title: How to know when to use numpy.linalg instead of scipy.linalg?
body: <p>Received wisdom is to prefer <code>scipy.linalg</code> over <code>numpy.linalg</code> functions. For doing linear algebra, ideally (and conveniently) I would like to combine the functionalities of <code>numpy.array</code> and <code>scipy.linalg</code> without ever looking towards <code>numpy.linalg</code>. This is not always possible and may become too frustrating. </p>
  
  <p>Is there a comparative checklist of equivalent functions from these two modules to quickly determine when to use <code>numpy.linalg</code> in case a function is absent in <code>scipy.linalg</code>? </p>
  
  <p>e.g. There are <code>scipy.linalg.norm()</code> and <code>numpy.linalg.norm()</code>, but there seem to be no scipy equivalents of <code>numpy.linalg.matrix_rank()</code> and <code>numpy.linalg.cond()</code>.</p>