Strange behavior when reshaping a NumPy array with non-contiguous memory layout
I'm building a feature where This might be a silly question, but I tried several approaches but none seem to work... I'm working with a peculiar scenario when trying to reshape a NumPy array that has a non-contiguous memory layout. I created a 2D array and then sliced it to obtain a view, but when I try to reshape this view into a different shape, it throws an behavior. Here's what I'm doing: ```python import numpy as np # Create a 5x5 array arr = np.arange(25).reshape(5, 5) # Slice to create a view (taking every second row) sliced_arr = arr[::2] print('Sliced Array Shape:', sliced_arr.shape) # Should print (3, 5) # Attempt to reshape the sliced array into (5, 3) reshaped_arr = sliced_arr.reshape(5, 3) # This raises an behavior ``` When I run the code, I get the following behavior: ``` ValueError: want to reshape array of size 15 into shape (5,3) ``` I thought that reshaping a view would work as it does with contiguous arrays. What am I missing? I've also checked the flags of `sliced_arr` using `sliced_arr.flags`, and it shows that it's not contiguous. Is there a way to reshape non-contiguous arrays, or do I need to make a copy of the array first? Any help would be appreciated! I'm using NumPy version 1.24.2 and Python 3.9. For context: I'm using Python on Ubuntu. What am I doing wrong? This is part of a larger web app I'm building. Has anyone else encountered this? This is my first time working with Python stable. What are your experiences with this? I'm working on a REST API that needs to handle this. What's the best practice here?