Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
Parameters:  a : array_like
axis : int, optional


Returns:  y : ndarray

See also
Notes
Numpy uses the IEEE Standard for Binary FloatingPoint for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. If positive or negative infinity are present the result is positive or negative infinity. But if both positive and negative infinity are present, the result is Not A Number (NaN).
Arithmetic is modular when using integer types (all elements of a must be finite i.e. no elements that are NaNs, positive infinity and negative infinity because NaNs are floating point types), and no error is raised on overflow.
Examples
>>> np.nansum(1)
1
>>> np.nansum([1])
1
>>> np.nansum([1, np.nan])
1.0
>>> a = np.array([[1, 1], [1, np.nan]])
>>> np.nansum(a)
3.0
>>> np.nansum(a, axis=0)
array([ 2., 1.])
When positive infinity and negative infinity are present
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
inf
>>> np.nansum([1, np.nan, np.inf, np.NINF])
nan