title: Convert list of tuples to structured numpy array
body: <p>I have a list of <code>Num_tuples</code> tuples that all have the same length <code>Dim_tuple</code></p>
  
  <pre><code>xlist = [tuple_1, tuple_2, ..., tuple_Num_tuples]
  </code></pre>
  
  <p>For definiteness, let's say <code>Num_tuples=3</code> and <code>Dim_tuple=2</code></p>
  
  <pre><code>xlist = [(1, 1.1), (2, 1.2), (3, 1.3)]
  </code></pre>
  
  <p>I want to convert <code>xlist</code> into a structured numpy array <code>xarr</code> using a user-provided list of column names <code>user_names</code> and a user-provided list of variable types <code>user_types</code></p>
  
  <pre><code>user_names = [name_1, name_2, ..., name_Dim_tuple]
  user_types = [type_1, type_2, ..., type_Dim_tuple]
  </code></pre>
  
  <p>So in the creation of the numpy array,</p>
  
  <pre><code>dtype = [(name_1,type_1), (name_2,type_2), ..., (name_Dim_tuple, type_Dim_tuple)]
  </code></pre>
  
  <p>In the case of my toy example desired end product would look something like:</p>
  
  <pre><code>xarr['name1']=np.array([1,2,3])
  xarr['name2']=np.array([1.1,1.2,1.3])
  </code></pre>
  
  <p>How can I slice <code>xlist</code> to create <code>xarr</code> without any loops?</p>