Metadata-Version: 1.1
Name: google-cloud-bigquery
Version: 0.28.0
Summary: Python Client for Google BigQuery
Home-page: https://github.com/GoogleCloudPlatform/google-cloud-python
Author: Google Cloud Platform
Author-email: googleapis-publisher@google.com
License: Apache 2.0
Description: Python Client for Google BigQuery
        =================================
        
            Python idiomatic client for `Google BigQuery`_
        
        .. _Google BigQuery: https://cloud.google.com/bigquery/what-is-bigquery
        
        |pypi| |versions|
        
        -  `Documentation`_
        
        .. _Documentation: https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/usage.html
        
        Quick Start
        -----------
        
        .. code-block:: console
        
            $ pip install --upgrade google-cloud-bigquery
        
        Fore more information on setting up your Python development environment, such as installing ``pip`` and on your system, please refer to `Python Development Environment Setup Guide`_ for Google Cloud Platform.
        
        .. _Python Development Environment Setup Guide: https://cloud.google.com/python/setup
        
        Authentication
        --------------
        
        With ``google-cloud-python`` we try to make authentication as painless as
        possible. Check out the `Authentication section`_ in our documentation to
        learn more. You may also find the `authentication document`_ shared by all
        the ``google-cloud-*`` libraries to be helpful.
        
        .. _Authentication section: https://google-cloud-python.readthedocs.io/en/latest/core/auth.html
        .. _authentication document: https://github.com/GoogleCloudPlatform/google-cloud-common/tree/master/authentication
        
        Using the API
        -------------
        
        Querying massive datasets can be time consuming and expensive without the
        right hardware and infrastructure. Google `BigQuery`_ (`BigQuery API docs`_)
        solves this problem by enabling super-fast, SQL queries against
        append-mostly tables, using the processing power of Google's infrastructure.
        
        .. _BigQuery: https://cloud.google.com/bigquery/what-is-bigquery
        .. _BigQuery API docs: https://cloud.google.com/bigquery/docs/reference/v2/
        
        Create a dataset
        ~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            from google.cloud import bigquery
            from google.cloud.bigquery import Dataset
        
            client = bigquery.Client()
        
            dataset_ref = client.dataset('dataset_name')
            dataset = Dataset(dataset_ref)
            dataset.description = 'my dataset'
            dataset = client.create_dataset(dataset)  # API request
        
        Load data from CSV
        ~~~~~~~~~~~~~~~~~~
        
        .. code:: python
        
            import csv
        
            from google.cloud import bigquery
            from google.cloud.bigquery import LoadJobConfig
            from google.cloud.bigquery import SchemaField
        
            client = bigquery.Client()
        
            SCHEMA = [
                SchemaField('full_name', 'STRING', mode='required'),
                SchemaField('age', 'INTEGER', mode='required'),
            ]
            table_ref = client.dataset('dataset_name').table('table_name')
        
            load_config = LoadJobConfig()
            load_config.skip_leading_rows = 1
            load_config.schema = SCHEMA
        
            # Contents of csv_file.csv:
            #     Name,Age
            #     Tim,99
            with open('csv_file.csv', 'rb') as readable:
                client.load_table_from_file(
                    readable, table_ref, job_config=load_config)  # API request
        
        Perform a query
        ~~~~~~~~~~~~~~~
        
        .. code:: python
        
            # Perform a query.
            QUERY = (
                'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
                'WHERE state = "TX" '
                'LIMIT 100')
            query_job = client.query(QUERY)  # API request
            rows = query_job.result()  # Waits for query to finish
        
            for row in rows:
                print(row.name)
        
        
        See the ``google-cloud-python`` API `BigQuery documentation`_ to learn how
        to connect to BigQuery using this Client Library.
        
        .. _BigQuery documentation: https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/usage.html
        
        .. |pypi| image:: https://img.shields.io/pypi/v/google-cloud-bigquery.svg
           :target: https://pypi.org/project/google-cloud-bigquery/
        .. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-bigquery.svg
           :target: https://pypi.org/project/google-cloud-bigquery/
        
Platform: Posix; MacOS X; Windows
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software 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 :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Internet
