nancumsum(x, axis, dtype=None, out=None, *, method='sequential')
This docstring was copied from numpy.nancumsum.
Some inconsistencies with the Dask version may exist.
Dask added an additional keyword-only argument method
.
method
method
Zeros are returned for slices that are all-NaN or empty.
Input array.
Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.
Type of the returned array and of the accumulator in which the elements are summed. If :None:None:`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.
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 ufuncs-output-type
for more details.
A new array holding the result is returned unless :None:None:`out` is specified, in which it is returned. The result has the same size as a, and the same shape as a if :None:None:`axis` is not None or a is a 1-d array.
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.
isnan
Show which elements are NaN.
numpy.cumsum
Cumulative sum across array propagating NaNs.
>>> np.nancumsum(1) # doctest: +SKIP array([1])This example is valid syntax, but we were not able to check execution
>>> np.nancumsum([1]) # doctest: +SKIP array([1])This example is valid syntax, but we were not able to check execution
>>> np.nancumsum([1, np.nan]) # doctest: +SKIP array([1., 1.])This example is valid syntax, but we were not able to check execution
>>> a = np.array([[1, 2], [3, np.nan]]) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... np.nancumsum(a) # doctest: +SKIP array([1., 3., 6., 6.])
>>> np.nancumsum(a, axis=0) # doctest: +SKIP array([[1., 2.], [4., 2.]])This example is valid syntax, but we were not able to check execution
>>> np.nancumsum(a, axis=1) # doctest: +SKIP array([[1., 3.], [3., 3.]])See :
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them