Python Development

Three very different reasons clients ask for Python

Python turns up in our work for reasons that have little to do with each other, and the engineering that follows is different in each case. It is worth separating them before scoping anything, because a team that is excellent at one is not automatically the right team for another.

1. Web services and APIs

Django when a project benefits from batteries included — an admin interface, an ORM, migrations, authentication and a permission model that exist on day one. FastAPI when the deliverable is a typed HTTP API, validation matters, and generated OpenAPI documentation saves argument with the client teams that consume it. Both are calm, well-documented choices that a new engineer can join without a long ramp.

2. Data work, analytics and machine learning

This is where Python has no serious competition. Ingestion and cleaning pipelines, scheduled transformations, reporting, model training and the far less glamorous work of getting a model behind an endpoint where an application can actually call it. The hard part is rarely the model — it is scheduling, reproducibility, data validation and knowing when yesterday’s numbers silently stopped arriving.

3. Automation and glue

The scripts that quietly hold a business together: moving files between systems, reconciling two exports that disagree, driving an API that has no client library, generating documents. Small individually, and collectively worth real money in hours returned.

How we keep Python projects maintainable

Python gives you enough freedom to make a mess, so the guardrails matter more than in a stricter language. We use type hints with mypy or pyright checking in CI, Ruff for linting and formatting so style stops being a discussion, uv or Poetry for reproducible dependency resolution, pytest with fixtures that spin up real dependencies in containers, and packaging into Docker images with a pinned interpreter version. Long-running jobs get idempotency and retries designed in rather than added after the first failed overnight run.

Being straight about performance

Python is not fast at raw computation, and pretending otherwise leads to unpleasant surprises. In practice this matters less than expected, because the heavy numerical libraries drop into optimised native code and most web services spend their time waiting on a database. When a genuine hot path shows up, the fix is usually to move that piece — to a native extension, or to a service written in .NET or Java — rather than to rewrite an entire working system.

The stack we work in

Python 3.12 and newer, Django and Django REST Framework, FastAPI with Pydantic, SQLAlchemy, Celery or RQ for background work, pandas and Polars for data transformation, PostgreSQL, Redis, Airflow or Prefect for orchestration, and Docker in CI. Frontends for these systems are built with React or Vue when a Django template stops being enough.

Engineers are available through outstaffing, or we can scope the work as a custom development project.