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        "author": "Michele Lacchia",
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            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.1",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
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        "description": "sklearn-pdtransform\n-------------------\n\nInstallation:\n\n.. code::\n\n    $ pip install pdtransform\n\nA little package with a few transformers to work with Pandas dataframes in the\nSklearn pipeline, which I found myself writing quite frequently. Example usage:\n\n.. code:: python\n\n   from pdtransform import DFTransform, DFFeatureUnion\n\n   pipeline = Pipeline([\n       ('ordinal_to_nums', DFTransform(_ordinal_to_nums, copy=True)),\n       ('union', DFFeatureUnion([\n           ('categorical', Pipeline([\n               ('select', DFTransform(lambda X: X.select_dtypes(include=['object']))),\n               ('fill_na', DFTransform(lambda X: X.fillna('NA'))),\n               ('one_hot', DFTransform(_one_hot_encode)),\n           ])),\n           ('numerical', Pipeline([\n               ('select', DFTransform(lambda X: X.select_dtypes(exclude=['object']))),\n               ('fill_median', DFTransform(lambda X: X.fillna(X.median()))),\n               ('add_features', DFTransform(_add_features, copy=True)),\n               ('remove_skew', DFTransform(_remove_skew, copy=True)),\n               ('find_outliers', DFTransform(_find_outliers, copy=True)),\n               ('normalize', DFTransform(lambda X: X.div(X.max())))\n           ])),\n       ])),\n   ])\n\n\nFor more information read `this blog post <http://signal-to-noise.xyz/why-you-should-use-scikit-learns-pipeline-object.html>`_.\n",
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        "keywords": "sklearn pandas transformers pipeline",
        "license": "MIT",
        "maintainer": "",
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        "name": "pdtransform",
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        "summary": "Sklearn transformers that work with Pandas dataframes",
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