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# numpy.loadtxt

`numpy.``loadtxt`(fname, dtype=<type 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False)

  Load data from a text file.

  Each row in the text file must have the same number of values.

  Parameters: | fname : file or string File or filename to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. dtype : data-type Data type of the resulting array. If this is a record data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. comments : string, optional The character used to indicate the start of a comment. delimiter : string, optional The string used to separate values. By default, this is any whitespace. converters : {} A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: `converters = {0: datestr2num}`. Converters can also be used to provide a default value for missing data: `converters = {3: lambda s: float(s or 0)}`. skiprows : int Skip the first skiprows lines. usecols : sequence Which columns to read, with 0 being the first. For example, `usecols = (1,4,5)` will extract the 2nd, 5th and 6th columns. unpack : bool If True, the returned array is transposed, so that arguments may be unpacked using `x, y, z = loadtxt(...)`
  Returns: | out : ndarray Data read from the text file.

  See also

  `scipy.io.loadmat`
    reads Matlab(R) data files

  Examples

      >>> from StringIO import StringIO   # StringIO behaves like a file object
      >>> c = StringIO("0 1\n2 3")
      >>> np.loadtxt(c)
      array([[ 0.,  1.],
             [ 2.,  3.]])

      >>> d = StringIO("M 21 72\nF 35 58")
      >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
      ...                      'formats': ('S1', 'i4', 'f4')})
      array([('M', 21, 72.0), ('F', 35, 58.0)],
            dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])

      >>> c = StringIO("1,0,2\n3,0,4")
      >>> x,y = np.loadtxt(c, delimiter=',', usecols=(0,2), unpack=True)
      >>> x
      array([ 1.,  3.])
      >>> y
      array([ 2.,  4.])

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