numpy.genfromtxt#

numpy.genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=" !#$%&'()*+, -./:;<=>?@[\\]^{|}~", replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes', *, ndmin=0, like=None)[source]#

Load data from a text file, with missing values handled as specified.

Each line past the first skip_header lines is split at the delimiter character, and characters following the comments character are discarded.

Parameters

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.
dtypedtype, optional: Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually.
commentsstr, optional: The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded.
delimiterstr, int, or sequence, optional: The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field.
skiprowsint, optional: skiprows was removed in numpy 1.10. Please use skip_header instead.
skip_headerint, optional: The number of lines to skip at the beginning of the file.
skip_footerint, optional: The number of lines to skip at the end of the file.
convertersvariable, optional: The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: converters = {3: lambda s: float(s or 0)}.
missingvariable, optional: missing was removed in numpy 1.10. Please use missing_values instead.
missing_valuesvariable, optional: The set of strings corresponding to missing data.
filling_valuesvariable, optional: The set of values to be used as default when the data are missing.
usecolssequence, optional: Which columns to read, with 0 being the first. For example, usecols = (1, 4, 5) will extract the 2nd, 5th and 6th columns.
names{None, True, str, sequence}, optional: If names is True, the field names are read from the first line after the first skip_header lines. This line can optionally be preceded by a comment delimiter. If names is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If names is None, the names of the dtype fields will be used, if any.
excludelistsequence, optional: A list of names to exclude. This list is appended to the default list [‘return’,’file’,’print’]. Excluded names are appended with an underscore: for example, file would become file_.
deletecharsstr, optional: A string combining invalid characters that must be deleted from the names.
defaultfmtstr, optional: A format used to define default field names, such as “f%i” or “f_%02i”.
autostripbool, optional: Whether to automatically strip white spaces from the variables.
replace_spacechar, optional: Character(s) used in replacement of white spaces in the variable names. By default, use a ‘_’.
case_sensitive{True, False, ‘upper’, ‘lower’}, optional: If True, field names are case sensitive. If False or ‘upper’, field names are converted to upper case. If ‘lower’, field names are converted to lower case.
unpackbool, optional: If True, the returned array is transposed, so that arguments may be unpacked using x, y, z = genfromtxt(...). When used with a structured data-type, arrays are returned for each field. Default is False.
usemaskbool, optional: If True, return a masked array. If False, return a regular array.
loosebool, optional: If True, do not raise errors for invalid values.
invalid_raisebool, optional: If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped.
max_rowsint, optional: The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file.

New in version 1.10.0.
encodingstr, optional: Encoding used to decode the inputfile. Does not apply when fname is a file object. The special value ‘bytes’ enables backward compatibility workarounds that ensure that you receive byte arrays when 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.
ndminint, optional: Same parameter as loadtxt

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.

Returns

outndarray: Data read from the text file. If usemask is True, this is a masked array.

See also

numpy.loadtxt: equivalent function when no data is missing.

Notes

When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields.
When the variables are named (either by a flexible dtype or with names), there must not be any header in the file (else a ValueError exception is raised).
Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces.

References

1: NumPy User Guide, section I/O with NumPy.

Examples

>>> from io import StringIO
>>> import numpy as np

Comma delimited file with mixed dtype

>>> s = StringIO(u"1,1.3,abcde")
>>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
... ('mystring','S5')], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
      dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

Using dtype = None

>>> _ = s.seek(0) # needed for StringIO example only
>>> data = np.genfromtxt(s, dtype=None,
... names = ['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
      dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

Specifying dtype and names

>>> _ = s.seek(0)
>>> data = np.genfromtxt(s, dtype="i8,f8,S5",
... names=['myint','myfloat','mystring'], delimiter=",")
>>> data
array((1, 1.3, b'abcde'),
      dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

An example with fixed-width columns

>>> s = StringIO(u"11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
...     delimiter=[1,3,5])
>>> data
array((1, 1.3, b'abcde'),
      dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', 'S5')])

An example to show comments

>>> f = StringIO('''
... text,# of chars
... hello world,11
... numpy,5''')
>>> np.genfromtxt(f, dtype='S12,S12', delimiter=',')
array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')],
  dtype=[('f0', 'S12'), ('f1', 'S12')])

numpy.savetxt

numpy.fromregex