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        "author": "Open Knowledge International",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
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        "description": "# tableschema-spss-py\n\n[![Travis](https://img.shields.io/travis/frictionlessdata/tableschema-spss-py/master.svg)](https://travis-ci.org/frictionlessdata/tableschema-spss-py)\n[![Coveralls](http://img.shields.io/coveralls/frictionlessdata/tableschema-spss-py/master.svg)](https://coveralls.io/r/frictionlessdata/tableschema-spss-py?branch=master)\n[![PyPi](https://img.shields.io/pypi/v/tableschema-spss.svg)](https://pypi.python.org/pypi/tableschema-spss)\n[![Github](https://img.shields.io/badge/github-master-brightgreen)](https://github.com/frictionlessdata/tableschema-spss-py)\n[![Gitter](https://img.shields.io/gitter/room/frictionlessdata/chat.svg)](https://gitter.im/frictionlessdata/chat)\n\nGenerate and load SPSS files based on [Table Schema](http://specs.frictionlessdata.io/table-schema/) descriptors.\n\n## Features\n\n- implements `tableschema.Storage` interface\n\n## Contents\n\n<!--TOC-->\n\n  - [Getting Started](#getting-started)\n    - [Installation](#installation)\n    - [Examples](#examples)\n  - [Documentation](#documentation)\n    - [Storage](#storage)\n  - [Contributing](#contributing)\n  - [Changelog](#changelog)\n\n<!--TOC-->\n\n## Getting Started\n\n### Installation\n\nThe package use semantic versioning. It means that major versions  could include breaking changes. It's highly recommended to specify `package` version range in your `setup/requirements` file e.g. `package>=1.0,<2.0`.\n\n```bash\npip install tableschema-spss\n```\n\n### Examples\n\nCode examples in this readme requires Python 3.3+ interpreter. You could see even more example in [examples](https://github.com/frictionlessdata/tableschema-spss-py/tree/master/examples) directory.\n\n> For this example your schema should be compatible with SPSS storage - https://github.com/frictionlessdata/tableschema-spss-py#creating-sav-files\n\n```python\nfrom tableschema import Table\n\n# Load and save table to SPSS\ntable = Table('data.csv', schema='schema.json')\ntable.save('data', storage='spss', base_path='dir/path')\n```\n\n## Documentation\n\nThe whole public API of this package is described here and follows semantic versioning rules. Everyting outside of this readme are private API and could be changed without any notification on any new version.\n\n### Storage\n\nPackage implements [Tabular Storage](https://github.com/frictionlessdata/tableschema-py#storage) interface (see full documentation on the link):\n\n![Storage](https://i.imgur.com/RQgrxqp.png)\n\nThis driver provides an additional API:\n\n#### `Storage(base_path=None)`\n\n- `base_path (str)` - a valid directory path where .sav files can be created and read. If no base_path is provided, the Storage object methods will accept file paths rather than bucket names.\n\n#### `storage.buckets`\n\nList all .sav and .zsav files at base path. Bucket list is only maintained if Storage has a valid base path, otherwise will return None.\n\n- `(str[]/None)` - returns bucket list or None\n\n#### With a base path\n\nWe can get storage with a specified base path this way:\n\n```python\nfrom tableschema_spss import Storage\n\nstorage_base_path = 'path/to/storage/dir'\nstorage = Storage(storage_base_path)\n```\n\nWe can then interact with storage buckets ('buckets' are SPSS .sav/.zsav files in this context):\n\n```python\nstorage.buckets  # list buckets in storage\nstorage.create('bucket', descriptor)\nstorage.delete('bucket')  # deletes named bucket\nstorage.delete()  # deletes all buckets in storage\nstorage.describe('bucket') # return tableschema descriptor\nstorage.iter('bucket') # yields rows\nstorage.read('bucket') # return rows\nstorage.write('bucket', rows)\n```\n\n#### Without a base path\n\nWe can also create storage without a base path this way:\n\n```python\nfrom tableschema_spss import Storage\n\nstorage = Storage()  # no base path argument\n```\n\nThen we can specify SPSS files directly by passing their file path (instead of bucket names):\n\n```python\nstorage.create('data/my-bucket.sav', descriptor)\nstorage.delete('data/my-bucket.sav')  # deletes named file\nstorage.describe('data/my-bucket.sav') # return tableschema descriptor\nstorage.iter('data/my-bucket.sav') # yields rows\nstorage.read('data/my-bucket.sav') # return rows\nstorage.write('data/my-bucket.sav', rows)\n```\n\nNote that storage without base paths does not maintain an internal list of buckets, so calling `storage.buckets` will return `None`.\n\n#### Reading .sav files\n\nWhen reading SPSS data, SPSS date formats, `DATE`, `JDATE`, `EDATE`, `SDATE`, `ADATE`, `DATETIME`, and `TIME` are transformed into Python `date`, `datetime`, and `time` objects, where appropriate.\n\nOther SPSS date formats, `WKDAY`, `MONTH`, `MOYR`, `WKYR`, `QYR`, and `DTIME` are not supported for native transformation and will be returned as strings.\n\n#### Creating .sav files\n\nWhen creating SPSS files from Table Schemas, `date`, `datetime`, and `time` field types must have a format property defined with the following patterns:\n\n- `date`: `%Y-%m-%d`\n- `datetime`: `%Y-%m-%d %H:%M:%S`\n- `time`: `%H:%M:%S.%f`\n\nTable Schema descriptors passed to `storage.create()` should include a custom `spss:format` property, defining the SPSS type format the data is expected to represent. E.g.:\n\n```json\n{\n    \"fields\": [\n        {\n            \"name\": \"person_id\",\n            \"type\": \"integer\",\n            \"spss:format\": \"F8\"\n        },\n        {\n            \"name\": \"name\",\n            \"type\": \"string\",\n            \"spss:format\": \"A10\"\n        },\n        {\n            \"type\": \"number\",\n            \"name\": \"salary\",\n            \"title\": \"Current Salary\",\n            \"spss:format\": \"DOLLAR8\"\n        },\n        {\n           \"type\": \"date\",\n           \"name\": \"bdate\",\n           \"title\": \"Date of Birth\",\n           \"format\": \"%Y-%m-%d\",\n           \"spss:format\": \"ADATE10\"\n        }\n    ]\n}\n```\n\n## Contributing\n\nThe project follows the [Open Knowledge International coding standards](https://github.com/okfn/coding-standards).\n\nRecommended way to get started is to create and activate a project virtual environment.\nTo install package and development dependencies into active environment:\n\n```\n$ make install\n```\n\nTo run tests with linting and coverage:\n\n```bash\n$ make test\n```\n\nFor linting `pylama` configured in `pylama.ini` is used. On this stage it's already\ninstalled into your environment and could be used separately with more fine-grained control\nas described in documentation - https://pylama.readthedocs.io/en/latest/.\n\nFor example to sort results by error type:\n\n```bash\n$ pylama --sort <path>\n```\n\nFor testing `tox` configured in `tox.ini` is used.\nIt's already installed into your environment and could be used separately with more fine-grained control as described in documentation - https://testrun.org/tox/latest/.\n\nFor example to check subset of tests against Python 2 environment with increased verbosity.\nAll positional arguments and options after `--` will be passed to `py.test`:\n\n```bash\ntox -e py27 -- -v tests/<path>\n```\n\nUnder the hood `tox` uses `pytest` configured in `pytest.ini`, `coverage`\nand `mock` packages. This packages are available only in tox envionments.\n\n## Changelog\n\nHere described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted [commit history](https://github.com/frictionlessdata/tableschema-spss-py/commits/master).\n\n#### v1.0\n\n- Initial driver release\n\n",
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