I've used pandas regularly for the past ~5 years and find its API intuitive enough not to complain. I can write and read decently long pandas chained expressions fluently, but I barely know any R. Am I unwittingly a hostage of an inferior API and don't know what I'm missing?
For instance, integer indexing in base R is df[row,col] rather than the iloc pandas stuff.
plot, print and summary (and generic function OOP more generally is really underappreciated).
Python is a better programming language, but R is a better data analysis environment.
And dplyr is an incredibly fluent DSL for doing data analysis (not quite as good for modelling though).
Seriously, I read the original vignette for dplyr in late 2013/early 2014 and within two weeks I'd switched most of my new analytical code over to it. So very, very good. Less idea-impedance match than any other environment, in my experience.
This might not seem like a big issue but in the beginning, these were my issues
1. Not so easy way to rename columns during aggregation
2. The group by generates its own grouped by data and hence you almost always need `reset_index`
3. Sometimes group by can convert a dataframe to series
4. Now `.loc` has provided bit consistent indexing/slicing, but earlier you had `.ix` `.iloc` and what not
These are something I can remember from top of my head. Of course all of these have solutions, but it makes pandas much more verbose. In R, these are just much more succinct.
In case you're interested in what's missing--I maintain a port of dplyr from R to python called siuba, and gave a talk recently on why pandas might be hard to use:
