This content originally appeared on Level Up Coding – Medium and was authored by Manalimran
My Favorite Tools That Took My Python Coding From Basic Scripts to Production-Ready Projects
1. Python (Official Extension by Microsoft)
The very first extension I install is the official Python extension by Microsoft. It provides everything: IntelliSense, code navigation, refactoring, linting, and debugging. I remember the first time I ran a Python script with breakpoints directly inside VS Code — it felt like magic compared to juggling between a terminal and an editor.
{
"python.pythonPath": "venv/bin/python",
"python.linting.enabled": true,
"python.linting.pylintEnabled": true
}2. Pylance: Lightning-Fast IntelliSense
The Pylance extension made my autocomplete buttery smooth. It leverages static type checking, so when I started adopting type hints in my projects, Pylance highlighted mismatches instantly. This not only improved code quality but also gave me confidence while working on large codebases.
def add_numbers(a: int, b: int) -> int:
return a + b
print(add_numbers("5", 10)) # Pylance warns here
This was a game-changer in catching silly type errors early.
3. Black Formatter
I used to waste time arguing about code style in pull requests until I discovered Black. With the VS Code Black extension, my code formats automatically on save. The best part? It enforces a single consistent style. I no longer think about where to put spaces or line breaks Black decides for me.
pip install black
Now, every Python file in my project looks like it was written by the same person, even in a team of five.
4. Flake8 for Linting
While Black formats code, Flake8 keeps it clean by enforcing PEP8 guidelines and spotting potential bugs. For example, it warns me about unused imports or variables that could cause confusion later.
pip install flake8
flake8 app.py
Pairing Flake8 with Black gave me the perfect balance of style + safety.
5. Jupyter Notebook Support
As a data scientist, Jupyter notebooks are my daily playground. The Jupyter extension in VS Code lets me run notebooks seamlessly without leaving my editor. I can execute Python cells, visualize plots, and even connect to remote Jupyter servers.
# Inside VS Code Jupyter cell
import matplotlib.pyplot as plt
plt.plot([1,2,3,4], [10,20,25,30])
plt.show()
This integration replaced my need to open the browser for quick experiments.
6. Python Docstring Generator
Writing docstrings used to feel boring, but this extension made it effortless. By typing """ after a function definition, it auto-generates a template based on my parameters. This small hack improved my documentation habits and saved hours in large projects.
def process_data(data, limit):
"""
Args:
data (list): input dataset
limit (int): maximum records
Returns:
list: processed dataset
"""
Now, I write cleaner and more maintainable code without extra effort.
7. Python Test Explorer
Testing is something I avoided early in my career, but once I discovered Python Test Explorer, everything changed. It integrates pytest and unittest directly into VS Code. I can run tests with a click, view results in the sidebar, and debug failing tests instantly.
pip install pytest
This gave me the discipline to test consistently and catch issues before deployment.
8. Docker Extension
Most of my Python apps run inside Docker containers, especially ML models or Flask APIs. The Docker extension in VS Code lets me build, run, and manage containers visually. I no longer have to memorize long Docker commands I manage everything right from the editor.
FROM python:3.10
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["python", "app.py"]
This made my deployment pipeline smoother and less error-prone.
9. REST Client for API Testing
When I started building Python APIs with Flask or FastAPI, I often switched between VS Code and Postman to test endpoints. The REST Client extension solved that. I can send HTTP requests right inside VS Code and view responses instantly.
### Test FastAPI endpoint
GET http://127.0.0.1:8000/users
This tiny extension replaced a heavy external tool for me.
10. SQLTools
Since most Python projects I worked on involved databases, SQLTools became a lifesaver. It connects directly to MySQL, PostgreSQL, or SQLite from inside VS Code. Instead of juggling between a DB client and editor, I query and debug right where I write code.
SELECT name, email FROM users WHERE active = true;
This is one of those hacks that drastically cut down my context-switching.
11. Python Environment Manager
Handling multiple Python environments (venv, conda, Docker) used to be a nightmare. The Environment Manager extension made it much easier to create, switch, and activate environments without touching the command line. It keeps projects isolated and dependencies clean.
12. Live Share for Collaboration
Finally, collaboration. With Live Share, I can pair program in Python with teammates in real-time. Whether we’re debugging ML models or writing Django code, we work together as if sitting at the same desk without screen sharing.
Final Thoughts: Extensions Made VS Code My Python IDE
When I first tried VS Code, I thought it was just a “text editor.” But with these extensions, it transformed into a full Python IDE lightweight, customizable, and powerful. These tools saved me time, improved my code quality, and made collaboration seamless.
Today, I can’t imagine writing Python without this setup.
12 VS Code Extensions Every Python Developer Should Be Using was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding – Medium and was authored by Manalimran