cumsum(x, axis=None, dtype=None, out=None, method='sequential')

This docstring was copied from numpy.cumsum.

Some inconsistencies with the Dask version may exist.

Dask added an additional keyword-only argument method .

method

method

Notes

Arithmetic is modular when using integer types, and no error is raised on overflow.

cumsum(a)[-1] may not be equal to sum(a) for floating-point values since sum may use a pairwise summation routine, reducing the roundoff-error. See sum for more information.

Parameters

a : array_like (Not supported in Dask)

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 :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.

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 ufuncs-output-type for more details.

Returns

cumsum_along_axis : ndarray.

A new array holding the result is returned unless :None:None:`out` is specified, in which case a reference to :None:None:`out` 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 the elements along a given axis.

See Also

diff

Calculate the n-th discrete difference along given axis.

sum

Sum array elements.

trapz

Integration of array values using the composite trapezoidal rule.

Examples

This example is valid syntax, but we were not able to check execution

>>> a = np.array([[1,2,3], [4,5,6]])  # doctest: +SKIP
... a  # doctest: +SKIP
array([[1, 2, 3],
       [4, 5, 6]])

This example is valid syntax, but we were not able to check execution

>>> np.cumsum(a)  # doctest: +SKIP
array([ 1,  3,  6, 10, 15, 21])

This example is valid syntax, but we were not able to check execution

>>> np.cumsum(a, dtype=float)     # specifies type of output value(s)  # doctest: +SKIP
array([  1.,   3.,   6.,  10.,  15.,  21.])

This example is valid syntax, but we were not able to check execution

>>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns  # doctest: +SKIP
array([[1, 2, 3],
       [5, 7, 9]])

This example is valid syntax, but we were not able to check execution

>>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows  # doctest: +SKIP
array([[ 1,  3,  6],
       [ 4,  9, 15]])

cumsum(b)[-1] may not be equal to sum(b)

This example is valid syntax, but we were not able to check execution

>>> b = np.array([1, 2e-9, 3e-9] * 1000000)  # doctest: +SKIP
... b.cumsum()[-1]  # doctest: +SKIP
1000000.0050045159

This example is valid syntax, but we were not able to check execution

>>> b.sum()  # doctest: +SKIP
1000000.0050000029

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.reductions.sum

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