How to use NumPy to directly create an a array that is made up of a few python nested loop of a uniform sized tuple/list?

Question

I have a tuple/list like so:

tup = (1, 2, 3, 4, 5)
f = [ (e, d, c, b, a) for a in tup for b in tup for c in tup for d in tup for e in tup ]

How do I create an equivalent array directly from NumPy without using np.array( f )? Is there some slicing technique that I should use on tup? Is what I am asking for possible?

I am specifically asking if NumPy can do this directly, for e.g via some slicing technique or some NumPy function, without using other python functions. I am trying to better understand the capabilities of NumPy.

Thank you.

Would itertools work : `np.array(list(itertools.product(tup,repeat=5)))`? — Divakar, Jun 19 '20 at 11:08
@Divakar Thanks. I know `itertools.product` can be used to create `f`, but it isn't specifically what I am asking for. — Sun Bear, Jun 19 '20 at 12:49
See https://stackoverflow.com/questions/28684492/numpy-equivalent-of-itertools-product — Jan Christoph Terasa, Jun 19 '20 at 13:00
From linked Q&A, use `cartesian_product_transpose(*([np.arange(1,6)]*5))`. — Divakar, Jun 19 '20 at 13:38

score 0 · Answer 1 · answered Jun 19 '20 at 11:08

0

you can use itertools.

import itertools
tup = (1,2,3,4,5)
list(itertools.product(tup, repeat=5))

answered Jun 19 '20 at 11:08

Florian H

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Thank you but this answer is not what I am looking for. I am specifically asking if numpy can do this directly, for e.g via some slicing technique for some numpy function. I will clarify this requirement in my question. – Sun Bear Jun 19 '20 at 12:36

How to use NumPy to directly create an a array that is made up of a few python nested loop of a uniform sized tuple/list?

1 Answers1