Slicing array with numpy?

Question

import numpy as np
r = np.arange(36)
r.resize((6, 6))

print(r)
# prints:
# [[ 0  1  2  3  4  5]
#  [ 6  7  8  9 10 11]
#  [12 13 14 15 16 17]
#  [18 19 20 21 22 23]
#  [24 25 26 27 28 29]
#  [30 31 32 33 34 35]]

print(r[:,::7])
# prints:
# [[ 0]
#  [ 6]
#  [12]
#  [18]
#  [24]
#  [30]]

print(r[:,0])
# prints:
# [ 0  6 12 18 24 30]

The r[:,::7] gives me a column, the r[:,0] gives me a row, they both have the same numbers. Would be glad if someone could explain to me why?

Answer 1

Because the step argument is greater than the corresponding shape so you'll just get the first "row". However these are not identical (even if they contain the same numbers) because the scalar index in [:, 0] flattens the corresponding dimension (so you'll get a 1D array). But [:, ::7] will keep the number of dimensions intact but alters the shape of the step-sliced dimension.