In the Python ecosystem, you rarely need to implement these "recipes" yourself because highly optimized versions already exist in the SciPy Stack. Reviews: Numerical Recipes - Lysator
scipy.optimize and numpy.linalg for production-ready code.Searching for a is tricky because the official Numerical Recipes books are not legally available for free in PDF format (more on this later). However, the scientific Python community has "re-cast" these recipes using modern libraries.
To understand why users search for "Numerical Recipes Python," one must understand the authority of the original work. First published in 1986, Numerical Recipes: The Art of Scientific Computing by Press, Teukolsky, Vetterling, and Flannery became the "bible" for scientists who needed to write code but were not computer scientists.
A top-tier Python conversion of Numerical Recipes does not simply translate for loops from C into Python (which would be incredibly slow). Instead, it uses via NumPy and just-in-time compilation via Numba.