Metadata-Version: 2.1
Name: tableschema-bigquery
Version: 0.6.4
Summary: Generate BigQuery tables, load and extract data, based on JSON Table Schema descriptors.
Home-page: https://github.com/frictionlessdata/jsontableschema-bigquery-py
Author: Open Knowledge Foundation
Author-email: info@okfn.org
License: MIT
Description: # tableschema-bigquery-py
        
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        Generate and load BigQuery tables based on [Table Schema](http://specs.frictionlessdata.io/table-schema/) descriptors.
        
        ## Features
        
        - implements `tableschema.Storage` interface
        
        ## Contents
        
        <!--TOC-->
        
          - [Getting Started](#getting-started)
            - [Installation](#installation)
            - [Examples](#examples)
          - [Documentation](#documentation)
            - [Storage](#storage)
          - [Contributing](#contributing)
          - [Changelog](#changelog)
        
        <!--TOC-->
        
        ## Getting Started
        
        ### Installation
        
        The 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`.
        
        ```bash
        pip install tableschema-bigquery
        ```
        
        To start using Google BigQuery service:
        - Create a new project - [link](https://console.developers.google.com/home/dashboard)
        - Create a service key - [link](https://console.developers.google.com/apis/credentials)
        - Download json credentials and set `GOOGLE_APPLICATION_CREDENTIALS` environment variable
        
        ### Examples
        
        Code examples in this readme requires Python 3.3+ interpreter. You could see even more example in [examples](https://github.com/frictionlessdata/tableschema-bigquery-py/tree/master/examples) directory.
        
        ```python
        import io
        import os
        import json
        from tableschema import Table
        from apiclient.discovery import build
        from oauth2client.client import GoogleCredentials
        
        # Prepare BigQuery credentials
        os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '.credentials.json'
        credentials = GoogleCredentials.get_application_default()
        service = build('bigquery', 'v2', credentials=credentials)
        project = json.load(io.open('.credentials.json', encoding='utf-8'))['project_id']
        
        # Load and save table to BigQuery
        table = Table('data.csv', schema='schema.json')
        table.save('data', storage='bigquery', service=service, project=project, dataset='dataset')
        ```
        
        ## Documentation
        
        The 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.
        
        ### Storage
        
        Package implements [Tabular Storage](https://github.com/frictionlessdata/tableschema-py#storage) interface (see full documentation on the link):
        
        ![Storage](https://i.imgur.com/RQgrxqp.png)
        
        This driver provides an additional API:
        
        #### `Storage(service, project, dataset, prefix='')`
        
        - `service (object)` - BigQuery `Service` object
        - `project (str)` - BigQuery project name
        - `dataset (str)` - BigQuery dataset name
        - `prefix (str)` - prefix for all buckets
        
        ## Contributing
        
        The project follows the [Open Knowledge International coding standards](https://github.com/okfn/coding-standards).
        
        Recommended way to get started is to create and activate a project virtual environment.
        To install package and development dependencies into active environment:
        
        ```
        $ make install
        ```
        
        To run tests with linting and coverage:
        
        ```bash
        $ make test
        ```
        
        For linting `pylama` configured in `pylama.ini` is used. On this stage it's already
        installed into your environment and could be used separately with more fine-grained control
        as described in documentation - https://pylama.readthedocs.io/en/latest/.
        
        For example to sort results by error type:
        
        ```bash
        $ pylama --sort <path>
        ```
        
        For testing `tox` configured in `tox.ini` is used.
        It'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/.
        
        For example to check subset of tests against Python 2 environment with increased verbosity.
        All positional arguments and options after `--` will be passed to `py.test`:
        
        ```bash
        tox -e py27 -- -v tests/<path>
        ```
        
        Under the hood `tox` uses `pytest` configured in `pytest.ini`, `coverage`
        and `mock` packages. This packages are available only in tox envionments.
        
        ## Changelog
        
        Here 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-bigquery-py/commits/master).
        
        #### v0.x
        
        Initial driver implementation.
Keywords: frictionless data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: develop
