The * operator can be used as a shorthand for np.multiply on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 * x2 array([[ 0., 1., 4.], [ 0., 4., 10.], [ …

If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred. If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.

Multiplication by scalars is not allowed, use * instead. Stacks of matrices are broadcast together as if the matrices were elements, respecting the signature (n,k),(k,m)->(n,m):

The cross product of a and b in R 3 is a vector perpendicular to both a and b. If a and b are arrays of vectors, the vectors are defined by the last axis of a and b by default, and these axes can have …

This generalizes to linear algebra operations on higher-dimensional arrays: the last 1 or 2 dimensions of a multidimensional array are interpreted as vectors or matrices, as appropriate for each operation.

Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python. It does this without making needless copies of data and usually leads to efficient algorithm …

For clarity, it is best to avoid the mathematical terms when referring to an array because the mathematical objects with these names behave differently than arrays (e.g. “matrix” multiplication is …

As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or slicing the array (using, for example, N integers), and via the methods and attributes of …

For array, * means element-wise multiplication, while @ means matrix multiplication; they have associated functions multiply() and dot(). For matrix, * means matrix multiplication, and for element …

Additionally, np.einsum('ij,jk', a, b) returns a matrix multiplication, while, np.einsum('ij,jh', a, b) returns the transpose of the multiplication since subscript ‘h’ precedes subscript ‘i’.