Data structures are used to organize and store data efficiently. Python provides several built-in data structures that allow for different kinds of operations and use cases.

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1. Lists

A list is an ordered, mutable collection of items. Lists can store elements of different data types.

Key Features:

• Ordered (maintains the sequence of elements).
• Allows duplicates.
• Mutable (can be modified after creation).

Examples:

# Creating a list
fruits = ["apple", "banana", "cherry"]

# Accessing elements
print(fruits[0])  # Output: apple

# Modifying elements
fruits[1] = "blueberry"

# Adding elements
fruits.append("orange")

# Removing elements
fruits.remove("cherry")

# Iterating through a list
for fruit in fruits:
    print(fruit)

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2. Tuples

A tuple is an ordered, immutable collection of items. It is similar to a list but cannot be modified after creation.

Key Features:

• Ordered.
• Immutable (cannot be changed after creation).
• Allows duplicates.

Examples:

# Creating a tuple
coordinates = (10, 20, 30)

# Accessing elements
print(coordinates[1])  # Output: 20

# Iterating through a tuple
for value in coordinates:
    print(value)

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3. Dictionaries

A dictionary is an unordered collection of key-value pairs. Keys must be unique and immutable, while values can be of any data type.

Key Features:

• Unordered (Python 3.7+ maintains insertion order).
• Keys are unique.
• Mutable (can add, modify, or delete items).

Examples:

# Creating a dictionary
person = {"name": "Alice", "age": 25, "city": "New York"}

# Accessing values
print(person["name"])  # Output: Alice

# Modifying values
person["age"] = 26

# Adding a key-value pair
person["job"] = "Engineer"

# Removing a key-value pair
del person["city"]

# Iterating through a dictionary
for key, value in person.items():
    print(f"{key}: {value}")

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4. Sets

A set is an unordered collection of unique items. It is useful for removing duplicates and performing mathematical operations like union, intersection, and difference.

Key Features:

• Unordered.
• No duplicate elements.
• Mutable (can add or remove items).

Examples:

# Creating a set
numbers = {1, 2, 3, 4}

# Adding elements
numbers.add(5)

# Removing elements
numbers.remove(3)

# Checking membership
print(2 in numbers)  # Output: True

# Set operations
set1 = {1, 2, 3}
set2 = {3, 4, 5}
print(set1.union(set2))      # Output: {1, 2, 3, 4, 5}
print(set1.intersection(set2))  # Output: {3}

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5. Strings (as a Data Structure)

Strings are immutable sequences of characters. While not traditionally thought of as a data structure, strings support many operations similar to lists.

Examples:

text = "hello"

# Accessing characters
print(text[1])  # Output: e

# Slicing
print(text[1:4])  # Output: ell

# Iterating through a string
for char in text:
    print(char)

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6. Advanced Data Structures

Python also provides libraries like collections and numpy for advanced data structures.

• Deque (from collections): Double-ended queue for fast appends and pops.pythonCopy codefrom collections import deque dq = deque([1, 2, 3]) dq.append(4) dq.appendleft(0) print(dq) # Output: deque([0, 1, 2, 3, 4])
• Arrays (from numpy): Efficient handling of numerical data.pythonCopy codeimport numpy as np arr = np.array([1, 2, 3, 4]) print(arr + 1) # Output: [2 3 4 5]

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Comparison of Data Structures

Feature	List	Tuple	Dictionary	Set
Ordered	✅	✅	❌ (3.7+: ✅)	❌
Mutable	✅	❌	✅	✅
Allows Duplicates	✅	✅	Keys: ❌, Values: ✅	❌
Access by Index	✅	✅	❌	❌

Understanding these data structures and their properties helps you choose the right one for your program’s needs.