Numpy transpose of 1D array not giving expected result

Transpose is a noop for one-dimensional arrays.

Add new axis and transpose:

>>> a[None].T
array([[1],
       [2],
       [3]])
>>> np.newaxis is None
True

Or reshape:

>>> a.reshape(a.shape+(1,))
array([[1],
       [2],
       [3]])

Or as @Sven Marnach suggested in comments, add new axis at the end:

>>> a[:,None]
array([[1],
       [2],
       [3]])

NumPy's transpose() effectively reverses the shape of an array. If the array is one-dimensional, this means it has no effect.

In NumPy, the arrays

array([1, 2, 3])

and

array([1,
       2,
       3])

are actually the same – they only differ in whitespace. What you probably want are the corresponding two-dimensional arrays, for which transpose() would work fine. Also consider using NumPy's matrix type:

In [1]: numpy.matrix([1, 2, 3])
Out[1]: matrix([[1, 2, 3]])

In [2]: numpy.matrix([1, 2, 3]).T
Out[2]: 
matrix([[1],
        [2],
        [3]])

Note that for most applications, the plain one-dimensional array would work fine as both a row or column vector, but when coming from Matlab, you might prefer using numpy.matrix.

A more concise way to reshape a 1D array into a 2D array is:

a = np.array([1,2,3]),  a_2d = a.reshape((1,-1)) or a_2d = a.reshape((-1,1))

The -1 in the shape vector means "fill in whatever number makes this work"