NumPy (pronounced “numb pie” or sometimes “numb pea”) is an extension to the Python programming language that adds support for large, multi-dimensional arrays, along with an extensive library of high-level mathematical functions to operate on these arrays.
Whenever possible express operations on data in terms of arrays and vector operations. Vector operations execute much faster than equivalent for loops
numpy.dot
Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned.
As of version 1.8, several of the routines in np.linalg can operate on a 'stack' of matrices. That is, the routine can calculate results for multiple matrices if they're stacked together. For example, A here is interpreted as two stacked 3-by-3 matrices:
np.random.seed(123)
A = np.random.rand(2,3,3)
b = np.random.rand(2,3)
x = np.linalg.solve(A, b)
print np.dot(A[0,:,:], x[0,:])
# array([ 0.53155137, 0.53182759, 0.63440096])
print b[0,:]
# array([ 0.53155137, 0.53182759, 0.63440096])
The official np docs specify this via parameter specifications like a : (..., M, M) array_like.