8000 GitHub - kipyin/records at v0.1.0
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to content

kipyin/records

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

51 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Records: Just Write SQL

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')

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.
  • Query results are iterators of standard Python dictionaries ({'column-name': 'value'})

Records is 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, 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
--------|------|----------|-----------------|--------------------------
hansolo |True  |Henry Ford|hansolo@gmail.com|2016-02-06 22:28:23.894202
...

Export your query results to CSV:

>>> print rows.dataset.csv
username,active,name,user_email,timezone
hansolo,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 also available, so you can add/remove columns/rows, add seperators, 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.

About

SQL for Humans™

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 99.1%
  • Makefile 0.9%
0