9 Python Libraries That Make Automation Ridiculously Easy

I've been writing Python long enough to develop a mild allergy to doing the same thing twice. If a task repeats, it's getting automated. If it might repeat, it's getting automated. If it happens once a month but annoys me for 30 seconds… you guessed it — automated.

The funny part? Most people try to do this with the same 5 libraries everyone talks about. They work. They're fine. They're also not where the real leverage is.

Below are 9 less-talked-about Python libraries that quietly turn "ugh, I'll do it later" into "done before my coffee cools down." I've used every pattern here in real projects. Some of these saved me hours. One of them saved me from a production incident at 2 a.m. (Ask me how I know.)

No fluff. Just sharp tools.

1. pexpect — Automate Interactive Programs Like a Wizard

Ever tried to automate something that keeps asking you questions in the terminal? SSH, FTP, installers, weird legacy CLIs that refuse to be scripted properly?

That's pexpect's home turf.

import pexpect

child = pexpect.spawn('ssh user@server.com')
child.expect('password:')
child.sendline('my_secret_password')
child.expect('$')
child.sendline('uptime')
child.interact()

Why it's gold: You can script conversations with programs, not just commands. I once used this to automate a deployment process that had three interactive prompts and zero documentation. It felt like cheating.

Pro tip: Combine this with cron and suddenly "manual server ritual" becomes "boring scheduled job."

2. watchfiles — React to File Changes in Real Time (The Fast Way)

Most people reach for watchdog. It works. watchfiles is faster, simpler, and built on Rust under the hood.

from watchfiles import watch

for changes in watch('./incoming'):
    print("Something changed:", changes)
    # Trigger your automation here

Use cases:

• Auto-process files dropped into a folder
• Rebuild assets when something changes
• Kick off scripts when a report appears

I use this to auto-validate data dumps the moment they land. No polling. No waiting. Just reaction.

3. plumbum — Shell Scripting, But Actually Pleasant

If you ever wrote Python that mostly calls shell commands… you've suffered enough.

plumbum makes shell commands feel like Python objects.

from plumbum import local

ls = local["ls"]
grep = local["grep"]

print((ls["-a"] | grep[".py"])())

Why it's different: You get pipes, redirection, and composition — without turning your code into a string-concatenation horror movie.

I replaced a 200-line bash script with ~40 lines of readable Python using this. My future self sent me a thank-you note.

4. python-fire — Turn Any Script into a CLI in 10 Seconds

You know that script you keep editing because you "just need one more flag"? Stop. Use fire.

import fire

def resize(path, width=800, height=600):
    print(f"Resizing {path} to {width}x{height}")

if __name__ == "__main__":
    fire.Fire(resize)

Now you get:

python script.py ./images --width=1024 --height=768

Why it's dangerous: You'll start turning everything into a CLI. And honestly? That's a good problem to have.

5. tenacity — Retry Logic Without Losing Your Sanity

Network calls fail. APIs time out. Databases blink. The difference between a fragile script and a production-ready one is retries.

from tenacity import retry, stop_after_attempt, wait_fixed

@retry(stop=stop_after_attempt(5), wait=wait_fixed(2))
def fetch_data():
    print("Trying...")
    raise Exception("Temporary failure")

Why it matters: Real automation lives in the real world. The real world is flaky.

I've seen this single library eliminate entire classes of "it failed once and never recovered" bugs.

6. streamz — Build Data Pipelines That React to Events

Think of streamz as "automation for flows, not scripts."

from streamz import Stream

source = Stream()

source.map(lambda x: x * 2).sink(print)

source.emit(10)
source.emit(20)

Now imagine:

• Files arriving
• Messages coming in
• Sensors updating
• Logs streaming

You don't run the pipeline. You feed it, and it reacts.

Where it shines: Real-time automation. Monitoring. Event-driven systems. The stuff that usually ends up as a messy while-true loop.

7. diskcache — Make Your Scripts Remember Things (Properly)

Automation that forgets everything is slow automation.

diskcache gives you a fast, persistent cache with almost zero effort.

from diskcache import Cache

cache = Cache('./mycache')

@cache.memoize()
def expensive_call(x):
    print("Computing...")
    return x * x

print(expensive_call(10))
print(expensive_call(10))  # This one is instant

Why this is underrated: Caching turns "runs in 10 minutes" into "runs in 10 seconds." At scale, this is the difference between "nice script" and "core infrastructure."

8. pywinauto — Automate Actual Windows Apps

Not everything has an API. Some things only have… buttons.

pywinauto lets you script Windows GUIs like a patient robot.

from pywinauto import Application

app = Application().start("notepad.exe")
app.Notepad.menu_select("Help->About Notepad")
app.Notepad.AboutNotepad.OK.click()

Yes, really. I've used this to automate a legacy accounting tool that predates REST, JSON, and probably electricity.

If it has a window, you can automate it.

9. boltons — The Missing Toolbox for Serious Scripts

boltons is a collection of ridiculously practical utilities: caching, iterutils, dictutils, timeutils, and more.

from boltons.iterutils import chunked

data = list(range(10))
print(list(chunked(data, 3)))

Why it sneaks into everything I write: It removes tiny, boring problems that quietly waste hours over a year. Automation isn't just big wins — it's death by a thousand paper cuts avoided.

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