15 Modern Python Libraries You Must Know in 2025! 🚀

I have discovered these few game changer modern set of libraries that you should must know in 2025.

Pythonworld

18 Apr 2025 — 11 min read

LEVEL UP YOUR PYTHON SKILLS WITH THESE

15 Modern Python Libraries You Must Know in 2025! 🚀

Hey Everyone, as we know python has a vast set of amazing libraries and many of them are really a game changer.

So, i have discovered these few game changer modern set of libraries that you should must know if you want to stay ahead in 2025.

1. Polars — The Blazing-Fast DataFrame Library

Polars is a blazingly fast DataFrame library written in Rust for manipulating structured data.

Why You Should Use It: This is 10x–100x faster than Pandas. It Supports lazy evaluation for large datasets and works natively with Apache Arrow

Docs : https://docs.pola.rs/

Installation

pip install polars

Example

This is simple example to create a DataFrame using Polars:

import polars as pl 
import datetime as dt 
 
df = pl.DataFrame( 
    { 
        "name": ["Alice Archer", "Ben Brown", "Chloe Cooper", "Daniel Donovan"], 
        "birthdate": [ 
            dt.date(1997, 1, 10), 
            dt.date(1985, 2, 15), 
            dt.date(1983, 3, 22), 
            dt.date(1981, 4, 30), 
        ], 
        "weight": [57.9, 72.5, 53.6, 83.1],  # (kg) 
        "height": [1.56, 1.77, 1.65, 1.75],  # (m) 
    } 
) 
 
print(df)

shape: (4, 4) 
┌────────────────┬────────────┬────────┬────────┐ 
│ name           ┆ birthdate  ┆ weight ┆ height │ 
│ ---            ┆ ---        ┆ ---    ┆ ---    │ 
│ str            ┆ date       ┆ f64    ┆ f64    │ 
╞════════════════╪════════════╪════════╪════════╡ 
│ Alice Archer   ┆ 1997-01-10 ┆ 57.9   ┆ 1.56   │ 
│ Ben Brown      ┆ 1985-02-15 ┆ 72.5   ┆ 1.77   │ 
│ Chloe Cooper   ┆ 1983-03-22 ┆ 53.6   ┆ 1.65   │ 
│ Daniel Donovan ┆ 1981-04-30 ┆ 83.1   ┆ 1.75   │ 
└────────────────┴────────────┴────────┴────────┘

2. Ruff — The Fastest Python formatter & Linter

Ruff is a blazing-fast linter written in Rust, designed to replace Flake8, Black, and isort in one tool.

Why You Should Use It: This is 20x faster than Flake8, supports auto-fixing issues and works as a formatter and linter

Docs : https://docs.astral.sh/ruff/

Installation

pip install ruff

Example

We can use uv to initialize a project:

uv init --lib demo

This command creates a Python project with the following structure:

demo 
  ├── README.md 
  ├── pyproject.toml 
  └── src 
      └── demo 
          ├── __init__.py 
          └── py.typed

We’ll then replace the contents of src/demo/__init__.py with the following code:

from typing import Iterable 
import os 
 
def sum_even_numbers(numbers: Iterable[int]) -> int: 
    """Given an iterable of integers, return the sum of all even numbers in the iterable.""" 
    return sum( 
        num for num in numbers 
        if num % 2 == 0 
    )

Next, we’ll add Ruff to our project:

uv add --dev ruff

We can then run the Ruff linter over our project via uv run ruff check:

$ uv run ruff check 
src/numbers/__init__.py:3:8: F401 [*] `os` imported but unused 
Found 1 error. 
[*] 1 fixable with the `--fix` option.

we can resolve the issue automatically by running ruff check --fix:

$ uv run ruff check --fix 
Found 1 error (1 fixed, 0 remaining).

3. PyScript — Run Python in the Browser

PyScript lets you write and execute Python code in the browser, similar to JavaScript.

Why You Should Use It: It enables Python-powered web apps, works directly in HTML and No backend needed!

Docs : https://docs.pyscript.net/2025.2.4/

Installation

We don’t need to install PyScript, instead of this simply add a <script> and link tag, to your HTML document's <head>

<!-- PyScript CSS --> 
<link rel="stylesheet" href="https://pyscript.net/releases/2025.2.4/core.css"> 
<!-- This script tag bootstraps PyScript --> 
<script type="module" src="https://pyscript.net/releases/2025.2.4/core.js"></script>

Example

Create a simple .html file and user <py-script> tag write your python code.

<!doctype html> 
<html> 
    <head> 
        <!-- Recommended meta tags --> 
        <meta charset="UTF-8"> 
        <meta name="viewport" content="width=device-width,initial-scale=1.0"> 
        <!-- PyScript CSS --> 
        <link rel="stylesheet" href="https://pyscript.net/releases/2025.2.4/core.css"> 
        <!-- This script tag bootstraps PyScript --> 
        <script type="module" src="https://pyscript.net/releases/2025.2.4/core.js"></script> 
    </head> 
    <body> 
        <!-- Now you can use <py-script> tag to write your python code inside--> 
        <py-script> 
          import sys 
          from pyscript import display 
           
          display(sys.version) 
        </py-script> 
    </body> 
</html>

4. Pandera — Data Validation for Pandas

Pandera helps validate Pandas DataFrames and Series using schema-based validation.

Why You Should Use It: It Catch data errors before processing, works like Pydantic, but for Pandas and supports unit testing for data!

Docs : https://pandera.readthedocs.io/en/stable/

Installation

pip install pandera

Example

import pandas as pd 
import pandera as pa 
 
# Data to validate 
df = pd.DataFrame({ 
    "column1": [1, 4, 0, 10, 9], 
    "column2": [-1.3, -1.4, -2.9, -10.1, -20.4], 
    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"], 
}) 
# Define schema 
schema = pa.DataFrameSchema({ 
    "column1": pa.Column(int, checks=pa.Check.le(10)), 
    "column2": pa.Column(float, checks=pa.Check.lt(-1.2)), 
    "column3": pa.Column(str, checks=[ 
        pa.Check.str_startswith("value_"), 
        # define custom checks as functions that take a series as input and 
        # outputs a boolean or boolean Series 
        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2) 
    ]), 
}) 
validated_df = schema(df) 
print(validated_df)

column1  column2  column3 
0        1     -1.3  value_1 
1        4     -1.4  value_2 
2        0     -2.9  value_3 
3       10    -10.1  value_2 
4        9    -20.4  value_1

5. Textual — Build TUI Apps in Python

Textual allows you to build modern Terminal UI apps (TUI) in Python with rich components.

Why You Should Use It: To Create beautiful terminal apps, Works with Rich for styling and No frontend experience needed!

Docs : https://textual.textualize.io/tutorial/

Installation

pip install textual

Example

A simple example to create a TUI Apps.

from textual.app import App, ComposeResult 
from textual.widgets import Label, Button 
 
class QuestionApp(App[str]): 
    def compose(self) -> ComposeResult: 
        yield Label("Do you love Textual?") 
        yield Button("Yes", id="yes", variant="primary") 
        yield Button("No", id="no", variant="error") 
    def on_button_pressed(self, event: Button.Pressed) -> None: 
        self.exit(event.button.id) 
 
if __name__ == "__main__": 
    app = QuestionApp() 
    reply = app.run() 
    print(reply)

Running this app will give you this result:

6. LlamaIndex — Build Custom AI Assistants

LlamaIndex simplifies indexing and querying large datasets for LLM-powered applications.

Why You Should Use It: It is used for RAG (Retrieval-Augmented Generation), Works with OpenAI GPT models and Handles structured and unstructured data.

Docs : https://docs.llamaindex.ai/en/stable/#getting-started

Installation

pip install llama-index

Example

Let’s start with a simple example using an agent that can perform basic multiplication by calling a tool. Create a file called starter.py:

Set an environment variable called OPENAI_API_KEY with an OpenAI API key.

import asyncio 
from llama_index.core.agent.workflow import AgentWorkflow 
from llama_index.llms.openai import OpenAI 
 
# Define a simple calculator tool 
def multiply(a: float, b: float) -> float: 
    """Useful for multiplying two numbers.""" 
    return a * b 
 
# Create an agent workflow with our calculator tool 
agent = AgentWorkflow.from_tools_or_functions( 
    [multiply], 
    llm=OpenAI(model="gpt-4o-mini"), 
    system_prompt="You are a helpful assistant that can multiply two numbers.", 
) 
 
async def main(): 
    # Run the agent 
    response = await agent.run("What is 1234 * 4567?") 
    print(str(response)) 
 
# Run the agent 
if __name__ == "__main__": 
    asyncio.run(main())

The result of \( 1234 \times 4567 \) is \( 5,678,678 \).

7. Robyn — The Fastest Python Web Framework

Robyn is a high-performance alternative to Flask and FastAPI, optimized for multi-core processing.

Why You Should Use It: It 5x faster than FastAPI. It Supports async and multi-threading and Uses Rust for speed

Docs : https://robyn.tech/documentation/en

Installation

pip install robyn

Example

Let’s create simple project by using this command :

$ python -m robyn --create

This, would result in the following output.

$ python3 -m robyn --create 
? Directory Path: . 
? Need Docker? (Y/N) Y 
? Please select project type (Mongo/Postgres/Sqlalchemy/Prisma): 
❯ No DB 
  Sqlite 
  Postgres 
  MongoDB 
  SqlAlchemy 
  Prisma

This will created a new application with the following structure.

├── src 
│   ├── app.py 
├── Dockerfile

You can now write a code in app.py file:

from robyn import Request 
 
@app.get("/") 
async def h(request: Request) -> str: 
    return "Hello, world"

You can use this command to run the server :

python -m robyn app.py

8. DuckDB — The Lightning-Fast In-Memory Database

DuckDB is an in-memory SQL database that is faster than SQLite for analytics.

Why You Should Use It: It is Blazing-fast for analytics, Works without a server and Easily integrates with Pandas & Polars

Docs : https://duckdb.org/docs/stable/clients/python/overview.html

Installation

pip install duckdb --upgrade

Example

A simple example with pandas dataframe :

import duckdb 
import pandas as pd 
 
pandas_df = pd.DataFrame({"a": [42]}) 
duckdb.sql("SELECT * FROM pandas_df")

┌───────┐ 
│   a   │ 
│ int64 │ 
├───────┤ 
│    42 │ 
└───────┘

9. Django — Full-Stack Web Framework

A Django high-level Python web framework for building secure, scalable applications quickly.

Why You Should Use It: It has Built-in ORM (Object-Relational Mapping), Authentication system included, Scalable & Secure and many more

Docs : https://docs.djangoproject.com/en/5.2/

Installation:

pip install django

Example:

Create a new Django project:

django-admin startproject myproject 
cd myproject 
python manage.py runserver

You should see you app is running on http:127.0.0.1:8000/

10. FastAPI — High-Performance APIs

FastAPI is a lightweight and fast Python web framework for building RESTful APIs with async support.

Why You Should Use It: Asynchronous support (built-in), Automatic OpenAPI & Swagger UI and Fast (built on Starlette & Pydantic)

Docs : https://fastapi.tiangolo.com/learn/

Installation:

pip install fastapi uvicorn

Example:

A simple example to create api using FastAPI:

# main.py 
from fastapi import FastAPI 
 
app = FastAPI() 
@app.get("/") 
def read_root(): 
    return {"message": "Hello, FastAPI!"}

Run the server:

uvicorn main:app --reload

You should see you app is running on http:127.0.0.1:8000/

11. LangChain — AI-Powered Applications

LangChain is python framework that simplifies working with LLMs (Large Language Models) like OpenAI’s GPT.

Why You Should Use It: Integrates with OpenAI, Hugging Face, and more, Chain multiple LLM calls together and Supports memory & retrieval-based queries

Docs : https://python.langchain.com/docs/introduction/

Installation:

pip install langchain

Example:

A simple example to create chatbot, using openAI model:

pip install -qU "langchain[openai]"

import getpass 
import os 
from langchain.chat_models import init_chat_model 
 
if not os.environ.get("OPENAI_API_KEY"): 
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ") 
model = init_chat_model("gpt-4o-mini", model_provider="openai")

model.invoke("Hello, world!")

You will see a response from chatbot

12. Pydantic — Data Validation & Parsing

Pydantic provides data validation using Python type hints, used in FastAPI.

Why You Should Use It: Automatic data validation, Type hint-based parsing and Works well with FastAPI

Docs : https://docs.pydantic.dev/latest/

Installation:

pip install pydantic

Example:

from pydantic import BaseModel 
 
class User(BaseModel): 
    name: str 
    age: int 
user = User(name="Aashish Kumar", age=25) 
print(user)      # User(name='Aashish Kumar', age=25) 
print(user.name) # 'Aashish Kumar' 
print(user.age)  # 25

13. Flet — Python for Web, Mobile & Desktop UI

Flet build modern web, desktop, and mobile apps using Python only (no HTML/CSS/JS).

Why You Should Use It: No need for JavaScript or frontend knowledge, Works on Web, Windows, macOS, and Linux and Reactive UI framework

Docs : https://flet.dev/docs/

Installation:

pip install flet

Example:

Let’s build a simple Counter App:

import flet 
from flet import IconButton, Page, Row, TextField, icons 
 
def main(page: Page): 
    page.title = "Flet counter example" 
    page.vertical_alignment = "center" 
    txt_number = TextField(value="0", text_align="right", width=100) 
    def minus_click(e): 
        txt_number.value = str(int(txt_number.value) - 1) 
        page.update() 
    def plus_click(e): 
        txt_number.value = str(int(txt_number.value) + 1) 
        page.update() 
    page.add( 
        Row( 
            [ 
                IconButton(icons.REMOVE, on_click=minus_click), 
                txt_number, 
                IconButton(icons.ADD, on_click=plus_click), 
            ], 
            alignment="center", 
        ) 
    ) 
flet.app(target=main)

Run the program:

python counter.py

The app will be started in a native OS window — what a nice alternative to Electron!

If you want to run the app as a web app, just replace the last line with:

flet.app(target=main, view=flet.AppView.WEB_BROWSER)

Run again and now you instantly get a web app:

14. Weaviate — Vector Database for AI & Search

Weaviate is a fast, open-source vector database for semantic search and AI applications.

Why You Should Use It: Ideal for AI-powered search, Stores text, images, and embeddings and Scales for large datasets

Docs : https://weaviate.io/developers/weaviate

Installation:

pip install -U weaviate-client

Example:

To run Weaviate with Docker using default settings, run this command from from your shell:

docker run -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:1.29.0

Docker instances default to http://localhost:8080

To connect to a local instance without authentication :

import weaviate 
 
client = weaviate.connect_to_local() 
print(client.is_ready())

15. Reflex — Web Apps in Python (Frontend + Backend)

Reflex is a full-stack web framework to build modern web apps in Python, similar to Streamlit but more customizable.

Why You Should Use It: Build React-like UI in Python, State management included and Backend + frontend in one place

Docs : https://reflex.dev/docs/getting-started/introduction/

Installation

pip install reflex

Example

You can use these command to create a reflex project:

mkdir my_app_name 
cd my_app_name 
reflex init

Create a simple app :

# app.py 
import reflex as rx 
import openai 
 
openai_client = openai.OpenAI() 
 
# Backend code 
class State(rx.State): 
    """The app state.""" 
    prompt = "" 
    image_url = "" 
    processing = False 
    complete = False 
    def get_image(self): 
        """Get the image from the prompt.""" 
        if self.prompt == "": 
            return rx.window_alert("Prompt Empty") 
        self.processing, self.complete = True, False 
        yield 
        response = openai_client.images.generate( 
            prompt=self.prompt, n=1, size="1024x1024" 
        ) 
        self.image_url = response.data[0].url 
        self.processing, self.complete = False, True 
 
# Frontend code 
def index(): 
    return rx.center( 
        rx.vstack( 
            rx.heading("DALL-E", font_size="1.5em"), 
            rx.input( 
                placeholder="Enter a prompt..", 
                on_blur=State.set_prompt, 
                width="25em", 
            ), 
            rx.button( 
                "Generate Image",  
                on_click=State.get_image, 
                width="25em", 
                loading=State.processing 
            ), 
            rx.cond( 
                State.complete, 
                rx.image(src=State.image_url, width="20em"), 
            ), 
            align="center", 
        ), 
        width="100%", 
        height="100vh", 
    ) 
# Add state and page to the app. 
app = rx.App() 
app.add_page(index, title="Reflex:DALL-E")

Run development server :

reflex run

You should see your app running at http://localhost:3000.

Final Thoughts

These 15 modern Python tools will help you build faster, smarter, and more scalable applications in 2025.

Which library are you most excited to try? Let me know in the comments!

LEVEL UP YOUR PYTHON SKILLS WITH THESE

15 Modern Python Libraries You Must Know in 2025! 🚀

1. Polars — The Blazing-Fast DataFrame Library

Installation

Example

2. Ruff — The Fastest Python formatter & Linter

Installation

Example

3. PyScript — Run Python in the Browser

Installation

Example

4. Pandera — Data Validation for Pandas

Installation

Example

5. Textual — Build TUI Apps in Python

Installation

Example

6. LlamaIndex — Build Custom AI Assistants

Installation

Example

7. Robyn — The Fastest Python Web Framework

Installation

Example

8. DuckDB — The Lightning-Fast In-Memory Database

Installation

Example

9. Django — Full-Stack Web Framework

Installation:

Example:

10. FastAPI — High-Performance APIs

Installation:

Example:

11. LangChain — AI-Powered Applications

Installation:

Example:

12. Pydantic — Data Validation & Parsing

Installation:

Example:

13. Flet — Python for Web, Mobile & Desktop UI

Installation:

Example:

14. Weaviate — Vector Database for AI & Search

Installation:

Example:

15. Reflex — Web Apps in Python (Frontend + Backend)

Installation

Example

Final Thoughts

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