Intro: How I Accidentally Broke My Own Python Ego

Three years ago, at 2:17 AM, I was refactoring a script I had written six months earlier. You know the kind. It worked. It shipped. It even impressed a client.

But reading it felt like reading someone else's bad handwriting.

Too many if statements. Manual file handling everywhere. A cron job duct-taped to a bash script duct-taped to hope.

That night, I didn't just refactor the code. I rewired how I think about Python.

I stopped asking "How do I write this?" And started asking "How do I never touch this again?"

Below are 14 Python hacks that changed my style permanently. Not flashy. Not trendy. Just brutally effective — especially if you care about automation.

I'm Ahmad. I've been writing Python professionally for over 4 years. And yes — some of these will make you uncomfortable. Good.

1. Pathlib Over Strings (Always)

If you're still manipulating paths as strings, you're working harder than needed.

from pathlib import Path
base = Path.home() / "projects" / "reports"
for file in base.glob("*.csv"):
    print(file.name)

Why this matters:

• Cross-platform by default
• Safer file operations
• Reads like English

Pro tip: Strings lie. Objects don't.

2. Automate Cleanup With contextlib

Manual cleanup is where bugs hide.

from contextlib import contextmanager
@contextmanager
def managed_file(path):
    f = open(path, "w")
    try:
        yield f
    finally:
        f.close()

Now your automation scripts don't leak resources when things explode. And they will explode.

3. dataclasses Replace Half Your Boilerplate

I used to write 20-line classes. Now I don't.

from dataclasses import dataclass
@dataclass
class Job:
    name: str
    retries: int
    timeout: int

Cleaner configs = fewer bugs = calmer weekends.

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4. itertools for Invisible Performance Wins

Loops inside loops are silent killers.

from itertools import islice
def chunked(iterable, size):
    it = iter(iterable)
    while True:
        chunk = list(islice(it, size))
        if not chunk:
            return
        yield chunk

Use this in:

• Batch APIs
• File processing
• Data pipelines

You'll thank yourself later.

5. functools.lru_cache Is Free Speed

If you compute the same thing twice, you've already lost.

from functools import lru_cache
@lru_cache(maxsize=128)
def expensive_lookup(x):
    return x ** 3

Automation loves caching. Your CPU does too.

6. subprocess Instead of Bash Scripts

If it's part of your system, it belongs in Python.

import subprocess
result = subprocess.run(
    ["ls", "-la"],
    capture_output=True,
    text=True
)
print(result.stdout)

One language. One error-handling model. Less chaos.

7. concurrent.futures for Parallel Automation

Stop waiting on I/O sequentially.

from concurrent.futures import ThreadPoolExecutor
def process(item):
    return item * 2
with ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(process, range(10)))

This alone shaved minutes off one of my pipelines.

8. tempfile for Safer Automation

Temporary files shouldn't be your responsibility.

import tempfile
with tempfile.TemporaryDirectory() as tmp:
    print("Working in", tmp)

No cleanup scripts. No surprises.

9. argparse Turns Scripts Into Tools

If your script has arguments, it deserves respect.

import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--env", required=True)
args = parser.parse_args()
print(f"Running in {args.env}")

This is how one-off scripts become internal products.

10. sqlite3 for Lightweight Automation State

Not everything needs Redis or Postgres.

import sqlite3
conn = sqlite3.connect("state.db")
cur = conn.cursor()
cur.execute("CREATE TABLE IF NOT EXISTS jobs (id INTEGER)")
conn.commit()

Perfect for:

• Cron jobs
• Local automation
• Offline tools

11. hashlib for Idempotent Workflows

If automation isn't idempotent, it's dangerous.

import hashlib
def fingerprint(data):
    return hashlib.sha256(data.encode()).hexdigest()

Hashes prevent duplicate work and accidental reruns.

12. logging Over print (No Exceptions)

Print statements lie when things go wrong.

import logging
logging.basicConfig(level=logging.INFO)
logging.info("Job started")

Logs tell the story of failure. And failures are inevitable.

13. csv and json Are Automation Glue

Stop reinventing data formats.

import csv
with open("data.csv") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row)

Simple formats scale surprisingly far.

14. Write Code You Can Delete

This is the real hack.

If your automation:

• Is modular
• Is readable
• Has clear inputs/outputs

You can throw it away without fear.

That's mastery.

Final Thought

The best Python code doesn't look clever. It looks inevitable.

I didn't learn these hacks from tutorials. I learned them from broken pipelines, missed deadlines, and code I hated maintaining.

If this article saved you even one refactor at 2 AM — it did its job.

"Make it work. Make it right. Make it disappear."

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