Dictionaries and Sets: Efficient Data Retrieval #

Welcome back to our programming tutorial series! Today, we’ll explore two powerful data structures in Python: dictionaries and sets. These structures allow you to store and retrieve data efficiently, especially when working with unique values or key-value pairs.

What Are Dictionaries? #

A dictionary is an unordered collection of key-value pairs, where each key is unique. You can use a dictionary to store related pieces of information and access them using the corresponding key.

Creating Dictionaries: #

# Empty dictionary
my_dict = {}

# Dictionary with key-value pairs
person = {
    "name": "Alice",
    "age": 30,
    "city": "New York"
}

You can access dictionary values by referring to their keys:

print(person["name"])  # Outputs: Alice
print(person["age"])  # Outputs: 30

Adding, Updating, and Removing Dictionary Entries #

You can add new key-value pairs or update existing ones in a dictionary:

# Adding a new entry
person["job"] = "Engineer"

# Updating an existing entry
person["age"] = 31

# Removing an entry
del person["city"]

print(person)  # Outputs: {'name': 'Alice', 'age': 31, 'job': 'Engineer'}

Dictionary Methods #

Python provides several built-in methods to work with dictionaries:

# Get all keys
print(person.keys())  # Outputs: dict_keys(['name', 'age', 'job'])

# Get all values
print(person.values())  # Outputs: dict_values(['Alice', 31, 'Engineer'])

# Get key-value pairs
print(person.items())  # Outputs: dict_items([('name', 'Alice'), ('age', 31), ('job', 'Engineer')])

# Check if a key exists
if "name" in person:
    print("Name is present in the dictionary.")

Nested Dictionaries #

Dictionaries can also contain other dictionaries, allowing you to store more complex data structures.

people = {
    "person1": {
        "name": "Alice",
        "age": 30
    },
    "person2": {
        "name": "Bob",
        "age": 25
    }
}

print(people["person1"]["name"])  # Outputs: Alice

What Are Sets? #

A set is an unordered collection of unique elements. Sets are useful when you want to store non-duplicate items or perform mathematical set operations like union, intersection, and difference.

Creating Sets: #

# Empty set
my_set = set()

# Set with unique elements
fruits = {"apple", "banana", "cherry", "apple"}
print(fruits)  # Outputs: {'apple', 'banana', 'cherry'}

Set Operations #

Python sets support various operations for handling unique elements:

# Adding elements
fruits.add("orange")

# Removing elements
fruits.remove("banana")

# Set operations
set1 = {1, 2, 3}
set2 = {3, 4, 5}

# Union (all unique elements from both sets)
print(set1 | set2)  # Outputs: {1, 2, 3, 4, 5}

# Intersection (common elements in both sets)
print(set1 & set2)  # Outputs: {3}

# Difference (elements in set1 but not in set2)
print(set1 - set2)  # Outputs: {1, 2}

Practical Exercise: Phone Book Application #

Now that you understand dictionaries and sets, let’s create a simple phone book application using dictionaries:

  1. Write a program that allows the user to:
    • Add a new contact (name and phone number).
    • Search for a contact by name.
    • Remove a contact by name.
  2. Use a set to keep track of unique contact names.

Here’s a starter example:

phone_book = {}

def add_contact(name, phone):
    if name in phone_book:
        print(f"{name} is already in the phone book.")
    else:
        phone_book[name] = phone
        print(f"Added {name}: {phone}")

def search_contact(name):
    if name in phone_book:
        print(f"{name}: {phone_book[name]}")
    else:
        print(f"{name} is not found in the phone book.")

def remove_contact(name):
    if name in phone_book:
        del phone_book[name]
        print(f"Removed {name}")
    else:
        print(f"{name} is not found in the phone book.")

# Example usage
add_contact("Alice", "123-456-7890")
add_contact("Bob", "987-654-3210")
search_contact("Alice")
remove_contact("Bob")

What’s Next? #

You’ve just learned how to work with dictionaries and sets, essential tools for managing key-value pairs and unique collections of data. In the next post, we’ll dive into practical applications of these data structures to solve real-world problems.

Happy coding, and we’ll see you in the next lesson!