Rolling window for 1D arrays in Numpy?

Question

Is there a way to efficiently implement a rolling window for 1D arrays in Numpy?

For example, I have this pure Python code snippet to calculate the rolling standard deviations for a 1D list, where observations is the 1D list of values, and n is the window length for the standard deviation:

stdev = [] for i, data in enumerate(observations[n-1:]):     strip = observations[i:i+n]     mean = sum(strip) / n     stdev.append(sqrt(250*sum([(s-mean)**2 for s in strip])/(n-1))) 

Is there a way to do this completely within Numpy, i.e., without any Python loops? The standard deviation is trivial with numpy.std, but the rolling window part completely stumps me.

I found this blog post regarding a rolling window in Numpy, but it doesn't seem to be for 1D arrays.

Answer 1

Just use the blog code, but apply your function to the result.

i.e.

numpy.std(rolling_window(observations, n), 1) 

where you have (from the blog):

def rolling_window(a, window):     shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)     strides = a.strides + (a.strides[-1],)     return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) 

Answer 2

Starting in Numpy 1.20, you can directly get a rolling window with sliding_window_view:

from numpy.lib.stride_tricks import sliding_window_view  sliding_window_view(np.array([1, 2, 3, 4, 5, 6]), window_shape = 3) # array([[1, 2, 3], #        [2, 3, 4], #        [3, 4, 5], #        [4, 5, 6]])