👀 Views: 100 💬 Answers: 1 📅 Created: 2025-06-09

numpy slicing views Python

I've encountered a strange issue with I'm integrating two systems and I'm working with a question where slicing a view of a NumPy array appears to be causing unintended modifications to the original array... I am using NumPy version 1.24.1 and I expected that changes to the slice wouldn't affect the original array. Here’s a simplified version of my code: ```python import numpy as np original_array = np.array([[1, 2, 3], [4, 5, 6]]) slice_view = original_array[0, :] # Modifying the slice_view slice_view[0] = 10 print("Original Array after modifying slice:", original_array) ``` When I run this code, the output is: ``` Original Array after modifying slice: [[10 2 3] [ 4 5 6]] ``` As you can see, modifying `slice_view` also changes `original_array`, which is not the behavior I expected since I thought I was working with a view that should not reflect changes back to the original array. I have tried using `np.copy()` to create a copy of the slice and modifying that, which works as expected: ```python copied_slice = np.copy(original_array[0, :]) copied_slice[0] = 20 print("Original Array after modifying the copy:", original_array) ``` This correctly outputs the original array without changes: ``` Original Array after modifying the copy: [[10 2 3] [ 4 5 6]] ``` Is there a way to create a slice that doesn't affect the original array? I’ve read that using views can be efficient, but this behavior is unexpected. Am I misunderstanding how slicing works in NumPy? I'd love to hear your thoughts on this. This is for a CLI tool running on Ubuntu 22.04. Hoping someone can shed some light on this.