fastest way to create a nxn matrix with numpy containing zeros [duplicate]

The best way to speed up the creation of this matrix would be to skip using the matrix class entirely and just use np.zeros:

a = np.zeros((16, 16))

Skipping the use of matrix gives a 10x speedup:

%%timeit
a = np.matrix(np.zeros((16, 16)))
4.95 µs ± 50.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

%%timeit
a = np.zeros((16, 16))
495 ns ± 2.18 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

numpy.matrix has been deprecated:

Note It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future.

Edit: There's a nice discussion about the reasons behind matrix's deprecation that Paul Panzer linked to in the comments.

A common reason why people use matrix instead of array is so that a * b will perform matrix multiplication (instead of pairwise multiplication, like it will for standard array's). However, you can now use the shiny new matrix multiplication operator @ to easily perform matrix multiplication using standard arrays:

a = np.arange(2*2).reshape(2,2)
b = np.arange(2*2, 2*2*2).reshape(2,2)
print('a\n%s\n' % a)
print('b\n%s\n' % b)
print('a * b (pairwise multiplication)\n%s\n' % (a * b))
print('a @ b (matrix multiplication)\n%s\n' % (a @ b))

Output:

a
[[0 1]
 [2 3]]

b
[[4 5]
 [6 7]]

a * b (pairwise multiplication)
[[ 0  5]
 [12 21]]

a @ b (matrix multiplication)
[[ 6  7]
 [26 31]]