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    "info": {
        "author": "Pierluigi Failla",
        "author_email": "{name}.{surname}@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 1 - Planning",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Documentation",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: System :: Installation/Setup",
            "Topic :: System :: Software Distribution"
        ],
        "description": "# `pipesnake`\n\n*a pandas sklearn-inspired pipeline data processor*\n\n`pipesnake` is a data processing pipeline able to handle Pandas Dataframes. In many cases\nDataframes are used to clean-up data, pre-processing it and to perform feature engineering, \n`pipesnake` tries to simplify these steps, creating complex pipelines.\n\n[documentation](docs/source/index.rst); [examples](examples/README.md);\n\n## Why?\n\nTwo easy reasons:\n* in many cases Pandas DataFrame is super easy to build _feature extractor_ or _data preocessors_\n* in many cases it is useful to have a pipeline that can process both `x` and `y` at the same time\n\n# How can you use `pipesnake` ?\n\n## Install\n\nThe easy way:\n\n`pip install --upgrade https://github.com/pierluigi-failla/pipesnake/tarball/master`\n\nto get the latest version available on GitHub, or:\n\n`pip install pipesnake` \n\nto install the latest stable version on [PyPi](https://pypi.python.org).\n\n## Coding\n\nYou can build your own pipelines combining `SeriesPipe` and `ParallelPipe`, both of them can handle list \nof `Transformer`. \n\nAn inherited `Transformer` object is a class which implements the abstract \n`base.Transformer` methods:\n\n```python\nfrom pipesnake.base import Transformer\n\nclass MyTransformer(Transformer):\n    def __init__(self, name=None, <your params>):\n        Transformer.__init__(self, name=name, ...)\n\n    def fit_x(self, x):\n        <your implementation>\n\n    def fit_y(self, y):\n        <your implementation>\n\n    def transform_x(self, x):\n        <your implementation>\n\n    def transform_y(self, y):\n        <your implementation>\n\n    def inverse_transform_x(self, x):\n        <your implementation>\n\n    def inverse_transform_y(self, y):\n        <your implementation>\n```\n\nYou can find some `Transformers` already implemented in `pipesnake.transformers`. \n\nOnce you have all the needed `Transformers` you can create pipelines for feature engineering or data \nprocessing using `SeriesPipe` or `ParallelPipe`:\n\n```python\nfrom pipesnake.pipe import ParallelPipe\nfrom pipesnake.pipe import SeriesPipe\n\npipe = SeriesPipe(transformers=[\n    ParallelPipe(transformers=[\n        MyTransformer1(<params>),\n        MyTransformer2(<params>),\n    ]),\n    MyTransformer3(<params>),\n])\n```\n\nMore info in the [documentation]() and in the [examples](examples/README.md).\n\n# Batteries included\n\n`pipesnake` comes with several transformers included:\n\nModule | Name | Short Description\n--- | --- | ---\n`pipenskae.transformers.combiner` | `Combiner` | Apply user function to a column or a set of columns\n`pipenskae.transformers.combiner` | `Roller` | Apply the provided function rolling within a given window\n`pipenskae.transformers.converter` | `Category2Number` | Convert categorical to number\n`pipenskae.transformers.deeplearning` | `LSTMPacker` | Pack rows in order to be used as input for LSTM networks\n`pipenskae.transformers.dropper` | `DropDuplicates` | Drop duplicated rows and/or cols\n`pipenskae.transformers.dropper` | `DropNanCols` | Drop cols with nans\n`pipenskae.transformers.dropper` | `DropNanRows` | Drop rows with nans\n`pipenskae.transformers.financial` | `ToReturn` | Convert columns to `financial return`: r_t = (x_t - x_{t-1}) / x_{t-1}\n`pipenskae.transformers.imputer` | `ReplaceImputer` | Impute NaNs replacing them\n`pipenskae.transformers.imputer` | `KnnImputer` | Impute NaNs using K-nearest neighbors\n`pipenskae.transformers.misc` | `ToNumpy` | Convert `x` and `y` to a particular numpy type\n`pipenskae.transformers.misc` | `ColumnRenamer` | Rename `x` and `y` columns\n`pipenskae.transformers.misc` | `Copycat` | Copy the datasets forward\n`pipenskae.transformers.scaler` | `MinMaxScaler` | Min max scaler\n`pipenskae.transformers.scaler` | `StdScaler` | Standard deviation scaler\n`pipenskae.transformers.scaler` | `MadScaler` | Median absolute deviation scaler\n`pipenskae.transformers.scaler` | `UnitLenghtScaler` | Scale the feature vector to have norm 1.0\n`pipenskae.transformers.selector` | `ColumnSelector` | Select a given list of column names to keep\n`pipenskae.transformers.stats` | `ToSymbolProbability` | Convert values in columns to their probabilities\n\n# How can you contribute to `pipesnake` ?\n\nFirst of all grab a copy of the repository: \n\n`git clone https://github.com/scikit-learn/scikit-learn.git`\n\nyou can run tests just running `run_tests.py`. \n\nThere is a bunch of things you can contribute as far as `pipesnake` is at its early stages:\n\n* **improvements**: make the library bugfixed, faster, parallel, nicer, cleaner...;\n* **documentation**: this library uses Sphinx to generate documentation, so feel free to enrich it;\n* **samples**: create examples about using the library;\n* **transformers**: develop new-general-purpose transformers to share with the community;\n* **tests**: code better tests to extend the coverage and reduce code regression;\n\nor whatever you may thing is relevant to make `pipesnake` better.\n\n\n",
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