Python Dictionary Explained with Examples

In Python, a dictionary is one of the most powerful and flexible data structures. It allows you to store data in key-value pairs, where each key must be unique, and values can be of any data type. Dictionaries are extremely useful for organizing data, improving code readability, and performing quick lookups. In this tutorial, we will explore Python dictionaries in detail, complete with examples and best practices.

What is a Python Dictionary?

A dictionary in Python is defined using curly braces {}, with each key-value pair separated by a colon :. You can think of it as a real-world dictionary, where the word is the key and its meaning is the value.

# Example of a Python dictionary
student = {
    "name": "Alice",
    "age": 21,
    "major": "Computer Science"
}

print(student)

Output:

{'name': 'Alice', 'age': 21, 'major': 'Computer Science'}

Here, name, age, and major are keys, and their corresponding values are “Alice”, 21, and “Computer Science”.

Accessing Dictionary Values

You can access dictionary values using their keys within square brackets [] or using the get() method.

print(student["name"])  # Output: Alice
print(student.get("major"))  # Output: Computer Science

If you try to access a key that doesn’t exist using [], Python raises a KeyError. However, get() returns None instead of throwing an error, making it safer for optional keys.

Adding and Updating Dictionary Elements

Dictionaries are mutable, meaning you can change their content after creation. You can add new key-value pairs or update existing ones easily.

# Add a new key-value pair
student["grade"] = "A"

# Update existing key
student["age"] = 22

print(student)

Output:

{'name': 'Alice', 'age': 22, 'major': 'Computer Science', 'grade': 'A'}

Removing Elements from a Dictionary

You can remove items using several methods such as pop(), del, and clear().

# Remove by key
student.pop("grade")

# Remove a specific key-value pair
del student["major"]

# Clear all items
student.clear()

print(student)

Output:

{}

Looping Through a Dictionary

Python provides different ways to loop through keys, values, or both.

student = {"name": "Alice", "age": 22, "major": "Computer Science"}

# Loop through keys
for key in student:
    print(key)

# Loop through values
for value in student.values():
    print(value)

# Loop through both keys and values
for key, value in student.items():
    print(f"{key}: {value}")

Dictionary Methods

Here are some commonly used dictionary methods in Python:

• keys() – returns all keys in the dictionary
• values() – returns all values
• items() – returns key-value pairs as tuples
• update() – merges another dictionary or updates existing keys
• copy() – creates a shallow copy of the dictionary

info = {"brand": "Toyota", "year": 2020}
extra = {"color": "blue", "type": "SUV"}

info.update(extra)
print(info)

Output:

{'brand': 'Toyota', 'year': 2020, 'color': 'blue', 'type': 'SUV'}

Dictionary Comprehension

Python allows you to create dictionaries using dictionary comprehensions — a concise way to generate dictionaries from iterable data.

squares = {x: x*x for x in range(1, 6)}
print(squares)

Output:

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Nested Dictionaries

A dictionary can also contain another dictionary as a value. This is useful for representing structured data such as JSON objects or configuration files.

students = {
    "student1": {"name": "Alice", "age": 21},
    "student2": {"name": "Bob", "age": 23}
}

print(students["student2"]["name"])  # Output: Bob

Real-World Example: Counting Word Frequency

Dictionaries are great for counting occurrences of items. Here’s a practical example that counts word frequency in a sentence:

sentence = "python dictionary explained with examples python tutorial"
words = sentence.split()

frequency = {}

for word in words:
    frequency[word] = frequency.get(word, 0) + 1

print(frequency)

Output:

{'python': 2, 'dictionary': 1, 'explained': 1, 'with': 1, 'examples': 1, 'tutorial': 1}

Conclusion

Python dictionaries are versatile and essential for modern programming. Whether you’re building APIs, managing data, or working with JSON, understanding how dictionaries work will make your code cleaner and more efficient. Mastering this data structure is a must for every Python developer.

Now that you’ve learned how to use dictionaries, try combining them with lists and sets for even more powerful data handling.