NumPy：使用nditer迭代numpy数组的外部尺寸

Question

I am unable to iterate over the outer axis of a numpy array.

import numpy as np

a = np.arange(2*3).reshape(2,3)
it = np.nditer(a)
for i in it:
    print i

and this gives, as one would expect:

0
1
2
3
4
5

I would, however, like the output to come in threes, such that I have iterated over the outer axes:

(0, 1, 2)
(3, 4, 5)

I know of many ways in which I can achieve this, but after pouring over the nditer documentation , I can't seem to find a solution using nditer. I am using this as an opportunity to learn nditer. So I would prefer not using other solutions, unless it is genuinely more efficient or pythonic.

Answer 1

It's easier to control the iteration with a plain for :

In [17]: a
Out[17]: 
array([[0, 1, 2],
       [3, 4, 5]])
In [18]: for row in a:
    ...:     print(row)
    ...:     
[0 1 2]
[3 4 5]

Doing this with nditer is just plain awkward. Unless you need broadcasting use cython as described at the end of the page, nditer does not offer any speed advantages. Even with cython , I've gotten better speeds with memoryviews than with nditer .

Look at np.ndindex . It creates a dummy array with reduced dimensions, and does a nditer on that:

In [20]: for i in np.ndindex(a.shape[0]):
    ...:     print(a[i,:])
    ...:     
[[0 1 2]]
[[3 4 5]]

Got it:

In [31]: for x in np.nditer(a.T.copy(), flags=['external_loop'], order='F'):
    ...:     print(x)

[0 1 2]
[3 4 5]

Like I said - awkward

I recently explored the difference between direct iteration and nditer over a 1d structured array: https://stackoverflow.com/a/43005985/901925

Answer 2

You can iterate it over just like you iterate over a 1D array to get the output like you want.

 for k,v in enumerate(a):
      print(v)