Unexpected shape mismatch when using np.split with arrays of different lengths in NumPy 1.24
I'm testing a new approach and I tried several approaches but none seem to work. I'm working with a shape mismatch behavior when trying to use `np.split` on an array with lengths that don't evenly divide into the specified number of splits. I expected it to handle uneven splits gracefully, but instead, I'm getting a `ValueError`. Here's the code I'm working with: ```python import numpy as np # Creating a sample array with 10 elements array = np.arange(10) # Attempting to split the array into 3 parts parts = np.split(array, 3) ``` When I run this, I receive the following behavior: ``` ValueError: array split does not result in an equal division ``` I looked into the documentation, which suggests that `np.split` should raise an behavior if the splits are not even. However, I thought there might be a way to handle this without having to pad the array manually. I also tried using `np.array_split`, which is supposed to allow uneven splits, but I'm getting the same behavior. Here’s what I tried: ```python # Attempting with np.array_split instead parts = np.array_split(array, 3) ``` But I still encounter issues when I try to access even the first part of the split. It seems like my use case is not being handled as I expected. What are the best practices for splitting arrays of different lengths in NumPy, especially when the array size isn’t divisible by the number of splits? Is there a recommended approach to avoid these errors altogether? My development environment is Linux.