Python Development
Python is the right tool for a wide range of problems, and I have worked
across the full spectrum — web frameworks, data libraries, NLP, web scraping,
and automation. I choose the framework based on what the project needs:
Django when you want a full-featured framework with an ORM, admin, and
batteries included; Flask when you need something lightweight and explicit;
FastAPI when you need speed, async support, and automatic API documentation.
What's included
Django application development
Full Django projects — models, views, templates, admin interface, authentication,
form handling, middleware, signals, and management commands. Django REST
Framework for API-first Django applications. Celery for background task queues.
The framework used as it was designed, not fought against.
Flask API development
Lightweight REST APIs and web services in Flask — Blueprints for project
structure, SQLAlchemy for database access, Marshmallow for serialisation,
Flask-JWT-Extended for authentication, and Gunicorn for production serving.
Flask used correctly: explicit, minimal, and well-structured.
FastAPI development
Async-first REST APIs with automatic OpenAPI and ReDoc documentation, Pydantic
models for request and response validation, dependency injection, OAuth2 with
JWT, and async database access with SQLAlchemy. FastAPI is my recommendation
for new Python API projects that need to be fast and well-documented.
SQLAlchemy and ORM
Database modelling in SQLAlchemy — declarative models, relationships, migrations
with Alembic, session management, and raw SQL where the ORM is not the right
tool. Both the Core and ORM layers used where appropriate.
Web scraping and automation
BeautifulSoup4 and Requests for structured web scraping. Selenium for
JavaScript-rendered pages. Scrapy for larger crawling workloads. Automation
scripts for file processing, report generation, and workflow orchestration.
Data analysis and NLP
Pandas and NumPy for tabular data. NLP pipelines for text classification,
keyword extraction, n-gram analysis, and entity recognition. Deployed as
REST APIs consumed by web frontends — not just Jupyter notebooks.
Good fit if you need
A Python backend for a web or mobile application. A data collection and
analysis pipeline. An automation tool that replaces manual repetitive work.
A FastAPI microservice with excellent documentation. A Django application
with a full admin interface and authentication built in.