10 Insanely Useful Python Libraries You Haven’t Heard Of (Yet!)

Discover hidden Python gems!

10 Insanely Useful Python Libraries You Haven’t Heard Of (Yet!)

Python’s vast ecosystem is filled with well-known libraries like NumPy, Pandas, and TensorFlow. But beneath the surface, there are hidden gems — powerful yet under-the-radar libraries that can supercharge your workflow.

In this article, I’ll introduce you to 10 insanely useful Python libraries that you probably haven’t heard of (yet!). These libraries will help you write cleaner, more efficient, and more powerful code.

Python Libraries

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1. Rich — Beautiful Terminal Output

Have you ever struggled with making your terminal output more readable? Rich is a game-changer. It allows you to print beautifully formatted text, tables, progress bars, and even syntax-highlighted code.

GitHub - Textualize/rich: Rich is a Python library for rich text and beautiful formatting in the…

Rich is a Python library for rich text and beautiful formatting in the terminal. - Textualize/rich

Why You’ll Love It:

Stunning terminal formatting

Built-in Markdown and syntax highlighting

Easy to use

Installation:

pip install rich

Example Usage:

from rich import print 
 
print("Hello, [bold magenta]World[/bold magenta]!", ":vampire:", locals())

2. Typer — The Easiest Way to Build CLI Apps

If you’ve built command-line applications with argparse, you know it can be tedious. Typer makes it effortless, utilize Python’s type hints for auto-documentation and validation.

GitHub - fastapi/typer: Typer, build great CLIs. Easy to code. Based on Python type hints.

Typer, build great CLIs. Easy to code. Based on Python type hints. - fastapi/typer

Why You’ll Love It:

Intuitive syntax

Auto-generates help documentation

Works seamlessly with FastAPI

Installation:

pip install typer

Example Usage:

• Create a file main.py with:

def main(name: str): 
    print(f"Hello {name}")

• Run your application with the typer command:

// Run your application 
$ typer main.py run 
 
// You get a nice error, you are missing NAME 
Usage: typer [PATH_OR_MODULE] run [OPTIONS] NAME 
Try 'typer [PATH_OR_MODULE] run --help' for help. 
╭─ Error ───────────────────────────────────────────╮ 
│ Missing argument 'NAME'.                          │ 
╰───────────────────────────────────────────────────╯ 
 
 
// You get a --help for free 
$ typer main.py run --help 
 
Usage: typer [PATH_OR_MODULE] run [OPTIONS] NAME 
 
Run the provided Typer app. 
 
╭─ Arguments ───────────────────────────────────────╮ 
│ *    name      TEXT  [default: None] [required]   | 
╰───────────────────────────────────────────────────╯ 
╭─ Options ─────────────────────────────────────────╮ 
│ --help          Show this message and exit.       │ 
╰───────────────────────────────────────────────────╯ 
 
// Now pass the NAME argument 
$ typer main.py run Camila 
 
Hello Camila 
 
// It works! 🎉

3. Loguru — The Ultimate Logging Library

Python’s built-in logging module can be frustrating. Loguru simplifies logging with a cleaner API, structured logging, and automatic log rotation.

GitHub - Delgan/loguru: Python logging made (stupidly) simple

Python logging made (stupidly) simple. Contribute to Delgan/loguru development by creating an account on GitHub.

Why You’ll Love It:

No boilerplate setup

Pretty console output

Automatic file rotation

credit : loguru github

Installation:

pip install loguru

Example Usage:

from loguru import logger 
 
logger.debug("That's it, beautiful and simple logging!") 
 
# output 
# 2025-03-27 09:53:56.431 | DEBUG    | __main__:<module>:1 - That's it, beautiful and simple logging!

4. Pydantic — Data Validation Made Easy

If you work with APIs or data models, Pydantic is a must-have. It simplifies data validation using Python’s type hints, ensuring your data is always clean and structured.

GitHub - pydantic/pydantic: Data validation using Python type hints

Data validation using Python type hints. Contribute to pydantic/pydantic development by creating an account on GitHub.

Why You’ll Love It:

Type-safe data validation

Automatic data parsing

Integrates with FastAPI

Installation:

pip install pydantic

Example Usage:

from datetime import datetime 
from typing import Optional 
from pydantic import BaseModel 
 
class User(BaseModel): 
    id: int 
    name: str = 'John Doe' 
    signup_ts: Optional[datetime] = None 
    friends: list[int] = [] 
 
external_data = {'id': '123', 'signup_ts': '2017-06-01 12:22', 'friends': [1, '2', b'3']} 
user = User(**external_data) 
print(user) 
#> User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3] 
print(user.id) 
#> 123

5. Poetry — Modern Python Dependency Management

Tired of pip and requirements.txt? Poetry makes dependency management and packaging effortless, ensuring reproducible builds and streamlined workflows.

GitHub - python-poetry/poetry: Python packaging and dependency management made easy

Python packaging and dependency management made easy - python-poetry/poetry

Why You’ll Love It:

Simplifies package management

Handles virtual environments automatically

Faster and more reliable

credit : poetry Github

Installation:

pip install poetry

Example Usage:

poetry new my_project 
cd my_project 
poetry add requests

Checkout this article to setup you Django project with poetry:

Ditch requirements.txt: Elevate Your Django Projects with Poetry! 🚀

If you’re still managing dependencies with requirements.txt, it's time for an upgrade. While pip and virtualenv have…

6. httpx — Next-Gen HTTP Client

While requests is popular, httpx is its more powerful successor, supporting async requests and better performance.

GitHub - encode/httpx: A next generation HTTP client for Python. 🦋

A next generation HTTP client for Python. 🦋. Contribute to encode/httpx development by creating an account on GitHub.

Why You’ll Love It:

Async support

HTTP/2 support

Drop-in replacement for requests

Installation:

pip install httpx

Example Usage:

>>> import httpx 
>>> r = httpx.get('https://www.example.org/') 
>>> r 
<Response [200 OK]> 
>>> r.status_code 
200 
>>> r.headers['content-type'] 
'text/html; charset=UTF-8' 
>>> r.text 
'<!doctype html>\n<html>\n<head>\n<title>Example Domain</title>...'

Checkout this article to learn more about HTTPX:

Python HTTPX: The Ultimate Library for Fast & Powerful HTTP Requests! 🚀

When you working with APIs, web scraping, or making HTTP requests in Python, many developers default to the requests…

7. PyInstrument — Supercharge Performance Profiling

Need to find slow parts of your code? PyInstrument provides a beautiful, interactive report of where your code is spending time.

GitHub - joerick/pyinstrument: 🚴 Call stack profiler for Python. Shows you why your code is slow!

🚴 Call stack profiler for Python. Shows you why your code is slow! - joerick/pyinstrument

Why You’ll Love It:

Fast and lightweight

Intuitive flame graphs

Great for optimizing code

Installation:

pip install pyinstrument

Example Usage:

from pyinstrument import Profiler 
 
profiler = Profiler() 
profiler.start() 
 
# Your code here 
 
profiler.stop() 
print(profiler.output_text())

8. icecream — Debugging Made Sweet

Printing variables for debugging? icecream makes it cleaner and more informative with minimal effort.

GitHub - gruns/icecream: 🍦 Never use print() to debug again.

🍦 Never use print() to debug again. Contribute to gruns/icecream development by creating an account on GitHub.

Why You’ll Love It:

One-line debugging

Shows variable names automatically

Works inside Jupyter Notebooks

Installation:

pip install icecream

Example Usage:

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

Checkout this article to learn more about icecream

Python Debugging Like a Pro: Ditch print() and Use ic() Instead! 🚀

Stop spamming print(). Learn how ic() from IceCream module makes debugging faster and more insightful!

9. Faker — Generate Fake Data Easily

Need test data? Faker generates realistic fake names, emails, addresses, and more.

GitHub - joke2k/faker: Faker is a Python package that generates fake data for you.

Faker is a Python package that generates fake data for you. - joke2k/faker

Why You’ll Love It:

Supports multiple languages

Easily customizable

Great for testing

Installation:

pip install faker

Example Usage:

from faker import Faker 
fake = Faker() 
 
fake.name() 
# 'Lucy Cechtelar' 
 
fake.address() 
# '426 Jordy Lodge 
#  Cartwrightshire, SC 88120-6700' 
 
fake.text() 
# 'Sint velit eveniet. Rerum atque repellat voluptatem quia rerum. Numquam excepturi 
#  beatae sint laudantium consequatur. Magni occaecati itaque sint et sit tempore. Nesciunt 
#  amet quidem. Iusto deleniti cum autem ad quia aperiam. 
#  A consectetur quos aliquam. In iste aliquid et aut similique suscipit. Consequatur qui 
#  quaerat iste minus hic expedita. Consequuntur error magni et laboriosam. Aut aspernatur 
#  voluptatem sit aliquam. Dolores voluptatum est. 
#  Aut molestias et maxime. Fugit autem facilis quos vero. Eius quibusdam possimus est. 
#  Ea quaerat et quisquam. Deleniti sunt quam. Adipisci consequatur id in occaecati. 
#  Et sint et. Ut ducimus quod nemo ab voluptatum.'

10. Pendulum — Better Date and Time Handling

Python’s built-in datetime module can be tricky. Pendulum makes date/time handling easier and more intuitive.

GitHub - python-pendulum/pendulum: Python datetimes made easy

Python datetimes made easy. Contribute to python-pendulum/pendulum development by creating an account on GitHub.

Why You’ll Love It:

Human-friendly syntax

Time zone support

Works as a drop-in replacement for datetime

Installation:

pip install pendulum

Example Usage:

>>> import pendulum 
 
>>> now_in_paris = pendulum.now('Europe/Paris') 
>>> now_in_paris 
'2016-07-04T00:49:58.502116+02:00' 
 
# Seamless timezone switching 
>>> now_in_paris.in_timezone('UTC') 
'2016-07-03T22:49:58.502116+00:00' 
 
>>> tomorrow = pendulum.now().add(days=1) 
>>> last_week = pendulum.now().subtract(weeks=1) 
 
>>> past = pendulum.now().subtract(minutes=2) 
>>> past.diff_for_humans() 
'2 minutes ago' 
 
>>> delta = past - last_week 
>>> delta.hours 
23 
>>> delta.in_words(locale='en') 
'6 days 23 hours 58 minutes' 
 
# Proper handling of datetime normalization 
>>> pendulum.datetime(2013, 3, 31, 2, 30, tz='Europe/Paris') 
'2013-03-31T03:30:00+02:00' # 2:30 does not exist (Skipped time) 
 
# Proper handling of dst transitions 
>>> just_before = pendulum.datetime(2013, 3, 31, 1, 59, 59, 999999, tz='Europe/Paris') 
'2013-03-31T01:59:59.999999+01:00' 
>>> just_before.add(microseconds=1) 
'2013-03-31T03:00:00+02:00'

Wrapping Up

These 10 lesser-known Python libraries can boost your productivity, simplify your code, and make your development process smoother.

Which one will you try first? Let me know in the comments!

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