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Full-Stack Toolbox ๐Ÿงฐ: Python๐Ÿ Edition

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.

Gif introduction


1. Taipy

Taipy is an open-source Python library for building production-ready applications front-end & back-end.
It has been designed to expedite application development, from initial prototypes to production-ready applications.

๐Ÿ”‘ 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

Taipy


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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.

๐Ÿ”‘ 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)

Prefect


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.

๐Ÿ”‘ Features:

  • Pythonic framework
  • Interactive widgets
  • User-friendliness

Streamlit


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.

๐Ÿ”‘ 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

Airflow


5. Brython

Brython brings Python to the front-end, as the name regroups โ€œBrowserโ€ and โ€œPythonโ€.
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.

๐Ÿ”‘ Features:

  • Easy integration as it integrates the Python logic directly into the browser
  • Compatible with different web browsers

Brython


6. Dash

Dash, created by Plotly, is a web application framework using the completeness of Plotlysโ€™ components.
It is known to be a complete solution, and with that comes a longer time to master.

๐Ÿ”‘ Features:

  • Component-based architecture
  • Powerful interactive dashboards
  • Real-time data updates

Dash


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.

๐Ÿ”‘ Features:

  • Declarative pipeline setup
  • Opinionated structure
  • Versioning
  • Integration with Hadoop
  • Comprehensive metadata tracking

Dagster


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.

๐Ÿ”‘ Features:

  • SQL Expression language
  • Object-Relation Mapping (ORM)
  • Wide support for databases
  • Complex query support

SQL


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.

๐Ÿ”‘ Features:

  • Parallel processing
  • Scalability
  • Task scheduling
  • Asynchronous processing

Celery


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.

๐Ÿ”‘ Features:

  • Lightweight framework
  • Database support
  • Pythonic syntax

Peewee


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.

๐Ÿ”‘ Features:

  • Data Catalog
  • Notebooks integration
  • Project template
  • Opinionated as it enforces specific conventions

Kedro


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)

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biplobsd profile image
Biplob Sutradhar

Awesome list ๐ŸŒŸ

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marisogo profile image
Marine

Thank you @biplobsd :)

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jennyliu07 profile image
Jenny

Thanks. This is what I am looking for.

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proteusiq profile image
Prayson Wilfred Daniel

I am yet to try Taipy. It looks like what I need next. Thank you for sharing

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fernandezbaptiste profile image
Bap

Taipy looks really interesting.- thanks for sharing!

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rym_michaut profile image
Rym

Didnโ€™t know there was so many libraries for full stack Python development. Thanks for it

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vindevoy profile image
Yves Vindevogel

I would certainly also have a look at Flet.dev

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debadyuti profile image
Deb

Some of these libraries are outstanding and a couple of these are absolutely atrocious at scale.

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matijasos profile image
Matija Sosic

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?

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celtic34fr profile image
Gilbert ARMENGAUD

Very interesting article, but what's about Flask and Django ?