Python Dictionary (HashMap)

Introduction to Python dictionary.

A dictionary (dict) in Python is an unordered collection of key-value pairs, similar to HashMap in Java.

Characteristics:

  • Stores key-value pairs
  • Keys must be unique and hashable
  • Supports fast lookup, insertion, and deletion (O(1) average time complexity)
  • Ordered (since Python 3.7+)

Here are some key properties and operations with dictionary:

Creating a Dictionary

# Creating a dictionary
person = {
    "name": "John Doe",
    "age": 30,
    "city": "New York"
}
print(person)

Accessing Dictionary Values

print(person["name"])   # Output: John Doe
print(person.get("age"))  # Output: 30

Adding & Updating Dictionary Items

person["email"] = "john@example.com"  # Adding a new key-value pair
person["age"] = 31  # Updating an existing value
print(person)

Removing Items from Dictionary

person.pop("city")  # Removes "city"
del person["age"]   # Deletes "age"
print(person)

Iterating Over a Dictionary

for key, value in person.items():
    print(key, ":", value)

Checking If a Key Exists

if "name" in person:
    print("Key exists!")

Dictionary Comprehension

squared_numbers = {x: x*x for x in range(1, 6)}
print(squared_numbers)  # Output: {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

Nested Dictionary

student = {
    "name": "Alice",
    "marks": {"math": 90, "science": 85}
}
print(student["marks"]["math"])  # Output: 90

Using dict() Constructor

data = dict(name="Emma", age=25, country="Canada")
print(data)  # Output: {'name': 'Emma', 'age': 25, 'country': 'Canada'}

When to Use a Dictionary?

  • When you need fast lookups (O(1) average time complexity).
  • When storing related data together as key-value pairs.
  • When working with unordered data but requiring quick access.
  • When you need a flexible, dynamic data structure.
  • When data is naturally represented as key-value mappings.
  • When you need to avoid duplicate keys.
  • When building a configuration or settings store.
  • When you need to count occurrences of items (using collections.Counter).
  • When implementing caching mechanisms (e.g., memoization).
  • When handling JSON-like structured data.
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