Ch2. Python — Data Structures: Lists, Dicts, Tuples & Sets

Python’s Four Core Data Structures

Structure	Ordered	Mutable	Duplicates	Example
list	Yes	Yes	Yes	[1, 2, 3]
tuple	Yes	No	Yes	(1, 2, 3)
dict	Yes (3.7+)	Yes	Keys: No	{"a": 1}
set	No	Yes	No	{1, 2, 3}

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Lists

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

# Indexing (0-based)
print(fruits[0])    # apple
print(fruits[-1])   # apple (last item)

# Slicing
print(fruits[1:3])  # ['banana', 'cherry']

# Key methods
fruits.append("mango")      # Add to end
fruits.insert(1, "grape")   # Insert at index 1
fruits.remove("banana")     # Remove by value
fruits.pop()                # Remove last item
fruits.sort()               # Sort in place
print(len(fruits))          # Length

# List comprehension
squares = [x**2 for x in range(10)]
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# Conditional comprehension
evens = [x for x in range(20) if x % 2 == 0]
# [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

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Dictionaries

person = {
    "name": "Alice",
    "age": 30,
    "city": "New York"
}

# Access
print(person["name"])                   # Alice
print(person.get("age"))               # 30
print(person.get("job", "Unknown"))    # Unknown (default if missing)

# Add / modify
person["email"] = "alice@example.com"
person["age"] = 31

# Delete
del person["city"]

# Iterate
for key, value in person.items():
    print(f"{key}: {value}")

# Dictionary comprehension
word_lengths = {word: len(word) for word in ["python", "java", "javascript"]}
# {'python': 6, 'java': 4, 'javascript': 10}

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Tuples and Sets

# Tuples: immutable sequences (good for coordinates, RGB values)
coordinates = (40.7128, -74.0060)  # NYC coordinates
lat, lon = coordinates  # Unpacking

# Sets: unordered, no duplicates
numbers = {1, 2, 3, 3, 4, 4, 5}
print(numbers)  # {1, 2, 3, 4, 5}

# Set operations
a = {1, 2, 3, 4}
b = {3, 4, 5, 6}
print(a | b)  # Union: {1, 2, 3, 4, 5, 6}
print(a & b)  # Intersection: {3, 4}
print(a - b)  # Difference: {1, 2}

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Practical Example: Grade Manager

students = {
    "Alice": [90, 85, 92],
    "Bob": [78, 82, 75],
    "Charlie": [95, 88, 91]
}

# Calculate averages with dict comprehension
averages = {
    name: sum(scores) / len(scores)
    for name, scores in students.items()
}

# Sort and display
for name, avg in sorted(averages.items(), key=lambda x: x[1], reverse=True):
    print(f"{name}: {avg:.1f}")
# Alice: 89.0
# Charlie: 91.3
# Bob: 78.3

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