The Python Oracle

"Cloning" row or column vectors

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Chapters
00:00 &Quot;Cloning&Quot; Row Or Column Vectors
00:57 Accepted Answer Score 112
01:23 Answer 2 Score 62
02:04 Answer 3 Score 431
02:20 Answer 4 Score 10
02:41 Thank you

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Full question
https://stackoverflow.com/questions/1550...

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Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...

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Tags
#python #numpy #linearalgebra

#avk47



ANSWER 1

Score 431


Use numpy.tile:

>>> tile(array([1,2,3]), (3, 1))
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

or for repeating columns:

>>> tile(array([[1,2,3]]).transpose(), (1, 3))
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])



ACCEPTED ANSWER

Score 112


Here's an elegant, Pythonic way to do it:

>>> array([[1,2,3],]*3)
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

>>> array([[1,2,3],]*3).transpose()
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])

the problem with [16] seems to be that the transpose has no effect for an array. you're probably wanting a matrix instead:

>>> x = array([1,2,3])
>>> x
array([1, 2, 3])
>>> x.transpose()
array([1, 2, 3])
>>> matrix([1,2,3])
matrix([[1, 2, 3]])
>>> matrix([1,2,3]).transpose()
matrix([[1],
        [2],
        [3]])



ANSWER 3

Score 62


First note that with numpy's broadcasting operations it's usually not necessary to duplicate rows and columns. See this and this for descriptions.

But to do this, repeat and newaxis are probably the best way

In [12]: x = array([1,2,3])

In [13]: repeat(x[:,newaxis], 3, 1)
Out[13]: 
array([[1, 1, 1],
       [2, 2, 2],
       [3, 3, 3]])

In [14]: repeat(x[newaxis,:], 3, 0)
Out[14]: 
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

This example is for a row vector, but applying this to a column vector is hopefully obvious. repeat seems to spell this well, but you can also do it via multiplication as in your example

In [15]: x = array([[1, 2, 3]])  # note the double brackets

In [16]: (ones((3,1))*x).transpose()
Out[16]: 
array([[ 1.,  1.,  1.],
       [ 2.,  2.,  2.],
       [ 3.,  3.,  3.]])



ANSWER 4

Score 10


I think using the broadcast in numpy is the best, and faster

I did a compare as following

import numpy as np
b = np.random.randn(1000)
In [105]: %timeit c = np.tile(b[:, newaxis], (1,100))
1000 loops, best of 3: 354 µs per loop

In [106]: %timeit c = np.repeat(b[:, newaxis], 100, axis=1)
1000 loops, best of 3: 347 µs per loop

In [107]: %timeit c = np.array([b,]*100).transpose()
100 loops, best of 3: 5.56 ms per loop

about 15 times faster using broadcast