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SQL for Humans

Project description

Records: SQL for Humans™

Records is a very simple, but powerful, library for making raw SQL queries to Postgres databases.

This common task can be surprisingly difficult with the standard tools available. This library strives to make this workflow as simple as possible, while providing an elegant interface to work with your query results.

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

import records

db = records.Database('postgres://...')
rows = db.query('select * from active_users')    # or db.query_file('sqls/active-users.sql')

☤ The Basics

Grab one row at a time:

>>> rows[0]
Record(username='model-t', name='Henry Ford', active=True, timezone=datetime.datetime(2016, 2, 6, 22, 28, 23, 894202), user_email='model-t@gmail.com')

Or iterate over them:

for r in rows:
    spam_user(name=r.name, email=r.user_email)

Or store them all for later reference:

>>> rows.all()
[Record(username=...), Record(username=...), Record(username=...), ...]

☤ Features

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

Records is proudly powered by Psycopg2 and Tablib.

☤ Data Export Functionality

Records also features full Tablib integration, and allows you to export your results to CSV, XLS, JSON, HTML Tables, or YAML with a single line of code. Excellent for sharing data with friends, or generating reports.

>>> print rows.dataset
username|active|name      |user_email       |timezone
--------|------|----------|-----------------|--------------------------
model-t |True  |Henry Ford|model-t@gmail.com|2016-02-06 22:28:23.894202
...
  • Comma Seperated Values (CSV)

    >>> print rows.export('csv')
    username,active,name,user_email,timezone
    model-t,True,Henry Ford,model-t@gmail.com,2016-02-06 22:28:23.894202
    ...
    
  • YAML Ain’t Markup Language (YAML)

    >>> print rows.export('yaml')
    - {active: true, name: Henry Ford, timezone: '2016-02-06 22:28:23.894202', user_email: model-t@gmail.com, username: model-t}
    ...
    
  • JavaScript Object Notation (JSON)

    >>> print rows.export('json')
    [{"username": "model-t", "active": true, "name": "Henry Ford", "user_email": "model-t@gmail.com", "timezone": "2016-02-06 22:28:23.894202"}, ...]
    
  • Microsoft Excel (xls, xlsx)

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

You get the point. All other features of Tablib are also available, so you can sort results, add/remove columns/rows, remove duplicates, transpose the table, add separators, slice data by column, and more.

See the Tablib Documentation for more details.

☤ Installation

Of course, the recommended installation method is pip:

$ pip install records
✨🍰✨

☤ Thank You

Thanks for checking this library out! I hope you find it useful.

Of course, there’s always room for improvement. Feel free to open an issue so we can make Records better, stronger, faster.

v0.2.0 (02-10-2016)

  • Results are now represented as Record, a namedtuples class with dict-like qualities.
  • New ResultSet.export method, for exporting to various formats.
  • Slicing a ResultSet now works, and results in a new ResultSet.
  • Lots of bugfixes and improvements!

v0.1.0 (02-07-2016)

  • Initial release.

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