Python: Differentiating between row and column vectors

Question

Is there a good way of differentiating between row and column vectors in python? So far I'm using numpy and scipy and what I see so far is that If I was to give one a vector, say

from numpy import * Vector = array([1,2,3]) 

they wouldn't be able to say weather I mean a row or a column vector. Moreover:

array([1,2,3]) == array([1,2,3]).transpose() True 

Which in "real world" is simply untrue. I realize that most of the functions on vectors from the mentioned modules don't need the differentiation. For example outer(a,b) or a.dot(b) but I'd like to differentiate for my own convenience.

Answer 1

You can make the distinction explicit by adding another dimension to the array.

>>> a = np.array([1, 2, 3]) >>> a array([1, 2, 3]) >>> a.transpose() array([1, 2, 3]) >>> a.dot(a.transpose()) 14 

Now force it to be a column vector:

>>> a.shape = (3,1) >>> a array([[1],        [2],        [3]]) >>> a.transpose() array([[1, 2, 3]]) >>> a.dot(a.transpose()) array([[1, 2, 3],        [2, 4, 6],        [3, 6, 9]]) 

Another option is to use np.newaxis when you want to make the distinction:

>>> a = np.array([1, 2, 3]) >>> a array([1, 2, 3]) >>> a[:, np.newaxis] array([[1],        [2],        [3]]) >>> a[np.newaxis, :] array([[1, 2, 3]]) 

Answer 2

Use double [] when writing your vectors.

Then, if you want a row vector:

row_vector = array([[1, 2, 3]])    # shape (1, 3) 

Or if you want a column vector:

col_vector = array([[1, 2, 3]]).T  # shape (3, 1)