ReproduciblePython 🐍🐱‍👤

Materials associated with the PyCon 2018 workshop on reproducible analysis in Python.

The proposal for this workshop can be found in the proposal.md file.

Slides

🗒️ The slides for the workshop can be found here:

• Online html version: interactive slides
• PDF version

💬 Discussion

We will encourage discussions over the workshop, for this purpose we will be using an Etherpad. Click on the following link: https://public.etherpad-mozilla.org/p/ReproduciblePython

🗃️ The content

This material covers the basics of reproducible workflows in Python and is provided in the following sections:

0. Setup: installation instructions for the workshop
1. Setting up projects: advise on best practices to set up projects with a reproducibility-first approach
2. Working with data: information on how to use, archive, and share data
3. Processing data, workflows: producing automated wokrflows
4. All things testing: introduction to testing of standalone scripts and Jupyter notebooks
5. Making code public: how to share your code and being credited for it

🦄 Additional materials

These are complementary materials that you can follow at your own pace if you wanted to dive further.

• Getting started with Docker: introduction to containers and usage of repo2docker
• Sharing your interactive notebooks using Binder
• Introduction to Datalad

Solutions

The solutions to the tutorial can be found in the solutions folder. Make sure to read the solutions README first

🖥️ What do I need for this workshop?

The installation instructions can be found at http://bitsandchips.me/ReproduciblePython/Setup.html

Acknowledgements

The development of this material was funded by OpenDreamKit, a Horizon2020 European Research Infrastructure project (676541) that aims to advance the open source computational mathematics ecosystem.

This work is licensed under a Creative Commons Attribution 4.0 International License.