Python Myths Even Seniors Believe

This guide was originally published as a visual guide by DevMindS on the Soargram platform.

Here are 5 persistent myths that even experienced developers fall for — backed by real facts from industry experts.

Myth 1: "Python is slow because it's interpreted"
This is only half true. Python is indeed slower than C or Rust, but not because it's interpreted. Python code is first compiled to bytecode, which runs on the Python Virtual Machine (PVM) — similar to how Java works.

What actually makes Python slower is its dynamic nature. Every operation requires type lookups, method resolution, boxing/unboxing, and memory allocation. A simple expression like p.x + 2 in Python involves:

• Finding the type of p
• Calling getattribute()
• Unboxing values
• Boxing results

None of this depends on interpretation.

Myth 2: "Type hints make Python faster"
Type hints don't improve runtime performance. They're designed for static analysis tools (like mypy) and your IDE, not for the interpreter. The Python interpreter itself doesn't perform any type checking based on these hints.

In fact, type annotations are completely ignored at runtime. The static types are "completely useless from the point of view of optimization and performance".

And you can still break them:

def add(x: int, y: int) -> int:
    return x + y

add('hello ', 'world')  # Still works

Myth 3: "The GIL is a terrible design flaw"
The GIL (Global Interpreter Lock) has a bad reputation, but it actually sits in a sweet spot. It's "just good enough to be useful yet pointless enough to not be used" for complex threading.

The GIL protects Python from needing complex thread synchronization in the common single-threaded case. More importantly, it "means that the core Python developers are not forced to spend a lot of effort supporting a model of concurrency that will ultimately be a dead end".

Removing the GIL was tried in the past — Greg Stein produced a patch years ago, but "nobody seemed to want to pay the penalty it extracted in speed reduction".

Myth 4: "Static compilation of Python is easy with type hints"
Python's dynamic nature makes static compilation fundamentally difficult. As one performance engineer explained: "Everything can change".

Consider this:

import random

class Evil:
    if random.random() > 0.5:
        def hello(self):
            print('hello world')

Evil().hello()  # Works half the time

Legal Python. "This is not something to define in production, I hope. Half of the time it still works, half of the time it raises an exception. Good luck compiling it."

Even new() can return instances unrelated to the class being instantiated. The dynamic features that make Python awesome also make it impossible to fully optimize ahead-of-time.

Myth 5: "Senior devs memorize everything"
Seniors don't have encyclopedic knowledge of syntax. They know concepts, patterns, and trade-offs — not exact syntax. Experience isn't about memorization. It's about knowing "what to solve, not necessarily how to write it".

The real difference between juniors and seniors:

Juniors get stuck on how to write the code

Seniors get stuck on whether they should write it at all

Seniors Google basic syntax openly. They bookmark the same Stack Overflow thread for the 40th time without shame. "Exact syntax is a low-priority cache eviction."

The Bottom Line
The difference between a junior and a senior dev isn't knowing more syntax. It's knowing which tool to use, and when.

This guide was originally published as a visual guide by DevMindS on Soargram — a social network for visual guides. Read, save, and discuss it there.

Read the full guide on the website. Link in bio.

Comments

[Gunjan]

This was a great read because some Python myths persist even among experienced developers. One that stands out is the idea that "Python is always slow." While it's generally not as fast as compiled languages for raw computation, performance depends heavily on the problem being solved, the libraries used, and the overall application design. In many real-world projects, development speed, readability, and maintainability matter just as much as execution speed.
Another common misconception is that writing concise Python code automatically means writing good Python code. Readability and simplicity are usually more valuable than clever one-liners, especially when working in teams.