What is the quick way to create a 2d numpy array?

Hm, you could always use a list-comprehension if you already don't mind using an auxiliary list:

>>> np.array([list(range(i, i + 6)) for i in range(0, 55, 10)])
array([[ 0,  1,  2,  3,  4,  5],
       [10, 11, 12, 13, 14, 15],
       [20, 21, 22, 23, 24, 25],
       [30, 31, 32, 33, 34, 35],
       [40, 41, 42, 43, 44, 45],
       [50, 51, 52, 53, 54, 55]])

And here's a numpy way using broadcasting:

>>> np.arange(0, 51, 10).reshape(-1, 1) + np.arange(6)
array([[ 0,  1,  2,  3,  4,  5],
       [10, 11, 12, 13, 14, 15],
       [20, 21, 22, 23, 24, 25],
       [30, 31, 32, 33, 34, 35],
       [40, 41, 42, 43, 44, 45],
       [50, 51, 52, 53, 54, 55]])

This approach, especially for larger examples, will be faster and require much less auxiliary storage.

You can use an mgrid class instance for this.

import numpy as np

y, x = np.mgrid[:60:10, :6]
a = y + x
print(a)

output

[[ 0  1  2  3  4  5]
 [10 11 12 13 14 15]
 [20 21 22 23 24 25]
 [30 31 32 33 34 35]
 [40 41 42 43 44 45]
 [50 51 52 53 54 55]]

A minor variation is to use the Numpy sum function to perform the addition of the two arrays:

a = np.sum(np.mgrid[:60:10, :6], axis=0)

However, as user2357112 mentions in the comments, it's more efficient to just do the addition with the +operator rather than calling a function.

He also mentions that we can use ogrid to create column and row arrays, which consumes less RAM for y and x. And it's faster too.

import numpy as np
y, x = np.ogrid[:60:10, :6]
a = y + x
print(y, x, a, sep='\n\n')  

output

 [10]
 [20]
 [30]
 [40]
 [50]]

[[0 1 2 3 4 5]]

[[ 0  1  2  3  4  5]
 [10 11 12 13 14 15]
 [20 21 22 23 24 25]
 [30 31 32 33 34 35]
 [40 41 42 43 44 45]
 [50 51 52 53 54 55]]