In large enterprise applications, Python can be slow for CPU-intensive tasks and is limited by the Global Interpreter Lock (GIL) for concurrent execution. Python’s dynamic typing may cause errors during operation. To address these issues, developers should profile and optimise code, use speed-up tools like Cython, and ensure thorough testing and type checks.
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