Perform a reverse cumulative sum on a numpy array

This does it:

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

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)

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.