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

Load data from a text file.

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

fnamefile, str, pathlib.Path, list of str, generator

File, filename, list, or generator to read. If the filename extension is .gz or .bz2, the file is first decompressed. Note that generators must return bytes or strings. The strings in a list or produced by a generator are treated as lines.

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 or None, 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 or callable, optional

A function to parse all columns strings into the desired value, or a dictionary mapping column number to a parser function. 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, e.g. converters = lambda s: float(s.strip() or 0) will convert empty fields to 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.

quotecharunicode character or None, optional

The character used to denote the start and end of a quoted item. Occurrences of the delimiter or comment characters are ignored within a quoted item. The default value is quotechar=None, which means quoting support is disabled.

If two consecutive instances of quotechar are found within a quoted field, the first is treated as an escape character. See examples.

New in version 1.23.0.

likearray_like, optional

Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

New in version 1.20.0.


Data read from the text file.

See also

load, fromstring, fromregex

Load data with missing values handled as specified.

reads MATLAB data files


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.


>>> from io 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([(b'M', 21, 72.), (b'F', 35, 58.)],
      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.])

The converters argument is used to specify functions to preprocess the text prior to parsing. converters can be a dictionary that maps preprocessing functions to each column:

>>> s = StringIO("1.618, 2.296\n3.141, 4.669\n")
>>> conv = {
...     0: lambda x: np.floor(float(x)),  # conversion fn for column 0
...     1: lambda x: np.ceil(float(x)),  # conversion fn for column 1
... }
>>> np.loadtxt(s, delimiter=",", converters=conv)
array([[1., 3.],
       [3., 5.]])

converters can be a callable instead of a dictionary, in which case it is applied to all columns:

>>> s = StringIO("0xDE 0xAD\n0xC0 0xDE")
>>> import functools
>>> conv = functools.partial(int, base=16)
>>> np.loadtxt(s, converters=conv)
array([[222., 173.],
       [192., 222.]])

This example shows how converters can be used to convert a field with a trailing minus sign into a negative number.

>>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94')
>>> def conv(fld):
...     return -float(fld[:-1]) if fld.endswith(b'-') else float(fld)
>>> np.loadtxt(s, converters=conv)
array([[ 10.01, -31.25],
       [ 19.22,  64.31],
       [-17.57,  63.94]])

Using a callable as the converter can be particularly useful for handling values with different formatting, e.g. floats with underscores:

>>> s = StringIO("1 2.7 100_000")
>>> np.loadtxt(s, converters=float)
array([1.e+00, 2.7e+00, 1.e+05])

This idea can be extended to automatically handle values specified in many different formats:

>>> def conv(val):
...     try:
...         return float(val)
...     except ValueError:
...         return float.fromhex(val)
>>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2")
>>> np.loadtxt(s, delimiter=",", converters=conv, encoding=None)
array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00])

Note that with the default encoding="bytes", the inputs to the converter function are latin-1 encoded byte strings. To deactivate the implicit encoding prior to conversion, use encoding=None

>>> s = StringIO('10.01 31.25-\n19.22 64.31\n17.57- 63.94')
>>> conv = lambda x: -float(x[:-1]) if x.endswith('-') else float(x)
>>> np.loadtxt(s, converters=conv, encoding=None)
array([[ 10.01, -31.25],
       [ 19.22,  64.31],
       [-17.57,  63.94]])

Support for quoted fields is enabled with the quotechar parameter. Comment and delimiter characters are ignored when they appear within a quoted item delineated by quotechar:

>>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n')
>>> dtype = np.dtype([("label", "U12"), ("value", float)])
>>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"')
array([('alpha, #42', 10.), ('beta, #64',  2.)],
      dtype=[('label', '<U12'), ('value', '<f8')])

Two consecutive quote characters within a quoted field are treated as a single escaped character:

>>> s = StringIO('"Hello, my name is ""Monty""!"')
>>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"')
array('Hello, my name is "Monty"!', dtype='<U26')