5 Python Libraries So Good I Keep Reusing Them in Every Project

These libraries aren’t just popular — they’ve become essential in my daily development workflow.

Aashish Kumar

26 Jun 2025 — 3 min read

I don’t even think twice — I just install them by default.

5 Python Libraries So Good I Keep Reusing Them in Every Project

There’s a special kind of joy in discovering a Python library that just works — intuitive, powerful, and so versatile that it quietly becomes part of every project you touch.

Over the years, I’ve experimented with hundreds of Python libraries. Some were impressive but niche.

Others were bloated or over-engineered. But a handful stood the test of time — showing up in web apps, automation scripts, data pipelines, and even hobby projects.

Here are five such libraries I keep going back to, and why they might become your go-to tools too.

1. Pydantic — For Data Validation That Feels Like Magic

“Garbage in, garbage out” — and Pydantic ensures you don’t let garbage in.

Whether you’re building an API, working with user input, or loading data from external sources, validating and parsing data is unavoidable. Pydantic makes it effortless.

Why I use it everywhere:

Typed Models: Define your data structure like a Python class — and get type validation automatically.
Error Transparency: It gives detailed, readable error messages out of the box.
Fast: Built on top of dataclasses and Cython-optimized logic.

from pydantic import BaseModel 
 
class User(BaseModel): 
    id: int 
    name: str 
    is_active: bool = True 
 
user = User(id=1, name="Aashish")

I use Pydantic in FastAPI, Flask, data parsing scripts, and even CLI tools — it’s that flexible.

2. Rich — For Beautiful Terminal Output

Because your CLI deserves better than boring print() statements.

If you’ve ever built a command-line tool or just wanted better visibility in your logs, rich is a game-changer. It makes your terminal output pop — with colors, tables, progress bars, and even Markdown.

Why I keep using it:

No setup required — just import rich and go.
Great for debugging — especially when visualizing complex objects.
Enhances user experience in CLI apps without any GUI.

from rich import print 
from rich.progress import track 
 
print("[bold green]Deploying app...[/bold green]") 
 
for step in track(range(10), description="Processing..."): 
    pass

I use Rich in all my dev tools and internal scripts — it makes me feel like the user of a premium product.

3. Typer — The Fastest Way to Build CLI Apps

Like Click, but smarter and beautifully modern.

Typer is built by the same creator as FastAPI, and it brings the same philosophy: type hints + minimal boilerplate = clean, powerful code. With Typer, I can build full-featured CLI apps in minutes.

Why it’s irreplaceable:

Automatic help and command generation
Pythonic syntax — no weird decorators or verbose options
Async-ready if needed

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

I use Typer to build everything from internal tools to client-facing utilities — and users love the experience.

4. Tenacity — For Retrying Like a Pro

When something might fail, Tenacity gives you elegant retries — with exponential backoff and zero stress.

APIs time out. Network calls fail. File systems flake. Instead of writing clunky retry loops, Tenacity gives you a clean, configurable decorator.

Why I rely on it:

Simple to use but extremely powerful.
Configurable delays, retries, backoff strategies — all in one place.
Great logging so you know what failed and why.

from tenacity import retry, wait_exponential 
 
@retry(wait=wait_exponential(multiplier=1, min=4, max=10)) 
def get_data(): 
    # Try until it works 
    return unreliable_api_call()

I use Tenacity in all my backend services — it’s like insurance for flaky systems.

5. Loguru — For Logging Without the Pain

Built-in logging module? Powerful, yes. Friendly? Not so much.

Loguru is what logging in Python should feel like — zero config, meaningful output, and just enough magic.

Why I use it religiously:

Drop-in replacement for print() and logging.
Beautiful output with time, module, and level out of the box.
Rotating logs, filtering, file sinks — all simplified.

from loguru import logger 
 
logger.info("Service started") 
logger.error("Something went wrong")

Whether it’s a production app or a quick script, Loguru helps me see what my code is doing.

Final Thoughts

These five libraries have one thing in common: they respect the developer’s time. They don’t just work — they make your life easier, your code cleaner, and your output more professional.

You don’t need to memorize a massive API, write boilerplate, or fight with documentation. Instead, you just build.