sliding window of M-by-N shape numpy.ndarray

Question

I have a numpy array of shape (6,2)

[[00,01],
 [10,11],
 [20,21],
 [30,31],
 [40,41],
 [50,51]]

I need a sliding window with step size 1 an

Answer 1

Here is One-liner using Numpy >= v1.17

rowsJoined = 3

splits = np.vstack(np.split(x,np.array([[i, i + rowsJoined] for i in range(x.shape[0] - (rowsJoined - 1))]).reshape(-1))).reshape(-1, rowsJoined * x.shape[1]) 

Test

x = np.array([[00,1],
              [10,11],
              [20,21],
              [30,31],
              [40,41],
              [50,51]])

Result

[[ 0  1 10 11 20 21]
 [10 11 20 21 30 31]
 [20 21 30 31 40 41]
 [30 31 40 41 50 51]]

Test Performance On Large Array

import numpy as np
import time

x = np.array(range(1000)).reshape(-1, 2)
rowsJoined = 3

all_t = 0.
for i in range(1000):
    start_ = time.time()
    np.vstack(
        numpy.split(x,np.array([[i, i + rowsJoined] for i in range(x.shape[0] - (rowsJoined - 1))])
                    .reshape(-1))).reshape(-1, rowsJoined * x.shape[1])
    all_t += time.time() - start_

print('Average Time of 1000 Iterations on Array of Shape '
      '1000 x 2 is: {} Seconds.'.format(all_t/1000.))

Performance Result

Average Time of 1000 Iterations on Array of Shape 1000 x 2 is: 0.0016909 Seconds.