Transpose

A special form of reshaping is transposing. It just switches the two shape elements of the matrix. The transpose of a matrix  is a matrix  such that

which is resolved in the following way:

A = ...
shape(A) # (3,4)

B = A.T  # A transpose
shape(B) # (4,3)

transpose does not copy: transposition is very similar to reshaping. In particular, it does not copy the data either and just returns a view on the same array: 

A= array([[ 1., 2.],[ 3., 4.]]) 
B=A.T 
A[1,1]=5. 
B[1,1] # 5.0

Transposing a vector makes no sense since vectors are tensors of one dimension, that is, functions of one variable – the index. NumPy will, however, comply and return exactly the same object:

v = array([1., 2., 3.])
v.T # exactly the same vector!

What you have in mind when you want to transpose a vector is probably to create a row or column matrix. This is done using reshape:

v.reshape(-1, 1) # column matrix containing v
v.reshape(1, -1) # row matrix...