Python users often face performance issues due to the language's inherent dynamism, rendering it slower than languages like C or Java. While external libraries like NumPy or Numba offer performance improvements, they require sacrifices in terms of code abstraction and granularity. The aspiration to enhance Python's native speed encounters significant hurdles, with its dynamic nature being a fundamental barrier. Unlike statically typed languages, Python lacks strong guarantees about variable types, complicating optimizations. Therefore, while users seek faster Python out of the box, achieving this remains a challenging endeavor.
In many of the ways that matter, it's true. 'Pure' Python, without external libraries written in C, is nowhere near as fast at computation or object manipulation as C or C++, or Java, or Rust, or Go.
Users clamor for the same thing: Can we make Python natively faster, right out of the box? And over the years, a simple answer has taken shape: 'Maybe, but it's hard.'
The biggest obstacles to performance revolve around something baked into the language at its most fundamental level: its dynamism.
If you assign something a name in a language like C++ or Rust, assumptions about the language's behavior provide strong guarantees about what type of thing you're dealing with.
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