[IndianAstronaut]

Interesting. Although after spwnding a lot of time on data frames, I have grown to like the csv parsers in postgres where I can do a lot of the same things as data frames, but with clean sql instead of the sometimes odd data frame syntax.

R also stands out because it is so easy to run a wide variety of statistical methods easily on a data frame.

[mapcar]

It's amazing to think that R (or S) had data frames since the 70s and only now are other languages implementing them. There are some quirks of course, and pandas introduced some convenient features. But the R community has also provided its own improvements in the way of data.table, and now, dplyr.

[ajinkyakale]

Data.table package by matt dowle definitely deserves a mention! Its fast and I like the indexing functonalities it provides. The benchmark timings are pretty impressive.

[arun_sriniv]

@ajinkyakale, thanks. What'd be also interesting is to benchmark memory usage in addition to runtime.

[ajinkyakale]

I should have mentioned you (arun_sriniv) as the co-developer of data.table! Thanks for all the hard work. And yes, memory usage will be interesting as that is the bottleneck when it comes to large dataset. I am working on something on those lines. Will post something soon :)

[arun_sriniv]

No worries :-). And glad to hear you're working on it! Let me know if I can be of any help.