Hi William,
You can simply use a for loop for that task:
Python 3.8.2 | packaged by conda-forge | (default, Apr 24 2020, 07:56:27)
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IPython 7.14.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import numpy as np
In [2]: a = np.arange(3).reshape((1, 3))
In [3]: for x in a.T:
...: print(x)
...:
[0]
[1]
[2]
Best regards,
Hameer Abbasi
From: NumPy-Discussion
<numpy-discussion-bounces+einstein.edison=gmail....@python.org> on behalf of
William Ayd <william....@icloud.com>
Reply to: Discussion of Numerical Python <numpy-discussion@python.org>
Date: Tuesday, 19. May 2020 at 01:42
To: "numpy-discussion@python.org" <numpy-discussion@python.org>
Subject: Re: [Numpy-discussion] Using nditer + external_loop to Always Iterate
by Column
I am trying to use the nditer to traverse each column of a 2D array, returning
the column as a 1D array. Consulting the docs, I found this example which works
perfectly fine:
In [65]: a = np.arange(6).reshape(2,3)
In [66]: for x in np.nditer(a, flags=['external_loop'], order='F'):
...: print(x, end=' ')
...:
[0 3] [1 4] [2 5]
When changing the shape of the input array to (1, 3) however, this doesn’t
yield what I am hoping for any more (essentially [0], [1] [2]):
In [68]: for x in np.nditer(a, flags=['external_loop'], order='F'):
...: print(x, end=' ')
...:
[0 1 2]
I suspect this may have to do with the fact that the (1, 3) array is both C and
F contiguous, and it is trying to return as large of a 1D F-contiguous array as
it can. However, I didn’t see any way to really force it to go by columns. My
best guess was the itershape argument though I couldn’t figure out how to get
that to work and didn’t see much in the documentation.
Thanks in advance for the help!
- Will
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