Python optimization, activated by the -O command-line option or by setting the PYTHONOPTIMIZE environment variable, modifies the behavior of the Python interpreter in several ways to optimize the execution of Python programs. Here are the primary effects of enabling Python optimization:
Assertion Removal: The most immediate effect of Python optimization is the removal of assert statements from the code. Assertions are typically used for debugging to check for conditions that should be true. When Python is run with optimization, these checks are skipped, which can slightly improve performance, especially in code with many assertions[1][13].
Bytecode Generation: When optimization is enabled, Python generates bytecode files with a .pyo extension instead of the usual .pyc files. This distinction was more relevant in older versions of Python; in more recent versions, both optimized and non-optimized bytecode are stored in .pyc files, with the optimization level encoded in the filename[13][16].
Docstring Removal: At a higher level of optimization, specified by -OO or setting PYTHONOPTIMIZE to 2, Python also removes docstrings from the code. This can reduce the memory footprint of the program, as less string data is stored in memory. However, this means that help texts and other docstring-based information will not be available during runtime[13][16].
Performance Impact: While these changes can reduce the amount of data Python needs to handle, the actual performance improvement in execution speed is generally minimal. Python does not perform more aggressive optimizations like reordering code or speculative execution that compilers for languages like C or Java might do[13][16].
Debugging and Development: Because assertions and possibly docstrings are removed, debugging can be more challenging with optimization enabled. Therefore, it's typical to use optimization primarily in a production environment where code has already been thor...
