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Picking the right data type in Python starts with the basics. In Part 1 of Python’s data types, we cover NoneType, bool, int, float, and complex.

Working with many objects at once requires the right container. In Part 2 of Python’s data types, we cover str, bytes, tuple, list, bytearray, set, and frozenset.

In Part 3 of Python’s data types, we cover dict and the new frozendict, Python 3.15’s built-in immutable mapping, plus how to choose the right data type for the job.

In Python, two variables equal to 256 share the same object, but two variables equal to 512 don’t. The reason is Python’s object model. In this chapter, we cover identity, type, and value; how assignment and name binding actually work; the small-integer cache; and why mutating a list changes both aliases while reassigning a string doesn’t.

Validation tells you if data is okay. Parsing turns it into a type the rest of your code can trust. We walk through what parsing and validation look like across OOP, ECS, and microservices in Python.

We explore Python testing from unit tests to integration and end-to-end tests, mutation testing, property-based testing, and MC/DC. We see why coverage alone can’t tell you if your tests are any good.

We look at why static typing matters in Python and how type hints help you catch bugs before your code ever runs.

There is no escape.

Production-grade Python, coding with AI, and other things on my mind.