title: Efficiently add single row to Pandas Series or DataFrame
body: <p>I want to use Pandas to work with series in real-time. Every second, I need to add the latest observation to an existing series. My series are grouped into a DataFrame and stored in an HDF5 file.</p>
  
  <p>Here's how I do it at the moment:</p>
  
  <pre><code>&gt;&gt; existing_series = Series([7,13,97], [0,1,2]) 
  &gt;&gt; updated_series = existing_series.append( Series([111], [3]) )
  </code></pre>
  
  <p>Is this the most efficient way? I've read countless posts but cannot find any that focuses on efficiency with high-frequency data.</p>
  
  <p><strong>Edit</strong>: I just read about modules shelve and pickle. It seems like they would achieve what I'm trying to do, basically save lists on disks. Because my lists are large, is there any way not to load the full list into memory but, rather, efficiently append values one at a time?</p>