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 ofx
, unlessx
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 (ones) 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 toout
is returned. The result has the same shape asx
ifinclude_initial=False
.
See also
Notes
Arithmetic is modular when using integer types, and no error is raised on overflow.
cumulative_sum(a)[-1]
may not be equal tosum(a)
for floating-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]) >>> 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 tosum(c)
>>> c = np.array([1, 2e-9, 3e-9] * 1000000) >>> np.cumulative_sum(c)[-1] 1000000.0050045159 >>> c.sum() 1000000.0050000029