https://github.com/loleg/v-zug-recipe-parser

A data exploration at #openfooddata
https://github.com/loleg/v-zug-recipe-parser

Last synced: 19 days ago
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A data exploration at #openfooddata

• Host: GitHub
• URL: https://github.com/loleg/v-zug-recipe-parser
• Owner: loleg
• License: odbl-1.0
• Created: 2018-09-09T00:09:21.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-09-09T00:50:58.000Z (over 6 years ago)
• Last Synced: 2024-12-29T16:37:26.712Z (20 days ago)
• Language: Jupyter Notebook
• Homepage: https://hack.opendata.ch/project/228
• Size: 61.5 KB
• Stars: 1
• Watchers: 5
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
We got an interesting database of recipes to play with from [V-Zug Home](https://home.vzug.com/en/) at the Open Food Data Hackathon, used in a mobile application to program smart kitchen devices. We took a closer look and investigated ways of combining it with other data sources and tools following [challenge #12](https://hack.opendata.ch/project/223).

In the project repository there is a Jupyter [notebook](https://github.com/loleg/v-zug-recipe-parser/blob/master/explore-vzug-recipes.ipynb) written in Python which explores and visualizes the data, along with a [script](https://github.com/loleg/v-zug-recipe-parser/blob/master/convert.py) to convert the `XML` files we received according to a schema defined in [recipe.py](https://github.com/loleg/v-zug-recipe-parser/blob/master/recipe.py).

We created an example [Data Package](https://frictionlessdata.io/data-packages/) containing a summary of the dataset in `CSV` format, as well as a `JSON` formatted recipe schema proposal in [recipe.json](https://github.com/loleg/v-zug-recipe-parser/blob/master/data/recipe.json). These are proposed as a potential starting point for future discussions about developing an open standard, the advantages of which may include participation of the wider development community, better interaction with other manufacturers, and consumer trust.

# Preparation

No special libraries are required to use the parsing script. The conversion script `convert.py` references the [Python Data Analysis](https://pandas.pydata.org/) library for CSV file generation. The Jupyter notebook includes some data analysis using the Pandas, Numpy and Matplotlib libraries. You can find some setup [instructions here](https://forum.schoolofdata.ch/t/14-9-wikidata-zurich-workshop/267/2). The schema of this Data Package was inferred using [Frictionless Data](https://frictionlessdata.io/field-guide/) CLI tools.

# Research

In this project we conducted some background research of schemas used in other recipe application, particularly in the open source and cloud API areas. Here are some of the interesting links we have found:

- [OpenRecipeFormat](https://github.com/techhat/openrecipeformat) ([Walkthrough](https://open-recipe-format.readthedocs.io/en/latest/topics/tutorials/walkthrough.html))

- [Yummly API](https://developer.yummly.com/documentation)

- [Spoonacular API](https://spoonacular.com/food-api)

- [hRecipe Microformat](http://microformats.org/wiki/h-recipe)

# License

The licensing terms of this dataset have not yet been established. If you intend to use these data in a public or commercial product, check with each of the data sources for any specific restrictions.

This Data Package is made available by its maintainers under the [Public Domain Dedication and License v1.0](http://www.opendatacommons.org/licenses/pddl/1.0/), a copy of the full text of which is in [LICENSE.md](LICENSE.md).