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 tonumpy.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 arrayswith more than one dimensionaxis is required.

dtypedtype, optional

Type of the returned array and of the accumulator in which theelements are summed. Ifdtype is not specified, it defaultsto the dtype ofx, unlessx has an integer dtype witha 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 musthave the same shape and buffer length as the expected outputbut the type will be cast if necessary. SeeOutput type determinationfor more details.

include_initialbool, optional

Boolean indicating whether to include the initial value (zeros) asthe first value in the output. Withinclude_initial=Truethe 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 unlessout isspecified, in which case a reference toout is returned. Theresult has the same shape asx ifinclude_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 israised on overflow.

cumulative_sum(a)[-1] may not be equal tosum(a) forfloating-point values sincesum may use a pairwise summation routine,reducing the roundoff-error. Seesum for more information.

Examples

>>>a=np.array([1,2,3,4,5,6])>>>aarray([1, 2, 3, 4, 5, 6])>>>np.cumulative_sum(a)array([ 1,  3,  6, 10, 15, 21])>>>np.cumulative_sum(a,dtype=np.float64)# 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 columnsarray([[1, 2, 3],       [5, 7, 9]])>>>np.cumulative_sum(b,axis=1)# sum over columns for each of the 2 rowsarray([[ 1,  3,  6],       [ 4,  9, 15]])

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