Data work, automation, integrations and services where the available libraries decide the language, not the other way round.
We reach for it when the libraries do the heavy lifting, and say so when something else fits better.
Cleaning, transforming and reporting on data, where the tooling is genuinely without equal.
Manual processes turned into scheduled jobs with logging and alerts, so they run without someone remembering.
Talking to APIs, files and databases that were never designed to work with each other.
Python code can be reviewed by the analyst who owns the logic, not only by its author.
Pulling numbers out of several systems, reconciling them and producing something a person can act on. This is where Python earns its place and where nothing else is as quick.
Files that get moved, spreadsheets that get merged, reports that get assembled every Monday. Often the cheapest software a business ever buys.
Python has a library for almost every format and protocol, which turns a week of parsing into an afternoon.
Classification, extraction, recommendations, wired into an existing application rather than sold as a project of their own.
Small, well-defined services where being quick to write and easy to read matters more than raw speed.
Written for someone deciding, not for someone who already knows. Skip it if you do.
A general-purpose language designed to be readable, which is why it spread into places programming languages usually don't reach: research, finance, operations, science. If a task involves data, there's a good chance the best available library for it is Python.
Because of the ecosystem more than the language. For anything touching data, files, formats or machine learning, the mature tool already exists in Python and writing it again elsewhere is waste.
As the default for a web back end, in our view. It's perfectly capable of one, but if a system is mostly serving pages and calling a database we would rather use PHP or Node, where hosting is cheaper and maintainers are easier to find. We use Python where its libraries are the reason.
Python 2 is long dead and 3.8 is now out of support. The other trap is dependency management: a Python project without a locked, reproducible environment works on one machine and fails on another. We pin everything and build in containers, which removes the entire class of problem.
Out of support and no longer patched. The move to a current release is usually small and occasionally reveals one abandoned library.
The classic Python failure. It means the environment isn't described anywhere, and it's a day of work to fix permanently.
Automation that quietly runs the business but was written by someone who left. Getting it into version control with a test is cheap insurance.
We can, with Django, and we'll tell you when Node or PHP is the better fit for the team who will keep it. For data-heavy products Python is often the right centre.
Yes, and it's common work. It becomes a scheduled job with error handling, logging and alerts, and stops being one person’s laptop.
We build the pipelines, the data handling and the serving around models. For the modelling itself we're honest about where our expertise ends.
Probably not, unless data or machine learning is the core of it. For a normal web back end we would suggest PHP or Node and keep Python for what it's best at.
Yes, and it's common work. It usually means pinning the environment, adding error handling and a schedule, and putting it somewhere it can be watched — not rewriting it.
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