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        "author": "Greg Lamp",
        "author_email": "greg@yhathq.com",
        "bugtrack_url": null,
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            "Intended Audience :: Science/Research",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "dummipy\n=======\n\nQuickstart\n----------\n\n.. code:: bash\n\n    $ pip install dummipy\n\nLet it out of the box...\n\n::\n\n    from sklearn.linear_model import LinearRegression\n    from dummipy import cereal\n\n    type(cereal)\n    # CategoricalDataFrame\n\n    cereal.head()\n    reg = LinearRegression()\n    reg.fit(cereal[['mfr', 'vitamins', 'fat']], cereal.calories)\n\nInstallation\n------------\n\nYou'll need ```pandas`` <http://pandas.pydata.org/>`__, but any old\nversion will do the trick. There is no ``pandas`` version pegged in the\n``setup.py`` file so installing ``dummipy`` won't mess up your existing\nsci-py setup.\n\n.. code:: bash\n\n    $ pip install dummipy\n\nUse\n---\n\nJust use it like any old data frame. That's really all there is to it.\n\n::\n\n    import dummipy as dp\n\n    df = dp.CategoricalDataFrame({\n        \"x\": range(5),\n        \"y\": [\"a\", \"b\", \"c\", \"a\", \"b\"]\n    })\n\n\n    df = pd.read_csv(\"foo.csv\")\n    df = dp.CategoricalDataFrame(df)",
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        "keywords": null,
        "license": "BSD",
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        "maintainer_email": null,
        "name": "dummipy",
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        "summary": "Categorical variable friendly pandas data frames",
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