Swapping Columns with NumPy arrays

Question

When I have a=1 and b=2, I can write a,b=b,a so that a and b are interchanged with each other.

I use this matrix as an array:

   [ 1,  2,  0, -2]
   [ 0,  0,  1,  2]
   [ 0,  0,  0,  0]

Swapping the columns of a numpy array does not work:

import numpy as np

x = np.array([[ 1,  2,  0, -2],
   [ 0,  0,  1,  2],
   [ 0,  0,  0,  0]])

x[:,1], x[:,2] = x[:,2], x[:,1]

It yields:

   [ 1,  0,  0, -2]
   [ 0,  1,  1,  2]
   [ 0,  0,  0,  0]

So x[:,1] has simply been overwritten and not transferred to x[:,2].

Why is this the case?

Good question. It works as expected with regular lists, e.g. d = [0,1,2,3]; d[:2], d[2:] = d[2:], d[:2] yields [2, 3, 0, 1] — tobias_k
– tobias_k, Commented Jul 1, 2014 at 10:17

Ashoka Lella · Accepted Answer · 2014-07-01 10:26:22Z

33

If you're trying to swap columns you can do it by

print x
x[:,[2,1]] = x[:,[1,2]]
print x

output

[[ 1  2  0 -2]
 [ 0  0  1  2]
 [ 0  0  0  0]]
[[ 1  0  2 -2]
 [ 0  1  0  2]
 [ 0  0  0  0]]

The swapping method you mentioned in the question seems to be working for single dimensional arrays and lists though,

x =  np.array([1,2,0,-2])
print x
x[2], x[1] = x[1], x[2]
print x

output

[ 1  2  0 -2] 
[ 1  0  2 -2]

edited Jul 1, 2014 at 10:26

answered Jul 1, 2014 at 10:14

Ashoka Lella

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Comments

Community · Accepted Answer · 2017-05-23 12:19:30Z

15

When you use the x[:] = y[:] syntax with a numpy array, the values of y are copied directly into x; no temporaries are made. So when you do x[:, 1], x[:,2] = x[:, 2], x[:, 1], first the third column of x is copied directly into the second column, and then the second column is copied directly into the third.

The second column has already been overwritten by the third columns values by the time you copy the second column to the third, so you end up with the original values in the third column.

Numpy is designed to avoid copies where possible in order to improve performance. It's important to understand that list[:] returns a copy of the list, while np.array[:] returns a view of the numpy array.

edited May 23, 2017 at 12:19

CommunityBot

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answered Jul 1, 2014 at 10:20

dustyrockpyle

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Comments

Samit Saxena · Accepted Answer · 2020-05-18 19:06:37Z

1

if you need to swap the mth and the nth rows, you could you the following code:

temp = a[m,:].copy()
a[m,:]  = a[n,:]
a[n,:] =  temp

you can extrapolate the same concept for swapping the columns by changing the indices.

answered May 18, 2020 at 19:06

Samit Saxena

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Comments

Sunil · Accepted Answer · 2019-05-04 16:34:46Z

0

can you try something like:

arr = np.arange(10).reshape(5,2)

arr[:, [1,0]]

array([[1, 0],
       [3, 2],
       [5, 4],
       [7, 6],
       [9, 8]])

answered May 4, 2019 at 16:34

Sunil

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1 Comment

Alberto Chiusole Over a year ago

Works, but pay attention that this is just a view, not a copy.

Vinod Kumar · Accepted Answer · 2019-05-10 16:00:32Z

-1

A very simple solution would be to use swapaxes

x = x.swapaxes(1,2)

answered May 10, 2019 at 16:00

Vinod Kumar

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1 Comment

Alberto Chiusole Over a year ago

Wrong, this does not swap columns, it's "sort of" a transposition. arr = np.arange(10).reshape(5,2) print(np.array_equal(arr.swapaxes(0, 1), arr.T))

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