AbstractAlthough atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate the CPU‐based atomic C APIs into Python shared variables by C Foreign Function Interface for Python (CFFI) on all major platforms and utilises Cython to optimise calculation in CPython. Evidence shows that the resulting product, Shared Atomic Enterprise (SAE), could accelerate data operations on shared data types to a large extent. These findings provide a solid evidence base for the massive utilisation of Python atomic operations in parallel computation and concurrent programming.