Python Dictionaries: How to Create and Use Them

Dictionaries are one of Python’s most useful data structures. Instead of accessing values by a numeric index like you do with lists, you access them by a key — usually a string. If lists are like numbered shelves, dictionaries are like labeled drawers.

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How Do You Create a Dictionary in Python?

Dictionaries are declared using curly braces with key: value pairs separated by commas:

car = {
    "brand": "Toyota",
    "model": "Camry",
    "year": 2019,
}

The keys here are the strings "brand", "model", and "year". The values are "Toyota", "Camry", and 2019. Keys must be unique — if you use the same key twice, the second value silently overwrites the first.

You can also start with an empty dictionary and populate it later:

planets = {}

How Do You Access Values in a Dictionary?

You retrieve a value by specifying its key in square brackets — similar to indexing a list, but with a key instead of a number:

car = {
    "make": "Toyota",
    "model": "Camry",
}
print(car["make"])  # Toyota

If you try to access a key that doesn’t exist, you’ll get a KeyError. To avoid that, you can check first with the in keyword:

cars = {
    "ford": "f150",
    "toyota": "camry",
}
print("ford" in cars)   # True
print("gmc" in cars)    # False

How Do You Add and Update Dictionary Values?

Setting a value uses the same bracket syntax with the assignment operator (=):

planets = {}
planets["Earth"] = True
planets["Pluto"] = False
print(planets)  # {'Earth': True, 'Pluto': False}

If the key already exists, its value gets overwritten:

planets = {"Pluto": True}
planets["Pluto"] = False
print(planets["Pluto"])  # False

This makes dictionaries great for building up data dynamically inside a loop. Here’s a common pattern — splitting full names into a dictionary of first → last:

full_names = ["jack bronson", "jill mcarty", "john denver"]
names_dict = {}
for full_name in full_names:
    parts = full_name.split()
    names_dict[parts[0]] = parts[1]

print(names_dict)
# {'jack': 'bronson', 'jill': 'mcarty', 'john': 'denver'}

How Do You Delete From a Dictionary?

Use the del keyword to remove a key-value pair:

names_dict = {
    "jack": "bronson",
    "jill": "mcarty",
    "joe": "denver",
}
del names_dict["joe"]
print(names_dict)  # {'jack': 'bronson', 'jill': 'mcarty'}

Be careful — if you try to delete a key that doesn’t exist, you’ll get a KeyError. Check with in first if you’re not sure.

How Do You Check if a Key Exists?

The in keyword checks for the presence of a key. This is especially useful when counting occurrences — a very common dictionary pattern:

def count_enemies(enemy_names):
    enemies = {}
    for name in enemy_names:
        if name in enemies:
            enemies[name] += 1
        else:
            enemies[name] = 1
    return enemies

If the key already exists, we increment its value with the += operator. If it doesn’t, we set it to 1. Without the in check, accessing a missing key would raise a KeyError.

How Do You Loop Over a Dictionary?

You can iterate over a dictionary’s keys using the same for loop syntax you’d use with a list. The loop variable gets each key, and you use bracket notation to get the corresponding value:

fruit_sizes = {
    "apple": "small",
    "banana": "large",
    "grape": "tiny",
}
for name in fruit_sizes:
    size = fruit_sizes[name]
    print(f"name: {name}, size: {size}")

# name: apple, size: small
# name: banana, size: large
# name: grape, size: tiny

This is the most common way to iterate over dictionaries when you need both keys and values.

What Are Nested Dictionaries?

Dictionaries can contain other dictionaries as values. This lets you represent structured, hierarchical data:

progress = {
    "character_name": "Kaladin",
    "quests": {
        "bridge_run": {
            "status": "In Progress",
        },
        "talk_to_syl": {
            "status": "Completed",
        },
    },
}

You access nested values by chaining bracket notation:

status = progress["quests"]["bridge_run"]["status"]
print(status)  # In Progress

Each bracket digs one level deeper into the structure. This pattern is very common when working with JSON data from APIs and web services.

What Should You Learn After Python Dictionaries?

You now know both of Python’s core data structures: lists and dictionaries. Together with loops and conditionals, you have the tools to build surprisingly powerful programs. From here, most beginners move on to functions in more depth, or start learning about sets and more advanced data structures.

If you want to keep going through the full Python curriculum with hands-on exercises, check out the Learn to Code in Python course on Boot.dev.