l : scalar, optional
    The maximum 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 minimum. See `~numpy.ufunc.reduce`
    for details.

    .. versionadded:: 1.22.0

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

amax, fmax, maximum

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.min.

Examples
--------
>>> import numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
array([1.,  2.])
>>> np.nanmin(a, axis=1)
array([1.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmin([1, 2, np.nan, np.inf])
1.0
>>> np.nanmin([1, 2, np.nan, -np.inf])
-inf

r\