How to find the cumulative sum of numbers in a lis

Question

time_interval = [4, 6, 12]

I want to sum up the numbers like [4, 4+6, 4+6+12] in order to get the list t = [4, 10, 22].

I tried the following:

for i in time_interval:
    t1 = time_interval[0]
    t2 = time_interval[1] + t1
    t3 = time_interval[2] + t2
    print(t1, t2, t3)

4 10 22
4 10 22
4 10 22

Answer 1

This would be Haskell-style:

def wrand(vtlg):

    def helpf(lalt,lneu): 

        if not lalt==[]:
            return helpf(lalt[1::],[lalt[0]+lneu[0]]+lneu)
        else:
            lneu.reverse()
            return lneu[1:]        

    return helpf(vtlg,[0])

Answer 2

If You want a pythonic way without numpy working in 2.7 this would be my way of doing it

l = [1,2,3,4]
_d={-1:0}
cumsum=[_d.setdefault(idx, _d[idx-1]+item) for idx,item in enumerate(l)]

now let's try it and test it against all other implementations

import timeit
L=range(10000)

def sum1(l):
    cumsum=[]
    total = 0
    for v in l:
        total += v
        cumsum.append(total)
    return cumsum


def sum2(l):
    import numpy as np
    return list(np.cumsum(l))

def sum3(l):
    return [sum(l[:i+1]) for i in xrange(len(l))]

def sum4(l):
    return reduce(lambda c, x: c + [c[-1] + x], l, [0])[1:]

def this_implementation(l):
    _d={-1:0}
    return [_d.setdefault(idx, _d[idx-1]+item) for idx,item in enumerate(l)]


# sanity check
sum1(L)==sum2(L)==sum3(L)==sum4(L)==this_implementation(L)
>>> True    

# PERFORMANCE TEST
timeit.timeit('sum1(L)','from __main__ import sum1,sum2,sum3,sum4,this_implementation,L', number=100)/100.
>>> 0.001018061637878418

timeit.timeit('sum2(L)','from __main__ import sum1,sum2,sum3,sum4,this_implementation,L', number=100)/100.
>>> 0.000829620361328125

timeit.timeit('sum3(L)','from __main__ import sum1,sum2,sum3,sum4,this_implementation,L', number=100)/100.
>>> 0.4606760001182556 

timeit.timeit('sum4(L)','from __main__ import sum1,sum2,sum3,sum4,this_implementation,L', number=100)/100.
>>> 0.18932826995849608

timeit.timeit('this_implementation(L)','from __main__ import sum1,sum2,sum3,sum4,this_implementation,L', number=100)/100.
>>> 0.002348129749298096

Answer 3

values = [4, 6, 12]
total  = 0
sums   = []

for v in values:
  total = total + v
  sums.append(total)

print 'Values: ', values
print 'Sums:   ', sums

Running this code gives

Values: [4, 6, 12]
Sums:   [4, 10, 22]

Answer 4

If you're doing much numerical work with arrays like this, I'd suggest numpy, which comes with a cumulative sum function cumsum:

import numpy as np

a = [4,6,12]

np.cumsum(a)
#array([4, 10, 22])

Numpy is often faster than pure python for this kind of thing, see in comparison to @Ashwini's accumu:

In [136]: timeit list(accumu(range(1000)))
10000 loops, best of 3: 161 us per loop

In [137]: timeit list(accumu(xrange(1000)))
10000 loops, best of 3: 147 us per loop

In [138]: timeit np.cumsum(np.arange(1000))
100000 loops, best of 3: 10.1 us per loop

But of course if it's the only place you'll use numpy, it might not be worth having a dependence on it.

Answer 5

Try this:

result = []
acc = 0
for i in time_interval:
    acc += i
    result.append(acc)

Answer 6

lst = [4,6,12]

[sum(lst[:i+1]) for i in xrange(len(lst))]

If you are looking for a more efficient solution (bigger lists?) a generator could be a good call (or just use numpy if you really care about perf).

def gen(lst):
    acu = 0
    for num in lst:
        yield num + acu
        acu += num

print list(gen([4, 6, 12]))