GitLocker: The Coding Marketplace

Description:

records 0.6.0

Records: SQL for Humans™

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

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).

☤ The Basics
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')
Grab one row at a time:
>>> rows[0]
<Record {"username": "model-t", "active": true, "name": "Henry Ford", "user_email": "model-t@gmail.com", "timezone": "2016-02-06 22:28:23.894202"}>
Or iterate over them:
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:
>>> rows.all()
[<Record {"username": ...}>, <Record {"username": ...}>, <Record {"username": ...}>, ...]
If you’re only expecting one result:
>>> rows.first()
<Record {"username": ...}>
Other options include rows.as_dict() and rows.as_dict(ordered=True).

☤ Features

Iterated rows are cached for future reference.
$DATABASE_URL environment variable support.
Convenience Database.get_table_names method.
Command-line records tool for exporting queries.
Safe parameterization: Database.query('life=:everything', everything=42).
Queries can be passed as strings or filenames, parameters supported.
Transactions: t = Database.transaction(); t.commit().
Bulk actions: Database.bulk_query() & Database.bulk_query_file().

Records is proudly powered by SQLAlchemy
and Tablib.

☤ Data Export Functionality
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.
>>> 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 Separated 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'))
Pandas DataFrame
>>> rows.export('df')
username active name user_email timezone
0 model-t True Henry Ford model-t@gmail.com 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 for more details.

☤ Installation
Of course, the recommended installation method is pipenv:
$ pipenv install records[pandas]
✨🍰✨

☤ 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.

v0.6.0 (04-29-2024)

Support for Python 3.6+ only.
Support for SQLAlchemy 2+.
Dropped support for Python 2.7 and 3.4, with the move to SQLAlchemy 2+.

v0.5.1 (09-01-2017)

Depend on tablib[pandas].
Support for Bulk quies: Database.bulk_query() & Database.bulk_query_file().

v0.5.0 (11-15-2016)

Support for transactions: t = Database.transaction(); t.commit()

v0.4.3 (02-16-2016)

The cake is a lie.

v0.4.2 (02-15-2016)

Packaging fix.

v0.4.1 (02-15-2016)

Bugfix for Python 3.

v0.4.0 (02-13-2016)

Refactored to be fully powered by SQLAlchemy!
Support for all major databases (thanks, SQLAlchemy!).
Support for non-alphanumeric column names.
New Record class, for representing/accessing result rows.
ResultSet renamed RecordCollection.
Removed Interactive Mode from the CLI.

v0.3.0 (02-11-2016)

New record command-line tool available!
Various improvements.

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.