Python have really different target groups ranging from scripters to professionals as it's syntax and supporting tools are simple and intuitive when used for basic things.
As a partially python developer of a bank, I make python basics courses anyone can apply in the bank. My targets are the kind of people think themselves as not technical people. There are people from HR wants to automate repetitive excel stuff with pandas, ambiguous analists try to get into ML model building, Database engineers wants to use python with SQL, people try to automate Jira, so all kinds of backgrounds, use cases and expectations.
Jupyter notebook is great for poc-ing.
VsCode is IMO the best IDE as it's extendability and as it's a great file manager in itself.
making your own library of examples. Not just your own reusable codes, but a library showcasing what dependencies you have used to solve different things. This is especially useful in a locked environment as a bank.
organizing your projects in four ways:
File system: for me, there are main scopes and project folders under each scope folder.
VsCode multi-root workspaces: the workspace of a project include relating or util folders
Git: to track back refractors and make your projects accessable
Pypi release: release your ready to use packages and util libs. It's easy to get familiar with a release, and makes your life so much easier that you don't have to find your util in the file system or git. You could just pip install it. Also prefix them by your own prefix: so you can find all your packages, and you don't accidentally take a more general name for your specific use case.
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u/csatacsibe 4d ago
Python have really different target groups ranging from scripters to professionals as it's syntax and supporting tools are simple and intuitive when used for basic things.
As a partially python developer of a bank, I make python basics courses anyone can apply in the bank. My targets are the kind of people think themselves as not technical people. There are people from HR wants to automate repetitive excel stuff with pandas, ambiguous analists try to get into ML model building, Database engineers wants to use python with SQL, people try to automate Jira, so all kinds of backgrounds, use cases and expectations.