IPython Magics for BigQuery¶
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.
Quick Start¶
In order to use this library, you first need to go through the following steps:
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.
Supported Python Versions¶
Python >= 3.7
Unsupported Python Versions¶
Python == 3.5, Python == 3.6.
Mac/Linux¶
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install bigquery-magics
Windows¶
pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install bigquery-magics
Example Usage¶
To use these magics, you must first register them. Run the %load_ext bigquery_magics
in a Jupyter notebook cell.
%load_ext bigquery_magics
Perform a query¶
%%bigquery
SELECT name, SUM(number) as count
FROM 'bigquery-public-data.usa_names.usa_1910_current'
GROUP BY name
ORDER BY count DESC
LIMIT 3
More Examples¶
Migration Guide¶
Migrating from the google-cloud-bigquery, you need to run the following in a Jupyter notebook cell.
Before:
%load_ext google.cloud.bigquery
After:
%load_ext bigquery_magics
Changelog¶
For a list of all bigquery-magics releases:
