Create vertical NumPy arrays in Python

Question

I'm using NumPy in Python to work with arrays. This is the way I'm using to create a vertical array:

import numpy as np
a = np.array([[1],[2],[3]])

Is there a simple and more direct way to create vertical arrays?

possible duplicate of numpy convert row vector to column vector — ali_m
– ali_m, Commented Apr 15, 2015 at 19:26

David A Pugh · Accepted Answer · 2016-09-22 21:25:04Z

30

You can use reshape or vstack :

>>> a=np.arange(1,4)
>>> a
array([1, 2, 3])
>>> a.reshape(3,1)
array([[1],
       [2],
       [3]])
>>> np.vstack(a)
array([[1],
       [2],
       [3]])

Also, you can use broadcasting in order to reshape your array:

In [32]: a = np.arange(10)
In [33]: a
Out[33]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [34]: a[:,None]
Out[34]: 
array([[0],
       [1],
       [2],
       [3],
       [4],
       [5],
       [6],
       [7],
       [8],
       [9]])

edited Sep 22, 2016 at 21:25

David A Pugh

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answered Apr 15, 2015 at 19:00

Kasravnd

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2 Comments

Bhargav Rao Over a year ago

It would be better if you could add links to your function

Kasravnd Over a year ago

@BhargavRao i was doing that ;)

Bhargav Rao · Accepted Answer · 2015-04-15 19:10:16Z

4

You can also use np.newaxis (See Examples here)

>>> import numpy as np
>>> np.arange(3)[:, np.newaxis]
array([[0],
       [1],
       [2]])

As a side note

I just realized that you have used, from numpy import *. Do not do so as many functions from the Python generic library overlap with numpy (for e.g. sum). When you import * from numpy you lose the functionality of those functions. Hence always use :

import numpy as np

which is also easy to type.

edited Apr 15, 2015 at 19:10

answered Apr 15, 2015 at 19:02

Bhargav Rao

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Comments

mohamed martini · Accepted Answer · 2022-02-25 01:22:36Z

4

The best way in my experience is to use reshape(-1, 1) because you don't have to specify the size of the array. It works like this:

>>> a = np.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> a.reshape(-1, 1)
array([[0],
       [1],
       [2],
       [3],
       [4]])

answered Feb 25, 2022 at 1:22

mohamed martini

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Comments

Peter Mortensen · Accepted Answer · 2017-12-24 23:57:43Z

Simplicity and directness is in the eye of the beholder.

In [35]: a = np.array([[1],[2],[3]])
In [36]: a.flags
Out[36]:
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False
In [37]: b=np.array([1,2,3]).reshape(3,1)
In [38]: b.flags
Out[38]:
  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False

The first is shorter and owns its data. So in a sense the extra brackets are a pain, but it's a rather subjective one.

Or if you want something more like MATLAB you could use the np.matrix string format:

c=np.array(np.matrix('1;2;3'))
c=np.mat('1;2;3').A

But I usually don't worry about the OWNDATA flag. One of my favorite sample arrays is:

np.arange(12).reshape(3,4)

Other ways:

np.atleast_2d([1,2,3]).T
np.array([1,2,3],ndmin=2).T
a=np.empty((3,1),int);a[:,0]=[1,2,3] # OWNDATA

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