Port of dplyr and other related R packages in python, using pipda.
Project description
datar
A Grammar of Data Manipulation in python
Documentation | Reference Maps | Notebook Examples | API | Blog
datar
is a re-imagining of APIs of data manipulation libraries in python (currently only pandas
supported) so that you can manipulate your data with it like with dplyr
in R
.
datar
is an in-depth port of tidyverse
packages, such as dplyr
, tidyr
, forcats
and tibble
, as well as some functions from base R.
Installation
pip install -U datar
# install pdtypes support
pip install -U datar[pdtypes]
# install dependencies for modin as backend
pip install -U datar[modin]
# you may also need to install dependencies for modin engines
# pip install -U modin[ray]
Example usage
from datar import f
from datar.dplyr import mutate, filter, if_else
from datar.tibble import tibble
# or
# from datar.all import f, mutate, filter, if_else, tibble
df = tibble(
x=range(4), # or c[:4] (from datar.base import c)
y=['zero', 'one', 'two', 'three']
)
df >> mutate(z=f.x)
"""# output
x y z
<int64> <object> <int64>
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
<int64> <object> <int64>
0 0 zero 0
1 1 one 0
2 2 two 1
3 3 three 1
"""
df >> filter(f.x>1)
"""# output:
x y
<int64> <object>
0 2 two
1 3 three
"""
df >> mutate(z=if_else(f.x>1, 1, 0)) >> filter(f.z==1)
"""# output:
x y z
<int64> <object> <int64>
0 2 two 1
1 3 three 1
"""
# works with plotnine
# 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))
# very easy to integrate with other libraries
# for example: klib
import klib
from pandas import Series
from pipda import register_verb
from datar.datasets import iris
from datar.dplyr import pull
dist_plot = register_verb(Series, func=klib.dist_plot)
iris >> pull(f.Sepal_Length) >> dist_plot()
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