SQL for Humans™
… image:: https://img.shields.io/pypi/v/records.svg
:target: https://pypi.python.org/pypi/records
Records is a very simple, but powerful, library for making raw SQL queries
to most relational databases.
… image:: https://farm1.staticflickr.com/569/33085227621_7e8da49b90_k_d.jpg
Just write SQL. No bells, no whistles. 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.
Database support includes RedShift, Postgres, MySQL, SQLite, Oracle, and MS-SQL (drivers not included).
We know how to write SQL, so let’s send some to our database:
… code:: python
import records
db = records.Database('postgres://...')
rows = db.query('select * from active_users') # or db.query_file('sqls/active-users.sql')
Grab one row at a time:
… code:: python
>>> rows[0]
<Record {"username": "model-t", "active": true, "name": "Henry Ford", "user_email": "[email protected]", "timezone": "2016-02-06 22:28:23.894202"}>
Or iterate over them:
… code:: python
for r in rows:
print(r.name, r.user_email)
Values can be accessed many ways: row.user_email, row['user_email'], or row[3].
Fields with non-alphanumeric characters (like spaces) are also fully supported.
Or store a copy of your record collection for later reference:
… code:: python
>>> rows.all()
[<Record {"username": ...}>, <Record {"username": ...}>, <Record {"username": ...}>, ...]
If you’re only expecting one result:
… code:: python
>>> rows.first()
<Record {"username": ...}>
Other options include rows.as_dict() and rows.as_dict(ordered=True).
$DATABASE_URL environment variable support.Database.get_table_names method.records tool for exporting queries.Database.query('life=:everything', everything=42).t = Database.transaction(); t.commit().Database.bulk_query() & Database.bulk_query_file().Records is proudly powered by SQLAlchemy <http://www.sqlalchemy.org>_
and Tablib <https://tablib.readthedocs.io/en/latest/>_.
Records also features full Tablib integration, and allows you to export
your results to CSV, XLS, JSON, HTML Tables, YAML, or Pandas DataFrames with a single line of code.
Excellent for sharing data with friends, or generating reports.
… code:: pycon
>>> print(rows.dataset)
username|active|name |user_email |timezone
--------|------|----------|-----------------|--------------------------
model-t |True |Henry Ford|[email protected]|2016-02-06 22:28:23.894202
...
Comma Separated Values (CSV)
… code:: pycon
>>> print(rows.export('csv'))
username,active,name,user_email,timezone
model-t,True,Henry Ford,[email protected],2016-02-06 22:28:23.894202
...
YAML Ain’t Markup Language (YAML)
… code:: python
>>> print(rows.export('yaml'))
- {active: true, name: Henry Ford, timezone: '2016-02-06 22:28:23.894202', user_email: [email protected], username: model-t}
...
JavaScript Object Notation (JSON)
… code:: python
>>> print(rows.export('json'))
[{"username": "model-t", "active": true, "name": "Henry Ford", "user_email": "[email protected]", "timezone": "2016-02-06 22:28:23.894202"}, ...]
Microsoft Excel (xls, xlsx)
… code:: python
with open('report.xls', 'wb') as f:
f.write(rows.export('xls'))
Pandas DataFrame
… code:: python
>>> rows.export('df')
username active name user_email timezone
0 model-t True Henry Ford [email protected] 2016-02-06 22:28:23.894202
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 <https://tablib.readthedocs.io/>_ for more details.
Of course, the recommended installation method is pipenv <http://pipenv.org>_::
$ pipenv install records[pandas]
✨🍰✨
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 <https://github.com/kennethreitz/records/issues>_ so we can make Records better, stronger, faster.