The Python Dev Tool Stack I Wish I Had When I Started

I wasted years reinventing wheels I didn’t know existed.

The Python Dev Tool Stack I Wish I Had When I Started

When I first started coding in Python, I treated it like a glorified calculator. I wrote messy scripts, ran them manually, and had zero clue what a virtual environment was. Debugging meant printing variables. Version control? I was zipping folders named project_v3_FINAL_REALLY_THIS_TIME.

It took me years of trial, error, and Googling in frustration to discover the tools that would later transform how I work.

So, if you’re early in your Python journey — or even a few years in — this is the tool stack I wish someone had shown me. It would have saved me hundreds of hours, a few existential crises, and at least one hard drive.

1. Poetry — Dependency Management That Doesn’t Suck

I started with pip and requirements.txt, then fumbled through virtualenv. It worked — until it didn’t. Environments broke, packages clashed, and dependency hell was very real.

Then I found Poetry.

Manages dependencies and virtual environments in one tool

Uses a clean pyproject.toml instead of multiple config files

Makes publishing to PyPI a breeze

# Create a new project 
poetry new my-awesome-project 
 
# Add a package 
poetry add requests 
 
# Run your app in the virtualenv 
poetry run python main.py

If you’re still juggling pip, venv, and requirements.txt, Poetry will feel like upgrading from a flip phone to an iPhone.

2. Pre-Commit — Because Clean Code Should Be Automatic

Code formatting, linting, and catching silly mistakes shouldn’t be manual. That’s where pre-commit hooks come in.

With a single .pre-commit-config.yaml, you can enforce:

Black for formatting

Flake8 or Ruff for linting

isort for import sorting

Bandit for security checks

And it all runs before you commit.

# Install pre-commit 
pip install pre-commit 
 
# Add some hooks 
pre-commit install

No more “oops, I forgot to run the linter” moments.

3. Ruff — The Fastest Linter I’ve Ever Used

I used to run Flake8 + isort + pylint + mypy. Then I met Ruff, which replaced most of them with blazing speed.

Written in Rust (aka very fast)

Lints, formats, and autofixes common issues

Zero-config to start — opinionated defaults that just work

pip install ruff 
ruff check . 
ruff format .

If your CI/CD pipeline is crawling, Ruff will be your new best friend.

4. Rich — Beautiful Output for CLIs and Logs

I spent years printing logs that looked like this:

[INFO] Process started 
[ERROR] Something broke

Then I found Rich — a game-changer for making Python output actually readable.

With just a few lines, you can render:

Colorful tracebacks

Progress bars

Markdown

Tables and trees

from rich import print 
from rich.progress import track 
 
for i in track(range(100)): 
    do_something(i)

Bonus: Logging with Rich’s handler looks amazing out of the box.

5. Typer — CLI Apps Without the Boilerplate

I used to build command-line tools with argparse and a growing pile of if statements. It worked… barely.

Typer, from the same creator as FastAPI, changed everything. It’s built on top of Click, but uses Python 3.6+ type hints for super-clean syntax.

import typer 
 
app = typer.Typer() 
 
@app.command() 
def greet(name: str): 
    print(f"Hello {name}!") 
 
if __name__ == "__main__": 
    app()

In seconds, you’ve got a CLI that feels professional.

6. GitHub Actions — CI/CD That’s Not Just for DevOps Pros

Early on, I thought CI/CD was only for “real” engineers.

But with GitHub Actions, you can:

Run tests on every push

Lint and format automatically

Deploy your code

All from a single .yaml file in .github/workflows.

name: CI 
 
on: [push] 
 
jobs: 
  test: 
    runs-on: ubuntu-latest 
    steps: 
      - uses: actions/checkout@v4 
      - name: Set up Python 
        uses: actions/setup-python@v5 
        with: 
          python-version: "3.11" 
      - run: pip install -r requirements.txt 
      - run: pytest

You don’t need a DevOps degree. Just copy, tweak, and ship.

7. IPython + Jupyter — For When You Just Need to Play

When exploring data, debugging tricky logic, or just trying things out — Jupyter Notebooks and IPython are invaluable.

Instant feedback

Markdown + code in one place

Plot inline with Matplotlib or Seaborn

Not everything needs to be a script. Sometimes, hacking in a notebook is the fastest way to get clarity.

8. Task Runner Bonus: Invoke or Just

Python needs a good task runner. Makefiles are okay, but not Pythonic. I’ve used Invoke and Just to script project workflows:

Run tests

Lint code

Start servers

Example with Invoke:

from invoke import task 
 
@task 
def test(c): 
    c.run("pytest tests/")

Now you can just run invoke test instead of typing the full command every time.

Final Thoughts: Tooling Is a Superpower

You don’t need to master all of these tools today. But knowing they exist is half the battle.

If you’re just starting out, bookmark this. Pick one tool a week. Integrate it. Learn it. Soon, your workflow will be cleaner, faster, and way less painful.

These aren’t just tools — they’re force multipliers.

The right tool at the right time can level you up faster than any tutorial ever could.

Don’t just learn Python — learn how to work with Python like a pro.