Python: Differentiating between row and column vectors

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.

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]])

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)

The vector you are creating is neither row nor column. It actually has 1 dimension only. You can verify that by

• checking the number of dimensions myvector.ndim which is 1
• checking the myvector.shape, which is (3,) (a tuple with one element only). For a row vector is should be (1, 3), and for a column (3, 1)

Two ways to handle this

• create an actual row or column vector
• reshape your current one

You can explicitly create a row or column

row = np.array([    # one row with 3 elements
   [1, 2, 3]
]
column = np.array([  # 3 rows, with 1 element each
    [1],
    [2],
    [3]
])

or, with a shortcut

row = np.r_['r', [1,2,3]]     # shape: (1, 3)
column = np.r_['c', [1,2,3]]  # shape: (3,1)

Alternatively, you can reshape it to (1, n) for row, or (n, 1) for column

row = my_vector.reshape(1, -1)
column = my_vector.reshape(-1, 1)

where the -1 automatically finds the value of n.