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        "author": "Andriy Kushnir (Orhideous)",
        "author_email": "me@orhideous.name",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "tinsel\n======\n\n*Your data IS your schema*\n\n.. image:: https://img.shields.io/pypi/pyversions/tinsel.svg\n    :target: https://pypi.python.org/pypi/tinsel\n.. image:: https://img.shields.io/pypi/v/tinsel.svg\n    :target: https://pypi.python.org/pypi/tinsel\n.. image:: https://coveralls.io/repos/github/Orhideous/tinsel/badge.svg?branch=master\n    :target: https://coveralls.io/github/Orhideous/tinsel?branch=master\n.. image:: https://img.shields.io/travis/Orhideous/tinsel.svg\n    :target: https://travis-ci.org/Orhideous/tinsel\n.. image:: https://pyup.io/repos/github/Orhideous/tinsel/shield.svg\n    :target: https://pyup.io/repos/github/Orhideous/tinsel/\n\nThis tiny library helps to overcome excessive complexity in hand-written pyspark\ndataframe schemas.\n\nHow?\n----\n\nShape your data as ``NamedTuple`` or dataclasses - they can freely mix::\n\n    from dataclasses import dataclass\n    from tinsel import struct, transform\n    from typing import NamedTuple, Optional, Dict, List\n\n    @struct\n    @dataclass\n    class UserInfo:\n        hobby: List[str]\n        last_seen: Optional[int]\n        pet_ages: Dict[str, int]\n\n\n    @struct\n    class User(NamedTuple):\n        login: str\n        age: int\n        active: bool\n        info: Optional[UserInfo]\n\n\nTransform root node (``User`` in our case) into schema::\n\n    schema = transform(User)\n\n\nCreate some data, if necessary::\n\n    data = [\n        User(\n            login=\"Ben\",\n            age=18,\n            active=False,\n            info=None\n        ),\n        User(\n            login=\"Tom\",\n            age=32,\n            active=True,\n            info=UserInfo(\n                hobby=[\"pets\", \"flowers\"],\n                last_seen=16,\n                pet_ages={\"Jack\": 2, \"Sunshine\": 6}\n            )\n        )\n    ]\n\nAnd\u2026 voil\u00e0!::\n\n    from pyspark.sql import SparkSession\n\n    sc = SparkSession.builder.master('local').getOrCreate()\n\n    df = sc.createDataFrame(data=data, schema=schema)\n    df.printSchema()\n    df.show(truncate=False)\n\nThis will output::\n\n    root\n     |-- login: string (nullable = false)\n     |-- age: integer (nullable = false)\n     |-- active: boolean (nullable = false)\n     |-- info: struct (nullable = true)\n     |    |-- hobby: array (nullable = false)\n     |    |    |-- element: string (containsNull = false)\n     |    |-- last_seen: integer (nullable = true)\n     |    |-- pet_ages: map (nullable = false)\n     |    |    |-- key: string\n     |    |    |-- value: integer (valueContainsNull = false)\n\n\n    +-----+---+------+----------------------------------------------+\n    |login|age|active|info                                          |\n    +-----+---+------+----------------------------------------------+\n    |Ben  |18 |false |null                                          |\n    |Tom  |32 |true  |[[pets, flowers],, [Jack -> 2, Sunshine -> 6]]|\n    +-----+---+------+----------------------------------------------+\n\nFeatures\n--------\n* use native python types; no extra DSL, no cryptic API \u2014 just plain Python;\n* small and fast;\n* provide type shims for some types absent in Python, like ``long`` or ``short``;\n* nullable fields naturally fits into schema definition;\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n\n\n=======\nHistory\n=======\n\n0.2.0 (2018-08-28)\n------------------\n\n* Added dataclasses support\n\n0.1.0 (2018-08-28)\n------------------\n\n* First release on PyPI.\n\n\n",
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        "license": "MIT license",
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