{ "info": { "author": "Feature Labs Team", "author_email": "team@featurelabs.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7" ], "description": ".. image:: https://badge.fury.io/py/fl-henchman.svg?maxAge=2592000\n :target: https://badge.fury.io/py/fl-henchman\n.. image:: https://img.shields.io/conda/v/featurelabs/henchman.svg\n :target: https://anaconda.org/featurelabs/henchman\n.. image:: https://img.shields.io/conda/pn/featurelabs/henchman.svg \n :target: https://anaconda.org/featurelabs/henchman\n\n\nWelcome to Henchman!\n=====================\nHenchman is a collection of `open source\n`_ python\nutility functions for working in a jupyter notebook. With\nHenchman, you can rapidly prototype end-to-end data science\nworkflows. You can explore data with\n``henchman.diagnostics``, make interesting plots with\n``henchman.plotting``, and do feature selection and machine\nlearning with ``henchman.selection`` and \n``henchman.learning``. \n\nFor more information, visit the Henchman `documentation `_.\n\n\n\nWhy?\n~~~~~~~\nLife is full of reusable functions. Here's what separates\nHenchman:\n\n- **Easy Interactive Plotting**: We bypass the flexible Bokeh\n API in favor of a small, rigid collection of standard data\n analysis plots. With sliders and checkboxes, finding the\n right plot parameters can be done with a `single function call `_.\n\n.. image:: https://henchman.featurelabs.com/_images/timeseries.gif\n :align: center\n\n- **Memorable API, Extensive documentation**: We have a\n heavy emphasis on ease of use. That means all the\n functions are sorted into one of 4 semantically named\n modules and names should be easy to remember inside that\n module. Additionally, every function has a docstring, an\n example and a `documentation `_\n page.\n\n.. image:: http://henchman.featurelabs.com/_images/create_model_docs.png\n :width: 75%\n :align: center\n\n- **Novel Functionality**: We provide a few functions built\n from scratch to add to your data science workflow. There\n are methods to systematically find dataset attributes with\n `overview `_ and `warnings `_ from ``henchman.diagnostics`` and classes to\n select features in novel ways with `RandomSelect `_ and\n `Dendrogram `_ in ``henchman.selection``.\n\n\nInstall\n~~~~~~~~~\nTo install Henchman, run this command in your terminal:\n\n.. code-block:: console\n\n $ python -m pip install fl-henchman\n\nIf you are using conda, you can download the most recent build from our channel on Anaconda.org:\n\n.. code-block:: console\n\n $ conda install -c featurelabs henchman\n\nThese are the preferred methods to install Henchman, as it will always install the most recent stable release. You can download miniconda `from this page`_.\n\n.. _from this page: https://github.com/conda/conda\n\n\nThe sources for Henchman can be downloaded from the `Github repo`_.\n\nYou can either clone the public repository:\n\n.. code-block:: console\n\n $ git clone git://github.com/featurelabs/henchman\n\nOr download the `tarball`_:\n\n.. code-block:: console\n\n $ curl -OL https://github.com/featurelabs/henchman/tarball/master\n\nOnce you have a copy of the source, you can install it with:\n\n.. code-block:: console\n\n $ python setup.py install\n\n.. _Github repo: https://github.com/featurelabs/henchman\n.. _tarball: https://github.com/featurelabs/henchman/tarball/master\n\n\n\n\n\n\n\n\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/featurelabs/henchman", "keywords": "henchman", "license": "BSD 3-clause", "maintainer": "", "maintainer_email": "", "name": 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