TL;DR
Here are Python libraries that focus on different aspects of full-stack development; some focus on web application development, some on the back end, and others on both.
1. Taipy
Taipy is an open-source Python app builder for production-ready applications front-end & back-end.
It has been designed to expedite application development, from initial prototypes to production-ready applications.
🔑 Features:
- Extensive interactivity
- There are more customization capabilities for your layout, styling, etc. (no CSS needed)
- Multipage & multi-user applications
- Graphical Pipeline Editor
- Integration with Taipy Front-end capabilities for an end-to-end deployment
- Scheduling
- Notebook compatible
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2.Prefect
Prefect is a framework offering data pipeline development.
It sets itself apart from competitors like Airflow by focusing on simplicity and user-friendliness.
Prefect is a good in-between if you want a mature product with various features but an easier learning curve than Airflow.
🔑 Features:
- Intuitive and user-friendly control panel
- intelligent features such as caching
- Flow-based structure to ease into workflow organization
- Dynamic parametrization & dependency management
- Hybrid execution ( Local/Cloud)
3. Streamlit
Streamlit is a game changer for quickly and simply building web applications.
This data app framework focused their differentiation from more complete and complex solutions to a quick and user-friendly solution.
🔑 Features:
- Pythonic framework
- Interactive widgets
- User-friendliness
4.Airflow
Apache Airflow is an open-source platform designed for scheduling and monitoring workflows.
It's an excellent choice for orchestrating complex data pipelines and ETL processes, as it has been a prominent player in the pipeline landscape for the last decade.
The completeness of this library is associated with a steep learning curve.
🔑 Features:
- DAG-Based Workflow Definition
- The complete interface that includes visualization of DAGs tracks failures and manages retries.
- Various integration
- Dynamic Task Execution and Scheduling
- Python-Centric Design
- Community Support
5. Brython
Brython brings Python to the front-end, as the name regroups “Browser” and “Python”.
It introduces a unique concept of running Python code directly in web applications.
This innovative approach allows you to build interactive web experiences using Python.
🔑 Features:
- Easy integration as it integrates the Python logic directly into the browser
- Compatible with different web browsers
6. Dash
Dash, created by Plotly, is a web application framework using the completeness of Plotlys’ components.
It is known to be a complete solution, and with that comes a longer time to master.
🔑 Features:
- Component-based architecture
- Powerful interactive dashboards
- Real-time data updates
7. Dagster
Dagster, one of the newer libraries in this compilation, is a cloud-native data pipeline orchestration aiming to unify data integration, workflow orchestration, and monitoring.
Compared to other tools, Dagster emphasizes the DataOps aspect of the workflow creation and management.
🔑 Features:
- Declarative pipeline setup
- Opinionated structure
- Versioning
- Integration with Hadoop
- Comprehensive metadata tracking
8. SQLAlchemy
SQLAlchemy is a Python library used to handle database interaction.
It is a versatile toolkit that links Python programming and relational databases.
The time to master this library is longer, but the benefits are real.
🔑 Features:
- SQL Expression language
- Object-Relation Mapping (ORM)
- Wide support for databases
- Complex query support
9. Celery
Celery is a Python library used as a framework to build back-end applications.
It handles distributed task processing, job executions, and workflows.
It is a complete solution and extensive feature set with a steeper learning curve.
🔑 Features:
- Parallel processing
- Scalability
- Task scheduling
- Asynchronous processing
10. Peewee
Like SQAlchemy, Peewee is a Python library that simplifies database interaction.
It differs from its competitors with a user-friendly and simple setup.
This library is the best option for small to medium projects.
🔑 Features:
- Lightweight framework
- Database support
- Pythonic syntax
11. Kedro
Kedro is an open-source Python framework.
It provides a toolbox for production-ready data science pipelines.
Indeed, Kedro easily integrates with well-established Python ML libraries and provides a unified way to implement an end-to-end framework.
🔑 Features:
- Data Catalog
- Notebooks integration
- Project template
- Opinionated as it enforces specific conventions
Conclusion
Python is considered the go-to language for AI and Machine learning applications.
To streamline and automate these applications, Python has been witnessing the emergence of framework libraries.
These libraries facilitate web application development, back-end services, and the creation of open-source full-stack applications.
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Top comments (10)
Awesome list 🌟
Thank you @biplobsd :)
Thanks. This is what I am looking for.
I am yet to try Taipy. It looks like what I need next. Thank you for sharing
Taipy looks really interesting.- thanks for sharing!
Didn’t know there was so many libraries for full stack Python development. Thanks for it
I would certainly also have a look at Flet.dev
Some of these libraries are outstanding and a couple of these are absolutely atrocious at scale.
I like how Python has so many options for developing full-stack apps. From what I got it's mostly focused on data-driven apps, rather than typical SaaS-es?
Very interesting article, but what's about Flask and Django ?