Functions for manipulating Data in Python


Keywords
data-manipulation, pandas, python
License
BSD-1-Clause
Install
pip install plydata==0.2.0

Documentation

plydata

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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.

Installation

plydata only supports Python 3.

Official version

$ pip install plydata

Development version

$ pip install git+https://github.com/has2k1/plydata.git@master

Example

import 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
"""

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))
 )
./doc/images/readme-image.png

What about dplython or pandas-ply?

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.