The anatomy of an ndarray in NumPy looks like this red cube below: (source: Physics Dept, Cornell Uni)

Once you leave the 2D space and enter 3D or higher dimensional spaces, the concept of rows and columns doesn't make much sense anymore. But still you can intuitively understand 3D arrays. For instance, considering your example:

In [41]: b
Out[41]: 
array([[[ 1,  2,  3],
        [ 4,  5,  6]],

       [[ 7,  8,  9],
        [10, 11, 12]]])

In [42]: b.shape
Out[42]: (2, 2, 3)

Here the shape of b is (2, 2, 3). You can think about it like, we've two (2x3) matrices stacked to form a 3D array. To access the first matrix you index into the array b like b[0] and to access the second matrix, you index into the array b like b[1].

# gives you the 2D array (i.e. matrix) at position `0`
In [43]: b[0]
Out[43]: 
array([[1, 2, 3],
       [4, 5, 6]])


# gives you the 2D array (i.e. matrix) at position 1
In [44]: b[1]
Out[44]: 
array([[ 7,  8,  9],
       [10, 11, 12]])

However, if you enter 4D space or higher, it will be very hard to make any sense out of the arrays itself since we humans have hard time visualizing 4D and more dimensions. So, one would rather just consider the ndarray.shape attribute and work with it.

More information about how we build higher dimensional arrays using (nested) lists:

For 1D arrays, the array constructor needs a sequence (tuple, list, etc) but conventionally list is used.

In [51]: oneD = np.array([1, 2, 3,])    
In [52]: oneD.shape
Out[52]: (3,)

For 2D arrays, it's list of lists but can also be tuple of lists or tuple of tuples etc:

In [53]: twoD = np.array([[1, 2, 3], [4, 5, 6]])
In [54]: twoD.shape
Out[54]: (2, 3)

For 3D arrays, it's list of lists of lists:

In [55]: threeD = np.array([[[1, 2, 3], [2, 3, 4]], [[5, 6, 7], [6, 7, 8]]])

In [56]: threeD.shape
Out[56]: (2, 2, 3)

P.S. Internally, the ndarray is stored in a memory block as shown in the below picture. (source: Enthought)

I made a pull request to matplotlib that does exactly this, adding the ax3d.voxels function. Unfortunately, it hasn't been reviewed fully yet.

Update: This made it into matplotlib 2.1

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# your real data here - some 3d boolean array
x, y, z = np.indices((10, 10, 10))
voxels = (x == y) | (y == z)

ax.voxels(voxels)

plt.show()

If you have a dset like that, and you want to just get the 1 values, you could use nonzero, which "returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension.".

For example, we can make a simple 3d array:

>>> import numpy
>>> numpy.random.seed(29)
>>> d = numpy.random.randint(0, 2, size=(3,3,3))
>>> d
array([[[1, 1, 0],
        [1, 0, 0],
        [0, 1, 1]],

       [[0, 1, 1],
        [1, 0, 0],
        [0, 1, 1]],

       [[1, 1, 0],
        [0, 1, 0],
        [0, 0, 1]]])

and find where the nonzero elements are located:

>>> d.nonzero()
(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2]), array([0, 0, 1, 2, 2, 0, 0, 1, 2, 2, 0, 0, 1, 2]), array([0, 1, 0, 1, 2, 1, 2, 0, 1, 2, 0, 1, 1, 2]))
>>> z,x,y = d.nonzero()

If we wanted a more complicated cut, we could have done something like (d > 3.4).nonzero() or something, as True has an integer value of 1 and counts as nonzero.

Finally, we plot:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, -z, zdir='z', c= 'red')
plt.savefig("demo.png")

giving

Hello,

You'll probably want to create a structured grid out of this and then use a threshold filter. See: https://docs.pyvista.org/core/point-grids.html

Try re-writing the update function as

def update(val):
    frame = numpy.around(sframe.val)
    l.set_data(data[frame,:,:])

so that you do not need to re-create all of the matplotlib objects every update