I wasn’t trying to become “better at Python”
Four years into writing Python professionally, I hit an uncomfortable realization.
My code worked.
My scripts shipped.
But most of my day was spent gluing boring things together.
Retry logic.
Task orchestration.
Fake data.
Time handling.
Cron jobs that failed silently.
None of that made me a better engineer. It just made me tired.
So I stopped asking “What new tech should I learn?”
And started asking a better question:
“What part of this workflow feels stupidly manual?”
That mindset led me to these libraries.
Not trendy.
Not hype-driven.
Just tools that remove friction and let automation breathe.
If you already know Python but want it to feel cleaner, this is for you.
- Invoke – Stop Treating Automation Like an Afterthought
If your automation lives in Bash scripts, you’re already paying interest on technical debt.
Invoke let me pull deployment, testing, and housekeeping back into Python.
from invoke import task
@task
def build(c):
c.run("pytest")
c.run("docker build -t app .")
Why it matters:
Python logic > shell hacks
Discoverable tasks
Automation becomes code, not glue
Once I made this switch, I stopped dreading “ops” work.
- Faker – Because Bad Test Data Lies to You
Hardcoded test data creates fake confidence.
Faker gave my tests the chaos they deserved.
from faker import Faker
fake = Faker()
email = fake.email()
What changed:
- More realistic failures
- Better edge-case coverage
- Less “works on my machine” syndrome
If your test data never surprises you, it’s not doing its job.
- Tenacity – Retry Logic Without Shame
I used to write retry loops manually.
You probably did too.
Tenacity made retries declarative and obvious.
from tenacity import retry, stop_after_attempt
@retry(stop=stop_after_attempt(3))
def fetch():
...
Why this is automation gold:
- Centralized retry behavior
- Backoff strategies
- Zero boilerplate
This is how production code should read.
- Arrow – Datetime Without Mental Gymnastics
Python’s datetime API is powerful — and unnecessarily painful.
Arrow fixed that for me.
import arrow
expires = arrow.utcnow().shift(days=3)
Why this matters:
- Fewer timezone bugs
- Readable intent
- Less cognitive overhead
Time bugs don’t crash loudly. They rot systems quietly.
- Prefect – When Cron Jobs Start Lying to You
Cron tells you when something ran.
Prefect tells you what actually happened.
from prefect import flow, task
@task
def extract():
...
@flow
def pipeline():
extract()
pipeline()
What I gained:
Task retries
Failure visibility
Structured automation flows
Cron schedules jobs.
Prefect manages systems.
Big difference.
- Watchfiles – Event-Driven Automation Wins
Scheduled automation is fine.
Reactive automation is better.
from watchfiles import watch
for changes in watch("incoming"):
process(changes)
Real uses:
- Auto-ingesting files
- Live rebuilds
- Trigger-based pipelines
Once you automate reactions instead of time, you don’t go back.
- SQLModel – Databases Without the Ceremony
Most ORMs feel like paperwork.
SQLModel felt like relief.
from sqlmodel import SQLModel, Field
class User(SQLModel, table=True):
id: int | None = Field(default=None, primary_key=True)
email: str
Why it stuck:
- One model, many purposes
- Strong typing
- Minimal boilerplate
- Automation thrives on clarity.
- Ray – Parallelism That Doesn’t Fight You
Parallel Python used to feel hostile.
Ray changed that.
import ray
ray.init()
@ray.remote
def work(x):
return x * 2
What unlocked for me:
- Faster batch jobs
- Parallel experiments
- Scalable automation
Speed changes how ambitious you’re willing to be.
- Beanie – MongoDB That Feels Native
I avoided MongoDB automation for years.
Beanie fixed my resistance.
from beanie import Document
class Log(Document):
message: str
Why it works:
- Async-first
- Clean models
- Low friction persistence
When storage stops being painful, systems scale naturally.
The Pattern Most People Miss
None of these libraries are flashy.
They all do one thing exceptionally well:
They remove friction.
“Good automation isn’t about doing more. It’s about thinking less about the boring parts.”
Every tool here bought me back time — and let me spend it on problems that actually mattered.
Final Thought
If Python feels slow, messy, or fragile, it’s rarely your skill level.
It’s your tooling.
Automate ruthlessly.
Steal good ideas shamelessly.
And stop writing code just because “that’s how it’s always been done.”
— Ahmad
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