Hands-on Practical: Working with Collections

Now that you understand the different types of collections available in Python. lists, tuples, dictionaries, and sets. let's put that knowledge into practice. This section provides hands-on examples to help solidify your understanding of how to create, manipulate, and use these fundamental data structures. Follow along and try running these examples in your Python interpreter or script.

Working with Lists

Lists are ordered, mutable collections. They are perhaps the most common collection type you'll encounter. Let's manage a list of tasks.

1. Create a list: Start by creating a list of strings representing tasks.

   tasks = ["Review Chapter 3", "Start Chapter 4", "Practice list operations"]
   print(tasks)
   # Output: ['Review Chapter 3', 'Start Chapter 4', 'Practice list operations']
   

2. Access elements: Access elements using their index (remembering that indexing starts at 0).

   first_task = tasks[0]
   print(f"First task: {first_task}")
   # Output: First task: Review Chapter 3
   
   last_task = tasks[-1] # Negative indexing counts from the end
   print(f"Last task: {last_task}")
   # Output: Last task: Practice list operations
   

3. Add elements: Use append() to add an item to the end, or insert() to add at a specific position.

   tasks.append("Take a break")
   print(tasks)
   # Output: ['Review Chapter 3', 'Start Chapter 4', 'Practice list operations', 'Take a break']
   
   tasks.insert(1, "Read list documentation") # Insert at index 1
   print(tasks)
   # Output: ['Review Chapter 3', 'Read list documentation', 'Start Chapter 4', 'Practice list operations', 'Take a break']
   

4. Remove elements: Use remove() to remove the first occurrence of a value, or del to remove by index.

   tasks.remove("Take a break") # Remove by value
   print(tasks)
   # Output: ['Review Chapter 3', 'Read list documentation', 'Start Chapter 4', 'Practice list operations']
   
   del tasks[0] # Remove the item at index 0
   print(tasks)
   # Output: ['Read list documentation', 'Start Chapter 4', 'Practice list operations']
   

5. Iterate through a list: Use a for loop to process each item in the list.

   print("\nRemaining tasks:")
   for task in tasks:
       print(f"- {task}")
   # Output:
   # Remaining tasks:
   # - Read list documentation
   # - Start Chapter 4
   # - Practice list operations
   

Working with Tuples

Tuples are ordered, immutable collections. Once created, their contents cannot be changed. They are often used for fixed collections of items, like coordinates or RGB color values.

1. Create a tuple: Tuples are defined using parentheses ().

   point = (10, 20) # Represents an (x, y) coordinate
   rgb_color = (255, 0, 0) # Red color
   
   print(f"Point coordinates: {point}")
   # Output: Point coordinates: (10, 20)
   print(f"RGB Color: {rgb_color}")
   # Output: RGB Color: (255, 0, 0)
   

2. Access elements: Access elements just like lists, using indexing.

   x_coordinate = point[0]
   red_value = rgb_color[0]
   
   print(f"X coordinate: {x_coordinate}")
   # Output: X coordinate: 10
   print(f"Red value: {red_value}")
   # Output: Red value: 255
   

3. Immutability: Trying to change an element in a tuple results in an error. This is a defining feature.

   # This line will cause an error:
   # point[0] = 15
   # TypeError: 'tuple' object does not support item assignment
   

   Immutability makes tuples reliable for representing data that shouldn't change accidentally. They can also be used as keys in dictionaries (unlike lists).

Working with Dictionaries

Dictionaries store data as key-value pairs. They are unordered (in older Python versions) or insertion-ordered (in newer Python versions) and mutable. Keys must be unique and immutable (like strings, numbers, or tuples). Dictionaries provide fast lookups based on the key.

1. Create a dictionary: Use curly braces {} with key: value pairs.

   student_scores = {
       "Alice": 85,
       "Bob": 92,
       "Charlie": 78
   }
   print(student_scores)
   # Output: {'Alice': 85, 'Bob': 92, 'Charlie': 78}
   

2. Access values: Use the key inside square brackets [] to get the corresponding value.

   bob_score = student_scores["Bob"]
   print(f"Bob's score: {bob_score}")
   # Output: Bob's score: 92
   

   Trying to access a key that doesn't exist will raise a KeyError. You can use the .get() method for safer access, which returns None (or a specified default) if the key is not found.

   david_score = student_scores.get("David") # Key doesn't exist
   print(f"David's score: {david_score}")
   # Output: David's score: None
   
   david_score_default = student_scores.get("David", "Not found") # With default
   print(f"David's score: {david_score_default}")
   # Output: David's score: Not found
   

3. Add or modify entries: Assign a value to a new key to add it, or assign a new value to an existing key to modify it.

   # Add a new student
   student_scores["David"] = 88
   print(student_scores)
   # Output: {'Alice': 85, 'Bob': 92, 'Charlie': 78, 'David': 88}
   
   # Update Alice's score
   student_scores["Alice"] = 87
   print(student_scores)
   # Output: {'Alice': 87, 'Bob': 92, 'Charlie': 78, 'David': 88}
   

4. Remove entries: Use del to remove an entry by key.

   del student_scores["Charlie"]
   print(student_scores)
   # Output: {'Alice': 87, 'Bob': 92, 'David': 88}
   

5. Iterate through a dictionary: You can iterate through keys, values, or key-value pairs (items).

   print("\nStudent Scores:")
   # Iterate through keys (default iteration)
   for student in student_scores:
       print(f"- {student}: {student_scores[student]}")
   
   print("\nUsing .items():")
   # Iterate through key-value pairs
   for student, score in student_scores.items():
       print(f"- {student} scored {score}")
   
   # Output:
   # Student Scores:
   # - Alice: 87
   # - Bob: 92
   # - David: 88
   #
   # Using .items():
   # - Alice scored 87
   # - Bob scored 92
   # - David scored 88
   

Working with Sets

Sets are unordered collections of unique, immutable items. They are useful for membership testing, removing duplicates, and performing mathematical set operations.

1. Create a set: Use curly braces {} or the set() function. Note that {} creates an empty dictionary, so use set() for an empty set.

   # From a list with duplicates
   tags_list = ["python", "data", "web", "python", "data"]
   unique_tags = set(tags_list)
   print(unique_tags)
   # Output: {'data', 'python', 'web'} (Order may vary)
   
   # Direct creation
   allowed_users = {"alice", "bob", "charlie"}
   print(allowed_users)
   # Output: {'charlie', 'alice', 'bob'} (Order may vary)
   

2. Add elements: Use the add() method. Duplicates are automatically ignored.

   allowed_users.add("david")
   print(allowed_users)
   # Output: {'charlie', 'david', 'alice', 'bob'}
   
   allowed_users.add("alice") # Adding existing element has no effect
   print(allowed_users)
   # Output: {'charlie', 'david', 'alice', 'bob'}
   

3. Membership testing: Use the in operator to check if an element exists in the set. This is very efficient.

   if "bob" in allowed_users:
       print("Bob is an allowed user.")
   else:
       print("Bob is not allowed.")
   # Output: Bob is an allowed user.
   
   if "eve" in allowed_users:
       print("Eve is an allowed user.")
   else:
       print("Eve is not allowed.")
   # Output: Eve is not allowed.
   

4. Set operations: Sets support operations like union (|), intersection (&), difference (-), and symmetric difference (^).

   admin_users = {"alice", "eve"}
   
   # Union: all users in either set
   all_users = allowed_users | admin_users
   print(f"All users: {all_users}")
   # Output: All users: {'eve', 'charlie', 'david', 'alice', 'bob'}
   
   # Intersection: users in both sets
   common_users = allowed_users & admin_users
   print(f"Common users (admins who are allowed): {common_users}")
   # Output: Common users (admins who are allowed): {'alice'}
   
   # Difference: users in allowed_users but not in admin_users
   non_admin_allowed = allowed_users - admin_users
   print(f"Allowed users who are not admins: {non_admin_allowed}")
   # Output: Allowed users who are not admins: {'charlie', 'david', 'bob'}
   

Choosing the Right Structure: A Small Example

Imagine you need to store information about books in a library catalog.

• To store the ISBN (unique identifier) and title of each book, a dictionary is suitable: books_by_isbn = {"978-0134": "Python Crash Course", ...}. This allows quick lookup by ISBN.
• To store the sequence of books borrowed by a specific user in the order they were borrowed, a list is appropriate: borrow_history = ["Python Crash Course", "Fluent Python", ...]. Order matters, and the list can grow as more books are borrowed.
• To store a fixed set of genres like ('Fiction', 'Science', 'History'), a tuple could be used if you don't expect genres to change often during program execution.
• To quickly check if a book belongs to a special collection (e.g., "rare books"), a set of ISBNs for that collection would be efficient: rare_book_isbns = {"978-...", "978-..."}. Checking isbn in rare_book_isbns is fast.

This practical session covered creating, accessing, modifying, and iterating through lists, tuples, dictionaries, and sets. Experimenting with these examples is the best way to become comfortable using Python's collection types effectively. Remember to consider the characteristics of each type. order, mutability, uniqueness, key-value mapping. when deciding which one to use for your specific data organization needs.