Modify multiple columns in an array numpy

Question

I have a numpy array (nxn matrix), and I would like to modify only the columns which sum is 0. And I would like to assign the same value to all of these columns. To do that, I have first taken the index of the columns that sum to 0:

sum_lines = np.sum(mat_trans, axis = 0)
indices = np.where(sum_lines == 0)[0]

then I did a loop on those indices:

for i in indices:
    mat_trans[:, i] = rank_vect

so that each of these columns now has the value of the rank_vect column vector.

I was wondering if there was a way to do this without loop, something that would look like:

mat_trans[:, (np.where(sum_lines == 0)[0]))] = rank_vect

Thanks!

hpaulj · Accepted Answer · 2018-07-28 01:15:28Z

2

In [114]: arr = np.array([[0,1,2,3],[1,0,2,-3],[-1,2,0,0]])

In [115]: sumlines = np.sum(arr, axis=0)
In [116]: sumlines
Out[116]: array([0, 3, 4, 0])

In [117]: idx = np.where(sumlines==0)[0]
In [118]: idx
Out[118]: array([0, 3])

So the columns that we want to modify are:

In [119]: arr[:,idx]
Out[119]: 
array([[ 0,  3],
       [ 1, -3],
       [-1,  0]])

In [120]: rv = np.array([10,11,12])

If rv is 1d, we get a shape error:

In [121]: arr[:,idx] = rv
ValueError: shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (2,3)

But if it is a column vector (shape (3,1)) it can be broadcast to the (3,2) target:

In [122]: arr[:,idx] = rv[:,None]

In [123]: arr
Out[123]: 
array([[10,  1,  2, 10],
       [11,  0,  2, 11],
       [12,  2,  0, 12]])

answered Jul 28, 2018 at 1:15

hpaulj

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Comments

kevinkayaks · Accepted Answer · 2018-07-27 22:58:25Z

0

This should do the trick

mat_trans[:,indices] = np.stack((rank_vect,)*indices.size,-1)

Please test and let me know if it does what you want. It just stacks the rank_vect repeatedly to match the shape of the LHS on the RHS.

I believe this is equivalent to

for i in indices:
    mat_trans[:, i] = rank_vec

I'd be interested to know the speed difference

edited Jul 27, 2018 at 22:58

answered Jul 27, 2018 at 22:53

kevinkayaks

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2 Comments

hpaulj Over a year ago

Your use of stack and tuple repeat is works fine, but is actually a bit more work than needed. rank_vect just needs to have shape (n,1) to broadcast to target space.

Wendy Over a year ago

thanks for your answer, it indeed works but is longer than the for loop (10.4ms vs 4.2 ms on my matrix which is quite small (832x832)).

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