For monitoring purposes and to inform our reporting, it’s helpful to keep an eye on trends over time in some datasets, for example when tracking the progress of an mpox outbreak, monitoring pre-election polls or investigating migration trends.
This is where automation comes in handy: with the data being automatically grabbed from the source, reconfigured and channeled into a chart for visualization, allowing data journalists and their colleagues to notice report-worthy trends early on.
To establish such a workflow, we have been relying on free tools like Datawrapper and Github Actions to run a Python script.
Participants will be guided through setting up such a workflow step-by-step.
LEARNING OBJECTIVES:
🚀 In this session you will learn how to... - collect data from a url - parse the data into the needed format using Python's `pandas` library - use Python's `datawrapper` library to create a chart - set up the script to run automatically on Github Actions
🔍 PREREQUISITES & TOOLS:
- ✅ Datawrapper API token - ✅ Github Account (if you want to automate the chart update) - optional: Code text editor (if you prefer working with your own code for automation), such as Atom or Sublime Text - optional: Distill browser plugin (if you want to update on click) - optional: basic understanding of Python/coding helpful, but not required
