title: Calculating Mean of arrays with different lengths
body: <p>Is it possible to calculate the mean of multiple arrays, when they may have different lengths? I am using numpy. So let's say I have:</p>
  
  <pre><code>numpy.array([[1, 2, 3, 4, 8],    [3, 4, 5, 6, 0]])
  numpy.array([[5, 6, 7, 8, 7, 8], [7, 8, 9, 10, 11, 12]])
  numpy.array([[1, 2, 3, 4],       [5, 6, 7, 8]])
  </code></pre>
  
  <p>Now I want to calculate the mean, but ignoring elements that are 'missing' (Naturally, I can not just append zeros as this would mess up the mean)</p>
  
  <p>Is there a way to do this without iterating through the arrays?</p>
  
  <p>PS. These arrays are all 2-D, but will always have the same amount of coordinates for that array.  I.e. the 1st array is 5 and 5, 2nd is 6 and 6, 3rd is 4 and 4.</p>
  
  <p>An example:</p>
  
  <pre><code>np.array([[1, 2],    [3, 4]])
  np.array([[1, 2, 3], [3, 4, 5]])
  np.array([[7],       [8]])
  </code></pre>
  
  <p>This must give</p>
  
  <pre><code>(1+1+7)/3  (2+2)/2   3/1
  (3+3+8)/3  (4+4)/2   5/1
  </code></pre>
  
  <p>And graphically:</p>
  
  <pre><code>[1, 2]    [1, 2, 3]    [7]
  [3, 4]    [3, 4, 5]    [8]
  </code></pre>
  
  <p>Now imagine that these 2-D arrays are placed on top of each other with coordinates overlapping contributing to that coordinate's mean.</p>