Python/Numpy fast way to selecting every nth chunk in list

Question

Edited for the confusion in the problem, thanks for the answers!

My original problem was that I have a list [1,2,3,4,5,6,7,8], and I want to select every chunk of size x with gap of one. So if I want to select select every other chunk of size 2, the outcome would be [1,2,4,5,7,8]. A chunk size of three would give me [1,2,3,5,6,7].

I've searched a lot on slicing and I couldn't find a way to select chunks instead of element. Make multiple slice operations then join and sort seems a little too expensive. The input can either be a python list or numpy ndarray. Thanks in advance.

Answer 1

To me it seems, you want to skip one element between chunks until the end of the input list or array.

Here's one approach based on np.delete that deletes that single elements squeezed between chunks -

out = np.delete(A,np.arange(len(A)/(x+1))*(x+1)+x)

Here's another approach based on boolean-indexing -

L = len(A)
avoid_idx = np.arange(L/(x+1))*(x+1)+x
out = np.array(A)[~np.in1d(np.arange(L),avoid_idx)]

Sample run -

In [98]: A = [51,42,13,34,25,68,667,18,55,32] # Input list

In [99]: x = 2

# Thus, [51,42,13,34,25,68,667,18,55,32]
                ^        ^         ^        # Skip these

In [100]: np.delete(A,np.arange(len(A)/(x+1))*(x+1)+x)
Out[100]: array([ 51,  42,  34,  25, 667,  18,  32])

In [101]: L = len(A)
     ...: avoid_idx = np.arange(L/(x+1))*(x+1)+x
     ...: out = np.array(A)[~np.in1d(np.arange(L),avoid_idx)]
     ...: 

In [102]: out
Out[102]: array([ 51,  42,  34,  25, 667,  18,  32])

Answer 2

First off, you can create an array of indices then use np.in1d() function in order to extract the indices that should be omit then with a simple not operator get the indices that must be preserve. And at last pick up them using a simple boolean indexing:

>>> a = np.array([1,2,3,4,5,6,7,8])
>>> range_arr = np.arange(a.size)
>>> 
>>> a[~np.in1d(range_arr,range_arr[2::3])]
array([1, 2, 4, 6, 8])

General approach:

>>> range_arr = np.arange(np_array.size) 
>>> np_array[~np.in1d(range_arr,range_arr[chunk::chunk+1])]