To force an iteration to run along certain axes, I believe you should be using `op_axes`. Your diagnosis is correct that `external_loop` is trying to help you be more optimal, since it's purpose is exactly that: optimization.
Unfortunately, if you use `op_axes` you'll run into https://github.com/numpy/numpy/issues/9808.
On Tue, 19 May 2020 at 00:42, William Ayd firstname.lastname@example.org wrote:
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 ,  ):
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!
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