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).

• 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.

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.

Of course, the recommended installation method is pipenv:

$ pipenv install records[pandas]
✨🍰✨

As an added bonus, a records command-line tool is automatically included. Here's a screenshot of the usage information:

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.