Are you the type of developer who can’t resist building everything from scratch? Need a feature? Existing libraries? Never! You’d rather code it yourself! JSON to CSV conversion? Command-line dashboard? “Give me 30 minutes and Vim – I’ll code it barehanded!”
But true efficiency isn’t about how much code you can write; it’s about knowing when NOT to write code. Take Rich, one of my most frequently used libraries recently – it solves terminal rendering problems that used to keep you debugging all night with just 3 lines of code:
- Logging/Debug Output automatically becomes syntax-highlighted, collapsible panels (no more blinding
print(json.dumps())) - Tabular Data transforms from crooked
┐└─symbol hell into automatically aligned, paginated, print-quality displays - Progress Bars come with speed estimation, dynamic width adjustment, and even support for parallel tasks – while the version I spent half a day writing couldn’t even handle basic interrupt recovery
Rich and the other 7 libraries (see below) teach us one thing: using others’ wheels isn’t laziness – it’s investing time where you truly create value. If you’re tired of repeatedly solving problems that have already been perfectly solved, it’s time to upgrade your toolbox.
1. Rich — CLI ≠ Ugly
Remember when command-line output looked like Windows 95 styling? Rich fixes this – with style. One import makes terminal output look like it was designed in Figma. Tables, Markdown rendering, syntax-highlighted tracebacks, stress-free progress bars… it’s addictive.
python
from rich.console import Console
console = Console()
console.print("Hello, [bold magenta]world[/bold magenta]!")
Use Case: Logging that won’t make your eyes cross.
🧠 Pro Tip: rich.traceback.install() replaces ugly Python tracebacks with gorgeous, context-rich ones – zero extra work.
2. Typer — Fastest Way to Build Quality CLIs
No more argparse… I recommend Typer. Built on Click, it makes creating command-line interfaces (CLIs) incredibly simple using function signatures and type hints. Add docstrings, and you’ve basically written your help command.
python
import typer
def main(name: str):
typer.echo(f"Hello {name}")
if __name__ == "__main__":
typer.run(main)
Use Case: Creating polished CLI tools in 5 minutes.
Typer hints = better autocomplete and documentation = less --help time.
3. Pendulum — datetime Will Betray You
Ever tried subtracting two datetimes in Python and got… weird results? Yeah, that feeling. Pendulum is a direct datetime replacement that handles timezones, formatting, durations, and arithmetic like an adult.
python
import pendulum
dt = pendulum.now("UTC").add(days=3)
print(dt.to_datetime_string())
Use Case: Scheduling scripts, timezone manipulation, or dealing with daylight saving time.
Pendulum can parse human-readable time strings like “next Thursday at 5pm” in multiple locales.
4. Pydantic — Strong Typing Without the Fuss
I used to validate JSON manually. (Don’t judge.) Then I found Pydantic. Define a class with type hints, and you’re done – validation, documentation, parsing.
python
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str
is_active: bool = True
Use Case: Validating API responses, configurations, and input data.
Note: There’s a reason it became FastAPI’s backbone. But even outside the web world? It’s still a game-changer.
5. Faker — Real Data is Messy and Possibly Illegal
Whether you’re mocking APIs, seeding development databases, or just trying to generate convincing fake user profiles (hopefully not for nefarious reasons), Faker has you covered.
python
from faker import Faker fake = Faker() print(fake.name(), fake.email(), fake.address())
Use Case: Creating personalized dummy data.
😂 Try generating pirate names. Trust me.
6. Tqdm — Progress Bars for the Impatient
You’ve probably seen this before. But if you’re not using it regularly, we need to talk. Tqdm wraps any iterable and displays smart, responsive progress bars. Perfect for loops, downloads, or monitoring large jobs to avoid silent hangs.
python
from tqdm import tqdm
for i in tqdm(range(10000)):
pass
Use Case: Anything taking longer than 0.5 seconds.
Tip: Helps you catch infinite loops early.
7. Requests-HTML — Web Scraping Made Easy
I love requests. I can tolerate BeautifulSoup. But requests-html? It combines requests’ simplicity with headless browser parsing power. It even executes JavaScript! Meaning you can finally scrape modern websites with peace of mind.
python
from requests_html import HTMLSession
session = HTMLSession()
r = session.get('https://example.com')
r.html.render()
print(r.html.find('h1')[0].text)
Use Case: Scraping websites that hate traditional parsers.
It uses Pyppeteer under the hood. JS rendering with Python – without Selenium’s headaches.
8. Loguru — Logging Made Easy
Python’s default logging is… verbose and unintuitive. Loguru makes logging feel like writing in a diary – except with log levels, file rotation, and colored output.
python
from loguru import logger
logger.add("debug.log", rotation="1 MB")
logger.info("Processing started...")
Use Case: Debugging, production logging, and better sleep.
One line replaces primitive print() with a fully-configured logging system.