A simple way to achieve this is by using np.convolve. The idea behind this is to leverage the way the discrete convolution is computed and use it to return a rolling mean. This can be done by convolving with a sequence of np.ones of a length equal to the sliding window length we want. In order to do so we could define the following function: def moving_average(x, w): return np.convolve(x, np.ones(w), 'valid') / w This function will be taking the convolution of the sequence x and a sequence ... The multiple of 2 makes the sliding window slide 2 units at a time which is necessary for sliding over each tuple. Using numpy array slicing you can pass the sliding window into the flattened numpy array and do aggregates on them like sum. While solving a geometry problem, I came across an approach called Sliding Window Algorithm. Couldn't really find any study material/details on it. What is the algorithm about? Maybe the best option is to throw away sliding_window_view and implement it with numba.
