Frictionless Data is a progressive framework for building data infrastructure. Frictionless can do:
- data management
- data integration
- data flows
Frictionless allows you to describe datasets as a collection of one or more data tables, each having rows and columns, using a straightforward syntax. A described dataset can then be packaged and distributed as Frictionless Tabular Data Package.
This lesson aims to teach researchers the basic concepts and skills for using the Frictionless Data format to describe and package tabular data. The lesson uses a tabular dataset for an agricultural field experiment and teaches data description and metadata and data manipulation using the Frictionless Python modules.
After completing this lesson you will:
- Understand the concept and structure of a Frictionless Tabular Data Package
- Understand how Frictionless Data relates to FAIR Data
- Use Python to describe tables and columns
- Use Python to create a Frictionless Data Package
- Use Python to read a data package
This lesson uses example data from Agricultural field experiments and is directly relevant to agricultural scientists and ecologists.
Before you start
Before starting the training make sure you have completed the lesson set-up.
Prerequisites
This lesson requires Python 3.x.
A Jupyter Notebook is recommended for following the lesson exercises and saving work.
You should be familiar with CSV, tabular data and have an understanding of JSON.