Last week I was invited to participate in the Open Source Weekends meetup, so I set up a quick GrimoireLab demo for them. It was a surprise, part of my talk about the history of Bitergia. You can see the slides online:
The adoption of Python notebooks to perform data analysis has considerably increased, becoming a de-facto standard within data scientists communities.
Notebooks are interactive human-readable documents, which contain the analysis description and the results (e.g, figures, tables) as well as the Python code used to perform data analysis. Thus, notebooks help scientists to both keep track of the results of their work, and make it easy to share the code with others.
Inspired by this blog post about the most useful open source python libraries for data analytics in 2017, where in addition of libraries description and category, the author gave insights about how popular these libraries are, using metrics such as number of commits and contributors extracted mainly from their home repository in Github, we are going to run some analysis from different perspectives.