavior when trying to perform element-wise operations between two NumPy arrays of different shapes in a custom function. I'm using NumPy version 1.21.0. When I attempt to add a (3, 1) array to a (2, 3) array, I expect broadcasting to handle this smoothly, but I'm getting a `ValueError: operands could not be broadcast together with shapes (3,1) (2,3)`. Here's the code snippet I'm working with: ```python import numpy as np def custom_operation(arr1, arr2): return arr1 + arr2 arr1 = np.array([[1], [2], [3]]) # Shape (3, 1) arr2 = np.array([[4, 5, 6], [7, 8, 9]]) # Shape (2, 3) result = custom_operation(arr1, arr2) ``` I was under the impression that NumPy would be able to broadcast `arr1` across the first axis of `arr2`, but it seems like the shapes are incompatible. I've tried reshaping `arr1` with `arr1.reshape(1, 3)` and adding an extra dimension to `arr2` with `arr2[np.newaxis, :]` but that hasn't resolved the issue. Could someone help clarify how broadcasting works in this case and what I might be doing wrong? I’d appreciate any insights into how I can adjust my code to achieve the intended operation.