Rolling window for 1D arrays in Numpy?

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)

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]])

I tried using so12311's answer listed above on a 2D array with shape [samples, features] in order to get an output array with shape [samples, timesteps, features] for use with a convolution or lstm neural network, but it wasn't working quite right. After digging into how the strides were working, I realized that it was moving the window along the last axis, so I made some adjustments so that the window is moved along the first axis instead:

def rolling_window(a, window_size):
    shape = (a.shape[0] - window_size + 1, window_size) + a.shape[1:]
    strides = (a.strides[0],) + a.strides
    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)

NOTE: there is no difference in the output if you are only using a 1D input array. In my search this was the first result to get close to what I wanted to do, so I am adding this to help any others searching for a similar answer.

With only one line of code...

import pandas as pd

pd.Series(observations).rolling(n).std()

Rolling window for 1D arrays in Numpy?

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