# records

**Repository Path**: dingcx/records

## Basic Information

- **Project Name**: records
- **Description**: SQL for Humans™
- **Primary Language**: Unknown
- **License**: ISC
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Records: SQL for Humans™
========================


.. image:: https://img.shields.io/pypi/v/records.svg
    :target: https://pypi.python.org/pypi/records

.. image:: https://travis-ci.org/kennethreitz/records.svg?branch=master
    :target: https://travis-ci.org/kennethreitz/records

.. image:: https://img.shields.io/badge/SayThanks.io-☼-1EAEDB.svg
    :target: https://saythanks.io/to/kennethreitz



**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).*

----------

☤ The Basics
------------
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": "model-t@gmail.com", "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)``.

☤ 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 <http://www.sqlalchemy.org>`_
and `Tablib <http://docs.python-tablib.org/en/latest/>`_.

☤ 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.

.. code:: pycon

    >>> 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)**

.. code:: pycon

    >>> 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)**

.. code:: python

    >>> 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)**

.. code:: python

    >>> 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)**

.. 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 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 <http://docs.python-tablib.org/en/latest/>`_
for more details.

☤ Installation
--------------

Of course, the recommended installation method is `pipenv <http://pipenv.org>`_::

    $ pipenv install records[pandas]
    ✨🍰✨

☤ Command-Line Tool
-------------------

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

.. image:: http://f.cl.ly/items/0S14231R3p0G3w3A0x2N/Screen%20Shot%202016-02-13%20at%202.43.21%20AM.png
   :alt: Screenshot of Records Command-Line Interface.

☤ 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 <https://github.com/kennethreitz/records/issues>`_ so we can make Records better, stronger, faster.