title: Speeding up .ix in pandas
body: <p>I'm trying to speed up the following code.  'db' is a dictionary of DataFrames.  Is there a better/different way to structure things which would speed this up? </p>
  
  <pre><code>for date in dates:                           # 3,800 days
      for instrument in instruments:           # 100 instruments
          s = instrument.ticker                         
          current_bar = db[s].ix[date]
          # (current_bar.xxx then gets used for difference calculations.)
  </code></pre>
  
  <p>Here are the results:</p>
  
  <pre><code>%timeit speedTest()
  1 loops, best of 3: 1min per loop
  </code></pre>
  
  <p>This is for each individual call:</p>
  
  <pre><code>%timeit current_bar = db[s].ix[date]
  10000 loops, best of 3: 154 µs per loop
  </code></pre>
  
  <p>Any help/suggestions would be appreciated.
  Thanks</p>