MemoryError when reshaping large NumPy arrays with np.reshape in version 1.24
I'm trying to debug I've been researching this but I tried several approaches but none seem to work... This might be a silly question, but I'm working with a `MemoryError` when trying to reshape a large NumPy array using `np.reshape`. My current implementation looks like this: ```python import numpy as np # Creating a large array of 10 million elements large_array = np.arange(10_000_000) # Attempting to reshape into a 1000x10000 array reshaped_array = np.reshape(large_array, (1000, 10000)) ``` When I run this code, I get the following behavior: ``` MemoryError: Unable to allocate array with shape (1000, 10000) and data type int64 ``` I've verified that my machine has sufficient memory (32GB RAM) and that there are no other memory-intensive processes running. I also tried using `np.reshape` with the `order='F'` parameter, but I still get the same behavior. Additionally, I checked the output of `large_array.nbytes` which shows it uses about 80MB, so I expected reshaping it wouldn't require that much more memory. I wonder if there’s some limitation or best practice I'm missing regarding memory allocation for large arrays in NumPy. Is there a way to efficiently reshape large arrays without running into memory issues? Also, could it be related to the way I’m allocating the array or the specific version of NumPy? I’m currently using NumPy version 1.24.0. Any insights would be greatly appreciated! I'd really appreciate any guidance on this. My development environment is Ubuntu. Is there a better approach? This is part of a larger API I'm building. I'd really appreciate any guidance on this. I'm working with Python in a Docker container on Windows 11. I'm working in a CentOS environment. Any help would be greatly appreciated! What am I doing wrong?