# numpy.nancumsum¶

`numpy.``nancumsum`(a, axis=None, dtype=None, out=None)[source]

Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros.

Zeros are returned for slices that are all-NaN or empty.

New in version 1.12.0.

Parameters: a : array_like Input array. axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If `dtype` is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. See `doc.ufuncs` (Section “Output arguments”) for more details. nancumsum : 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 1-d array.

`numpy.cumsum`
Cumulative sum across array propagating NaNs.
`isnan`
Show which elements are NaN.

Examples

```>>> np.nancumsum(1)
array()
>>> np.nancumsum()
array()
>>> np.nancumsum([1, np.nan])
array([ 1.,  1.])
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nancumsum(a)
array([ 1.,  3.,  6.,  6.])
>>> np.nancumsum(a, axis=0)
array([[ 1.,  2.],
[ 4.,  2.]])
>>> np.nancumsum(a, axis=1)
array([[ 1.,  3.],
[ 3.,  3.]])
```

numpy.nancumprod

numpy.diff