LEVEL UP YOUR PYTHON SKILLS WITH THESE

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.

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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 built in Rust, and it's designed to replace Flake8, Black, and isort in a single utility.

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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 whose structure is as follows:

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

We'll replace the contents of src/demo/__init__.py with the following:

from typing import Iterable
import os

def sum_even_num(numbers: Iterable[int]) -> int:
    """Supplied an iterable of integers, return a sum of all even numbers from 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 enables you to write and execute Python code directly 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.

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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:

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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 replacement for FastAPI and Flask, built for multi-core processing.

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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

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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

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A high-level Python web framework for rapid development of secure, scalable applications.

Why You Should Use It: It has In-built ORM (Object-Relational Mapping), In-built Authentication system, Scalable & Secure and so on

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

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FFastAPI is a fast and lightweight 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

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LangChain is a python package whose purpose is to simplify programming LLMs 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

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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

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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!

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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:

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14. Weaviate — Vector Database for AI & Search

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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)

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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.

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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!