Data Types

5.1 Introduction to Data Types

What data types are, why choosing the right one matters, how dynamic typing works, and real-world examples of Python values mapped to their types.

5.2 Python Object Model

Why everything in Python is an object, and the three properties every object has — identity, type, and value — with id() and type() examples.

5.3 Numeric Data Types

Python's four numeric types — int, float, complex, and bool — with type conversion and the core arithmetic operators.

5.4 Sequence Data Types

Python's four sequence types — str, list, tuple, and range — their mutability differences, typical uses, and the shared operations that work across all of them.

5.5 Mapping Data Type

Python's dict — the built-in hash map, its key-value pairs, and the real-world use cases where dictionaries are the natural data structure.

5.6 Set Data Types

Python's set and frozenset — unordered collections of unique, hashable values — and the classic one-line list-deduplication pattern.

5.7 Binary Data Types

Python's binary types — bytes, bytearray, and memoryview — for working with raw, non-text data like files, sockets, and cryptographic operations.

5.8 NoneType

Python's None singleton, why it's distinct from 0 and an empty string, and why you should always compare to None with is, not ==.

5.9 Mutable vs Immutable Types

The difference between mutable and immutable objects in Python, which built-in types fall into each category, and how identity (id()) behaves differently for each.

5.10 Type Checking

type() vs isinstance() for checking an object's type, why isinstance() is preferred for validation because it handles subclassing, and what id() is used for.

5.11 Type Conversion

Implicit type conversion (int promoted to float in mixed arithmetic) vs explicit casting with int(), float(), and str() in Python.

5.12 Memory Representation

How Python stores objects on the heap, why a variable is a reference rather than a box holding a value, and how CPython's reference-counting garbage collector works.

5.13 Choosing the Right Data Type

How to pick the right Python data type for a given piece of data based on performance and memory, with a real-world selection table.

5.14 Data Types in DevOps

Where Python's built-in data types show up in real infrastructure tooling — configuration data, JSON, API responses, AWS/boto3 resources, and log processing.

5.15 Common Mistakes

Three type-related mistakes that catch even experienced developers off guard — unexpected type changes, the mutable default argument bug, and comparison pitfalls.

5.16 Best Practices

Best practices for working with Python data types — readability, consistency, and choosing efficient data structures.

5.17 Interview Questions

Frequently asked and scenario-based Python data type interview questions covering list vs tuple, bool as an int subclass, mutable defaults, and JSON type mapping.

5.18 Hands-on Exercises

Practice programs and mini projects for reinforcing Python data type concepts — type counting, deduplication, the mutable default bug, JSON parsing, and a small type profiler.

Data Types: Chapter Practice

Apply data types with a mini lab and knowledge check.