dask.array.nancumsum(x, axis, dtype=None, out=None, *, method='sequential')[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.

This docstring was copied from numpy.nancumsum.

Some inconsistencies with the Dask version may exist.

Dask added an additional keyword-only argument method.

method{‘sequential’, ‘blelloch’}, optional

Choose which method to use to perform the cumsum. Default is ‘sequential’.

  • ‘sequential’ performs the cumsum of each prior block before the current block.

  • ‘blelloch’ is a work-efficient parallel cumsum. It exposes parallelism by

    first taking the sum of each block and combines the sums via a binary tree. This method may be faster or more memory efficient depending on workload, scheduler, and hardware. More benchmarking is necessary.

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

New in version 1.12.0.

aarray_like (Not supported in Dask)

Input array.

axisint, optional

Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.

dtypedtype, 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.

outndarray, 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 Output type determination for more details.


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.

See also


Cumulative sum across array propagating NaNs.


Show which elements are NaN.


>>> np.nancumsum(1)  
>>> np.nancumsum([1])  
>>> 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.]])