Doesn't get simpler than this, I think:
a=[("13.5",100)]
b=[("14.5",100), ("15.5", 100)]
c=[("15.5",100), ("16.5", 100)]
input=[a,b,c]
from collections import Counter
print sum(
(Counter(dict(x)) for x in input),
Counter())
Note that Counter
(also known as a multiset) is the most natural data structure for your data (a type of set to which elements can belong more than once, or equivalently - a map with semantics Element -> OccurrenceCount. You could have used it in the first place, instead of lists of tuples.
Also possible:
from collections import Counter
from operator import add
print reduce(add, (Counter(dict(x)) for x in input))
Using reduce(add, seq)
instead of sum(seq, initialValue)
is generally more flexible and allows you to skip passing the redundant initial value.
Note that you could also use operator.and_
to find the intersection of the multisets instead of the sum.
The above variant is terribly slow, because a new Counter is created on every step. Let's fix that.
We know that Counter+Counter
returns a new Counter
with merged data. This is OK, but we want to avoid extra creation. Let's use Counter.update
instead:
update(self, iterable=None, **kwds) unbound collections.Counter method
Like dict.update() but add counts instead of replacing them.
Source can be an iterable, a dictionary, or another Counter instance.
That's what we want. Let's wrap it with a function compatible with reduce
and see what happens.
def updateInPlace(a,b):
a.update(b)
return a
print reduce(updateInPlace, (Counter(dict(x)) for x in input))
This is only marginally slower than the OP's solution.
Benchmark: http://ideone.com/7IzSx (Updated with yet another solution, thanks to astynax)
(Also: If you desperately want an one-liner, you can replace updateInPlace
by lambda x,y: x.update(y) or x
which works the same way and even proves to be a split second faster, but fails at readability. Don't :-))