Return minimum of an array or minimum along an axis, ignoring any NaNs. When allNaN slices are encountered a RuntimeWarning
is raised and Nan is returned for that slice.
Parameters: 

a : array_like 
Array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted. 
axis : {int, tuple of int, None}, optional 
Axis or axes along which the minimum is computed. The default is to compute the minimum of the flattened array. 
out : ndarray, optional 
Alternate output array in which to place the result. The default is None ; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See doc.ufuncs for details. 
keepdims : bool, optional 
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 min method of subclasses of ndarray . If the subclasses methods does not implement keepdims any exceptions will be raised. 
Returns: 

nanmin : ndarray 
An array with the same shape as a , with the specified axis removed. If a is a 0d 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
 Elementwise minimum of two arrays, ignoring any NaNs.

minimum
 Elementwise 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 FloatingPoint 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
>>> 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.NINF])
inf