title: read_csv converters for unknown columns
body: <p>I'm trying to read a csv file that holds several values in every cell and I want to encode them to a single int formatted byte to be stored in a pandas cell, (e.g. (1, 1) -> 771). For that I would like to use the converters parameter of the <code>read_csv</code> function. The problem is that I don't know the names of the columns before hand and the value to be passed to the converters should be a dict with the column names as keys. In fact I want to convert all columns with the same converter function. For that it would be better to write:</p>
  
  <pre><code>read_csv(fhand, converter=my_endocing_function)
  </code></pre>
  
  <p>than:</p>
  
  <pre><code>read_csv(fhand, converters={'col1':my_endocing_function,
                              'col2':my_endocing_function,
                              'col3':my_endocing_function,})
  </code></pre>
  
  <p>Is something like that possible? Right now to solve the issue I'm doing:</p>
  
  <pre><code>dataframe = read_csv(fhand)
  enc_func = numpy.vectorize(encoder.encode_genotype)
  dataframe = dataframe.apply(enc_func, axis=1)
  </code></pre>
  
  <p>But I guess that this approach might be less efficient.
  By the way I have similar doubts with the formatters used by the to_string method.</p>