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 vecto

Answer 1

If I want a 1x3 array, or 3x1 array:

import numpy as np
row_arr = np.array([1,2,3]).reshape((1,3))
col_arr = np.array([1,2,3]).reshape((3,1)))

Check your work:

row_arr.shape  #returns (1,3)
col_arr.shape  #returns (3,1)

I found a lot of answers here are helpful, but much too complicated for me. In practice I come back to shape and reshape and the code is readable: very simple and explicit.

Answer 2

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.

Answer 3

Here's another intuitive way. Suppose we have:

>>> a = np.array([1, 3, 4])
>>> a
array([1, 3, 4])

First we make a 2D array with that as the only row:

>>> a = np.array([a])
>>> a
array([[1, 3, 4]])

Then we can transpose it:

>>> a.T
array([[1],
       [3],
       [4]])

Answer 4

row vectors are (1,0) tensor, vectors are (0, 1) tensor. if using v = np.array([[1,2,3]]), v become (0,2) tensor. Sorry, i am confused.

Answer 5

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 6

The excellent Pandas library adds features to numpy that make these kinds of operations more intuitive IMO. For example:

import numpy as np
import pandas as pd

# column
df = pd.DataFrame([1,2,3])

# row
df2 = pd.DataFrame([[1,2,3]])

You can even define a DataFrame and make a spreadsheet-like pivot table.