Cython is a programming language and compiler that lets you write code that looks almost like Python but compiles down to C, producing extension modules that run substantially faster than pure Python for CPU-bound tasks. It's a superset of Python syntax, so most existing Python code is already valid Cython, but the real performance gains come from adding optional static type declarations that let the compiler skip Python's dynamic type-checking overhead and generate more efficient C code underneath.
Cython is used almost exclusively to speed up specific bottlenecks in Python codebases rather than to write entire applications from scratch. A common pattern is profiling a Python program, finding the small number of functions responsible for most of the runtime, and rewriting just those in Cython with type annotations, while leaving the rest of the codebase in plain Python. This targeted approach avoids the cost of rewriting an entire project in a lower-level language while still getting most of the performance benefit where it matters most.
It's especially prevalent in the scientific Python ecosystem, where libraries need to combine Python's ease of use with performance close to C for numerical computation. Repos on this page that use Cython include scikit-learn, which relies on it for performance-critical machine learning algorithms like tree-based models and clustering, and pandas, which uses Cython extensively in its core data manipulation routines to make operations on large datasets fast enough for real-world use.