Python bytecode caches reduce loading work rather than execution work

Python's compiled module cache saves repeated loading work; it does not make the same program execute its instructions faster. The module tutorial distinguishes these two costs explicitly.

CPython can compile imported modules and cache the result as version-tagged .pyc files under __pycache__. This happens automatically, so calling Python interpreted does not imply that compilation never occurs. The directly executed command-line module is an important exception: it is recompiled without storing that cache.

The practical distinction is between preparing code to run and performing its work. A warm module cache can affect startup without improving an expensive computation. NumPy vectorization moves loops into compiled operations addresses a different cost by changing where repeated operations execute.

This also qualifies the measurement principle in Desktop performance claims need workload measurements: loading time and active-work latency answer different questions. Evaluate cache effects against startup requirements, not as evidence that the underlying algorithm became faster.