performance optimization when calculating the rolling mean with numpy in a large dataset
I'm attempting to set up I'm testing a new approach and I need help solving I've searched everywhere and can't find a clear answer. I'm currently trying to compute a rolling mean over a large NumPy array (size ~10 million) using a window size of 1000. However, the performance is significantly slower than expected, and I suspect it might be related to how I'm implementing it. I started with the following code: ```python import numpy as np # Generate a large random array data = np.random.rand(10_000_000) window_size = 1000 rolling_mean = np.convolve(data, np.ones(window_size)/window_size, mode='valid') ``` While this code does work, it takes quite some time to execute. I noticed that the execution time can be quite variable, and occasionally it raises a MemoryError, especially when trying to work with larger windows. I also experimented with `np.lib.stride_tricks.sliding_window_view`, but the results were still not as performant as I would like: ```python from numpy.lib.stride_tricks import sliding_window_view windowed_data = sliding_window_view(data, window_shape=window_size) rolling_mean_view = np.mean(windowed_data, axis=1) ``` This approach also consumes a lot of memory and does not seem to improve performance. After profiling my code, I found that the convolution method is still the fastest, but it doesn't seem to scale well for larger datasets. Is there an efficient way to calculate rolling means in NumPy for very large arrays? Or would it be better to switch to another library like Pandas for this kind of operation? I'd appreciate any insights or alternative methods to handle this scenario effectively without running into performance bottlenecks. For context: I'm using Python on Ubuntu. Cheers for any assistance! The project is a application built with Python. Hoping someone can shed some light on this.