.. include:: global.rst

============================
Structuring Data In Python
============================


Overview
--------
Structuring data in Python involves organizing and storing data in ways that
make it efficient to access, modify, and analyze. Python provides several
built-in data structures and tools to support this.

Core Data Structures
--------------------

Lists
^^^^^
Lists are ordered, mutable collections.

- Defined using square brackets ``[]``
- Can store mixed data types
- Support indexing and slicing

Example::

    numbers = [1, 2, 3, 4]
    numbers.append(5)

Tuples
^^^^^^
Tuples are ordered, immutable collections.

- Defined using parentheses ``()``
- Cannot be changed after creation
- Useful for fixed data

Example::

    point = (10, 20)

Dictionaries
^^^^^^^^^^^^
Dictionaries store key-value pairs.

- Defined using curly braces ``{}``
- Keys must be unique and immutable
- Values can be any type

Example::

    user = {"name": "Alice", "age": 25}
    print(user["name"])

Sets
^^^^
Sets are unordered collections of unique elements.

- Defined using ``{}`` or ``set()``
- Automatically remove duplicates
- Support set operations

Example::

    unique_numbers = {1, 2, 2, 3}

Custom Data Structures
----------------------

Classes
^^^^^^^
Classes allow you to create custom data structures.

- Combine data (attributes) and behavior (methods)
- Support object-oriented design

Example::

    class Person:
        def __init__(self, name, age):
            self.name = name
            self.age = age

    p = Person("Alice", 25)

Data Classes
^^^^^^^^^^^^
Data classes simplify class creation for storing data.

- Introduced in Python 3.7
- Automatically generate ``__init__``, ``__repr__``, etc.

Example::

    from dataclasses import dataclass

    @dataclass
    class Person:
        name: str
        age: int

Nested Structures
-----------------
Data structures can be combined to represent complex data.

Example::

    students = [
        {"name": "Alice", "grades": [90, 85, 88]},
        {"name": "Bob", "grades": [78, 82, 80]}
    ]

Benefits:
- Represent real-world relationships
- Enable hierarchical data organization

Choosing the Right Structure
----------------------------
Consider the following:

- Use lists for ordered, changeable collections
- Use tuples for fixed data
- Use dictionaries for fast key-based lookup
- Use sets for uniqueness and membership testing

Summary
-------
Effective data structuring in Python helps:

- Improve performance
- Simplify code
- Enhance readability and maintainability

Common tools include lists, tuples, dictionaries, sets, and classes.