Because "import time_saver" should be a real thing.

If you've coded in Python for some time, then you've no doubt memorized the classics — requests, pandas, flask, perhaps even typer. But let's be real — Python's ecosystem is enormous. Thousands of gems go under the radar, humming along as they solve actual-world issues that would save you hours (or days) per week.

In this article, we're omitting the bare necessities and jumping into 10 underappreciated Python libraries that speed up development, simplify data cleaning, and make production-ready code a breeze — whether you're automating workflows, cleaning data, or smashing out production-ready code.

Let's cut to the chase.

1. Rich — Make Your CLI Output Beautiful

Use case: Pretty, nicely-printed terminal output with colors, tables, progress indicators, and the like.

Even if your command-line scripts still appear as boring walls of text, rich time is waiting on you. It changes boring console logs into beautiful, human-readable interfaces — with color, Markdown rendering, and even live progress updates.

from rich import print
from rich.console import Console
from rich.table import Table

console = Console()
table = Table(title="Daily Tasks")

table.add_column("Task", style="cyan")
table.add_column("Status", style="green")

table.add_row("Email cleanup", "✅ Done")
table.add_row("Deploy update", "⏳ In progress")

console.print(table)

Why it saves time: You can avoid the scribble of-print-formatting logic and have your scripts easily readable instantly for end users or team members.

2. Pendulum — Datetime Made Human

Use case: Simplify date and time manipulation.

It can be like a toxic relationship with datetime in Python — erratic, inconsistent, and a surprise waiting to happen. Meet Pendulum, the drop-in replacement that will make dealing with timezones, periods, and formats amazingly easy.

import pendulum

dt = pendulum.now('Europe/Berlin')
print(dt.add(days=3).format('dddd, MMMM D, YYYY'))

Why it saves time: No more timedelta troubles or timezone issues — Pendulum takes care of all that beautifully and naturally.

3. Loguru — Logging Without the Pain

Use case: Beautiful, zero-config logging.

In-built logging module of Python is expressive but… verbose. Loguru removes the boilerplate and provides you with one-line beautiful logging.

from loguru import logger

logger.add("debug.log", rotation="1 MB")
logger.info("Starting the server...")

Why it saves time: No messing around with handlers and formatters — Loguru works out the box. And its logs are color-coded, structured, and filter-friendly.

4. Pydantic — Data Validation That Feels Magical

Use case: Validate and parse data with ease.

Whether you're working with APIs or user input, Pydantic enforces data integrity without extra code. It uses Python type hints to validate fields automatically.

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int
u = User(name="Alice", age="25")  # auto-converted to int

print(u.dict())

Why it saves time: No more checking the type by hand. Pydantic transforms your data models into rugged schemas instantly.

5. Typer — Build CLI Tools Like a Pro

Use case: Quickly create command-line applications.

Developed by the author of FastAPI, Typer is the new cool way to develop CLI tools — beautiful, type-safe, and cool.

import typer

def greet(name: str, excited: bool = False):
    greeting = f"Hello {name}{'!' if excited else '.'}"
    typer.echo(greeting)

if __name__ == "__main__":
    typer.run(greet)

Why it saves time: It transforms tiny scripts into fully-fledged CLI tools with arg parlings, help messages, and type hints — Effortlessly.

6. Tqdm — Instant Progress Bars for Loops

Use case: Track progress in long-running loops.

If your scripts execute slow tasks, tqdm provides you one-line solution that can be used to visualize progress.

from tqdm import tqdm
import time

for _ in tqdm(range(100)):
    time.sleep(0.01)

Why it saves time: You know instantly where bottlenecks occur — guess no more if your script is "still running."

7. Textual — Build Modern TUIs (Text-Based UIs)

Use case: Create rich terminal apps with layout, widgets, and interactivity.

Textual (from the creators of Rich) enables you to create lovely, interactive apps directly in the terminal — think menus, dashboards, even chat clients.

pip install textual

Then run:

textual new my-dashboard

Why it saves time: For desktop tools or lightweight dashboards, you can ignore web frontends altogether and create beautiful terminal apps instead.

8. Polars — The Faster Alternative to Pandas

Use case: Lightning-fast DataFrame manipulation.

If pandas is too slow for bigger data sets, then Polars is your turbo Boost — written in Rust, optimized for speed.

import polars as pl

df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
print(df.select(pl.col("a") + pl.col("b")))

Why it saves time: 10 times faster operations, parallel operations, and a clear syntax that is akin to pandas users.

9. HTTPX — The Next-Gen HTTP Client

Use case: Async requests, HTTP/2, and modern networking.

HTTPX packages all you ever want requests to have — async support, pooling, and HTTP/2.

import httpx
import asyncio

async def fetch_data():
    async with httpx.AsyncClient() as client:
        r = await client.get("https://api.github.com")
        print(r.json())
asyncio.run(fetch_data())

Why it saves time: Great when making fast, simultaneous API calls — perfect in web scrapping or microservices.

10. DeepDiff — Spot the Difference Between Python Objects

Use case: Compare complex data structures easily.

During debugging, you frequently must know exactly what was the difference between two JSONs or between two dictionaries. DeepDiff accomplishes that on one line.

from deepdiff import DeepDiff

a = {"name": "Alice", "age": 25}
b = {"name": "Alice", "age": 26}
print(DeepDiff(a, b))

Why it saves time: No more hand-diffing nested dictionary changes — the library informs you exactly what is changed and where.

Bonus Mentions

Because 10 wasn't enough:

Sh — Run shell commands as if they were Python functions.

Prefect — Automate and orchestrate data pipelines effortlessly.

Alive-Progress — Progress bars that animate.

Faker — Generate fake data for testing instantly.

Wrapping Up

Python's strength has always been its ecosystem — thousands of clever tools built by developers solving real problems. The 10 libraries above are proof that you don't need to reinvent the wheel to write elegant, efficient code.

If you put even three of these into regular use, you'll be saving yourself thousands of hours per month — Whilst also making your code cleaner, faster, and more maintainable, naturally.

So next time you access your terminal, give one a try. You never know, this could be your new go-to import.

Better programmers don't develop more code — they use better tools.