Python's performance is significantly enhanced by leveraging C-backed libraries rather than writing raw Python code, as the language lacks Just-In-Time (JIT) compilation used in .NET and JavaScript. This approach matters to developers because it ensures that computationally intensive tasks are handled efficiently, bypassing Python’s slower interpreted bytecode execution.
This insight shifts how developers write Python, emphasizing the use of built-in functions and libraries like NumPy for optimal performance.
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