Perform a reverse cumulative sum on a numpy array

This does it:

np.cumsum(x[::-1])[::-1] 

The answers given so far seem to be all inefficient if you want the result stored in the original array. As well, if you want a copy, keep in mind this will return a view not a contiguous array and np.ascontiguousarray() is still needed.

How about

view=np.flip(x, 0)
np.cumsum(view, 0, out=view)
#x contains the reverse cumsum result and remains contiguous and unflipped

This modifies the flipped view of x which writes the data properly in reverse order back into the original x variable. It requires no non-contiguous views at the end of execution and is about as speed efficient as possible. I am guessing numpy will never add a reversecumsum method namely because the technique I describe is so trivially and efficiently possible. Albeit, it might be ever so slightly more efficient to have the explicit method.

Otherwise if a copy is desired, then the extra flip is required AND conversion back to a contiguous array, mainly if it will be used in many vector operations thereafter. A tricky part of numpy, but views and contiguity are something to be careful with if you are seriously interested in performance.

You can use .flipud() for this as well, which is equivalent to [::-1] https://docs.scipy.org/doc/numpy/reference/generated/numpy.flipud.html

In [0]: x = np.array([0,1,2,3,4])

In [1]: np.flipud(np.flipud(x).cumsum())
Out[1]: array([10, 10,  9,  7,  4]

.flip() is new as of NumPy 1.12, and combines the .flipud() and .fliplr() into one API. https://docs.scipy.org/doc/numpy/reference/generated/numpy.flip.html

This is equivalent, and has fewer function calls:

np.flip(np.flip(x, 0).cumsum(), 0)