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A web interface and API for searching the OLCC price list.
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This project represents a Django site built for the sole purpose of displaying Oregon Liquor Control Commission (OLCC) product, price and store data.

The models are designed to easily import product data directly from the OLCC and include enhanced functionality for interacting with the original data set.

The project was born simply because the data existed and was freely available.

You can view the live site at

OLCC CSV files


You are encouraged to fork the project and add new functionality. We'd love to see your pull requests! Please make sure to run tests before submitting a patch.

$ python test olcc

Please make sure your editor is configured to use the proper indentation style with 4-spaces and no tab characters.

Getting Started

After cloning the project into your python virtualenv, you'll first need to create a basic with the following contents:

DEBUG = True

Active your virtualenv and install the project dependencies:

$ pip install -r requirements.txt

To start the local development server simply run:

$ python run_gunicorn

You'll then need to create your local development database by running:

$ python syncdb

A development fixture has been provided to get you up and running quickly. You can import this fixture by running:

$ python loaddata olccdev

You can import fresh product data into your database by running:

$ python olccfetch

Potential Features

  • Price calculator: Per shot, oz, ml, 2oz bar pour.
  • Collapse product sizes onto a single detail page. This could potentially be done using the product code, which seem to have a common prefix for product type.
  • Find stores by proximity to the visitor's current location.
  • Parse the store hours into a machine readable format. Display stores as currently open/closed.
  • Product images: Scrape Creative Commons licensed photos or accept submissions.
  • Price monitoring: Get notified when your favorite item drops in price or is about to go up in price.
  • Price intelligence: Analyze historical price changes to predict future sales.
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