title: pandas.merge inexplicably slow
body: <p>The following works great:</p>
  
  <pre><code>times1h = pandas.DatetimeIndex(start='2010-01-01', end='2014-01-01', freq='1h')
  times10min = pandas.DatetimeIndex(start='2010-01-01', end='2014-01-01', freq='10T')
  wind=pandas.DataFrame({'wind':0}, index=times1h)
  power=pandas.DataFrame({'power':0}, index=times10min)
  %timeit pandas.merge(wind, power, how='inner', left_index=True, right_index=True)
  
  100 loops, best of 3: 5.2 ms per loop
  </code></pre>
  
  <p>The following is inexplicably slow. I just make the timestamps of the first dataframe non-unique and have it as a column rather than as an index:</p>
  
  <pre><code>times1h = pandas.DatetimeIndex(start='2010-01-01', end='2014-01-01', freq='1h')
  times10min = pandas.DatetimeIndex(start='2010-01-01', end='2014-01-01', freq='10T')
  wind=pandas.DataFrame({'time':pandas.concat([pandas.Series(times1h),     pandas.Series(times1h)]), 'wind':0})
  power=pandas.DataFrame({'power':0}, index=times10min)
  %timeit pandas.merge(wind, power, how='inner', left_on='time', right_index=True)
  
  1 loops, best of 3: 16.6 s per loop
  </code></pre>
  
  <p>Why is this so much slower? Can I do anything about this?</p>
  
  <p>I am trying to get a set of (x,y) points for a Power Curve fitting.</p>
  
  <p>I use pandas 0.13.1 because it's the one included in WinPython :)</p>