Introducing Records Sql For Humanstm

Introducing Records: SQL for Humans™

View fullsize Records is a very simple, but powerful, library for making raw SQLqueries to Postgres databases.

This common task can be surprisingly difficult with the standard toolsavailable. This library strives to make this workflow as simple aspossible, while providing an elegant interface to work with your queryresults.

We know how to write SQL, so let's send some to our database:

import recordsdb = records.Database('postgres://...')rows = db.query('select * from active_users')

☤ The Basics

Rows are represented as standard Python dictionaries: {'column-name': 'value'}.

Grab one row at a time:

>>> rows.next(){'username': 'hansolo', 'name': 'Henry Ford', 'active': True, 'timezone': datetime.datetime(2016, 2, 6, 22, 28, 23, 894202), 'user_email': 'hansolo@gmail.com'}

Or iterate over them:

for row in rows:spam_user(name=row['name'], email=row['user_email'])

Or store them all for later reference:

>>> rows.all()[{'username': ...}, {'username': ...}, {'username': ...}, ...]

☤ Features

HSTORE support, if available.
Iterated rows are cached for future reference.
$DATABASE_URL environment variable support.
Convenience Database.get_table_names method.
Queries can be passed as strings or filenames, parameters supported.
Safe parameterization: Database.query('life=%s', params=('42',))
Query results are iterators of standard Python dictionaries: {'column-name': 'value'}

Records is proudly powered by Psycopg2and Tablib.

☤ Data Export Functionality

Records also features full Tablib integration (my first popular project!), and allows you to exportyour results to CSV, XLS, JSON, or YAML with a single line of code.

Excellent for sharing data with friends, or generating reports.

>>> print rows.datasetusername|active|name |user_email |timezone--------|------|----------|-----------------|--------------------------hansolo |True |Henry Ford|hansolo@gmail.com|2016-02-06 22:28:23.894202...

Export your query results to CSV:

>>> print rows.dataset.csvusername,active,name,user_email,timezonehansolo,True,Henry Ford,hansolo@gmail.com,2016-02-06 22:28:23.894202...

YAML:

>>> print rows.dataset.yaml- {active: true, name: Henry Ford, timezone: '2016-02-06 22:28:23.894202', user_email: hansolo@gmail.com, username: hansolo}...

JSON:

>>> print rows.dataset.json[{"username": "hansolo", "active": true, "name": "Henry Ford", "user_email": "hansolo@gmail.com", "timezone": "2016-02-06 22:28:23.894202"}, ...]

Excel:

with open('report.xls', 'wb') as f:f.write(rows.dataset.xls)

You get the point. Of course, all other features of Tablib are alsoavailable, so you can sort results, add/remove columns/rows, removeduplicates, tranpose the table, add separators, slice data by column,and more.

See the Tablib Documentationfor more details.

☤ Installation

Of course, the recommended installation method is pip:

$ pip install records

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