Python Dictionaries: The Data Structure You'll Use Every Day

If I had to pick one Python data structure to teach a beginner first, it would be Python dictionaries. They map keys to values, they’re fast, and they show up everywhere — JSON data, function arguments, database rows, configuration. Understanding dictionaries deeply will make you a better Python developer.

Creating and Accessing Dictionaries

A dictionary is defined with curly braces and key-value pairs:

user = {
    "name": "Kaikobud",
    "age": 28,
    "city": "Dhaka",
    "skills": ["Python", "Go", "Docker"]
}

# Access by key
print(user["name"])       # Kaikobud

# Safe access with .get() — returns None if key doesn't exist
print(user.get("email"))  # None
print(user.get("email", "not set"))  # "not set"

Always prefer .get() when a key might not exist — accessing a missing key with user["email"] raises a KeyError.

Adding, Updating, and Removing Items

# Add a new key
user["email"] = "kai@kaiko.dev"

# Update an existing key
user["age"] = 29

# Remove a key
del user["city"]

# Remove and return a value
age = user.pop("age")

# Check if a key exists
if "email" in user:
    print("Email found")

Looping Over Dictionaries

# Loop over keys (default)
for key in user:
    print(key)

# Loop over values
for value in user.values():
    print(value)

# Loop over key-value pairs — the most common pattern
for key, value in user.items():
    print(f"{key}: {value}")

Dictionary Comprehensions

Just like list comprehensions, you can build dictionaries in one line:

numbers = [1, 2, 3, 4, 5]
squares = {n: n ** 2 for n in numbers}
# {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Conclusion

Python dictionaries are the backbone of data handling in Python. They’re used in API responses, function kwargs, configuration objects, and almost every non-trivial program. Get comfortable with .get(), .items(), and dictionary comprehensions — you’ll use all three constantly.

Read next: Python List Comprehensions: Write Less, Do More

External resource: Python Docs — Dictionaries