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Hey friends, 4 years ago I posted an article about Python's 3 major drawbacks and got tons of backlash.

After using Python for 4 years, I'm here to roast it again.

As a programmer, were you also drawn in by Python's "simple and easy to learn" slogan? But when you actually dug deeper, did you encounter all sorts of frustrating moments? Today let's objectively review Python's lesser-known flaws.

Performance Bottleneck: Speed Is a Hard Limit

Python's biggest complaint is definitely its runtime speed. Compared to compiled languages like C/C++, Python, as an interpreted language, does have inherent disadvantages in execution efficiency. Although you can improve performance with JIT compilers or rewriting key parts in Cython, this "patchwork" approach can never solve the fundamental problem. In scenarios requiring large-scale data processing or high real-time performance, Python's efficiency shortcomings are particularly obvious. This is why many high-performance computing projects still choose C++ or Rust over Python.

The Two Sides of Type System

Python's dynamic typing does lower the barrier to entry for beginners, but it also brings many hidden dangers. The lack of static type checking means many errors only surface at runtime, posing challenges for large project maintenance. Although Python 3.5 introduced type hints, this feels more like "patching after the fact" — far less complete than the type systems in Java or C#. The arbitrary switching of variable types may seem flexible, but it easily leads to code logic chaos. Imagine: a variable that's a string one second and an integer the next — maintaining code like that is a nightmare.

The Dilemma of Concurrent Programming

The GIL (Global Interpreter Lock) makes Python perform poorly in multi-threaded parallel computing. Although Python provides multiprocessing solutions to bypass GIL limitations, this introduces additional overhead for inter-process communication. In today's era of multi-core processors, this flaw in Python is particularly glaring. While async programming offers some solutions, the learning curve is steep, and it can't fully leverage multi-core advantages. This is why Node.js, Go, and other languages are more favored in high-concurrency scenarios.

Version Compatibility Issues

The migration from Python 2 to Python 3 can be described as one of the most painful upgrades in programming language history. Although Python 2 has officially retired, this decade-plus transition period is enough to illustrate Python's backward compatibility shortcomings. A massive amount of legacy code needed rewriting, third-party libraries needed adaptation, bringing enormous burden to developers. Even now, compatibility between different Python 3 minor versions isn't perfect either. This "version fragmentation" brings unnecessary trouble to project maintenance.

Over-Encapsulated Standard Library

Python is famous for "batteries included" — the standard library is indeed rich, but sometimes too bloated. Many module designs have issues with over-encapsulation, leading to complex and hard-to-understand APIs. Worse, some standard library implementations have uneven quality with insufficient performance optimization. Over time, many standard library components have been completely surpassed by third-party libraries (like requests over urllib), but they can't be removed due to compatibility, creating an awkward "historical burden."

Absence from Mobile Development

Python has almost zero presence in mobile development. Whether Android or iOS, Python lacks native support. Although projects like Kivy and BeeWare try to fill this gap, their maturity and performance can't match native solutions like Kotlin or Swift. In this mobile-first era, Python's weakness on mobile limits its application scenarios. While other languages are moving toward full-stack development, Python still firmly holds its ground on the server side.

Over-Reliance on Third-Party Libraries

Hidden behind Python's thriving ecosystem is a crisis: developers are overly dependent on third-party libraries. Installing a simple tool might require downloading dozens of dependencies, creating "dependency hell." Worse, many popular libraries are poorly maintained and have security risks. PyPI may have hundreds of thousands of packages, but quality is uneven. Once a critical library stops being maintained, the entire project may face a refactoring risk. This contradiction of ecosystem prosperity coexisting with fragility makes Python projects have hidden costs.

Conclusion

Python is like a friend with equally prominent strengths and weaknesses: it's easy to learn but not rigorous enough; the ecosystem is thriving but over-dependent; it's versatile but limited in performance. Because of this, we should evaluate more rationally when choosing a tech stack: there is no perfect language, only suitable scenarios. Next time you hear the slogan "Life is short, I use Python," consider this: Python does lower the barrier to programming, but the challenges of engineering practice never disappear. Perhaps acknowledging a language's limitations is the first step to using it well.

Author: 程序员晚枫 (Programmer Wanfeng), specializing in AI programming training. After completing the tutorial 30 Lectures · AI Programming Bootcamp created with Turing Press, beginners can start working on AI projects.

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