records

Install the records library using pipenv. To include support for Pandas DataFrames, use the [pandas] extra.

$ pipenv install records[pandas]

Use records.Database to establish a connection via a connection string. You can execute raw SQL strings using .query() or load SQL from a file using .query_file(). Supported databases include RedShift, Postgres, MySQL, SQLite, Oracle, and MS-SQL (drivers not included).

import records

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

Records integrates with Tablib to allow exporting query results to multiple formats using the .export(format) method. Supported formats include:

• csv (Comma Separated Values)
• yaml (YAML)
• json (JSON)
• xls or xlsx (Microsoft Excel)
• df (Pandas DataFrame)
• html (HTML Tables)

Note: For Excel exports, you must write the returned bytes to a file.

Query results can be iterated over directly or accessed by index. Individual records allow field access via attribute name (row.field), dictionary key (row['field']), or integer index (row[index]). Fields with non-alphanumeric characters are supported.

Key methods for result sets:

• rows.all(): Returns a list of all records.
• rows.first(): Returns only the first record.
• rows.as_dict(): Returns results as a dictionary.
• rows.as_dict(ordered=True): Returns results as an ordered dictionary.