title: Read Space-separated Data with Pandas
body: <p>I used to read my data with <code>numpy.loadtxt()</code>. However, lately I found out in <a href="https://stackoverflow.com/questions/22616283/parallel-processing-with-numpy-loadtxt">SO</a>, that <code>pandas.read_csv()</code> is much more faster.</p>
  
  <p>To read these data I use: </p>
  
  <pre><code>pd.read_csv(filename, sep=' ',header=None)
  </code></pre>
  
  <p>The problem that I encounter right now is that in my case the separator can differ from one space, <em>x</em> spaces to even a tab. </p>
  
  <p>Here how my data could look like:</p>
  
  <pre><code>56.00     101.85 52.40 101.85 56.000000 101.850000 1
  56.00 100.74 50.60 100.74 56.000000 100.740000 2
  56.00 100.74 52.10 100.74 56.000000 100.740000 3
  56.00 102.96 52.40 102.96 56.000000 102.960000 4
  56.00 100.74 55.40 100.74 56.000000 100.740000 5
  </code></pre>
  
  <p>That leads to results like:</p>
  
  <pre><code>     0       1     2       3     4       5   6       7   8
  0   56     NaN   NaN  101.85  52.4  101.85  56  101.85   1
  1   56  100.74  50.6  100.74  56.0  100.74   2     NaN NaN
  2   56  100.74  52.1  100.74  56.0  100.74   3     NaN NaN
  3   56  102.96  52.4  102.96  56.0  102.96   4     NaN NaN
  4   56  100.74  55.4  100.74  56.0  100.74   5     NaN NaN
  </code></pre>
  
  <p>I have to specify that my data are >100 MB. So I can not preprocess the data or clean them first.
  Any ideas how to get this fixed?</p>