issue_comments

Thanks @mschrimpf. Hopefully we can get multi-dimensional groupbys, too.

Is there a way of vectorizing these calls with that in mind? I.e. apply a method for each group.

I can't think of anything immediately, and doubt there's an easy way given it doesn't exist yet (though that logic can be a trap!). There's some hacky pandas reshaping you may be able to do to solve this as a one-off. Otherwise it does likely require a concerted effort with numbagg.

I occasionally hit this issue too, so as keen as you are to find a solution. Thanks for giving it a try.

Thanks for the issue @mschrimpf

.sel is slow per operation, mainly because it's a python function call (although not the only reason - it's also doing a set of checks / potential alignments / etc). When I say 'slow', I mean about 0.5ms: In [6]: %timeit d.sel(a='a', b='a') 1000 loops, best of 3: 522 µs per loop

While there's an overhead, the time is fairly consistent regardless of the number of items it's selecting. For example: In [11]: %timeit d.sel(a=d['a'], b=d['b']) 1000 loops, best of 3: 1 ms per loop

So, as is often the case in the pandas / python ecosystem, if you can write code in a vectorized way, without using python in the tight loops, it's fast. If you need to run python in each loop, it's much slower.

Does that resonate?

While I think not the main point here, there might be some optimizations on sel. It runs isinstance 144 times! And initializes a collection 13 times? Here's the %prun of the 0.5ms command: