iable, 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. 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.

        .. versionadded:: 1.10.0
    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'.

        .. versionadded:: 1.14.0
    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 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
           <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(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')])

    Nrü