title: Pandas: convert list of string tuples to dataframe faster?
body: <p>From a text field I have the following <strong>input</strong> series, containing geographic coordinate tuples as a string:</p>
  
  <pre><code>import pandas as pd
  
  coords = pd.Series([
     '(29.65271977700047, -82.33086252299967)',
     '(29.652914019000434, -82.42682220199964)',
     '(29.65301114200048, -82.36455186899968)',
     '(29.642610841000476, -82.29853169599966)',
  ])
  </code></pre>
  
  <p>I would like to parse the numbers in these tuples and end up with the following <strong>result</strong> DataFrame:</p>
  
  <pre><code>         lat        lon
  0  29.652720 -82.330863
  1  29.652914 -82.426822
  2  29.653011 -82.364552
  3  29.642611 -82.298532
  </code></pre>
  
  <p>This is what I have come up with:</p>
  
  <pre><code>str_coords = coords.str[1:-1].str.split(', ')
  latlon = str_coords.apply(pd.Series).astype(float)
  latlon.columns = ['lat', 'lon']
  </code></pre>
  
  <p>My <strong>problem</strong>: The call to <code>.apply(pd.Series)</code> takes "forever" on the real list, which has around 1.2 million entries. Is there a faster way?</p>