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            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
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        "description": "===============================\r\nFlatson\r\n===============================\r\n\r\n.. image:: https://img.shields.io/travis/scrapinghub/flatson.svg\r\n        :target: https://travis-ci.org/scrapinghub/flatson\r\n\r\n.. image:: https://img.shields.io/pypi/v/flatson.svg\r\n        :target: https://pypi.python.org/pypi/flatson\r\n\r\n\r\nA tool to flatten JSON-like objects, allowing to configure via an annotated JSON schema\r\n\r\n* Free software: BSD license\r\n* Documentation: https://flatson.readthedocs.org.\r\n\r\nFeatures\r\n--------\r\n\r\n* Flattens Python dictionaries using a JSON schema\r\n* Supports per-field configuration via the schema\r\n\r\nUsage::\r\n\r\n    >>> from flatson import Flatson\r\n    >>> schema = {\r\n            \"$schema\": \"http://json-schema.org/draft-04/schema\",\r\n            \"type\": \"object\",\r\n            \"properties\": {\r\n                \"name\": {\"type\": \"string\"},\r\n                \"age\": {\"type\": \"number\"},\r\n                \"address\": {\r\n                    \"type\": \"object\",\r\n                    \"properties\": {\"city\": {\"type\": \"string\"}, \"street\": {\"type\": \"string\"}}\r\n                },\r\n                \"skills\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\r\n            }\r\n        }\r\n    >>> sample = {\r\n                \"name\": \"Claudio\", \"age\": 42,\r\n                \"address\": {\"city\": \"Paris\", \"street\": \"Rue de Sevres\"},\r\n                \"skills\": [\"hacking\", \"soccer\"]}\r\n    >>> f = Flatson(schema)\r\n    >>> f.fieldnames\r\n    ['address.city', 'address.street', 'age', 'name', 'skills']\r\n    >>> f.flatten(sample)\r\n    ['Paris', 'Rue de Sevres', 42, 'Claudio', '[\"hacking\",\"soccer\"]']\r\n\r\nYou can get a dict with the field names order preserved::\r\n\r\n    >>> f.flatten_dict(sample)\r\n    OrderedDict([('address.city', 'Paris'), ('address.street', 'Rue de Sevres'), ('age', 42), ('name', 'Claudio'), ('skills', '[\"hacking\",\"soccer\"]')])\r\n\r\nYou can also configure array serialization behavior through the schema (default JSON)::\r\n\r\n    >>> schema = {\r\n            \"$schema\": \"http://json-schema.org/draft-04/schema\",\r\n            \"type\": \"object\",\r\n            \"properties\": {\r\n                \"name\": {\"type\": \"string\"},\r\n                \"skills\": {\r\n                    \"type\": \"array\",\r\n                    \"items\": {\"type\": \"string\"},\r\n                    \"flatson_serialize\": {\"method\": \"join_values\"},\r\n                }\r\n            }\r\n        }\r\n    >>> f = Flatson(schema)\r\n    >>> f.flatten({\"name\": \"Salazar\", \"skills\": [\"hacking\", \"socker\", \"partying\"]})\r\n    ['Salazar', 'hacking,socker,partying']\r\n\r\n\r\n\r\n\r\n\r\nHistory\r\n-------\r\n\r\n0.1.0 (2015-06-02)\r\n---------------------\r\n\r\n* First release on PyPI.",
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