7 Underrated Python Tools That Boost My Productivity Every Day

You probably haven’t heard of half of these — but they’ve quietly saved me hundreds of hours.

7 Underrated Python Tools That Boost My Productivity Every Day

From faster debugging to smarter automation — these lesser-known gems make Python development smoother, faster, and surprisingly delightful.

When people talk about Python productivity, they usually focus on the big names — VS Code, Jupyter, Black, and so on. But in my experience, real day-to-day efficiency often comes from the smaller, quieter tools. The ones you don’t see in every tutorial, but that quietly supercharge your workflow.

I’ve spent the last decade writing Python almost daily — for everything from automation scripts to full-blown SaaS apps. Along the way, I’ve discovered a handful of tools that consistently make me faster, calmer, and more effective.

This isn’t a greatest-hits list. These are tools I genuinely use almost every single day. And if you’re not using them yet, I think you’ll walk away with at least one that becomes a new favorite.

1. rich — Turn Your Console into a Dashboard

Make your terminal output beautiful — not just functional.

I use rich whenever I want to print something — and I want it to actually make sense at a glance. Whether it’s logs, tables, JSON, or exceptions, rich makes everything easier to parse and prettier to look at.

Exception tracebacks are colorized and easier to read

You can print beautiful tables with just a few lines of code

Debugging structured data like JSON becomes way more visual

from rich import print 
from rich.table import Table 
 
table = Table(title="My TODOs") 
table.add_column("Task", style="cyan") 
table.add_column("Status", style="green") 
 
table.add_row("Write Medium article", "✅") 
table.add_row("Refactor API", "🛠️") 
 
print(table)

Once you use it, plain print() feels like a downgrade.

2. icecream — The Debugger’s Secret Weapon

Forget print() debugging — this is faster and smarter.

icecream (yes, that’s the name) is a tiny tool that makes debugging with print statements way less painful. Instead of writing print("x:", x), just write ic(x), and it tells you what you printed and where it came from.

from icecream import ic 
 
x = 42 
ic(x)  # Outputs: ic| x: 42

It shows the expression and the value

Return outputs filename and line number

This requires zero setup and works instantly

I use it as a “middle ground” between print-debugging and firing up a full debugger like pdb.

3. taskipy — The Lightweight Task Runner I Didn’t Know I Needed

Like make, but modern and Pythonic.

You’ve probably added shell commands to a Makefile or npm run script. taskipy brings that convenience to Python. Just define tasks in pyproject.toml, and run them with task <name>.

[tool.taskipy.tasks] 
lint = "flake8 src" 
test = "pytest tests" 
dev = "uvicorn app.main:app --reload"

Then in your terminal:

task dev

It centralizes your dev commands

It makes onboarding easier (others don’t need to memorize your CLI rituals)

It is cleaner than a bunch of shell scripts

4. pydantic (for data validation everywhere)

Yes, it powers FastAPI — but it’s useful even if you’re not building an API.

Most people know pydantic from FastAPI. But I use it outside web dev just as much — especially when I’m working with config files, CLI inputs, or any external data source.

It validate and parse untrusted data in one step

Raise helpful errors automatically

Autogenerates docs and IDE hints

from pydantic import BaseModel 
 
class Config(BaseModel): 
    name: str 
    retries: int = 3 
    debug: bool = False 
 
config = Config(name="my-script", debug=True)

Suddenly, your configuration has type safety and self-documentation.

5. typer — CLIs Without the Boilerplate

A beautiful way to write command-line interfaces in Python.

Writing a CLI used to mean wrestling with argparse. Now I reach for typer — a modern, FastAPI-inspired library that makes it fun to write CLI tools.

import typer 
 
def main(name: str): 
    print(f"Hello {name}!") 
 
typer.run(main)

Zero boilerplate

Type-safe arguments

Built-in help generation and argument parsing

I use typer for everything from quick scripts to full developer tools. It scales gracefully and keeps things clean.

6. httpx — Requests, But Async-Friendly

If you use requests, you’ll love httpx.

Python’s beloved requests library is great — but it doesn’t support async out of the box. httpx does, and adds a bunch of nice touches along the way.

Drop-in replacement for requests

Fully async support when you need it

Better timeout/retry handling

import httpx 
 
response = httpx.get("https://httpbin.org/get") 
print(response.json())

I still use requests occasionally — but when I want async, httpx is my go-to.

7. watchdog — Automate Actions When Files Change

This tiny tool powers a ton of developer automation.

Need to rerun a script whenever a file changes? watchdog is the underlying library behind many file-watching tools — and you can use it directly in Python.

Auto-reload when developing static sites or scripts

Monitor logs or config files for changes

Trigger custom logic on file edits

Example:

from watchdog.observers import Observer 
from watchdog.events import FileSystemEventHandler 
 
class Handler(FileSystemEventHandler): 
    def on_modified(self, event): 
        print(f"Modified: {event.src_path}") 
 
observer = Observer() 
observer.schedule(Handler(), path=".", recursive=True) 
observer.start()

I’ve built mini-automation systems around watchdog — it’s like a lightweight Zapier in your terminal.

Final Thoughts: Small Tools, Big Wins

These tools aren’t flashy. You probably won’t see them topping Reddit threads or YouTube tutorials. But they quietly eliminate friction from my workflow every day — whether it’s shaving 5 seconds off a task or making my logs readable without squinting.

That adds up. And in a world where productivity is often about focus and momentum, I’ll take every small win I can get.

So here’s my challenge to you:
Pick one of these tools. Try it today. See how it feels in your own workflow.

Chances are, it’ll earn a permanent spot in your dev toolbox — just like it did in mine.

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