The Most Underrated Python Libraries (And Why They Deserve More Love)

Everyone talks about Pandas and NumPy — but these quiet heroes might just save your next project.

The Most Underrated Python Libraries (And Why They Deserve More Love)

Python is everywhere — from web development and data science to automation and AI. With over 400,000 libraries available on PyPI, it’s no surprise that a handful dominate the spotlight: NumPy, Pandas, Flask, TensorFlow… the usual suspects.

But what about the hidden gems? The libraries that don’t trend on GitHub or star in conference keynotes — yet quietly solve real problems with elegance and power?

This article dives into some of the most underrated Python libraries that deserve far more attention than they get. Whether you’re a seasoned developer or a curious beginner, these tools might just become your new favorites.

1. Rich — Make Your Terminal Beautiful

Why it’s underrated: Despite being a game-changer for terminal applications, Rich still flies under the radar for many developers.

What it does: Rich makes it incredibly easy to create beautiful and interactive terminal outputs — think colored text, tables, progress bars, markdown, and even syntax-highlighted code blocks — all without breaking a sweat.

from rich.console import Console 
from rich.table import Table 
 
console = Console() 
table = Table(title="Underrated Libraries") 
 
table.add_column("Library", style="cyan") 
table.add_column("Why It Rocks", style="magenta") 
 
table.add_row("Rich", "Beautiful terminal UI in seconds") 
console.print(table)

Why it deserves more love:

Logging, debugging, or even just making your CLI scripts more readable becomes fun. For dev tools, internal dashboards, or one-off scripts — Rich is a gem.

2. Pillow — The Silent Workhorse of Image Processing

Why it’s underrated: Pillow doesn’t get the same excitement as OpenCV, but it’s one of the simplest ways to work with images in Python.

What it does: It lets you open, manipulate, and save images in many formats. You can crop, resize, apply filters, draw shapes, and add text — all in a few lines of code.

from PIL import Image, ImageDraw 
 
img = Image.new("RGB", (200, 100), color=(73, 109, 137)) 
draw = ImageDraw.Draw(img) 
draw.text((10, 10), "Hello Pillow!", fill=(255, 255, 0)) 
img.save("pillow_example.png")

Why it deserves more love:

For basic image tasks, Pillow is lightweight, easy to learn, and incredibly efficient. You don’t always need OpenCV’s complexity — and that’s where Pillow shines.

3. Toolz — Functional Programming Made Simple

Why it’s underrated: While libraries like functools and itertools get some love, Toolz often gets overlooked — despite making functional programming in Python feel elegant.

What it does: Toolz provides a set of functional utilities — like compose, curry, map, filter, and more — optimized for Pythonic workflows.

from toolz import curry, compose 
 
@curry 
def add(x, y): 
    return x + y 
 
double_add = compose(lambda x: x * 2, add(3)) 
print(double_add(4))  # (4 + 3) * 2 = 14

Why it deserves more love:

It turns your data pipelines into readable, testable, functional code. Great for data scientists, ETL engineers, or anyone tired of nested for-loops and if-statements.

4. Whoosh — Build Your Own Search Engine

Why it’s underrated: Full-text search often sends developers scrambling to Elasticsearch, but Whoosh offers a surprisingly capable in-Python alternative.

What it does: Whoosh lets you build fast, custom search indexes — with ranking, scoring, and even query parsing — all without a server.

from whoosh.index import create_in 
from whoosh.fields import Schema, TEXT 
import os 
 
schema = Schema(title=TEXT(stored=True), content=TEXT) 
os.mkdir("indexdir") 
ix = create_in("indexdir", schema)

Why it deserves more love:

It’s pure Python, easy to set up, and perfect for lightweight search functionality in apps, blogs, note-taking tools, or knowledge bases.

5. Sh — Pythonic Shell Scripting

Why it’s underrated: If you’ve ever used subprocess and felt the pain, Sh will feel like a dream.

What it does: It wraps shell commands in Python functions — so you can run ls, git, or docker commands as if they were native Python calls.

import sh 
 
print(sh.ls("-l")) 
print(sh.git.status())

Why it deserves more love:

It’s clean, readable, and reduces boilerplate. For automation, scripting, or devops tasks, Sh can be a joy to use.

Final Thoughts

Popular libraries dominate for a reason — but that doesn’t mean they’re always the right tool for the job. The beauty of Python lies in its ecosystem, and often, the most delightful libraries are the ones that quietly do their job without the hype.

So the next time you’re building something, take a second to look beyond the obvious choices. You might just find that perfect little gem — one that makes your code simpler, cleaner, and just more fun.

Got a favorite underrated Python library that you think deserves more love? Share it in the comments! Let’s spread the word — one import statement at a time.