NumPy

numpy.loadtxt

numpy.loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None)[source]

Load data from a text file.

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

Parameters
fnamefile, str, or pathlib.Path

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.

dtypedata-type, optional

Data-type of the resulting array; default: float. If this is a structured 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.

commentsstr or sequence of str, optional

The characters or list of characters used to indicate the start of a comment. None implies no comments. For backwards compatibility, byte strings will be decoded as ‘latin1’. The default is ‘#’.

delimiterstr, optional

The string used to separate values. For backwards compatibility, byte strings will be decoded as ‘latin1’. The default is whitespace.

convertersdict, optional

A dictionary mapping column number to a function that will parse the column string into the desired value. 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.

skiprowsint, optional

Skip the first skiprows lines, including comments; default: 0.

usecolsint or 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.

Changed in version 1.11.0: When a single column has to be read it is possible to use an integer instead of a tuple. E.g usecols = 3 reads the fourth column the same way as usecols = (3,) would.

unpackbool, optional

If True, the returned array is transposed, so that arguments may be unpacked using x, y, z = loadtxt(...). When used with a structured data-type, arrays are returned for each field. Default is False.

ndminint, optional

The returned array will have at least ndmin dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2.

New in version 1.6.0.

encodingstr, optional

Encoding used to decode the inputfile. Does not apply to input streams. The special value ‘bytes’ enables backward compatibility workarounds that ensures you receive byte arrays as results if possible and passes ‘latin1’ encoded strings to converters. Override this value to receive unicode arrays and pass strings as input to converters. If set to None the system default is used. The default value is ‘bytes’.

New in version 1.14.0.

max_rowsint, optional

Read max_rows lines of content after skiprows lines. The default is to read all the lines.

New in version 1.16.0.

Returns
outndarray

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.

New in version 1.10.0.

The strings produced by the Python float.hex method can be used as input for floats.

Examples

>>> from io import StringIO   # StringIO behaves like a file object
>>> c = StringIO(u"0 1\n2 3")
>>> np.loadtxt(c)
array([[0., 1.],
       [2., 3.]])
>>> d = StringIO(u"M 21 72\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
...                      'formats': ('S1', 'i4', 'f4')})
array([(b'M', 21, 72.), (b'F', 35, 58.)],
      dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])
>>> c = StringIO(u"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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