numpy.cumulative_sum#

numpy.cumulative_sum(x, /, *, axis=None, dtype=None, out=None, include_initial=False)[source]#

Return the cumulative sum of the elements along a given axis.

This function is an Array API compatible alternative to numpy.cumsum.

Parameters:
xarray_like

Input array.

axisint, optional

Axis along which the cumulative sum is computed. The default (None) is only allowed for one-dimensional arrays. For arrays with more than one dimension axis is required.

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 x, unless x 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.

include_initialbool, optional

Boolean indicating whether to include the initial value (zeros) as the first value in the output. With include_initial=True the shape of the output is different than the shape of the input. Default: False.

Returns:
cumulative_sum_along_axisndarray

A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same shape as x if include_initial=False.

See also

sum

Sum array elements.

trapezoid

Integration of array values using composite trapezoidal rule.

diff

Calculate the n-th discrete difference along given axis.

Notes

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

cumulative_sum(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.

Examples

>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> a
array([1, 2, 3, 4, 5, 6])
>>> np.cumulative_sum(a)
array([ 1,  3,  6, 10, 15, 21])
>>> np.cumulative_sum(a, dtype=float)  # specifies type of output value(s)
array([  1.,   3.,   6.,  10.,  15.,  21.])

>>> b = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.cumulative_sum(b,axis=0)  # sum over rows for each of the 3 columns
array([[1, 2, 3],
       [5, 7, 9]])
>>> np.cumulative_sum(b,axis=1)  # sum over columns for each of the 2 rows
array([[ 1,  3,  6],
       [ 4,  9, 15]])

cumulative_sum(c)[-1] may not be equal to sum(c)

>>> c = np.array([1, 2e-9, 3e-9] * 1000000)
>>> np.cumulative_sum(c)[-1]
1000000.0050045159
>>> c.sum()
1000000.0050000029
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