Not the OP but I agree with them: Little things, like some functions want to be called with a tuple of dimensions, np.zeros((rows, cols)) others just wan

Not the OP but I agree with them: Little things, like some functions want to be called with a tuple of dimensions, np.zeros((rows, cols)) others just want to be called like np.random.randn(n, m) The 1d array is a huge, fundamental design flaw in numpy. It makes zero sense that I can do matrix-vector multiplication against both an nx1 2d array as well as a 1d array. The latter is complete nonsense. When you slice a column from a matrix, and get a not an nx1 vector, but a 1d array, it makes me want to shell out $10,000 for matlab (yes, I know I can get a column vector with the slice A[:, [2]], but I shouldn't have to). This problem leaks out into the ecosystem. For example, when you try to use scipy to integrate an ODE, and pass it an initial condition vector that is nx1, the scipy integrator will silently coerce your vector to a 1d array, pass it to your RHS function, which then either blows up, or more likely, produces silently wrong result because of numpy's insane array broadcasting rules. This problem further leaks into the ridiculous function hstack. If you just used the function vstack, which made a 2x3 matrix from 2 1d 3 element arrays, you might imagine that hstack would produce a 3 x 2 matrix. But no. It creates a 1d 6 element array. For what you wanted, you actually need np.column_stack. I think the way Eigen handles this is the most intuitive. You do linear algebra with 2d objects, and cast to arrays for elementwise operations. There is also a huge inconsistency between what numpy exposes as an object oriented interface vs a "functional" interface. What I mean by this, is that I can call x.sum() on an array, but not x.diff(). For that, I need np.diff(x). There seems to be no pattern to what is exposed as a method vs a function. The array slicing api is also really inconsistent. For instance, given a 3 element array x, a = x[5] is an IndexError. However, this perfectly fine a = x[2:5] I just can't forgive that this is not also an IndexError.