Well Python and Panda has been making an R equivalent for a while now, Pandas. Pandas and other work has made a Python a good choice for doing data science work (Though I prefer R and started in Python years ago). The Pandas and Python Data Science developers have been awesome. Now the Python Community not so much (In regards to flaming R).
That said, the hoops you have to jump through when interfacing with stock R code and dplyr make me think that having an operator that is less generic than `%>%` makes sense -- `X %>% foo()` is equivalent to `foo(X)`; except if it isn't -- `X %>% plot(x~y, data=.)` is not equivalent to `plot(X, x~y, data=.)`. And sometimes you need tricks like `X %>% { plot(.$x, .$y) }` to work around the nonobvious behaviors.
I find most impressive the work that this project did to emulate the lazy evaluation that is a hallmark of dplyr. I wasn't aware that python had anything like this ability.