Why has Python become the preferred language for scientific computing rather than JavaScript?

Python's dominance in the field of scientific computing: From initial choice to widespread application today

A web developer is puzzled by the widespread use of Python in the field of scientific computing. He is familiar with JavaScript's dominance in web development, thanks to its rich libraries, avoiding duplicate wheels. However, he struggled to understand why Python rather than JavaScript became the first choice in the field of scientific computing, especially during the early stages of scientific computing library development. In addition, with the increasing convergence of programming language syntax sugar, why not directly develop AI libraries with JavaScript?

The key lies in the fundamental differences in the underlying architecture of Python and JavaScript and how these differences affect their applicability in different fields. The single-threaded characteristics of JavaScript and its shortcomings in precise timing and calculation are the main reasons why it is not suitable for scientific computing. Scientific computing usually involves a large number of complex numerical operations and precise time control, while JavaScript's single-threaded model and its limitations on the accuracy of floating-point numbers make it difficult to comply with these tasks. In contrast, Python supports multithreaded processing and has a powerful numerical computing library, such as NumPy, which provides efficient vectorized operations and higher numerical computing accuracy, which is crucial for scientific computing.

Therefore, these features of Python make it a better choice than JavaScript when initially choosing to develop scientific computing libraries. Although the syntax of modern programming languages ​​is becoming increasingly similar, the underlying language features are still the key to determining their applicability in a specific domain. JavaScript has advantages in browser-side applications and rich web development libraries, while Python's advantages in scientific computing and data analysis stem from its language design and rich scientific computing library ecosystem. These fundamental differences determine their application preferences in different fields and are not easily changed by convergence of syntactic sugars.

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