Honestly, in a lot of cases language performance doesn't matter as long as you have a good numerics library. For example, here's one implementation of the projection step in my current project, which does fluid simulation on unstructured tetrahedral meshes and relies heavily on SciPy's sparse matrices:

  def __init__(self, mesh):
    div_matrix = op_mesh.n_to_nm1_boundary.T * op_mesh.F_matrix
    boundary_matrix = op_mesh.F_matrix - op_mesh.FB_matrix
    self.C_matrix = sparse.vstack((div_matrix, boundary_matrix))
    self.CCT_matrix = self.C_matrix * self.C_matrix.T

  def Project(self, velocities):
    lam = linalg.cg(-self.CCT_matrix, self.C_matrix * velocities)[0]
    return velocities + self.C_matrix.T * lam

It's pretty simple (constrained least squares optimization) and it's missing some stuff (preconditioner), but it runs fast enough (~1 minute/frame for > 1M tetrahedra), and only required minimal testing: it's all built up from operators I was already using elsewhere. Since it's the fourth or fifth projection method I've tried for this code, that makes a big difference. The fact that I'm using Python is almost immaterial: all of the "interesting" code is library calls.