Category: Python

Python provides several built-in data structures that are essential for writing efficient and effective programs. These data structures can be categorized into primitive (or simple) data types and non-primitive (or complex) data types. Here's an overview of the most commonly used Python data structures: Primitive Data Types Integer (int) Represents whole numbers. Example: x = 10 Float (float) Represents real numbers with decimal points. Example: y = 3.14 String (str) Represents a sequence of characters. Example: s = "Hello, World!" Boolean (bool) Represents True or False values. Example: flag = True Non-Primitive Data Types List An ordered, mutable collection of items. Allows duplicate elements. Defined using square brackets []. Example: lst = [1, 2, 3, "apple", 3.14] Tuple An ordered, immutable collection of items. Allows duplicate elements. Defined using parentheses (). Example: tpl = (1, 2, 3, "apple", 3.14) Set An unordered collection of unique items. Defined using curly braces {} or the set() function. Example: st = {1, 2, 3, "apple", 3.14} Example using set(): st = set([1, 2, 3, "apple", 3.14]) Dictionary An unordered collection of key-value pairs. Keys must be unique and immutable. Defined using curly braces {} with key-value pairs separated by colons : and pairs separated by commas ,. Example: dct = {"name": "Alice", "age": 30, "city": "New York"} Specialized Data Structures Deque (Double-Ended Queue) A generalization of stacks and queues that supports adding and removing elements from either end efficiently. Part of the collections module. Example: python Copy code from collections import deque dq = deque([1, 2, 3]) dq.append(4) dq.appendleft(0) NamedTuple A tuple subclass with named fields. Part of the collections module. Example: python Copy code from collections import namedtuple Point = namedtuple('Point', ['x', 'y']) p = Point(1, 2) print(p.x, p.y) OrderedDict A dictionary subclass that remembers the order in which keys were inserted. Part of the collections module. Example: python Copy code from collections import OrderedDict od = OrderedDict() od['a'] = 1 od['b'] = 2 DefaultDict A dictionary subclass that provides a default value for missing keys. Part of the collections module. Example: python Copy code from collections import defaultdict dd = defaultdict(int) dd['a'] += 1 Example Usage Here is an example demonstrating the use of these data structures in a simple program: python Copy code # List fruits = ["apple", "banana", "cherry"] fruits.append("date") print(fruits) # Output: ['apple', 'banana', 'cherry', 'date'] # Tuple coordinates = (10.0, 20.0) print(coordinates) # Output: (10.0, 20.0) # Set unique_numbers = {1, 2, 3, 2, 1} unique_numbers.add(4) print(unique_numbers) # Output: {1, 2, 3, 4} # Dictionary person = {"name": "Alice", "age": 30} person["city"] = "New York" print(person) # Output: {'name': 'Alice', 'age': 30, 'city': 'New York'} # Deque from collections import deque queue = deque([1, 2, 3]) queue.append(4) queue.appendleft(0) print(queue) # Output: deque([0, 1, 2, 3, 4]) # NamedTuple from collections import namedtuple Car = namedtuple('Car', ['make', 'model', 'year']) my_car = Car('Toyota', 'Corolla', 2020) print(my_car.make) # Output: Toyota # OrderedDict from collections import OrderedDict ordered_dict = OrderedDict() ordered_dict['first'] = 1 ordered_dict['second'] = 2 print(ordered_dict) # Output: OrderedDict([('first', 1), ('second', 2)]) # DefaultDict from collections import defaultdict default_dict = defaultdict(int) default_dict['count'] += 1 print(default_dict) # Output: defaultdict(, {'count': 1}) Understanding and utilizing these data structures effectively can significantly enhance your programming capabilities in Python.

Category: Python

Python Data Structures

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