how to repeat a numpy vector to create an array with the rest of array being zeros

Question

I would like the create the following numpy array, based on the following vector e = numpy.array([1,0,0,0,0,0])

a = [ [e, 0, ---, 0],
      [0, e, ---, 0],
      -
      -
      [0, 0, ---, e]]

(Note: The 0 in this array is thus a zero vector and not scalar)

and thus;

a = [ [1,0,0,0,0,0, 0,0,0,0,0,0, ---, 0,0,0,0,0,0],
      [0,0,0,0,0,0, 1,0,0,0,0,0, ---, 0,0,0,0,0,0],
      -
      -
      [0,0,0,0,0,0, 0,0,0,0,0,0  ---, 1,0,0,0,0,0]]

The solutions does not have to make use of e. The structure of the first array (based on e) is due the underlying linear algebra of the problem I'm tackling.

I have looked at tile and repeat from numpy. However, I was not able to create a with these functions. Ideally, I would like to use a numpy function as speed is quite important for my implementation.

---

EDIT: e is an numpy array and not a python list

EDIT: added some extra information

Answer 1

Initially the suggestion from 'Michael Szczesny', was the one I ended up using. However, I found out as well that the Kronecker product is the mathematical operation which I was looking for.

From this StackOverflow answer it seems(/seemed) that the SciPy implementation works better than the NumPy one. This answer is quite old (2013). However, I do not have enough reputation to ask a followup question.

Maybe someone else would benefit from this information

Answer 2

I can offer a solution with a single for loop:

import numpy as np

# Define the sizes of the array.
a, b = 4, 6

matrix = np.zeros(shape=(a*b,b), dtype=int)
for i in range(b):
    matrix[i*a,i] = 1
print(matrix.T)