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# numpy.loadtxt

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

  Load data from a text file.

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

  Parameters : | fname : file or str File, filename, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators should return byte strings for Python 3k. dtype : data-type, optional Data-type of the resulting array; default: float. 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 : str, optional The character used to indicate the start of a comment; default: ‘#’. delimiter : str, optional The string used to separate values. By default, this is any whitespace. converters : dict, optional 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 (but see also `genfromtxt`): `converters = {3: lambda s: float(s.strip() or 0)}`. Default: None. skiprows : int, optional Skip the first skiprows lines; default: 0. usecols : sequence, optional Which columns to read, with 0 being the first. For example, `usecols = (1,4,5)` will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using `x, y, z = loadtxt(...)`. When used with a record data-type, arrays are returned for each field. Default is False. ndmin : int, optional The returned array will have at least ndmin dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0
  Returns : | out : ndarray Data read from the text file.

  See also

  `load`, `fromstring`, `fromregex`

  `genfromtxt`
    Load data with missing values handled as specified.
  `scipy.io.loadmat`
    reads MATLAB data files

  Notes

  This function aims to be a fast reader for simply formatted files. The `genfromtxt` function provides more sophisticated handling of, e.g., lines with missing values.

  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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