https://github.com/boydgreenfield/query

Interactive SQL database exploration in Python
https://github.com/boydgreenfield/query

Last synced: about 2 months ago
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Interactive SQL database exploration in Python

• Host: GitHub
• URL: https://github.com/boydgreenfield/query
• Owner: boydgreenfield
• License: mit
• Created: 2014-05-04T01:39:24.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T21:14:27.000Z (over 2 years ago)
• Last Synced: 2024-04-17T14:11:46.176Z (5 months ago)
• Language: Python
• Size: 1.76 MB
• Stars: 175
• Watchers: 10
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# query

Python module for quick, interactive exploration of SQL databases. Designed especially for use with IPython. Light wrapper on top of Pandas (>= 0.16) and SQLAlchemy (>= 0.9.9). The most recent release is available on [PyPI](https://pypi.python.org/pypi/query) and can be installed via `pip install query` with the proper dependencies.

[![Build Status](https://travis-ci.org/boydgreenfield/query.svg?branch=master)](https://travis-ci.org/boydgreenfield/query)

## Quickstart

```python

from query import QueryDb

db = QueryDb(demo=True)

```

But the real joy comes when using query interactively:

![Interactive query use demo #1 ](docs/images/interactive_demo.gif?raw=True)

## Key functionality

A few key functions to remember:

* `db`: The main database object. Print it in IPython to see a list of tables and their key attributes.

* `db.inspect.*`: Tab-completion across the database's tables and columns. Print any table to see its columns and their types.

* `db.query()`: Query the database with a raw SQL query. Returns a `pandas DataFrame` object by default, but can return a `sqlalchemy result` object if called with `return_as="result"`.

## Roadmap

Further improvements are planned, including some of the below. Please feel free to open an Issue with desired features or submit a pull request.

* **Plotting**: Graphical display of queried data (some of this can easily be done off the current `DataFrame` object, but it could be better integrated)

* **More Convenience Methods**: Additional convenience methods, like ``.tail()`` and ``.where()``

* **DB Schemas**: Direct output of database schema diagrams

* **Password Input via IPython**: Currently entering a DB password uses `getpass` in the user's terminal. Being able to enter the password directly into IPython would be ideal (while also not writing it into any history)

* **More/Better Documentation**: Enough said.