Numpy how to iterate over columns of array?

Question

Suppose I have and m x n array. I want to pass each column of this array to a function to perform some operation on the entire column. How do I iterate over the columns of the array?

For example, I have a 4 x 3 array like

1  99 2
2  14 5
3  12 7
4  43 1

for column in array:
  some_function(column)

where column would be "1,2,3,4" in the first iteration, "99,14,12,43" in the second, and "2,5,7,1" in the third.

Solution 1

Just iterate over the transposed of your array:

for column in array.T:
   some_function(column)

Solution 2

This should give you a start

>>> for col in range(arr.shape[1]):
    some_function(arr[:,col])


[1 2 3 4]
[99 14 12 43]
[2 5 7 1]

Solution 3

For a three dimensional array you could try:

for c in array.transpose(1, 0, 2):
    do_stuff(c)

See the docs on how array.transpose works. Basically you are specifying which dimension to shift. In this case we are shifting the second dimension (e.g. columns) to the first dimension.

Solution 4

You can also use unzip to iterate through the columns

for col in zip(*array):
   some_function(col)

Solution 5

for c in np.hsplit(array, array.shape[1]):
    some_fun(c)

View more solutions

Comments

[ev-br]

Can't you use an index --- stackoverflow.com/questions/4455076/…

[Ibrahim Muhammad]

What would be a good way to combine the result back into a single array?

[gronostaj]

It doesn't look pythonic to me.

[drevicko]

For those wondering, array.T isn't costly, as it just changes the 'strides' of array (see this answer for an interesting discussion)

[Neil G]

@gronostaj Of course it's Pythonic. How else would you solve this problem when you want to iterate over an arbitrary axis of a multidimensional array?

[gronostaj]

@NeilG This question is strictly about 2-dimensional arrays.

[Rufus]

Is there a way of iterating which keeps the vectors as column vectors?

[Bill]

Interesting. This returns tuples instead of arrays. And it's much faster.

[Ela782]

I have a hunch that the result of this might depend on the storage order of the numpy array ('C' or 'F') - it may return columns in one case and rows in the other. I'm not sure though - just a warning, better check before using this. It doesn't look safe.

[rv.kvetch]

This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From Review

[Admin]

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