[rbanffy]

It really depends on the size of the list you want to process. If it's 10 items, pandas is overkill (and probably slower). If it's a million items, pandas is a great solution.

I have a nagging feeling there is an easier way to do this, but my quick and dirty solution was

    def merge_list1(l):
    other_dict = defaultdict(lambda: 0)
        for t, c in ((i['thing'], i['count']) for i in l):
            other_dict[t] += c
        return ({'thing': k, 'count': other_dict[k]} for k in other_dict)

which is still readable, but probably far from optimal.

[Izkata]

> If it's a million items, pandas is a great solution.

Possibly not even then, it depends on how much you're doing and I feel like the topic at hand might be around that tipping point. We have some rather slow code that, profiling it, turned out to spend something like 60-70% of its time just converting between python types and native types when moving data in and out of the dataframe.

[rbanffy]

True. If there are millions of different “things” conversion times will end up dominating. If they are just a handful, then the libraries will be able to do a lot more work with parallel operations and converting the output will be very quick