Create a two-dimensional array with two one-dimensional arrays

Question

My function (name CovexHull(point)) accepts the argument as a two-dimensional array:

hull = ConvexHull(points)

Session

In [1]: points.ndim
Out[1]: 2
In [2]: points.shape
Out[2]: (10, 2)
In [3]: points
Out[3]:
array([[ 0. ,  0. ],
       [ 1. ,  0.8],
       [ 0.9,  0.8],
       [ 0.9,  0.7],
       [ 0.9,  0.6],
       [ 0.8,  0.5],
       [ 0.8,  0.5],
       [ 0.7,  0.5],
       [ 0.1,  0. ],
       [ 0. ,  0. ]])

points is a NumPy array with ndim 2.

I have two different NumPy arrays (tp and fp) like below:

In [4]: fp.ndim
Out[4]: 1
In [5]: fp.shape
Out[5]: (10,)
In [6]: fp
Out[6]:
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.4,
        0.5, 0.6,  0.9,  1. ])

How can I create a two-dimensional NumPy array effectively (like points mentioned above) with tp and fp?

Peter Mortensen · Accepted Answer · 2022-09-10 11:20:36Z

105

If you wish to combine two 10 element one-dimensional arrays into a two-dimensional array, np.vstack((tp, fp)).T will do it.

np.vstack((tp, fp)) will return an array of shape (2, 10), and the T attribute returns the transposed array with shape (10, 2) (i.e., with the two one-dimensional arrays forming columns rather than rows).

>>> tp = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> tp.ndim
1
>>> tp.shape
(10,)

>>> fp = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> fp.ndim
1
>>> fp.shape
(10,)

>>> combined = np.vstack((tp, fp)).T
>>> combined
array([[ 0, 10],
       [ 1, 11],
       [ 2, 12],
       [ 3, 13],
       [ 4, 14],
       [ 5, 15],
       [ 6, 16],
       [ 7, 17],
       [ 8, 18],
       [ 9, 19]])

>>> combined.ndim
2
>>> combined.shape
(10, 2)

edited Sep 10, 2022 at 11:20

Peter Mortensen

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answered Jul 17, 2013 at 21:49

ijmarshall

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2 Comments

Jaime Over a year ago

+1, but you can get the right shape directly with np.column_stack.

Daniel C Jacobs Over a year ago

This should not be the accepted answer. column_stack is much more straightforward (see Aminu's answer)

Peter Mortensen · Accepted Answer · 2022-09-10 11:21:21Z

54

You can use NumPy's column_stack:

np.column_stack((tp, fp))

edited Sep 10, 2022 at 11:21

Peter Mortensen

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answered Apr 20, 2019 at 6:43

Aminu Kano

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2 Comments

eric Over a year ago

This did the trick, accepted answer is less transparent

Leonard Over a year ago

This is the right way. I'm surprised how this is not the first answer that shows up when googled.

Keiku · Accepted Answer · 2023-01-17 11:38:47Z

9

Another way is to use np.transpose. It seems to be used occasionally, but it is not readable, so it is a good idea to use Aminu Kano's answer.

import numpy as np

tp = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
fp = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
combined = np.transpose((tp, fp))
combined
# Out[3]:
# array([[ 0, 10],
#        [ 1, 11],
#        [ 2, 12],
#        [ 3, 13],
#        [ 4, 14],
#        [ 5, 15],
#        [ 6, 16],
#        [ 7, 17],
#        [ 8, 18],
#        [ 9, 19]])
combined.ndim
# Out[4]: 2
combined.shape
# Out[5]: (10, 2)

edited Jan 17, 2023 at 11:38

answered Aug 5, 2021 at 4:49

Keiku

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3 Comments

Daniel C Jacobs Over a year ago

Even this is better than the accepted answer. vstack is not useful for OPs goal.

NelsonGon Over a year ago

I think column_stack as in stackoverflow.com/a/55770994/10323798 is better and does what one would expect than vstack which gives the wrong shape.

Keiku Over a year ago

Thank you for your comment. I changed the text of the answer.

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