You Cannot Miss These 8 Python Libraries in 2025

Python has exploded in popularity to become one of the world‘s most used programming languages, especially for artificial intelligence and machine learning applications. Much of this growth is thanks to its extensive collection of open source libraries that allow developers to quickly build powerful applications and ML models.

According to the 2022 Stack Overflow Survey, Python is the 4th most used programming language with 42% of respondents using it. For data science and machine learning, Python is even more dominant, with 83% of data scientists and ML engineers using Python according to the Anaconda 2022 State of Data Science report.

Python‘s rich ecosystem of libraries is a key reason for its lead in AI/ML and continued growth in other domains like web development, devops, mobile apps, and more. In this article, I will share 8 Python libraries that have unique and valuable capabilities that you may have overlooked but are worth adding to your toolkit in 2024.

1. Kivy & KivyMD – Android Apps in Python

Mobile apps are often associated with languages like Java, Kotlin, Swift and Flutter/Dart, but what if you could use Python instead? The Kivy framework and KivyMD library make it possible to develop fully-featured Android and iOS apps completely in Python.

Kivy provides cross-platform support for core mobile functionality like touch input, device sensors, and camera access. On top of this, KivyMD implements Material Design UI components including buttons, menus, navigation, forms and more.

While mobile developers are increasingly adopting Flutter for cross-platform apps, Kivy is great for Python developers who want to apply their skills to mobile or quickly prototype app ideas. Here‘s a basic "Hello World" app in Kivy:

from kivy.app import App
from kivy.uix.label import Label

class MyApp(App):
    def build(self):
        return Label(text=‘Hello world‘)

if __name__ == ‘__main__‘:
    MyApp().run()

Kivy adoption has grown steadily to over 15k GitHub stars and 5k questions on Stack Overflow. It may not replace native mobile dev tools, but it‘s a unique and valuable option for the Python community.

2. Brython – Python in the Browser

JavaScript has long been the only option for frontend web development, but Brython aims to change that. Brython is an implementation of Python 3 that runs in the browser and can interact with the DOM and Web APIs.

With Brython, you can manipulate HTML elements, handle events, make HTTP requests, and implement complex client-side logic all in Python rather than JS. Simply load the brython.js script and put your Python code inside <script type="text/python"> tags.

<script type="text/python">
from browser import document, alert

def click(event):
    alert("Hello from Python!")

document["mybutton"].bind("click", click)
</script>

<button id="mybutton">Click me!</button>

Brython has gained popularity, especially among data scientists who want to create web UIs and visualizations without having to learn a new language. It has over 6k GitHub stars and an active community.

While not a full replacement for JS frameworks yet, Brython excels for functionality like dashboards, form-based apps, and adding interactivity to data-driven pages. It also works well with Python-based backend frameworks like Flask and Django.

3. PyWebIO – Web UIs for Terminal Scripts

PyWebIO is a unique library that can turn simple terminal-based Python scripts into interactive web applications with just a few lines of code. It provides a set of input and output functions that work in both the terminal and web browser.

Simply import PyWebIO and replace input() and print() calls with pywebio.input.*() and pywebio.output.*() equivalents. For example, to prompt the user for their name in a script, you might use:

from pywebio.input import input
from pywebio.output import put_text

name = input("What‘s your name?")
put_text(f"Hello {name}!")

When you run this script from the terminal with python myscript.py, it will prompt the user and echo the response as usual. But if you run it with pywebio myscript.py, it will open a web page that does the same I/O in the browser.

PyWebIO has many more input types like dates, files, and checkboxes, as well as output functions for displaying markdown, tables, charts, and more. You can create a basic web app frontend with no HTML/CSS knowledge needed.

For data scientists and ML engineers, PyWebIO is a great way to quickly add a web UI to scripts for tasks like exploratory data analysis, model testing, and results visualization. While not a full web framework, it‘s a simple way to make Python scripts accessible to non-technical colleagues.

4. ColabCode – VS Code in Google Colab

Google Colab notebooks provide a free, cloud-hosted environment for Python and ML development, with access to GPUs and TPUs. They have become a go-to for training models without expensive hardware. However, the Jupyter-based UI can feel limited and unfamiliar compared to a desktop IDE.

Enter ColabCode – a library that brings the power of Visual Studio Code to Colab notebooks. With a single !pip install colabcode command, you can launch a full VS Code IDE in your browser, connected to the Colab runtime.

from colabcode import ColabCode
ColabCode()

This gives you access to all of VS Code‘s features including intelligent code editing, Git integration, extensions, and more. But you‘re still using Colab‘s CPU/GPU/TPU backends and can easily move between notebook and IDE views.

Over 10k Colab notebooks on GitHub already use ColabCode. It‘s a game-changer for Colab power users and anyone who wants the familiarity of a real IDE in the cloud.

5. Voila – Jupyter Notebooks as Web Apps

Jupyter notebooks are the default for Python data analysis and model building, but they are not an ideal format for presenting results to others. If you send a .ipynb file, the recipient needs to have Jupyter installed and running to view it, and there‘s no interactivity unless they run the code cells themselves.

Voila solves this by converting Jupyter notebooks into standalone web applications with the click of a button. When you run a notebook with voila notebook.ipynb, Voila will launch a web app that:

• Strips out the code cells, just showing text, visualizations, and output
• Renders interactive widgets from the ipywidgets library
• Allows changing themes, page layout, and other display options

This turns your notebook into a dashboard or report that anyone can use without needing to install or understand Jupyter. They can interact with sliders, dropdowns, and other controls to update the analysis.

Voila is a valuable tool for data scientists who need to deliver results to business stakeholders or create public-facing data apps. In combination with ipywidgets, it can turn a notebook into a fully-featured web application with minimal web development.

6. Flask – Python Web Development

Flask is a lightweight web application framework that has become the foundation for many Python-powered websites, APIs, and services. Its simplicity and flexibility have made it the go-to for developers who want to quickly build and deploy web backends.

At its core, Flask maps HTTP requests to Python functions and renders return values as responses. Here‘s a minimal "Hello World" Flask app:

from flask import Flask

app = Flask(__name__)

@app.route(‘/‘)
def hello():
    return ‘Hello World!‘

But don‘t let the simplicity fool you – Flask can scale to large, complex applications. It has an extensive ecosystem of extensions for databases, authentication, async processing, and more. You can use it to create REST APIs, server-side rendered websites, and real-time streaming services.

According to the Python Developers Survey 2022, Flask is the 3rd most used web framework after Django and FastAPI. It‘s popular for microservices architectures, rapid prototyping, and projects where performance is crucial.

For ML engineers, Flask is often used for serving models through APIs, either for internal use or as part of end-user applications. It can load a trained model, preprocess incoming data, and return model predictions as JSON endpoints. Frameworks like MLFlow also use Flask under-the-hood for their inference server.

7. Dear PyGUI – Python Desktop Apps

While web apps get most of the attention, there‘s still a lot of demand for powerful, native desktop applications. Dear PyGUI is a newer Python framework for building modern desktop GUIs that rival the performance and polish of C++.

The key to Dear PyGUI‘s performance is its GPU-accelerated rendering engine built on top of OpenGL. This allows it to smoothly render complex interfaces with thousands of interactive elements.

Dear PyGUI provides a declarative, reactive API similar to modern web frameworks like React and Vue. You define the interface using Python functions that return layout primitives, and Dear PyGUI automatically updates the rendered output when the underlying data changes.

Here‘s a simple Dear PyGUI app with a button that updates a counter:

from dearpygui.dearpygui import *

def button_callback(sender, app_data, user_data):
    add_value("click_counter", 1)

with window("Counter App"):
    add_button("Click Me", callback=button_callback)
    add_text("Clicks: ")
    add_same_line()
    add_text("0", tag="click_counter")

start_dearpygui()

Dear PyGUI is quickly gaining popularity, with over 7k stars on GitHub, thanks to its ease-of-use and performance. It‘s a great choice for creating desktop tools, visualizations, and games with Python.

8. ExplainerDashboard – ML Model Dashboards

My final recommendation is a powerful library for visualizing and debugging machine learning models – ExplainerDashboard.

When developing an ML model, it‘s not enough to just look at a single accuracy metric. You need to dig into the fine-grained performance across different subsets of data, examine feature importances, and analyze individual predictions to gain full confidence in your model.

ExplainerDashboard automates the creation of interactive dashboards that allow you to explore all facets of model behavior. With a single function call, it provides visualizations for:

• High-level model performance metrics and plots
• Feature importance analysis and comparisons
• Individual prediction explanations with SHAP values
• What-if analysis to see how changes in inputs affect outputs
• Text and image explanations for NLP and computer vision models

ExplainerDashboard works with models from scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, and more. Here‘s an example of explaining an sklearn random forest model:

from explainerdashboard import ClassifierExplainer, ExplainerDashboard
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model.fit(X_train, y_train)

explainer = ClassifierExplainer(model, X_test, y_test) 
ExplainerDashboard(explainer).run()

This will launch an interactive dashboard web app that looks like:

The dashboard lets you select individual data points and see how the model arrives at its predictions. You can compare feature values to the overall dataset, and simulate what-if scenarios. It‘s a powerful tool for building trust and understanding of ML models.

ExplainerDashboard has quickly gained adoption with hundreds of GitHub stars and a growing user base. It saves significant development time compared to building model analysis UIs from scratch.

For data scientists and ML engineers, ExplainerDashboard is a must-have tool for the model development process. It also helps facilitate conversations with business stakeholders and domain experts during model iteration and debugging.

Comparison and Summary

To summarize, here‘s a quick comparison of our 8 featured Python libraries:

Library	Domain	GitHub Stars	Key Benefit
Kivy/KivyMD	Mobile apps	15,000+	Build cross-platform mobile UIs in Python
Brython	Web development	6,600+	Run Python in the browser instead of JavaScript
PyWebIO	Web development	3,100+	Convert terminal scripts to web UIs
ColabCode	Data science	2,400+	Use VS Code IDE in Google Colab notebooks
Voila	Data science	3,700+	Turn Jupyter notebooks into standalone web apps
Flask	Web development	60,100+	Create web apps and APIs with Python
Dear PyGUI	Desktop apps	7,700+	Build performant desktop UIs with GPU rendering
ExplainerDashboard	Data science	900+	Generate dashboards for analyzing ML models

While they serve different use cases, these libraries all demonstrate the versatility and power of the Python ecosystem. Whether you‘re building a web app, analyzing data, or creating desktop software, there‘s a Python library that can help you be more productive.

Conclusion and Call-to-Action

As you‘ve seen, Python‘s ecosystem is incredibly rich and constantly evolving. Even as an experienced Python developer, it‘s impossible to keep up with all the new libraries and frameworks that emerge each year.

My goal with this article was to shed light on 8 libraries that you may not have encountered before but are worth exploring in 2024. They each bring something unique to the table, whether it‘s a new platform, improved performance, or a streamlined workflow.

I encourage you to take some time to check out the documentation and code for the libraries that piqued your interest. Try them out on a small project, or see how they could fit into your existing workflow. You may just find a new favorite tool that makes you even more productive with Python.

At the same time, don‘t feel pressured to adopt new libraries just because they are hyped. Part of being a good developer is knowing when to stick with tried-and-true tools and when to experiment with cutting-edge techniques. Focus on the libraries that genuinely solve a problem or fill a gap for you.

Finally, I‘d love to hear your thoughts and experiences with these libraries. Feel free to share your own tips, use cases, or favorite Python hidden gems in the comments below. Let‘s keep the conversation going and continue to explore the ever-expanding world of Python development together.