Python Data Structures

Lists

Lists are ordered, mutable collections that can hold a variety of object types. They are one of the most versatile data structures in Python.

                        # Creating a list

                        my_list = [1, 2, 3, 'apple', 'banana']

                        # Accessing elements

                        first_item = my_list[0] # 1
                            (zero-based indexing)

                        # Modifying elements

                        my_list[1] = 'orange'

                        # Common methods

                        my_list.append('grape') # Add to
                            end

                        my_list.insert(2, 'pear') # Insert at
                            position

                        removed_item = my_list.pop() # Remove and return last item

Lists support slicing, concatenation, and many other operations that make them extremely useful for storing collections of items.

Tuples

Tuples are similar to lists but are immutable (cannot be changed after creation). They are often used for fixed collections of items.

                        # Creating a tuple

                        my_tuple = (1, 'apple',
                        3.14)

                        # Accessing elements

                        second_item = my_tuple[1] #
                            'apple'

                        # Tuple unpacking

                        a, b, c = my_tuple # a=1, b='apple', c=3.14

                        # Single element tuple (note the comma)

                        single_tuple = (42,)

Tuples are more memory efficient than lists and can be used as dictionary keys (since they're immutable).

Dictionaries

Dictionaries store key-value pairs and provide fast lookups. Keys must be immutable types (strings, numbers, tuples).

                        # Creating a dictionary

                        student = {

                            'name': 'Alice',

                            'age': 22,

                            'courses': ['Math', 'Physics']

                        }

                        # Accessing values

                        name = student['name'] #
                            'Alice'

                        age = student.get('age') #
                            22

                        # Adding/updating items

                        student['grade'] = 'A'

                        student.update({'age': 23, 'city': 'New York'})

                        # Dictionary methods

                        keys = student.keys() # ['name', 'age', 'courses', 'grade',
                            'city']

                        values = student.values() # ['Alice', 23, ['Math', 'Physics'], 'A',
                            'New York']

Sets

Sets are unordered collections of unique elements. They are useful for membership testing and eliminating duplicate entries.

                        # Creating a set

                        fruits = {'apple', 'banana', 'orange'}

                        numbers = set([1, 2,
                        3, 2, 1]) # {1, 2, 3}

                        # Set operations

                        fruits.add('pear')

                        fruits.remove('banana')

                        # Set methods

                        a = {1, 2, 3}

                        b = {3, 4, 5}

                        union = a.union(b) # {1, 2, 3, 4, 5}

                        intersection = a.intersection(b) # {3}

                        difference = a.difference(b) # {1, 2}

Advanced Data Structures

Python also offers more specialized data structures in the collections module.

                        # Importing from collections module

                        from collections import defaultdict, Counter, deque, namedtuple

                        # defaultdict - dictionary with default values

                        word_counts = defaultdict(int)

                        word_counts['apple'] += 1 # No KeyError

                        # Counter - counts hashable objects

                        colors = ['red', 'blue',
                        'red', 'green', 'blue', 'red']

                        color_counts = Counter(colors) # {'red': 3, 'blue': 2, 'green':
                            1}

                        # deque - double-ended queue

                        d = deque([1, 2, 3])

                        d.appendleft(0) # [0, 1, 2,
                            3]

                        d.pop() # removes 3

                        # namedtuple - tuple with named fields

                        Point = namedtuple('Point', ['x', 'y'])

                        p = Point(10, 20)

                        p.x # 10

                        p.y # 20