Performance guide with np.concatenate on large lists of arrays in NumPy 1.24.0
I'm attempting to set up I've looked through the documentation and I'm still confused about This might be a silly question, but I'm working with important performance optimization when using `np.concatenate` to combine a large list of arrays in NumPy 1.24.0. Specifically, I've constructed a list of 1000 NumPy arrays, each of shape (1000, 1000). The goal is to concatenate them along the first axis, but the operation takes an unexpectedly long time and consumes a lot of memory. Here's a simplified version of my code: ```python import numpy as np # Create a list of 1000 arrays of shape (1000, 1000) arrays = [np.random.rand(1000, 1000) for _ in range(1000)] # Attempting to concatenate the arrays result = np.concatenate(arrays, axis=0) ``` When I run this, it takes several minutes and my system starts swapping due to high memory usage. I tried using `np.vstack` and `np.stack`, but they don't seem to provide any performance benefits and still lead to memory issues. Is there a more efficient way to concatenate a large number of arrays without running into these performance bottlenecks? Are there any best practices or alternative approaches I should consider for handling large datasets with NumPy? Any help or insights would be greatly appreciated! This is part of a larger web app I'm building. Has anyone else encountered this? I'm using Python stable in this project. How would you solve this?