Metadata-Version: 2.1
Name: google-cloud-bigquery
Version: 1.6.0
Summary: Google BigQuery API client library
Home-page: https://github.com/GoogleCloudPlatform/google-cloud-python
Author: Google LLC
Author-email: googleapis-packages@google.com
License: Apache 2.0
Description: Python Client for Google BigQuery
        =================================
        
        |pypi| |versions|
        
        Querying massive datasets can be time consuming and expensive without the
        right hardware and infrastructure. Google `BigQuery`_ solves this problem by
        enabling super-fast, SQL queries against append-mostly tables, using the
        processing power of Google's infrastructure.
        
        -  `Client Library Documentation`_
        -  `Product Documentation`_
        
        .. |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/
        .. _BigQuery: https://cloud.google.com/bigquery/what-is-bigquery
        .. _Client Library Documentation: https://googlecloudplatform.github.io/google-cloud-python/latest/bigquery/index.html
        .. _Product Documentation: https://cloud.google.com/bigquery/docs/reference/v2/
        
        Quick Start
        -----------
        
        In order to use this library, you first need to go through the following steps:
        
        1. `Select or create a Cloud Platform project.`_
        2. `Enable billing for your project.`_
        3. `Enable the Google Cloud Datastore API.`_
        4. `Setup Authentication.`_
        
        .. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
        .. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
        .. _Enable the Google Cloud Datastore API.:  https://cloud.google.com/bigquery
        .. _Setup Authentication.: https://googlecloudplatform.github.io/google-cloud-python/latest/core/auth.html
        
        Installation
        ~~~~~~~~~~~~
        
        Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
        create isolated Python environments. The basic problem it addresses is one of
        dependencies and versions, and indirectly permissions.
        
        With `virtualenv`_, it's possible to install this library without needing system
        install permissions, and without clashing with the installed system
        dependencies.
        
        .. _`virtualenv`: https://virtualenv.pypa.io/en/latest/
        
        
        Mac/Linux
        ^^^^^^^^^
        
        .. code-block:: console
        
            pip install virtualenv
            virtualenv <your-env>
            source <your-env>/bin/activate
            <your-env>/bin/pip install google-cloud-bigquery
        
        
        Windows
        ^^^^^^^
        
        .. code-block:: console
        
            pip install virtualenv
            virtualenv <your-env>
            <your-env>\Scripts\activate
            <your-env>\Scripts\pip.exe install google-cloud-bigquery
        
        Example Usage
        -------------
        
        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)
        
Platform: Posix; MacOS X; Windows
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Classifier: Topic :: Internet
Provides-Extra: pyarrow
Provides-Extra: pandas
