How can I iterate through numpy 3d array

You could do something ugly as

for i in range(len(your_array)):
    for j in range(len(your_array[i])):
        print(your_array[i][j])

b = a.reshape(-1, 3)
for triplet in b:
    ...

Apparently you want to iterate on the first 2 dimensions of the array, returning the 3rd (as 1d array).

In [242]: y = np.array([[[27, 27, 28],
     ...:         [27, 14, 28]],
     ...: 
     ...:        [[14,  5,  4],
     ...:         [ 5,  6, 14]]])

Double loops are fine, as is reshaping to a (4,2) and iterating.

nditer isn't usually needed, or encouraged as an iteration mechanism (its documentation needs a stronger disclaimer). It's really meant for C level code. It isn't used much in Python level code. One exception is the np.ndindex function, which can be useful in this case:

In [244]: for ij in np.ndindex(y.shape[:2]):
     ...:     print(ij, y[ij])
     ...:     
(0, 0) [27 27 28]
(0, 1) [27 14 28]
(1, 0) [14  5  4]
(1, 1) [ 5  6 14]

ndindex uses nditer in multi_index mode on a temp array of the specified shape.

Where possible try to work without iteration. Iteration, with any of these tricks, is relatively slow.

Think of it as having arrays within an array. So within array v you have array a which in turn contains the triplets b

import numpy as np
na = np.array

v=na([[[27, 27, 28], [27, 14, 28]], [[14,  5,  4],[ 5,  6, 14]]])

for a in v:
    for b in a:
        print b

Output:

[27 27 28]
[27 14 28]
[14  5  4]
[ 5  6 14]

Alternatively you could do the following,

v2 = [b for a in v for b in a]

Now all your triplets are stored in v2

[array([27, 27, 28]),
 array([27, 14, 28]),
 array([14,  5,  4]),
 array([ 5,  6, 14])]

..and you can access them like a 1D array eg

print v2[0]

gives..

array([27, 27, 28])

Another alternative (useful for arbitrary dimensionality of the array containing the n-tuples):

a_flat = a.ravel()
n = 3   
m = len(a_flat)/n
[a_flat[i:i+n] for i in range(m)]

or in one line (slower):

[a.ravel()[i:i+n] for i in range(len(a.ravel())/n)]

or for further usage within a loop:

for i in range(len(a.ravel())/n):
    print a.ravel()[i:i+n]

Reshape the array A (whose shape is n1, n2, 3) to array B (whose shape is n1 * n2, 3), and iterate through B. Note that B is just A's view. A and B share the same data block in the memory, but they have different array headers information where records their shapes, and changing values in B will also change A's value. The code below:

    a = np.array([[[27, 27, 28],[27, 14, 28]],
                  [[14,  5,  4],[ 5,  6, 14]]])   
    b = a.reshape((-1, 3))   
    for last_d in b:
        a, b, c = last_d
        # do something with last_d or a, b, c