Unexpected Memory Allocation Issues When Using np.empty vs np.zeros for Large Arrays
Can someone help me understand I've been working on this all day and I'm building a feature where I've hit a wall trying to I'm sure I'm missing something obvious here, but I’m working with an unexpected memory allocation scenario with creating large NumPy arrays in Python 3.10 using NumPy version 1.24.3. When I try to create a large empty array using `np.empty`, I seem to be running into memory limitations, while `np.zeros` works fine. Here's what I've tried: ```python import numpy as np # Attempting to create a large empty array large_empty_array = np.empty((10000, 10000)) print(large_empty_array) ``` When I run this code, I get a `MemoryError`, indicating that I can’t allocate enough memory. However, when I switch to using `np.zeros`, it works without any issues: ```python # Creating a large zero-filled array large_zero_array = np.zeros((10000, 10000)) print(large_zero_array) ``` I would expect that since `np.empty` does not initialize the memory, it should be more efficient and use less memory. Is there a limit on how the memory is allocated for uninitialized arrays? I’m running this on a machine with 16GB RAM. Any insights or recommendations on best practices for using `np.empty` vs `np.zeros`, especially with large datasets, would be greatly appreciated. Is there a way to troubleshoot or understand this behavior better? I'm working on a service that needs to handle this. Is there a better approach? For context: I'm using Python on Linux. I'd be grateful for any help. I recently upgraded to Python latest. I'm open to any suggestions. My team is using Python for this mobile app. How would you solve this?