title: How to efficiently handle European decimal separators using the pandas read_csv function?
body: <p>I'm using <code>read_csv</code> to read CSV files into Pandas data frames. My CSV files contain large numbers of decimals/floats. The numbers are encoded using the European decimal notation:</p>
  
  <pre><code>1.234.456,78
  </code></pre>
  
  <p>This means that the '.' is used as the thousand separator and the ',' is the decimal mark.</p>
  
  <p>Pandas 0.8. provides a <code>read_csv</code> argument called 'thousands' to set the thousand separator. Is there an additional argument to provide the decimal mark as well? If no, what is the most efficient way to parse a European style decimal number?</p>
  
  <p>Currently I'm using string replace which I consider to be a significant performance penalty. The coding I'm using is this:</p>
  
  <pre><code># Convert to float data type and change decimal point from ',' to '.'
  f = lambda x: string.replace(x, u',', u'.')
  df['MyColumn'] = df['MyColumn'].map(f)
  </code></pre>
  
  <p>Any help is appreciated.</p>