Data Structures

Lists and Tuples

Creating and Accessing Lists

Definition: A list is a mutable, ordered collection of items. Lists can contain elements of mixed data types.

Creating a List:

fruits = ["apple", "banana", "cherry"]

Accessing Elements:

By Index:

print(fruits[0])  # Output: "apple"

Using Slicing:

print(fruits[1:3])  # Output: ["banana", "cherry"]

List Methods

Append an Item:

fruits.append("orange")
print(fruits)  # Output: ["apple", "banana", "cherry", "orange"]

Insert an Item:

fruits.insert(1, "kiwi")
print(fruits)  # Output: ["apple", "kiwi", "banana", "cherry"]

Remove an Item:

fruits.remove("banana")
print(fruits)  # Output: ["apple", "kiwi", "cherry"]

Sort the List:

fruits.sort()
print(fruits)  # Output: ["apple", "cherry", "kiwi"]

Tuples vs. Lists

Tuples:

• Immutable (cannot be changed after creation).
• Created with parentheses ().

colors = ("red", "green", "blue")

Lists:

• Mutable (can be modified).
• Created with square brackets [].

When to Use:

• Use tuples for fixed collections of data.
• Use lists when data needs to change over time.

person = ("John", 25)  # Tuple
shopping_list = ["eggs", "milk", "bread"]  # List

Dictionaries and Sets

Creating and Accessing Dictionaries

Definition: A dictionary is an unordered collection of key-value pairs.

student = {"name": "Alice", "age": 22, "grade": "A"}

Accessing Values:

print(student["name"])  # Output: "Alice"
print(student.get("age"))  # Output: 22

Dictionary Methods

Add or Update a Key-Value Pair:

student["major"] = "Computer Science"
print(student)  # Output: {"name": "Alice", "age": 22, "grade": "A", "major": "Computer Science"}

Remove a Key:

student.pop("grade")
print(student)  # Output: {"name": "Alice", "age": 22, "major": "Computer Science"}

Iterate Through a Dictionary:

for key, value in student.items():
    print(key, value)

Sets and Their Applications

Definition:

A set is an unordered collection of unique items.

numbers = {1, 2, 3, 4}

Set Operations:

Union:

set1 = {1, 2, 3}
set2 = {3, 4, 5}
print(set1 | set2)  # Output: {1, 2, 3, 4, 5}

Intersection:

print(set1 & set2)  # Output: {3}

Difference:

print(set1 - set2)  # Output: {1, 2}

Applications:

Removing duplicates from a list:

data = [1, 2, 2, 3, 4, 4, 5]
unique_data = set(data)
print(unique_data)  # Output: {1, 2, 3, 4, 5}

Comprehensions in Python

Comprehensions in Python provide a concise way to create lists, dictionaries, and sets. They help make code more readable and efficient by replacing traditional loops with a more compact syntax.

List Comprehensions:

List comprehensions allow you to create a new list by applying an expression to each item in an iterable.

squares = [x**2 for x in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16]

Dictionary Comprehensions:

Dictionary comprehensions allow the creation of dictionaries using a similar syntax to list comprehensions.

square_dict = {x: x**2 for x in range(5)}
print(square_dict)  # Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

Set Comprehensions:

Set comprehensions work similarly to list comprehensions but create sets instead of lists.

unique_squares = {x**2 for x in range(5)}
print(unique_squares)  # Output: {0, 1, 4, 9, 16}

Summary

• Lists: Mutable, ordered, and versatile.
• Tuples: Immutable and used for fixed collections.
• Dictionaries: Key-value pairs, unordered, and fast for lookups.
• Sets: Unordered collections of unique elements.
• Comprehensions: Provide concise, readable ways to create data structures.