Last chance to join PythonDAB!
The 10th cohort of my Python, Data, and AI bootcamp (PythonDAB) starts on November 5th. If you want to get fluent with these technologies in a small, collaborative cohort that emphasizes discussion, interaction, and hands-on practice, then this might be the program for you. Get more information at https://lernerpython.com/bootcamp/, or contact me at reuven@LernerPython.com.
Earlier this month, Anthropic (the soon-to-be-public company behind the Claude AI models) told the world that they were no longer using Git for version control. The reason? Their pace of software development, thanks to AI, is too fast for Git. They needed to find another solution, and thus developed their own, in-house version control system called Scion (https://www.anthropic.com/research/redesigning-source-control).
I wouldn't say that Git is perfect; you might argue that as someone who has taught it for about 20 years, I benefit from the fact that it's famously confusing, with a steep learning curve. But I have very much enjoyed using it for a variety of personal and professional projects. Compared with previous version-control systems I used, such as Subversion and CVS (not to mention RCS), Git is a dream – especially when it comes to branching and merging.
Git became a favorite among developers in no small part because of GitHub, the company (now part of Microsoft) that made it easy and straightforward for developers to collaborate. (I maintain that it was brilliant for GitHub to give open-source projects free access to their system. That encouraged no small number of open-source projects to switch to Git, and to use GitHub for hosting.)
Pull requests, which began as a way to let outsiders contribute to open-source projects, have become a standard method for collaboration in the software industry, including many commercial companies.
This week, we'll look at data about GitHub: How many people use GitHub? How many pushes do we see in a given year? How many repositories are there? What countries produce the most pushes? And have we seen any serious changes in the last year, since agentic coding took over the industry?
I should note that GitHub isn't the same as Git, and that there are both free and commercial alternatives to GitHub. However, it's a dominant player in the Git-hosting ecosystem, and we can assume these numbers are at least somewhat representative of the world as a whole.
Data and five questions
This week's data comes from GitHub itself -- and more specifically, from what it calls the "GitHub Innovation Graph," with data (of course) hosted on GitHub at https://github.com/github/innovationgraph/ . This data reflects public (i.e., largely open-source) activity on GitHub, aggregated and published every quarter since 2020.
The data comes as a set of eight CSV files, each describing a different facet -- pushes, repositories, and licenses, among others.
Paid subscribers, both to Bamboo Weekly and to my LernerPython+data membership program (https://LernerPython.com) get all of the questions and answers, as well as downloadable data files, downloadable versions of my notebooks, one-click access to my notebooks, and invitations to monthly office hours.
Learning goals for this week include: Reading multiple files, joins, grouping, pivot tables, window functions, and plotting with Plotly.
Here are my five questions and tasks for this week. I'll be back tomorrow with my solutions and explanations.
- Clone the repository from GitHub. Read all eight files into a dictionary, in which the filenames are the keys and the data frames (from reading the CSV files) are the values. Create a line plot showing, in total, the number of pushes that GitHub has seen per year. How do you explain the numbers for 2026?
- What 10 economies (i.e., countries plus territories that aren't quite countries) have the greatest number of repositories in 2026? Using data from https://github.com/datasets/population, which 10 countries have the greatest number of repositories per capita in 2026?