title: Python Pandas - Read csv file containing multiple tables
body: <p>I have a single <code>.csv</code> file containing multiple tables.</p>
  
  <p>Using Pandas, what would be the best strategy to get two DataFrame <code>inventory</code> and <code>HPBladeSystemRack</code> from this one file ?</p>
  
  <p>The input <code>.csv</code> looks like this:</p>
  
  <pre><code>Inventory       
  System Name            IP Address    System Status
  dg-enc05                             Normal
  dg-enc05_vc_domain                   Unknown
  dg-enc05-oa1           172.20.0.213  Normal
  
  HP BladeSystem Rack         
  System Name               Rack Name   Enclosure Name
  dg-enc05                  BU40  
  dg-enc05-oa1              BU40        dg-enc05
  dg-enc05-oa2              BU40        dg-enc05
  </code></pre>
  
  <p>The best I've come up with so far is to convert this <code>.csv</code> file into Excel workbook (<code>xlxs</code>), split the tables into sheets and use:</p>
  
  <pre><code>inventory = read_excel('path_to_file.csv', 'sheet1', skiprow=1)
  HPBladeSystemRack = read_excel('path_to_file.csv', 'sheet2', skiprow=2)
  </code></pre>
  
  <p>However:</p>
  
  <ul>
  <li>This approach requires <code>xlrd</code> module.</li>
  <li>Those log files have to be analyzed in real time, so that it would be way better to find a way to analyze them as they come from the logs.</li>
  <li>The real logs have far more tables than those two.</li>
  </ul>