 If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the original `a`.
    If the value is anything but the default, then
    `keepdims` will be passed through to the `max` method
    of sub-classes of `ndarray`.  If the sub-classes methods
    does not implement `keepdims` any exceptions will be raised.
initial : scalar, optional
    The minimum value of an output element. Must be present to allow
    computation on empty slice. See `~numpy.ufunc.reduce` for details.

    .. versionadded:: 1.22.0
where : array_like of bool, optional
    Elements to compare for the maximum. See `~numpy.ufunc.reduce`
    for details.

    .. versionadded:: 1.22.0

Returns
-------
nanmax : ndarray
    An array with the same shape as `a`, with the specified axis removed.
    If `a` is a 0-d array, or if axis is None, an ndarray scalar is
    returned.  The same dtype as `a` is returned.

See Also
--------
nanmin :
    The minimum value of an array along a given axis, ignoring any NaNs.
amax :
    The maximum value of an array along a given axis, propagating any NaNs.
fmax :
    Element-wise maximum of two arrays, ignoring any NaNs.
maximum :
    Element-wise maximum of two arrays, propagating any NaNs.
isnan :
    Shows which elements are Not a Number (NaN).
isfinite:
    Shows which elements are neither NaN nor infinity.

amin, fmin, minimum

Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.max.

Examples
--------
>>> import numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmax(a)
3.0
>>> np.nanmax(a, axis=0)
array([3.,  2.])
>>> np.nanmax(a, axis=1)
array([2.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmax([1, 2, np.nan, -np.inf])
2.0
>>> np.nanmax([1, 2, np.nan, np.inf])
inf

r\