{ "info": { "author": "Florian Finkernagel", "author_email": "finkernagel@imt.uni-marburg.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Programming Language :: Python" ], "description": "# dppd\n\n| Build status: | [![Build Status](https://travis-ci.com/TyberiusPrime/dppd.svg?branch=master)](https://travis-ci.com/TyberiusPrime/dppd)|\n|---------------|-----------------------------------------------------------------------------|\n| Documentation | https://dppd.readthedocs.io/en/latest/\n\n| Code style | ![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n\nDppd is a python [dplyr](dplyr.tidyverse.org) clone.\n\n\nIt allows you to use code like this\n\n\n```python\n >>> from plotnine.data import mtcars\n >>> from dppd import dppd\n >>> dp, X = dppd()\n >>> dp(mtcars).mutate(kwh = X.hp * 0.74).groupby('cyl').filter_by(X.kwh.rank() < 2).ungroup().pd\n cyl name mpg disp hp drat wt qsec vs am gear carb kwh\n 5 6 Valiant 18.1 225.0 105 2.76 3.460 20.22 1 0 3 1 77.70\n 18 4 Honda Civic 30.4 75.7 52 4.93 1.615 18.52 1 1 4 2 38.48\n 21 8 Dodge Challenger 15.5 318.0 150 2.76 3.520 16.87 0 0 3 2 111.00\n 22 8 AMC Javelin 15.2 304.0 150 3.15 3.435 17.30 0 0 3 2 111.00\n```\n\n\nBriefly, it uses a data-manipulater instance (dp above) together with a proxied \nreference to the latest created DataFrame (the X above) to achive for pandas what dpylr's \nnon-standard-evaluation based verbs does for R.\n\n\nPlease see our full documentation at https://dppd.readthedocs.io/en/latest/\nfor more details and a list of the supported verbs.\n\n\nAlso check out [dppd_plotnine](https://github.com/TyberiusPrime/dppd_plotnine)", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/TyberiusPrime/dppd", "keywords": "", "license": "mit", "maintainer": "", "maintainer_email": 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