title: 3-dimensional array in numpy
body: <p>New at Python and Numpy, trying to create 3-dimensional arrays. My problem is that the order of the dimensions are off compared to Matlab. In fact the order doesn't make sense at all.</p>
  
  <p>Creating a matrix:</p>
  
  <pre><code>x = np.zeros((2,3,4))
  </code></pre>
  
  <p>In my world this should result in 2 rows, 3 columns and 4 depth dimensions and it should be presented as:</p>
  
  <pre><code>[0 0 0      [0 0 0      [0 0 0      [0 0 0
   0 0 0]      0 0 0]      0 0 0]      0 0 0] 
  </code></pre>
  
  <p>Seperating on each depth dimensions.
  Instead it is presented as</p>
  
  <pre><code>[0 0 0 0      [0 0 0 0
   0 0 0 0       0 0 0 0
   0 0 0 0]      0 0 0 0]
  </code></pre>
  
  <p>That is, 3 rows, 4 column and 2 depth dimensions. That is, the first dimension is the "depth". To further add to this problem, importing an image with OpenCV the color dimension is the last dimension, that is, I see the color information as the depth dimension. This complicates things greatly if all I want to do is try something on a known smaller 3-dimensional array.</p>
  
  <p>Have I misunderstood something? If not, why the heck is numpy using such a unintuitive way of working with 3D-dimensional arrays?</p>