title: Parsing data with variable number of columns
body: <p>I have several .txt files with 140k+ lines each. They all have three types of data, which are a mix of string and floats: 
  - 7 col
  - 14 col 
  - 18 col</p>
  
  <p>What is the best and fastest way to parse such data?</p>
  
  <p>I tried to use numpy.genfromtxt with usecols=np.arange(0,7) but obviously cuts out the 14 and 18 col data. </p>
  
  <pre class="lang-py prettyprint-override"><code># for 7 col data
  load = np.genfromtxt(filename, dtype=None, names=('day', 'tod', 'condition', 'code', 'type', 'state', 'timing'), usecols=np.arange(0,7))
  </code></pre>
  
  <p>I would like to parse the data as efficiently as possible.</p>