This is a template repository, which lets you create a quick API around your Frictionless Data Package. This could be useful in several ways: as a microservice for your SPA frontend, for integration with Web-based workflows, for more paginated access to larger datasets, for setting up a cheap and simple Data as a service offering.
The open source code is based on Python and Pandas, and can be easily extended to fit the needs of your data science project.
Place a datapackage.json file and data folder with your own data to start setting up an API.
If you have not used Data Packages before, an easy way to get started is to convert your dataset to a CSV file (or a set of CSV files), in UTF-8 format - which you can create with any spreadsheet program. Then, use the Data Package CLI or Create Frictionless Data tool to generate a Data Package by clicking the "Load" button and then adding and defining the columns and metadata. "Download" and place the resulting files here. Visit frictionlessdata.io for more advice on this.
This repository contains a minimalist backend service API based on the Falcon framework and Pandas DataPackage Reader. To run:
cd api
virtualenv env
. env/bin/activate
pip install -Ur requirements.txt
python server.py
(Alternatively: use Pipenv and run pipenv install && pipenv run python server.py)
At this point you should see the message "Serving on port..."
Soon there will be a webpage where you can test the API. Until then ...
Test the API using a REST client such as RESTer with queries such as:
http://localhost:8000/[my resource name]?[column]=[query]
For instance, if you wish to use ours users Resource to test the running server you could:
Or filter values with:
http://localhost:8000/users?name=Paul
You can adjust the amount of output with a page and per_page parameter in your query.
This project is licensed by its maintainers under the MIT License.
If you intended to use these data in a public or commercial product, please check the data sources themselves for any specific restrictions.