subsampling every nth entry in a numpy array

by

You can use numpy’s slicing, simply start:stop:step.

>>> xs
array([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4])
>>> xs[1::4]
array([2, 2, 2])

This creates a view of the the original data, so it’s constant time. It’ll also reflect changes to the original array and keep the whole original array in memory:

>>> a
array([1, 2, 3, 4, 5])
>>> b = a[::2]         # O(1), constant time
>>> b[:] = 0           # modifying the view changes original array
>>> a                  # original array is modified
array([0, 2, 0, 4, 0])

so if either of the above things are a problem, you can make a copy explicitly:

>>> a
array([1, 2, 3, 4, 5])
>>> b = a[::2].copy()  # explicit copy, O(n)
>>> b[:] = 0           # modifying the copy
>>> a                  # original is intact
array([1, 2, 3, 4, 5])

This isn’t constant time, but the result isn’t tied to the original array. The copy also contiguous in memory, which can make some operations on it faster.

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