numpy.nansum

numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=<no value>)
[source]

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
In NumPy versions <= 1.9.0 Nan is returned for slices that are allNaN or empty. In later versions zero is returned.
Parameters: 

a : array_like 
Array containing numbers whose sum 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 sum is computed. The default is to compute the sum of the flattened array. 
dtype : datatype, optional 
The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of a is used. An exception is when a has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact. 
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. The casting of NaN to integer can yield unexpected results. 
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 mean or sum methods of subclasses of ndarray . If the subclasses methods does not implement keepdims any exceptions will be raised. 
Returns: 

nansum : ndarray. 
A new array holding the result is returned unless out is specified, in which it is returned. The result has the same size as a , and the same shape as a if axis is not None or a is a 1d array. 
See also

numpy.sum
 Sum across array propagating NaNs.

isnan
 Show which elements are NaN.

isfinite
 Show which elements are not NaN or +/inf.
Notes
If both positive and negative infinity are present, the sum will be Not A Number (NaN).
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.])
>>> np.nansum([1, np.nan, np.inf])
inf
>>> np.nansum([1, np.nan, np.NINF])
inf
>>> from numpy.testing import suppress_warnings
>>> with suppress_warnings() as sup:
... sup.filter(RuntimeWarning)
... np.nansum([1, np.nan, np.inf, np.inf]) # both +/ infinity present
nan