Despite certain limitations, Python has achieved a milestone as the most popular language on GitHub. Traditionally, Python has been considered slower than compiled languages due to:
Limited memory management
Lack of true multithreading
Dynamic typing, which can hinder performance
Indeed, most interpreted languages are generally slower than compiled ones, with Python often cited as up to 100 times slower than languages like C. However, Python wasn’t created to compete on compilation speed; instead, its goal is to streamline software creation.
This milestone coincides with the recent release of Python 3.13 on October 7th, bringing significant improvements that directly address some of these core limitations.
Major Updates in Python 3.13 with Impact on AI and Machine Learning
Python’s new features are especially impactful for artificial intelligence (AI) and machine learning (ML) applications:
Experimental GIL Deactivation (Global Interpreter Lock): The GIL has historically been a major hurdle to true multithreading in CPU-bound tasks. Python 3.13 introduces an option to disable the GIL, enabling native multithreading for the first time.
JIT (Just-In-Time) Compilation: This experimental feature analyzes code and compiles the most execution-intensive parts, effectively reducing the time and resources consumed by real-time interpretation.
These enhancements solidify Python’s position not only as a leader in readability and practicality but also as a strong contender in performance.
A Point of Debate: TypeScript vs. JavaScript
Some argue that GitHub's ranking separates TypeScript from JavaScript incorrectly, as TypeScript is fundamentally an extension of JavaScript with added static typing and future-facing features. In essence, TypeScript builds upon JavaScript, adding structure and scalability that many developers value, especially in large-scale projects. The two languages are tightly linked, with TypeScript code often being compiled back to JavaScript to run in browsers and environments where JavaScript is the standard.
If TypeScript and JavaScript were combined in GitHub’s rankings, Python would fall back to the second position. This discrepancy has led to debate within the developer community, as many consider TypeScript to be simply an enhancement of JavaScript rather than a completely separate language. By combining their popularity, the JavaScript ecosystem as a whole remains unrivaled in terms of sheer usage across both web and server-side development.
However, Python’s unique combination of simplicity, readability, and its continuous evolution—along with the steady rise of TypeScript—illustrates that the landscape of programming language popularity is far from static. Python’s surge to the top signifies more than a shift in preference; it reflects the growing importance of developer productivity, ease of learning, and the explosive demand for AI and data science capabilities, where Python truly shines.
The Rise of Python: A Trend Reflecting Developer Needs
Python's meteoric rise highlights a key trend: modern developers increasingly prioritize productivity, ease of prototyping, and rapid development cycles over raw performance. The language's practical syntax, coupled with a rich ecosystem of libraries and frameworks like TensorFlow, Pandas, and Flask, makes it an ideal choice for AI, data science, and web development.
Python has also cemented itself as a favorite for education. Its simple, readable syntax allows new programmers to learn foundational concepts without the complexity of lower-level languages. As Python evolves, these foundational benefits continue to be enhanced by features aimed at more experienced developers, making it a language that truly spans a developer's career.
The release of Python 3.13 represents an ongoing effort to address some of the more technical criticisms of the language. With experimental JIT compilation and GIL deactivation, Python aims to enhance its performance while retaining the simplicity that has driven its adoption. The GIL, in particular, has long been a source of contention among developers building multithreaded applications, and this new flexibility promises to expand Python’s applicability in high-performance scenarios.
Python's adaptability is undeniable, but Java often takes the lead for enterprise-grade backend systems. Outsourcing Java development services offers unmatched stability, scalability, and reliability—the perfect complement to Python's versatility for tackling diverse software challenges.
Conclusion
Python's achievement in surpassing JavaScript on GitHub's ranking is a reflection of the language's adaptability and the growing emphasis on developer efficiency. While debates around the ranking metrics—such as whether TypeScript should be considered distinct from JavaScript—continue, there's no doubt that Python has proven its staying power and relevance in the modern development landscape.
With its recent updates, Python is not just a language of convenience but is transforming into one that can compete in terms of performance, especially in areas critical to AI and machine learning. Whether or not it stays at the top of GitHub's rankings, Python's influence on how we write and think about code will undoubtedly continue to grow.
