advanced patterns when using np.reshape on a view of a NumPy array
I've been researching this but I've been researching this but After trying multiple solutions online, I still can't figure this out... I've searched everywhere and can't find a clear answer. I'm working with an unexpected question when trying to reshape a view of a NumPy array. I have a 2D array and I'm creating a view of it using slicing. When I attempt to use `np.reshape()` on this view, I receive the behavior message `ValueError: want to reshape array of size 6 into shape (2,3)` even though the original array can be reshaped to that shape. Here's a simplified version of my code: ```python import numpy as np data = np.array([[1, 2, 3], [4, 5, 6]]) view = data[:, 1:3] # Create a view of the original array print(view.shape) # This will show (2, 2) reshaped = np.reshape(view, (2, 3)) # I expect this to work ``` I expected that since the view is derived from the original array, reshaping it to (2, 3) should be possible. However, it seems like it's treating the view as a separate array with its own shape. I already double-checked that the total number of elements matches, as I thought that was a requirement for reshaping, but I'm clearly missing something here. Can anyone guide to understand why this is happening? Am I misusing `np.reshape()` on a view? This is with NumPy version 1.24.3. Thanks in advance for your assistance! This is part of a larger CLI tool I'm building. Am I missing something obvious? Is there a simpler solution I'm overlooking? Thanks for your help in advance! I'm working on a REST API that needs to handle this. Hoping someone can shed some light on this.