f the file.
skip_footer : int, optional
    The number of lines to skip at the end of the file.
converters : variable, 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)}``.
missing : variable, optional
    `missing` was removed in numpy 1.10. Please use `missing_values`
    instead.
missing_values : variable, optional
    The set of strings corresponding to missing data.
filling_values : variable, optional
    The set of values to be used as default when the data are missing.
usecols : 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.
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. Any content before the comment delimiter is
    discarded. 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.
excludelist : sequence, 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_`.
deletechars : str, optional
    A string combining invalid characters that must be deleted from the
    names.
defaultfmt : str, optional
    A format used to define default field names, such as "f%i" or "f_%02i".
autostrip : bool, optional
    Whether to automatically strip white spaces from the variables.
replace_space : char, 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.
unpack : bool, 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.
usemask : bool, optional
    If True, return a masked array.
    If False, return a regular array.
loose : bool, optional
    If True, do not raise errors for invalid values.
invalid_raise : bool, 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_rows : int,  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.
encoding : str, 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'.

    .. versionchanged:: 2.0
        Before NumPy 2, the default was ``'bytes'`` for Python 2
        compatibility. The default is now ``None``.

ndmin : int, optional
    Same parameter as `loadtxt`

    .. versionadded:: 1.23.0
${ARRAY_FUNCTION_LIKE}

    .. versionadded:: 1.20.0

Returns
-------
out : ndarray
    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 variables are named (either by a flexible dtype or with a `names`
  sequence), 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.
* Custom converters may receive unexpected values due to dtype
  discovery.

References
----------
.. [1] NumPy User Guide, section `I/O with NumPy
       <https://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.

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

Comma delimited file with mixed dtype

>>> s = StringIO("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, 'abcde'),
      dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '<U5')])

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("11.3abcde")
>>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
...     delimiter=[1,3,5])
>>> data
array((1, 1.3, 'abcde'),
      dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '<U5')])

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')])

Nr