I Interviewed 10 Python Developers — Here’s What They Wish They Knew Earlier

They learned Python the hard way — so you don’t have to.

I Interviewed 10 Python Developers — Here’s What They Wish They Knew Earlier

I recently spoke with 10 professional Python developers, each at different stages in their careers — from junior coders to senior engineers. I asked them a simple question:

“What do you wish you knew earlier in your Python journey?”

The answers were surprisingly consistent — and surprisingly relatable. Whether you’re just starting out or have written thousands of lines of Python, chances are you’ll find something here that makes you say, “I wish someone told me that too.”

Let’s dive into their most valuable lessons, and how you can apply them today.

1. Knowing Python ≠ Writing Pythonic Code

Almost every developer I spoke to said they thought they were writing good Python — until they learned what “Pythonic” really meant.

Pythonic code emphasizes readability, simplicity, and the use of idiomatic constructs. Here’s what that means in practice:

Unpythonic:

numbers = [1, 2, 3, 4, 5] 
squared = [] 
for n in numbers: 
    squared.append(n * n)

Pythonic:

squared = [n ** 2 for n in numbers]

Read the Zen of Python (import this) and start paying attention to code quality—not just code correctness.

2. Mastering the Standard Library Saves You Hours

“I spent months building tools I later found already existed in the standard library.”

Python’s standard library is a treasure trove of utilities: itertools, collections, datetime, functools, and many others.

Underrated modules you should explore:

pathlib — Object-oriented file system paths

enumerate() — Cleaner loops with indexes

defaultdict — A game-changer for dictionaries

argparse — For writing CLI tools

Instead of jumping to external libraries, always ask: “Can I do this with what Python already gives me?”

3. Understanding Mutability and References is Crucial

“I lost days debugging weird bugs caused by lists and dictionaries behaving unexpectedly.”

Here’s a quick example:

def append_item(my_list=[]): 
    my_list.append(1) 
    return my_list 
 
print(append_item())  # [1] 
print(append_item())  # [1, 1] ← Wait, what?!

This happens because default arguments are evaluated only once when the function is defined — not each time it’s called.

What you should do instead:

def append_item(my_list=None): 
    if my_list is None: 
        my_list = [] 
    my_list.append(1) 
    return my_list

Once you understand mutability and reference behavior, a lot of Python’s quirks start to make sense.

4. Learning OOP Early Helps — Even if You Don’t Use It Often

“I avoided classes because I thought they were overkill. Now I use them to write cleaner code and testable modules.”

Even if you write mostly functional scripts, understanding object-oriented programming (OOP) helps structure larger projects, use design patterns, and write extensible code.

If terms like __init__, self, inheritance, and encapsulation still confuse you, take the time to build a real-world class-based module—like a simple BankAccount or Inventory system.

class BankAccount: 
    def __init__(self, balance=0): 
        self.balance = balance 
     
    def deposit(self, amount): 
        self.balance += amount 
     
    def withdraw(self, amount): 
        self.balance -= amount

Trust me, this foundation will pay dividends later.

5. Dependency Hell Is Real — Learn Virtual Environments Early

“I once broke three projects by running pip install without a virtual environment."

If you’re not using venv or poetry, you’re just one pip install away from disaster.

Essential tools:

venv — Built-in and simple

virtualenv — More features, great for older Python versions

poetry or uv — For dependency management and reproducible builds

Use them religiously. You’ll thank yourself when your app runs smoothly across different machines and team members.

6. Testing Isn’t Optional — It’s Freedom

“I used to think writing tests slowed me down. Now I know they let me move faster with confidence.”

Start small:

Use pytest for writing simple test functions.

Test edge cases and failure scenarios — not just the happy path.

Write tests before refactoring or adding new features.

Learn how to use assert, fixtures, and mocks. You’ll feel like a wizard catching bugs before they happen.

7. Don’t Ignore Type Hints and Linters

“I thought type hints were for statically typed languages. Turns out they’re amazing for large Python codebases.”

While Python is dynamically typed, tools like mypy can catch a surprising number of bugs at lint-time—before you even run the code.

Add type hints gradually:

def greet(name: str) -> str: 
    return f"Hello, {name}"

Pair that with linters like flake8, pylint, or ruff to enforce style and consistency.

This habit elevates your code from good to great.

8. You Don’t Need to Memorize Everything — Learn to Read Docs

“The best Python devs I know aren’t the ones who memorize syntax. They’re the ones who Google better and read docs daily.”

Being a pro developer is more about problem-solving than memorization.

Bookmark these:

https://docs.python.org

https://pypi.org

https://realpython.com

https://stackoverflow.com

Make a habit of reading docstrings, exploring source code, and writing your own documentation — it will make you an invaluable teammate.

9. The Python Ecosystem Is Huge — Don’t Go It Alone

“I spent too long stuck in tutorial hell. I should’ve joined a community or worked on real projects earlier.”

Some ideas to get out of the bubble:

Contribute to open source on GitHub

Join Python Discord servers or Reddit forums

Follow #Python on X/Twitter or LinkedIn

Attend local meetups or online conferences like PyCon

The more you interact with the community, the faster you’ll learn — and stay motivated.

10. Your First Job Won’t Be Perfect — But It Will Teach You Everything

“I wish someone told me not to wait for the perfect Python job. Just get in, and learn by doing.”

Your first role might involve legacy code, weird stacks, or boring tasks — but you’ll gain experience that tutorials can never teach.

Key things to look for:

A supportive team willing to mentor

Real-world codebases and deployment experience

A mix of challenges and learning opportunities

Don’t chase the perfect job. Chase the first good opportunity — and grow from there.

Final Thoughts: The Journey Is Non-Linear — And That’s Okay

Every developer I spoke with took a different path. Some started with Flask, others with data science. Some felt stuck for months. Others bloomed quickly.

But what united them? A love for the language, and a willingness to keep learning.

So here’s the takeaway:
You don’t need to get it all right today. But if you stay curious, reflective, and humble — you’ll get better every day.

If you found this article helpful, consider following me for more developer stories and practical Python advice. Drop a comment — what do you wish you knew earlier in your journey?