5 ms·

This solution is really bad, read it in as a dataframe and use a groupby...

• deleted 5y ago
  [deleted]
• halfdan 5y ago
  That's even worse. You don't need to throw in a massive library like pandas, especially not when OP is build a basic API. Sure, if you're in a data science-y project where pandas is already a dependency, go nuts.
  • rbanffy 5y ago
    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 5y ago
      > 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 5y ago
        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