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Table of contents

  * Overview
  * Docs
  * Quick Start
  * Roadmap
  * Contributions

 

viper

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PyPI version pages-build Code style: black

    Simple, expressive pipeline syntax to transform and manipulate
    data with ease

Overview

viper is a Python package that provides a simple, expressive way to
work with data. It allows you to easily manipulate and transform data
using a pipeline syntax similar to that of dplyr.

Pipelining your DataFrame manipulation operations offers several
benefits:

  * improved code readability (no need to 'comment the what')
  * no need to save intermediate dataframes
  * ability to chain a long sequence of operations in a single
    command
  * thinking of coding as a series of transformations between the
    input and the desired output can improve the design and make it
    less coupled

Docs

Complete documentation is available here.

Quick Start

Installation:

 pip install viper-df

Here is an example of how to use viper to analyze the famed mtcars
dataset.

We want to find:

  * the average consumption, expressed in Miles/(US) gallon
  * the average power

Furthermore:

  * only consider those cars that weigh more than 2000lbs
  * group the results by the number of cylinders and number of gears
  * arrange in descending orders by the grouping variables

 import viper as v
 from viper.data import mtcars
 
 v.pipeline(
     mtcars,
     v.rename(
         "hp = power",
         "mpg = consumption",
     ),
     v.mutate(
         consumption=lambda r: 1 / r["consumption"]
     ),
     v.filter(
         lambda r: r["wt"] > 2
     ),
     v.group_by("cyl", "gear"),
     v.summarize(
         "power = mean()",
         "consumption = mean()"
     ),
     v.arrange(
         "cyl desc",
         "gear desc"
     ),
 )
 #                power  consumption
 # cyl gear
 # 8   5     299.500000     0.064979
 #     3     194.166667     0.068824
 # 6   5     175.000000     0.050761
 #     4     116.500000     0.050875
 #     3     107.500000     0.050989
 # 4   5      91.000000     0.038462
 #     4      85.000000     0.041259
 #     3      97.000000     0.046512

Here you can find more examples, particularly on joins.

Roadmap

The future development of the package will probably focus on:

  * adding pivot_longerand pivot_wider functions
  * adding more join_* functions

Contributions

You are welcome to contribute to the project or open issues if you
have any ideas.

 
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