From my point of view, such function is a bit of a corner-case to be added to numpy. And it doesn’t justify it’s naming anymore. It is not one operation anymore. It is a cumsum and prepending 0. And it is very difficult to argue why prepending 0 to cumsum is a part of cumsum.

What I would rather vouch for is adding an argument to `np.diff` so that it leaves first row unmodified.

def diff0(a, axis=-1):
    """Differencing which appends first item along the axis"""
    a0 = np.take(a, [0], axis=axis)
    return np.concatenate([a0, np.diff(a, n=1, axis=axis)], axis=axis)
This would be more sensible from conceptual point of view. As difference can not be made, the result is the difference from absolute origin. With recognition that first non-origin value in a sequence is the one after it. And if the first row is the origin in a specific case, then that origin is correctly defined in relation to absolute origin.

Then, if origin row is needed, then it can be prepended in the beginning of a procedure. And np.diff and np.cumsum are inverses throughout the sequential code.

np.diff0 was one the first functions I had added to my numpy utils and been using it instead of np.diff quite a lot.

I think general flag to prevent fencepost errors could be added to all functions, where required, so that the flow is seamless retains initial dimension length. Taking some time to ensure consistency across numpy in this dimension could be of long term value.

E.g. rolling functions in numbagg and bottleneck leave nans, because there is no other sensible value to go there instead. While in this case, sensible value exists. Just not in `cumsum` function.

On 11 Aug 2023, at 15:53, Juan Nunez-Iglesias <> wrote:

I'm very sensitive to the issues of adding to the already bloated numpy API, but I would definitely find use in this function. I literally made this error (thinking that the first element of cumsum should be 0) just a couple of days ago! What are the plans for the "extended" NumPy API after 2.0? Is there a good place for these variants?

On Fri, 11 Aug 2023, at 2:07 AM, wrote:
`cumsum` computes the sum of the first k summands for every k from 1.
Judging by my experience, it is more often useful to compute the sum of
the first k summands for every k from 0, as `cumsum`'s behaviour leads
to fencepost-like problems.
For example, `cumsum` is not the inverse of `diff`. I propose adding a
function to NumPy to compute cumulative sums beginning with 0, that is,
an inverse of `diff`. It might be called `cumsum0`. The following code
is probably not the best way to implement it, but it illustrates the
desired behaviour.

def cumsum0(a, axis=None, dtype=None, out=None):
   Return the cumulative sum of the elements along a given axis,
   beginning with 0.

   cumsum0 does the same as cumsum except that cumsum computes the sum
   of the first k summands for every k from 1 and cumsum, from 0.

   a : array_like
       Input array.
   axis : int, optional
       Axis along which the cumulative sum is computed. The default
       (None) is to compute the cumulative sum over the flattened
   dtype : dtype, 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 `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
       :ref:`ufuncs-output-type` for more details.

   cumsum0_along_axis : ndarray.
       A new array holding the result is returned unless `out` is
       specified, in which case a reference to `out` is returned. If
       `axis` is not None the result has the same shape as `a` except
       along `axis`, where the dimension is smaller by 1.

   See Also
   cumsum : Cumulatively sum array elements, beginning with the first.
   sum : Sum array elements.
   trapz : Integration of array values using the composite trapezoidal rule.
   diff : Calculate the n-th discrete difference along given axis.

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

   ``cumsum0(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.

a = np.array([[1, 2, 3], [4, 5, 6]])
   array([[1, 2, 3],
          [4, 5, 6]])
   array([ 0,  1,  3,  6, 10, 15, 21])
np.cumsum0(a, dtype=float)  # specifies type of output value(s)
   array([ 0.,  1.,  3.,  6., 10., 15., 21.])

np.cumsum0(a, axis=0)  # sum over rows for each of the 3 columns
   array([[0, 0, 0],
          [1, 2, 3],
          [5, 7, 9]])
np.cumsum0(a, axis=1)  # sum over columns for each of the 2 rows
   array([[ 0,  1,  3,  6],
          [ 0,  4,  9, 15]])

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

b = np.array([1, 2e-9, 3e-9] * 1000000)

   empty = a.take([], axis=axis)
   zero = empty.sum(axis, dtype=dtype, keepdims=True)
   later_cumsum = a.cumsum(axis, dtype=dtype)
   return concatenate([zero, later_cumsum], axis=axis, dtype=dtype, out=out)
NumPy-Discussion mailing list --
To unsubscribe send an email to
Member address:
NumPy-Discussion mailing list --
To unsubscribe send an email to
Member address: