title: What are the relative advantages of extending NumPy in Cython vs Boost.Python?
body: <p>I need to speed up some algorithms working on NumPy arrays. They will use <code>std::vector</code> and some of the more advanced STL data structures.</p>
  
  <p>I've narrowed my choices down to Cython (which now wraps most STL containers) and Boost.Python (which now has built-in support for NumPy). </p>
  
  <p>I know from my experience as a programmer that sometimes it takes <em>months</em> of working with a framework to uncover its hidden issues (because they are rarely used as talking points by its disciples), so your help could potentially save me a lot of time.</p>
  
  <p>What are the relative advantages and disadvantages of extending NumPy in Cython vs Boost.Python?</p>