performance optimization when calculating means of large NumPy arrays using np.mean
I'm attempting to set up Quick question that's been bugging me - I've been banging my head against this for hours... I'm maintaining legacy code that I'm currently working on a data processing task where I need to compute the mean of a large NumPy array, but I'm experiencing important performance optimization. I'm using NumPy version 1.24.2, and my array is of shape (10, 10000, 1000), which is quite large. When I run the following code: ```python import numpy as np # Generate a large random array large_array = np.random.random((10, 10000, 1000)) # Calculate the mean along the first axis mean_values = np.mean(large_array, axis=0) ``` This operation takes a considerable amount of time, often exceeding 5 seconds. I have also tried using the `keepdims=True` parameter, but it does not seem to improve the performance: ```python mean_values = np.mean(large_array, axis=0, keepdims=True) ``` I've checked my system resources and they seem fine, so I'm wondering if there are any optimizations or best practices when working with large arrays in NumPy that I might be missing. Is there a more efficient way to compute the mean, or perhaps a different approach that could help improve the performance? Any insights would be greatly appreciated! This is happening in both development and production on Debian. Hoping someone can shed some light on this. I'm working on a web app that needs to handle this. The stack includes Python and several other technologies. I'm working in a CentOS environment. Am I approaching this the right way? I'd love to hear your thoughts on this. Could this be a known issue?