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That's R (and S/S+ before it), for better or worse.

Dataframes aren't first class in Julia? I guess it's provided by a library. But Julia has really good vector and matrix support with broadcasting, like R and unlike Python, which relies on NumPy for that (Pandas builds on top of NumPy).

The R and Julia syntax are better than Python for vectorized operations, although NumPy and Pandas mostly make up for it using the magic methods.

In terms of keystroke efficiency, nothing beats the array languages. One might think APL is a little too terse, whereas Pandas is a little on the verbose side.

As it's been mentioned, that's pretty much R. Unfortunately if you need to do anything that can't be coerced into a dataframe it's quite ugly.

What about R?

Array languages (APL/J/K/Q) all do this well, particularly kdb