Full-Stack Toolbox 🧰: Python🐍 Edition
Tags: python, fullstack, programming, opensource
Published: 2024-01-03 13:57:03

##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](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8eodxlxdlgr0xh6lrvhz.gif)

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## 1. [Taipy](https://github.com/Avaiga/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

![Taipy](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1rk0w837hwaq0akybw6o.png)

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## 2.[Prefect](https://github.com/PrefectHQ/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)

![Prefect](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/p94g40hwv04gkfmvklda.png)

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## 3. [Streamlit](https://github.com/streamlit/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

![Streamlit](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ddntytwa95tgp37kykcc.png)

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## 4.[Airflow](https://github.com/apache/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

![Airflow](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/lm89iyieg07c2rgzm2jb.png)

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## 5. [Brython](https://github.com/brython-dev/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

![Brython](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9a7w7lqd9b5rtpahnolr.png)

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## 6. [Dash](https://github.com/plotly/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

![Dash](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/yp5xw24ulak6mnwwos69.png)

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## 7. [Dagster](https://github.com/dagster-io/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

![Dagster](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/hz78m7rein4asbyp9buf.png)

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## 8. [SQLAlchemy](https://github.com/sqlalchemy/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

![SQL](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/owtvt0kv5gt45r33ulra.png)

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## 9. [Celery](https://github.com/celery/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

![Celery](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/p50my0xtbncfskb4y1ga.png)
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## 10. [Peewee](https://github.com/coleifer/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

![Peewee](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8tmvc7y9vkb2mdpj88jq.png)

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## 11. [Kedro ](https://github.com/kedro-org/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

![Kedro](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/x2z12ofiibpd5c9ggs7s.png)

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## 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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