Python offers three primary mutable collection types: lists, sets, and dictionaries. Each has unique characteristics suited for different use cases.

Lists

• Ordered collections allowing duplicate elements
• Use cases:
  • Storing sequences in a specific order
  • Keeping collections with potential duplicates
  • Algorithms requiring indexed element access

Example: Tracking visitor entries chronologically

visitors = ["Alice", "Bob", "Charlie", "David", "Alice", "Eve"]

# Get the 3rd visitor
third_visitor = visitors[2]  # "Charlie"

# Add a new visitor
visitors.append("Frank")

# Find the first occurrence of Alice
first_alice_index = visitors.index("Alice")  # 0

# Count how many times Alice visited
alice_visits = visitors.count("Alice")  # 2

print(f"Total visitors today: {len(visitors)}")
print(f"Visitors in order: {', '.join(visitors)}")

Output:

Total visitors today: 7
Visitors in order: Alice, Bob, Charlie, David, Alice, Eve, Frank

Sets

• Unordered collections of unique items
• Use cases:
  • Removing duplicates
  • Membership testing
  • Set operations (union, intersection, difference)
  • Storing unique values without order importance

Example: Tracking unique daily visitors

unique_visitors = {"Alice", "Bob", "Charlie", "David", "Alice", "Eve"}

# Check if Frank has visited
has_frank_visited = "Frank" in unique_visitors  # False

# Add a new unique visitor
unique_visitors.add("Frank")

# Try to add Alice again (won't change the set)
unique_visitors.add("Alice")

# Get the total number of unique visitors
total_unique_visitors = len(unique_visitors)

print(f"Total unique visitors: {total_unique_visitors}")
print(f"Unique visitors: {', '.join(unique_visitors)}")

# Set operations
yesterday_visitors = {"Alice", "Bob", "George", "Hannah"}
new_visitors_today = unique_visitors - yesterday_visitors
returning_visitors = unique_visitors.intersection(yesterday_visitors)

print(f"New visitors today: {', '.join(new_visitors_today)}")
print(f"Returning visitors: {', '.join(returning_visitors)}")

Output:

Total unique visitors: 6
Unique visitors: Alice, Charlie, Bob, David, Frank, Eve
New visitors today: David, Frank, Charlie, Eve
Returning visitors: Alice, Bob

Dictionaries

• Unordered key-value pairs (keys must be unique and immutable)
• Use cases:
  • Storing associated data
  • Fast value lookups by key
  • Counting item occurrences

Example: Managing detailed visitor information

visitor_info = {
    "Alice": {"age": 28, "membership": "Gold", "visits": 3},
    "Bob": {"age": 35, "membership": "Silver", "visits": 1},
    "Charlie": {"age": 42, "membership": "Bronze", "visits": 2},
    "David": {"age": 31, "membership": None, "visits": 1}
}

# Get Alice's information
alice_info = visitor_info["Alice"]
print(f"Alice's membership: {alice_info['membership']}")

# Update Bob's visit count
visitor_info["Bob"]["visits"] += 1

# Add a new visitor
visitor_info["Eve"] = {"age": 39, "membership": "Gold", "visits": 1}

# Get all Gold members
gold_members = [name for name, info in visitor_info.items() if info["membership"] == "Gold"]
print(f"Gold members: {', '.join(gold_members)}")

# Calculate average age of visitors
total_age = sum(info["age"] for info in visitor_info.values())
average_age = total_age / len(visitor_info)
print(f"Average visitor age: {average_age:.1f}")

# Find the visitor with the most visits
most_frequent_visitor = max(visitor_info, key=lambda x: visitor_info[x]["visits"])
print(f"Most frequent visitor: {most_frequent_visitor}")

Output:

Alice's membership: Gold
Gold members: Alice, Eve
Average visitor age: 35.0
Most frequent visitor: Alice

These examples demonstrate how each data structure can be used effectively in a visitor tracking system:

• Lists are great for maintaining the order of visitors and allowing duplicate entries.
• Sets are perfect for quickly checking unique visitors and performing set operations.
• Dictionaries are ideal for storing and retrieving complex visitor information, with fast lookups based on visitor names.