title: Dictionary versus NumPy array performance Python
body: <p>I am working with multiple NumPy 2-dimension arrays (matrices), and I want to get some rows, or columns, from them (same rows or columns indexes for each of the 3 matrices, each time). I was wondering if I should use dictionary or not. </p>
  
  <p>If I do it with a dictionary, then each row of each matrix would be indexed by a word, and would a list of values that interest me. E.g, <code>myDict['word']</code> would contain <code>[1 5 2 49 0 2]</code>.</p>
  
  <p>If I do it with an array <code>myArray</code>, for each <code>i</code> I would have an array contained within <code>myArray[i]</code>. E.g, <code>myArray[5]</code> would contain <code>array([[1 2 4 9 1 23]])</code>.</p>
  
  <p>On these I need to implement basic <code>get</code> operations (get rows or get columns), some matrix multiplications but never sorting or insertions.
  I know I can do it both ways, my question is mainly of performance. Which do you think would be the faster and simplier?</p>
  
  <p>Thanks a lot!</p>