Python is widely admired for its simplicity, versatility, and extensive ecosystem of libraries, but despite its many strengths, there are some features that many developers wish for that won’t be making their way into the language anytime soon. While Python continues to evolve and improve, a few commonly requested additions have been met with resistance or are simply not on the horizon. These are not necessarily bad ideas, but they highlight the trade-offs that come with Python’s design philosophy.
One of the most frequently mentioned requests is the introduction of a statically typed, compiled version of Python. The idea of a statically typed Python, where variables and functions are explicitly typed and compiled directly to machine code, is an alluring one. It promises to address performance concerns by removing the overhead of dynamic typing, which contributes to Python’s relative slowness. Static typing would also offer developers stronger guarantees about code behavior, making it easier to catch errors early. However, this vision is unlikely to come to fruition in the near future, if ever.
While Python has introduced type hints (thanks to PEP 484), these are designed for static analysis at edit time, using tools like linters. They are not used by the Python runtime, and the language’s design maintains a focus on flexibility and dynamic typing. In fact, PEP 484 explicitly states that Python will remain a dynamically typed language, with type hints being optional rather than mandatory. This approach ensures that Python retains its core identity as a flexible, high-level language, prioritizing readability and ease of use over rigid type systems.
For developers who are determined to achieve better performance with static typing, alternatives like Cython and mypyc exist. These tools enable certain parts of a Python program to be statically typed and compiled for performance gains. However, they come with their own trade-offs. Cython, for example, requires developers to write some code in C and limit the use of Python’s runtime for maximum efficiency. In essence, while it’s possible to make Python run faster by eliminating some of its dynamic features, achieving a truly statically typed, compiled version of Python would require significant sacrifices to its core dynamic nature.