numpy.multiply# numpy. multiply (x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True [, signature]) = <ufunc 'multiply'> # Multiply arguments …

The matmul function implements the semantics of the @ operator introduced in Python 3.5 following PEP 465. It uses an optimized BLAS library when possible (see numpy.linalg). …

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. If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a …

For example, if you have a 256x256x3 array of RGB values, and you want to scale each color in the image by a different value, you can multiply the image by a one-dimensional array with 3 …

>>> np. prod (a, axis = 1) array([ 2., 12.]) >>> np. prod (a, axis = 0) array([3., 8.]) Or select specific elements to include: >>> np . prod ([ 1. , np . nan , 3. ], where = [ True , False , True ]) …

>>> data = np. array ([[1, 2], [5, 3], [4, 6]]) >>> data array([[1, 2], [5, 3], [4, 6]]) >>> data. max (axis = 0) array([5, 6]) >>> data. max (axis = 1) array([2, 5, 6]) Once you’ve created your matrices, …

Return the cross product of two (arrays of) vectors. 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 …

NumPy performs operations element-by-element, so multiplying 2D arrays with * is not a matrix multiplication – it’s an element-by-element multiplication. (The @ operator, available since …

The Einstein summation convention can be used to compute many multi-dimensional, linear algebraic array operations. einsum provides a succinct way of representing these. A non …

Given two tensors, a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a’s and b’s elements (components) over the axes specified by …