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

Answer 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.

Answer 2

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]])

Answer 3

A very simple solution would be to use swapaxes

x = x.swapaxes(1,2)

Answer 4

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.

Answer 5

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]