SciPy

numpy.nansum

numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=0)[source]

Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.

In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN 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, optional

Axis along which the sum is computed. The default is to compute the sum of the flattened array.

dtype : data-type, 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.

New in version 1.8.0.

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.

New in version 1.8.0.

keepdims : bool, optional

If 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 arr.

New in version 1.8.0.

Returns :

y : ndarray or numpy scalar

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).

Numpy integer arithmetic is modular. If the size of a sum exceeds the size of an integer accumulator, its value will wrap around and the result will be incorrect. Specifying dtype=double can alleviate that problem.

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
>>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
nan

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