Removing every nth element in an array

Question

How do I remove every nth element in an array?

import numpy as np

x = np.array([0,10,27,35,44,32,56,35,87,22,47,17])
n = 3  # remove every 3rd element

...something like the opposite of x[0::n]? I've tried this, but of course it doesn't work:

for i in np.arange(0,len(x),n):
    x = np.delete(x,i)

score 17 · Accepted Answer · answered Feb 20 '14 at 23:56

17

You're close... Pass the entire arange as subslice to delete instead of attempting to delete each element in turn, eg:

import numpy as np

x = np.array([0,10,27,35,44,32,56,35,87,22,47,17])
x = np.delete(x, np.arange(0, x.size, 3))
# [10 27 44 32 35 87 47 17]

answered Feb 20 '14 at 23:56

Jon Clements

4

That is actually much better done as `np.delete(x, slice(None, None, 3))`. – Jaime Feb 21 '14 at 00:42
1

@Jaime very nice observation - didn't think to check if `np.delete` took native `slice` objects! There doesn't seem to be any speed advantage though even with a million item array (in fact it's ever so *slightly slower*) - but it's good to know - thanks. – Jon Clements Feb 21 '14 at 00:54
1

That's weird... On my system, removing every third item of a 1 million item long array is about 1.7x faster with the slice: `a = np.random.rand(1e6); %timeit np.delete(a, np.arange(0, 1e6, 3)) --> 100 loops, best of 3: 14.5 ms per loop; %timeit np.delete(a, slice(None, None, 3)) --> 100 loops, best of 3: 8.41 ms per loop`. – Jaime Feb 21 '14 at 01:38
1

@Jaime Using that exact code: 372/365... But swaps around occasionally - umm.... – Jon Clements Feb 21 '14 at 01:43
1

@Jaime In the interests of science, I tested this as well, but on a much larger array. I got that slice was moderately faster per loop, .251 to .267 ms. – kingledion Mar 05 '17 at 22:27
you can also do `np.delete(x, np.s_[::3])` see the last example [here](https://numpy.org/doc/stable/reference/generated/numpy.delete.html) and the doc for `np.s_` [here](https://numpy.org/doc/stable/reference/generated/numpy.s_.html) – Levi Baguley Aug 18 '21 at 15:35

score 2 · Answer 2 · answered Apr 10 '20 at 12:41

I just add another way with reshaping if the length of your array is a multiple of n:

import numpy as np

x = np.array([0,10,27,35,44,32,56,35,87,22,47,17])
x = x.reshape(-1,3)[:,1:].flatten()
# [10 27 44 32 35 87 47 17]

On my computer it runs almost twice faster than the solution with np.delete (between 1.8x and 1.9x to be honnest).

You can also easily perfom fancy operations, like m deletions each n values etc.

2 Answers2