title: Why is pandas applying the same values on both sides of an asymmetric error bar?
body: <p>I'm trying to plot a series with asymmetric error bars using pandas and matplotlib with the following code:</p>
  
  <pre><code>d = {'high_delta': {1: 0.6,
    2: 0.1,
    3: 0.2,
    4: 0.1,
    5: 0.1,
    6: 0.1,
    7: 0.1,
    8: 0.1,
    9: 0.2,
    10: 0.1},
   'low_delta': {1: 0.2,
    2: 0.1,
    3: 0.1,
    4: 0.1,
    5: 0.1,
    6: 0.1,
    7: 0.1,
    8: 0.1,
    9: 0.1,
    10: 0.4},
   'p_hat': {1: 0.2,
    2: 0.1,
    3: 0.3,
    4: 0.3,
    5: 0.1,
    6: 0.3,
    7: 0.2,
    8: 0.2,
    9: 0.1,
    10: 0.8}}
  
  df = pandas.DataFrame(d)
   df['p_hat'].plot(yerr=df[['low_delta', 'high_delta']].T.values)
  (df.p_hat + df.high_delta).plot(style='.')
  (df.p_hat - df.low_delta).plot(style='*')
  </code></pre>
  
  <p>The lower bounds always seem to match what I would expect, but instead of adding the values on the upper bound it seems to be adding the values from the lower bound again.</p>
  
  <p>How should the errors be passed into matplotlib so that the error bars are rendered correctly?</p>