plydata is a library that provides a grammar for data manipulation.
The grammar consists of verbs that can be applied to pandas
dataframes or database tables. It is based on the R packages
dplyr, tidyr and forcats. plydata uses the >> operator
as a pipe symbol, alternatively there is the ply(data, *verbs)
function that you can use instead of >>.
At present the only supported data store is the pandas dataframe. We expect to support sqlite and maybe postgresql and mysql.
plydata only supports Python 3.
Official version
$ pip install plydataDevelopment version
$ pip install git+https://github.com/has2k1/plydata.git@masterimport pandas as pd import numpy as np from plydata import define, query, if_else, ply # NOTE: query is the equivalent of dplyr's filter but with # slightly different python syntax for the expressions df = pd.DataFrame({ 'x': [0, 1, 2, 3], 'y': ['zero', 'one', 'two', 'three']}) df >> define(z='x') """ x y z 0 0 zero 0 1 1 one 1 2 2 two 2 3 3 three 3 """ df >> define(z=if_else('x > 1', 1, 0)) """ x y z 0 0 zero 0 1 1 one 0 2 2 two 1 3 3 three 1 """ # You can pass the dataframe as the # first argument query(df, 'x > 1') # same as `df >> query('x > 1')` """ x y 2 2 two 3 3 three """ # You can use the ply function instead of the >> operator ply(df, define(z=if_else('x > 1', 1, 0)), query('z == 1') ) """ x y z 2 2 two 1 3 3 three 1 """ # The >>= operator can be used to modify the dataframe # if there is a single operation df >>= define(two_x='2*x') df """ x y two_x 0 0 zero 0 1 1 one 2 2 2 two 4 3 3 three 6 """ # df >>= define(two_x='2*x') >> define(three_x='3*x') # is two operations and does not work
plydata piping works with plotnine.
from plotnine import ggplot, aes, geom_line df = pd.DataFrame({'x': np.linspace(0, 2*np.pi, 500)}) (df >> define(y='np.sin(x)') >> define(sign=if_else('y >= 0', '"positive"', '"negative"')) >> (ggplot(aes('x', 'y')) + geom_line(aes(color='sign'), size=1.5)) )
dplython and pandas-ply are two other packages that have a similar objective to plydata. The big difference is plydata does not use a placeholder variable (X) as a stand-in for the dataframe. For example:
diamonds >> select(X.carat, X.cut, X.price) # dplython diamonds >> select('carat', 'cut', 'price') # plydata select(diamonds, 'carat', 'cut', 'price') # plydata
For more, see the documentation.