How to efficiently calculate the moving average of a large 1D array using NumPy without using loops?
I've been struggling with this for a few days now and could really use some help. I'm not sure how to approach I've searched everywhere and can't find a clear answer. I tried several approaches but none seem to work..... I'm trying to calculate the moving average of a large 1D NumPy array, and I'm looking for a way to do this efficiently without using explicit loops. I'm aware that using a loop would be quite slow for large datasets. Here's a simplified version of what I've attempted so far: ```python import numpy as np # Sample data data = np.random.rand(1000000) window_size = 100 result = np.zeros(data.shape) for i in range(len(data) - window_size + 1): result[i] = np.mean(data[i:i + window_size]) ``` While this works, it takes a significant amount of time for larger arrays. I would like to know if there's a more efficient, vectorized method in NumPy to achieve the same result. I've read about using convolution with a kernel, but I'm not sure how to implement that correctly. Here's what I found: ```python kernel = np.ones(window_size) / window_size result_conv = np.convolve(data, kernel, mode='valid') ``` This seemed promising, but I'm unsure about what the `mode='valid'` does in this context, and whether it will give me the same moving average as my loop-based method. Could anyone clarify if this approach is valid and if there are any caveats? Also, are there any other techniques in NumPy that I should consider for calculating moving averages efficiently? Thanks in advance! I'm working on a CLI tool that needs to handle this. I'd really appreciate any guidance on this. I'm open to any suggestions. This is part of a larger mobile app I'm building. Is this even possible? I'm working on a microservice that needs to handle this.