Dictionary Fundamentals

A dictionary in Python is represented by curly braces {} and stores data in key-value pairs. The structure uses a hash algorithm to compute memory addresses for storing key-value combinations. Since hash tables are non-contiguous, dictionaries cannot be sliced and their contents maintain no specific order based on insertion. Dictionary keys must be hashable objects, meaning they must be immutable data types such as:

• int
• str
• tuple
• bool

Values can store any type of data.

data_store = {
    42: "answer",
    "name": "zhang",
    (10, 20): "coordinates",
    "scores": [95, 88, 92],
    True: {"nested": "value"}
}
print(data_store)
print(data_store[42])
print(data_store[True])
print(data_store["scores"])
print(data_store[(10, 20)])
# Note: True evaluates to 1 in numeric contexts

Adding Elements

Two primary methods exist for adding key-value pairs:

sample = {}
sample["artist"] = "picasso"
sample[0] = "genesis"
print(sample)

sample.setdefault("language")
print(sample)
sample.setdefault("language", "python")
print(sample)

When a key exists, the assignment performs an update; otherwise, it creates a new entry.

Removing Elements

Available removal methods include pop(), del, clear(), and popitem():

inventory = {
    "apple": 5,
    "banana": 3,
    "cherry": 7,
    "dates": [1, 2]
}
inventory.pop("apple")
removed_item = inventory.popitem()  # Random removal
print(inventory)

del inventory["banana"]
print(inventory)

inventory.clear()
print(inventory)

Updating Elements

The update() method merges dictionaries:

profile = {"id": 123, "username": "alice", "role": "user"}
updates = {"id": 456, "username": "bob", "status": "active"}
profile.update(updates)
print(profile)
print(updates)

Existing keys are modified, while new keys are added.

Accessing Elements

Two approaches for value retrieval:

catalog = {
    "book1": "python",
    "book2": "java",
    "book3": "javascript"
}
print(catalog["book1"])
print(catalog.get("book2"))
print(catalog.get("nonexistent"))  # Returns None
print(catalog["missing"])  # Raises KeyError

Iteration Techniques

Multiple iteration patterns are available:

library = {
    "title": "Deep Learning",
    "author": "Ian Goodfellow",
    "year": 2016,
    "pages": 775
}

# Iterate over keys
for k in library.keys():
    print(k)

# Iterate over values
for v in library.values():
    print(v)

# Iterate over key-value pairs
for key, value in library.items():
    print(f"{key}: {value}")

# Tuple unpacking example
info = ("data", "science", 2023)
field, specialty, year = info
print(field, specialty, year)

Nested Dictionaries

Complex data structures can be created through nesting:

company = {
    "name": "TechCorp",
    "founded": 2010,
    "ceo": {
        "name": "Sarah Chen",
        "age": 45
    },
    "products": [
        {"name": "CloudSync", "version": "2.0"},
        {"name": "DataVault", "version": "1.5"}
    ]
}

# Access nested data
print(company["products"][1]["version"])
print(company["ceo"]["name"])
print(company["name"])

Advanced Dictionary Operations

Complex manipulations with nested structures:

project = {
    "team": ["john", "mary", "bob"],
    "budget": 50000,
    "modules": {
        "frontend": ["react", "redux"],
        "backend": ["django", "postgresql"]
    }
}

# Add team member
project["team"].append("alice")
print(project)

# Capitalize first member
project["team"][0] = project["team"][0].title()
print(project)

# Add new module
project["modules"]["devops"] = ["docker", "kubernetes"]
print(project)

# Remove backend technology
project["modules"]["backend"].remove("postgresql")
print(project)