Analyzing the composition of beer recipes and visualize results, built with Django, Pandas and Plotly.

Live website: https://www.beer-analytics.com/

The goal is to expand the knowledge how different beer styles are typically brewed and potentially uncover trends in craft/home brewing.

The project has two components:

1. a recipe database with reliable data (clean and normalized, reduce outliers and bad data)
2. a user interface to execute data analysis (filtering, slicing and dicing) and present results in a visually appealing way

• Python 3.8
• pip
• virtualenv (ideally)
• yarn (JS package manager)

• yarn install
• Initialize virtualenv and enable it
• Install Python dependencies: pip -r requirements.txt
• Configure the application (see below)
• Apply database migrations python makemigrations + python manage.py migrate
• Load initial data python manage.py load_initial_data

Provide a .env file in the beer_analytics folder. An example can be found in beer_analytics/.env.example.

Per default the application starts with "dev" settings. Use DJANGO_SETTINGS_MODULE to use different settings according to the environment:

# Production settings
DJANGO_SETTINGS_MODULE=beer_analytics.settings_prod

# Dev settings
DJANGO_SETTINGS_MODULE=beer_analytics.settings_dev

After setting up the application, recipes can be imported via CLI in various formats. Each recipe must have a unique id assigned, which can be an arbitrary string.

BeerXML:

python manage.py load_beerxml_recipe recipe.xml unique_id

MMUM format:

python manage.py load_mmum_recipe recipe.json unique_id

BeerSmith format:

python manage.py load_beersmith_recipe recipe.bsmx unique_id

For information about the security policy and know security issues, see SECURITY.md.

Want to contribute to this project? See CONTRIBUTING.md.

This software is available under the Beerware License.