Chapter 8 Overview: Core Data Types, Data Structures (of Data Types) and Utilities in Python

Python Data Types

Python’s built-in data types are broadly categorized into simple (primitive) types and complex (collection) types.

Simple Data Types

• int: Integer numbers, e.g., 42, -7.
• float: Floating-point numbers, e.g., 3.14, -0.001.
• complex: Complex numbers with real and imaginary parts, e.g., 2 + 3j.
• bool: Boolean values, True or False.
• str: Unicode text strings, e.g., "Hello".
• bytes: Immutable sequences of bytes, used for binary data.
• bytearray: Mutable sequences of bytes.
• NoneType: The special None object representing absence of a value.

Complex (Collection) Data Types

• list: Ordered, mutable sequences of items, e.g., [1, 2, 3].
• tuple: Ordered, immutable sequences of items, e.g., (1, 2, 3).
• set: Unordered collections of unique items, e.g., {1, 2, 3}.
• frozenset: Immutable sets.
• dict: Key-value mappings, e.g., {'name': 'Alice', 'age': 30}.

Underlying Data Structures of Python Data Types

Understanding the underlying data structures helps to grasp how Python manages data efficiently under the hood.

• List: Implemented as a dynamic array, allowing fast indexing and amortized efficient append operations.
• Tuple: Similar to lists but immutable; also implemented as a fixed-size array, providing fast access.
• Set: Implemented as a hash table, enabling fast membership testing, insertion, and deletion.
• FrozenSet: An immutable hash table based collection similar to sets.
• Dictionary (dict): Built as a highly optimized hash table for mapping keys to values with average O(1) lookup, insertion, and deletion.

Other simple types like int, float, bool, and str have specialized implementations optimized for performance and memory, but are not built on collection data structures.

Python Iterables

Iterables are objects capable of returning their elements one at a time, allowing you to loop over them using a for loop or other iteration contexts.

• List: Iterates over elements in order.
• Tuple: Iterates over elements in order.
• Set: Iterates over unique elements in arbitrary order.
• Dictionary: Iterates over keys by default.
• Range: Represents a sequence of numbers; efficient and lazy evaluated iterable.
• String: Iterable over characters.
• Generator: Special iterables that produce items lazily using yield statements.
• File objects: Iterable over lines in a file.
• Enumerate: Iterable that yields index and value pairs from another iterable.
• Zip: Iterable that aggregates elements from multiple iterables.

Python Utilities: Functions and More

Python provides many utilities to work with data structures and iterables effectively.

• Functions: Encapsulate reusable blocks of code to perform tasks or computations.
• Itertools Module: Offers utilities for efficient looping, such as chain, cycle, combinations, and more.
• List Comprehensions: (COVERED IN CH-10) Concise syntax to create lists by iterating and optionally filtering.
• Generator Expressions: (COVERED IN CH-10) Similar to list comprehensions but produce generators for lazy evaluation.
• Lambda Functions: (COVERED IN CH-10) Small anonymous functions defined with the lambda keyword.
• Map: (COVERED IN CH-10) Applies a function to all items in an iterable, returning an iterator.
• Filter: (COVERED IN CH-10) Filters elements from an iterable based on a function that returns True or False.
• Reduce: (COVERED IN CH-10) Aggregates iterable items using a function to produce a single cumulative value (available in functools module).