NumPy Input and Output: loadtxt() function

Last update on August 19 2022 21:50:38 (UTC/GMT +8 hours)

numpy.loadtxt() function

The loadtxt() function is used to load data from a text file.

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

Syntax:

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

Version: 1.15.0

Parameter:

Name	Description	Required / Optional
fname	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. file, str, or pathlib.Path	Required
dtype	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. data-type	Optional
comments	The characters or list of characters used to indicate the start of a comment; default: ‘#’. str or sequence	Optional
delimiter	The string used to separate values. By default, this is any whitespace. str	Optional
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 (but see also genfromtxt): converters = {3: lambda s: float(s.strip() or 0)}. Default: None. dict	Optional
skiprows	Skip the first skiprows lines; default: 0. int	Optional
usecols	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. New in version 1.11.0. Also 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. int or sequence	Optional
unpack	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. bool	Optional
ndmin	The returned array will have at least ndmin dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. int	Optional
encoding	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’. str	Optional

Returns: out : ndarray

Data read from the text file.

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.

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

NumPy.loadtxt() method Example-1:

>>> import numpy as np
>>> from io import StringIO   # StringIO behaves like a file object
>>> a = StringIO("0 2\n4 6")
>>> np.loadtxt(a)

Output:

array([[0., 2.],
       [4., 6.]])

NumPy.loadtxt() method Example-2:

>>> import numpy as np
>>> from io import StringIO 
>>> b = StringIO("M 25 78\nF 37 55")
>>> np.loadtxt(b, dtype={'names': ('gender', 'age', 'weight'), 'formats': ('S1', 'i4', 'f4')})

Output:

array([(b'M', 25, 78.), (b'F', 37, 55.)],
      dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])

NumPy.loadtxt() method Example-3:

>>> import numpy as np
>>> from io import StringIO 
>>> a = StringIO("2,0,3\n4,0,5")
>>> x, y = np.loadtxt(a, delimiter=',', usecols=(0, 2), unpack=True)
>>> x

Output:

array([2., 4.])

NumPy.loadtxt() method Example-4:

>>> import numpy as np
>>> from io import StringIO 
>>> a = StringIO("2,0,3\n4,0,5")
>>> x, y = np.loadtxt(a, delimiter=',', usecols=(0, 2), unpack=True)
>>> y

Output:

array([3., 5.])

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