title: Plot pandas dataframe containing NaNs
body: <p>I have GPS data of ice speed from three different GPS receivers. The data are in a pandas dataframe with an index of julian day (incremental from the start of 2009).</p>
  
  <p>This is a subset of the data (the main dataset is 3487235 rows...):</p>
  
  <pre><code>                    R2          R7         R8
  1235.000000 116.321959  100.805197  96.519977
  1235.000116 NaN         100.771133  96.234957
  1235.000231 NaN         100.584559  97.249262
  1235.000347 118.823610  100.169055  96.777833
  1235.000463 NaN         99.753551   96.598350
  1235.000579 NaN         99.338048   95.283989
  1235.000694 113.995003  98.922544   95.154067
  </code></pre>
  
  <p>The dataframe has form:</p>
  
  <pre>
  Index: 6071320 entries, 127.67291667 to 1338.51805556
  Data columns:
  R2    3487235  non-null values
  R7    3875864  non-null values
  R8    1092430  non-null values
  dtypes: float64(3)
  </pre>
  
  <p>R2 sampled at a different rate to R7 and R8 hence the NaNs which appear systematically at that spacing.</p>
  
  <p>Trying <code>df.plot()</code> to plot the whole dataframe (or indexed row locations thereof) works fine in terms of plotting R7 and R8, but doesn't plot R2. Similarly, just doing <code>df.R2.plot()</code> also doesn't work. The only way to plot R2 is to do <code>df.R2.dropna().plot()</code>, but this also removes NaNs which signify periods of no data (rather than just a coarser sampling frequency than the other receivers).</p>
  
  <p>Has anyone else come across this? Any ideas on the problem would be gratefully received :)</p>