datar VS pipda

datar does not only mimic the piping syntax, but follows the API design from dplyr as much as possible, and is tested with its test cases.

df = tibble( x=range(4), y=['zero', 'one', 'two', 'three'] ) df >> mutate(z=f.x) """# output x y z 0 0 zero 0 1 1 one 1 2 2 two 2 3 3 three 3 """ df >> mutate(z=if_else(f.x>1, 1, 0)) """# output: x y z 0 0 zero 0 1 1 one 0 2 2 two 1 3 3 three 1 """ df >> filter(f.x>1) """# output: x y 0 2 two 1 3 three """ df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1) """# output: x y z 0 2 two 1 1 3 three 1 """ ``` Works with plotnine ```python example grabbed from https://github.com/has2k1/plydata import numpy from datar.base import sin, pi from plotnine import ggplot, aes, geom_line, theme_classic df = tibble(x=numpy.linspace(0, 2*pi, 500)) (df >> mutate(y=sin(f.x), sign=if_else(f.y>=0, "positive", "negative")) >> ggplot(aes(x='x', y='y')) + theme_classic() + geom_line(aes(color='sign'), size=1.2)) ```  Easy to integrate with other libraries ```python import klib from pipda import register_verb from datar.datasets import iris from datar.dplyr import pull dist_plot = register_verb(func=klib.dist_plot) iris >> pull(f.Sepal_Length) >> dist_plot() ```  For more detailed and advanced usage, see https://pwwang.github.io/datar/

I wrote a framework (https://github.com/pwwang/pipda) that fits this situation, and it makes me easier to port those APIs as they are in python. I am not only following the documentation of the original APIs but looking into the R source code of them so that I can recover most parts of them. I wouldn't say it's perfect, due to the difference between the languages, but I would say it the closest and most covered port of dplyr/tidyr and related packages in python.