I am not sure if the approach I've been using in sympy to convert a MutableDenseMatrix to a numpy.array or numpy.matrix is a good current practice.

I have a symbolic matrix like:

g = sympy.Matrix( [[   x,  2*x,  3*x,  4*x,  5*x,  6*x,  7*x,  8*x,   9*x,  10*x],
                   [x**2, x**3, x**4, x**5, x**6, x**7, x**8, x**9, x**10, x**11]] )

and I am converting to a numpy.array doing:

g_func = lambda val: numpy.array( g.subs( {x:val} ).tolist(), dtype=float )

where I get an array for a given value of x.

Is there a better built-in solution in SymPy to do that?

Thank you!

This answer is based on the advices from Krastanov and asmeurer. This little snippet uses sympy.lambdify:

from sympy import lambdify
from sympy.abc import x, y

g = sympy.Matrix([[   x,  2*x,  3*x,  4*x,  5*x,  6*x,  7*x,  8*x,   9*x,  10*x],
                  [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]])
s = (x, y)
g_func = lambdify(s, g, modules='numpy')

where g is your expression containing all symbols grouped in s.

If modules='numpy' is used the output of function g_func will be a np.ndarray object:

g_func(2, 3)
#array([[     2,      4,      6,      8,     10,     12,     14,     16,       18,     20],
#       [     9,     27,     81,    243,    729,   2187,   6561,  19683,    59049, 177147]])

g_func(2, y)
#array([[2, 4, 6, 8, 10, 12, 14, 16, 18, 20],
#       [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]], dtype=object)

If modules='sympy' the output is a sympy.Matrix object.

g_func = lambdify(vars, g, modules='sympy')
g_func(2, 3)
#Matrix([[2,  4,  6,   8,  10,   12,   14,    16,    18,     20],
#        [9, 27, 81, 243, 729, 2187, 6561, 19683, 59049, 177147]])

g_func(2, y)
#Matrix([[   2,    4,    6,    8,   10,   12,   14,   16,    18,    20],
#        [y**2, y**3, y**4, y**5, y**6, y**7, y**8, y**9, y**10, y**11]])