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 str

File or filename to read. If the filename extension is .gz or .bz2, the file is first decompressed.

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: converters = {3: lambda s: float(s 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(...). The default is False.

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.])