title: What's the difference between dtype and converters in pandas.read_csv?
body: <p>pandas function read_csv() reads a .csv file. Its documentation is <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="noreferrer">here</a> </p>
  
  <p>According to documentation, we know:</p>
  
  <blockquote>
    <p>dtype : Type name or dict of column -> type, default None Data type
    for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32}
    (Unsupported with engine=’python’)</p>
  </blockquote>
  
  <p>and </p>
  
  <blockquote>
    <p>converters : dict, default None Dict of functions for converting
    values in certain columns. Keys can either be integers or column
    labels</p>
  </blockquote>
  
  <p>When using this function, I can call either
  <code>pandas.read_csv('file',dtype=object)</code> or <code>pandas.read_csv('file',converters=object)</code>. Obviously, converter, its name can says that data type will be converted but I wonder the case of dtype?</p>