08 Modern Python Libraries That Deserve More Hype in 2025 🚀

Python’s ecosystem is massive — but these underrated gems are quietly making developers 10x faster.

08 Modern Python Libraries That Deserve More Hype in 2025 🚀

Python continues to dominate the programming world in 2025, and it’s not just because of its simplicity.

The real magic lies in its ever-evolving ecosystem of powerful libraries.

While giants like NumPy, Pandas, and FastAPI continue to shine, there’s a fresh wave of modern libraries quietly revolutionizing the way we write Python code.

These aren’t your usual suspects — these are the underrated gems that can boost your productivity, streamline your projects, and make coding a lot more enjoyable.

Let’s explore 08 modern Python libraries that deserve way more hype this year.

1. Reflex — Build Web Apps with Pure Python

What if you could build interactive frontend apps without touching HTML, CSS, or JavaScript?

Docs : https://github.com/reflex-dev/reflex

Reflex (formerly Pynecone) is doing just that. It lets you create full-stack web apps entirely in Python, including beautiful UIs — think React components but Pythonic.

• Uses a declarative syntax like React
• Handles frontend + backend with a single codebase
• Hot reload, state management, routing — all included

Perfect for Python devs who want to build web apps without leaving their comfort zone.

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

Let’s create a simple app :

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")

Hit this command to run development server :

reflex run

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

2. Robocorp / RPA Framework — Python for Automation Engineers

If you’re into RPA (Robotic Process Automation), Robocorp and its rpaframework package are a game changer.

• Automate Excel, PDFs, emails, websites, APIs — even legacy systems
• Built on top of Robot Framework with Python extensibility
• Ships with prebuilt automation libraries

It’s time to stop writing brittle Selenium scripts and level up your automation game.

Docs : https://sema4.ai/docs/automation/quickstart-guide/python

3. Typer — The Modern Way to Build CLIs

You’ve heard of Click. You’ve used argparse. But Typer, built by the creator of FastAPI, takes things to another level.

• Type hints become your CLI arguments
• Auto-generates help docs and command trees
• Built-in validation, tab completion, and more

Whether you’re building internal tools or shipping open-source projects, Typer makes CLI development elegant and fun.

Docs : https://typer.tiangolo.com/

Installation

pip install typer

Example

Create a file main.py with:

def main(name: str): 
    print(f"Hello {name}")

This script doesn’t even use Typer internally. But you can use the typer command to run it as a CLI application.

Run your application with the typer command:

💬 Run your application 
typer main.py run 
 
💬 You get a nice error, you are missing NAME 
Usage: typer [PATH_OR_MODULE] run [OPTIONS] NAME 
Try 'typer [PATH_OR_MODULE] run --help' for help. 
╭─ Error ───────────────────────────────────────────╮ 
│ Missing argument 'NAME'.                          │ 
╰───────────────────────────────────────────────────╯ 
 
 
💬 You get a --help for free 
typer main.py run --help 
 
Usage: typer [PATH_OR_MODULE] run [OPTIONS] NAME 
 
Run the provided Typer app. 
 
╭─ Arguments ───────────────────────────────────────╮ 
│ *    name      TEXT  [default: None] [required]   | 
╰───────────────────────────────────────────────────╯ 
╭─ Options ─────────────────────────────────────────╮ 
│ --help          Show this message and exit.       │ 
╰───────────────────────────────────────────────────╯ 
 
💬 Now pass the NAME argument 
typer main.py run Camila 
 
Hello Camila 
 
💬 It works! 🎉

This is the simplest use case, not even using Typer internally, but it can already be quite useful for simple scripts.

4. Pydantic V2 — The Data Validation Powerhouse (Now Even Faster)

While not exactly underrated, Pydantic 2.0 has been rebuilt from the ground up with performance in mind. It now uses Rust under the hood, making it blazing fast.

• Data validation + settings management
• Compatible with FastAPI, Typer, and more
• Works great with async code and complex nested data models

If you’re handling external data (APIs, user input, etc.), Pydantic is your best friend.

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

Installation

pip install pydantic

Example

To see Pydantic at work, let’s start with a simple example, creating a custom class that inherits from BaseModel:

from datetime import datetime 
 
from pydantic import BaseModel, PositiveInt 
 
 
class User(BaseModel): 
    id: int   
    name: str = 'John Doe'   
    signup_ts: datetime | None   
    tastes: dict[str, PositiveInt]   
 
 
external_data = { 
    'id': 123, 
    'signup_ts': '2019-06-01 12:22',   
    'tastes': { 
        'wine': 9, 
        b'cheese': 7,   
        'cabbage': '1',   
    }, 
} 
 
user = User(**external_data)   
 
print(user.id)   
#> 123 
print(user.model_dump())   
""" 
{ 
    'id': 123, 
    'name': 'John Doe', 
    'signup_ts': datetime.datetime(2019, 6, 1, 12, 22), 
    'tastes': {'wine': 9, 'cheese': 7, 'cabbage': 1}, 
} 
"""

If validation fails, Pydantic will raise an error with a breakdown of what was wrong:

# continuing the above example... 
 
from datetime import datetime 
from pydantic import BaseModel, PositiveInt, ValidationError 
 
 
class User(BaseModel): 
    id: int 
    name: str = 'John Doe' 
    signup_ts: datetime | None 
    tastes: dict[str, PositiveInt] 
 
 
external_data = {'id': 'not an int', 'tastes': {}}   
 
try: 
    User(**external_data)   
except ValidationError as e: 
    print(e.errors()) 
    """ 
    [ 
        { 
            'type': 'int_parsing', 
            'loc': ('id',), 
            'msg': 'Input should be a valid integer, unable to parse string as an integer', 
            'input': 'not an int', 
            'url': 'https://errors.pydantic.dev/2/v/int_parsing', 
        }, 
        { 
            'type': 'missing', 
            'loc': ('signup_ts',), 
            'msg': 'Field required', 
            'input': {'id': 'not an int', 'tastes': {}}, 
            'url': 'https://errors.pydantic.dev/2/v/missing', 
        }, 
    ] 
    """

5. Haystack — Build LLM Apps, the Right Way

If you’re building anything with Large Language Models or RAG (Retrieval Augmented Generation), Haystack is your go-to framework.

• Built-in support for vector stores, LLM orchestration, pipelines
• Plug-and-play with OpenAI, Cohere, Hugging Face, and more
• Enables production-ready LLM applications

Think of it as LangChain’s more structured and battle-tested cousin.

Docs : https://docs.haystack.deepset.ai/docs/get_started

Installation

pip install haystack-ai

Example

In the example below, we show how to set an API key using a Haystack Secret. However, for easier use, you can also set an OpenAI key as an OPENAI_API_KEY environment variable.

from haystack import Pipeline, Document 
from haystack.utils import Secret 
from haystack.document_stores.in_memory import InMemoryDocumentStore 
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever 
from haystack.components.generators.chat import OpenAIChatGenerator 
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder 
from haystack.dataclasses import ChatMessage 
 
# Write documents to InMemoryDocumentStore 
document_store = InMemoryDocumentStore() 
document_store.write_documents([ 
    Document(content="My name is Jean and I live in Paris."), 
    Document(content="My name is Mark and I live in Berlin."), 
    Document(content="My name is Giorgio and I live in Rome.") 
]) 
 
# Build a RAG pipeline 
prompt_template = [ 
    ChatMessage.from_system("You are a helpful assistant."), 
    ChatMessage.from_user( 
        "Given these documents, answer the question.\n" 
        "Documents:\n{% for doc in documents %}{{ doc.content }}{% endfor %}\n" 
        "Question: {{question}}\n" 
        "Answer:" 
    ) 
] 
 
# Define required variables explicitly 
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables={"question", "documents"}) 
 
retriever = InMemoryBM25Retriever(document_store=document_store) 
llm = OpenAIChatGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY")) 
 
rag_pipeline = Pipeline() 
rag_pipeline.add_component("retriever", retriever) 
rag_pipeline.add_component("prompt_builder", prompt_builder) 
rag_pipeline.add_component("llm", llm) 
rag_pipeline.connect("retriever", "prompt_builder.documents") 
rag_pipeline.connect("prompt_builder", "llm.messages") 
 
# Ask a question 
question = "Who lives in Paris?" 
results = rag_pipeline.run( 
    { 
        "retriever": {"query": question}, 
        "prompt_builder": {"question": question}, 
    } 
) 
 
print(results["llm"]["replies"])

6. Polars — Lightning-Fast DataFrames for Python

Move over Pandas — Polars is here to crunch data 10x faster using Rust-powered performance.

• Blazing speed and low memory usage
• Lazy evaluation and parallel execution
• Familiar DataFrame API (with a few smart twists)

If you’re working with large datasets, Polars will change the way you think about performance.

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   │ 
└────────────────┴────────────┴────────┴────────┘

7. Fugue — Write Spark Code Without Writing Spark Code

Ever wanted to scale your Pandas code to Spark or Dask without rewriting it? Fugue makes that possible.

• Write once, run anywhere (Pandas, Dask, Spark)
• Supports SQL + DataFrame transformations
• Ideal for ETL and big data workflows

It’s your bridge between local dev and big data production.

Docs : https://github.com/fugue-project/fugue

Installation

pip install fugue

Example

The Fugue API is a collection of functions that are capable of running on Pandas, Spark, Dask, and Ray. The simplest way to use Fugue is the transform() function. This lets users parallelize the execution of a single function by bringing it to Spark, Dask, or Ray. In the example below, the map_letter_to_food() function takes in a mapping and applies it on a column. This is just Pandas and Python so far (without Fugue).

import pandas as pd 
from typing import Dict 
 
input_df = pd.DataFrame({"id":[0,1,2], "value": (["A", "B", "C"])}) 
map_dict = {"A": "Apple", "B": "Banana", "C": "Carrot"} 
 
def map_letter_to_food(df: pd.DataFrame, mapping: Dict[str, str]) -> pd.DataFrame: 
    df["value"] = df["value"].map(mapping) 
    return df

Now, the map_letter_to_food() function is brought to the Spark execution engine by invoking the transform() function of Fugue. The output schema and params are passed to the transform() call. The schema is needed because it's a requirement for distributed frameworks. A schema of "*" below means all input columns are in the output.

from pyspark.sql import SparkSession 
from fugue import transform 
 
spark = SparkSession.builder.getOrCreate() 
sdf = spark.createDataFrame(input_df) 
 
out = transform(sdf, 
               map_letter_to_food, 
               schema="*", 
               params=dict(mapping=map_dict), 
               ) 
# out is a Spark DataFrame 
out.show()

+---+------+ 
| id| value| 
+---+------+ 
|  0| Apple| 
|  1|Banana| 
|  2|Carrot| 
+---+------+

8. Rich / Textual — Terminal UIs That Look Like Magic

Tired of boring terminal outputs? Enter Rich and its big brother Textual.

• Beautiful formatting for logs, tables, Markdown, syntax highlighting
• Textual lets you build full-blown TUIs (Terminal User Interfaces)
• Great for dashboards, monitoring tools, and CLIs

It’s like React, but for your terminal. Yes, it’s that cool.

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

Installation

pip install textual rich

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:

To effortlessly add rich output to your application, you can import the rich print method, which has the same signature as the builtin Python function. Try this:

from rich import print 
 
print("Hello, [bold magenta]World[/bold magenta]!", ":vampire:", locals())

Final Thoughts

The Python ecosystem is more vibrant than ever in 2025. While the old favorites still hold their ground, these rising stars deserve your attention. They reflect where the community is heading — toward better performance, developer experience, and intelligent tooling.

So the next time you start a project, explore beyond the usual suspects. You might just find a new favorite in this list.

✍️ If you enjoyed this article, don’t forget to clap, share, or follow me for more Python insights, modern dev tools, and productivity hacks!

Got a hidden gem of a library you love? Drop it in the comments — I’d love to check it out.