“I think what they have is acceptance from the scientific community as a tool that is considered to be universal,” Theodore Gray says of Pérez’s group. “And that’s the thing that Mathematica never really so far has achieved.” There are now 1.3 million of these notebooks hosted publicly on Github. They’re in use at Google, Bloomberg, and NASA; by musicians, teachers, and AI researchers; and in “almost every country on Earth.”

Out of the box, Python is a much less powerful language than the Wolfram Language that powers Mathematica. But where Mathematica gets its powers from an army of Wolfram Research programmers, Python’s bare-bones core is supplemented by a massive library of extra features—for processing images, making music, building AIs, analyzing language, graphing data sets—built by a community of open-source contributors working for free. Python became a de facto standard for scientific computing because open-source developers like Pérez happened to build useful tools for it; and open-source developers have flocked to Python because it happens to be the de facto standard for scientific computing. Programming-language communities, like any social network, thrive—or die—on the strength of these feedback loops.