loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None, *, like=None)
Each row in the text file must have the same number of values.
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.
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.
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.
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 '#'.
The string used to separate values. For backwards compatibility, byte strings will be decoded as 'latin1'. The default is whitespace.
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.
Skip the first :None:None:`skiprows` lines, including comments; default: 0.
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.
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.
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.
The returned array will have at least :None:None:`ndmin` dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2.
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'.
Read :None:None:`max_rows` lines of content after :None:None:`skiprows` lines. The default is to read all the lines.
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.
Data read from the text file.
Load data from a text file.
genfromtxt
Load data with missing values handled as specified.
scipy.io.loadmat
reads MATLAB data files
>>> 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.])
This example shows how :None:None:`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={0: conv, 1: conv}) array([[ 10.01, -31.25], [ 19.22, 64.31], [-17.57, 63.94]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.fromfile
numpy.genfromtxt
numpy.savetxt
numpy.load
numpy.fromregex
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