bundles / numpy latest / numpy / cumsum
_ArrayFunctionDispatcher
numpy:cumsum
source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/_core/fromnumeric.py :2838
Signature
def cumsum ( a , axis = None , dtype = None , out = None ) Summary
Return the cumulative sum of the elements along a given axis.
Parameters
a: array_likeInput array.
axis: int, optionalAxis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array.
dtype: dtype, optionalType of the returned array and of the accumulator in which the elements are summed. If
dtypeis not specified, it defaults to the dtype ofa, unlessahas 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, optionalAlternative 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-typefor more details.
Returns
cumsum_along_axis: ndarray.A new array holding the result is returned unless
outis specified, in which case a reference tooutis returned. The result has the same size asa, and the same shape asaifaxisis not None orais a 1-d array.
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.
Examples
import numpy as np a = np.array([[1,2,3], [4,5,6]]) a np.cumsum(a)✓
np.cumsum(a, dtype=float) # specifies type of output value(s)
✗np.cumsum(a,axis=0) # sum over rows for each of the 3 columns np.cumsum(a,axis=1) # sum over columns for each of the 2 rows✓
b = np.array([1, 2e-9, 3e-9] * 1000000)
✓b.cumsum()[-1] b.sum()✗
See also
- cumulative_sum
Array API compatible alternative for
cumsum.- diff
Calculate the n-th discrete difference along given axis.
- sum
Sum array elements.
- trapezoid
Integration of array values using composite trapezoidal rule.
Aliases
-
numpy.cumsum