That statement only feels true to me if you interpret the word "power" as an exact synonym for "performance." Which is a definition that is valid, but also just

That statement only feels true to me if you interpret the word "power" as an exact synonym for "performance." Which is a definition that is valid, but also just about perfect for leading someone to miss the point. Numpy approaches the performance you could get with Fortran. Mostly because its core is written in Fortran. What Numpy offers that Fortran never did, though, is leverage. The article mentions, but doesn't really do justice to, the sheer volume of interoperability that Numpy has enabled. It's not just that all these libraries were built on top of Numpy. It's also that their common Numpy substrate makes them all deeply interoperable with each other. And that works both above and below the boundary. You can swap out BLAS and LAPACK for something else - say, CUDA, or a distributed representation - and as long as the replacement also speaks Numpy's language, you can plug it into existing libraries that were originally written against Numpy. In short: Fortran gets you performance. Numpy gets you that, and also productivity. I would argue that that actually makes Numpy more powerful than what was possible with just Fortran.