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Python Dictionaries Overview

Python dictionaries are mutable, unordered collections of key-value pairs. They provide fast O(1) average time complexity for lookups, insertions, and deletions.

In a hash table (or Python dictionary), there isn’t a concept of a fixed “index” for key-value pairs like in a list—the data is stored based on the hash of the key, not in a specific order. - Dictionaries are unordered (before Python 3.7) or insertion-ordered (Python 3.7+), but you still can’t access by index directly. - If you really need the “index” (position in the list of items), you could convert the items to a list and use .index().

Python dictionaries can be used to tackle tasks that involve the manipulation and analysis of strings, validating password strengths, and managing personnel data.

Key Characteristics

  • Key-Value Pairs: Each element is a key-value pair
  • Unordered: Elements are not stored in any specific order (Python 3.7+ maintains insertion order)
  • Mutable: Can add, modify, or remove key-value pairs
  • Hashable Keys: Keys must be hashable (immutable)
  • Unique Keys: No duplicate keys allowed

Basic Operations

Creating Dictionaries

# Empty dictionary
empty_dict = {}

# Dictionary with initial values
person = {'name': 'John', 'age': 30, 'city': 'New York'}

# Using dict() constructor
numbers = dict(one=1, two=2, three=3)

# From list of tuples
pairs = [('a', 1), ('b', 2), ('c', 3)]
dict_from_pairs = dict(pairs)

Accessing and Modifying

age = person.get('age', 0)  # Returns default value if key doesn't exist

person = {'name': 'John', 'age': 30}

# Accessing values
name = person['name']  # Raises KeyError if key doesn't exist

# Adding/Modifying
person['city'] = 'New York'  # Add new key-value pair
person['age'] = 31  # Modify existing value

# Removing
del person['age']  # Raises KeyError if key doesn't exist
city = person.pop('city', 'Unknown')  # Returns default if key doesn't exist
person.clear()  # Remove all items

# Accessing keys via known values
matched_key = [key for key, val in book_ratings.items() if val == value]

Common Methods

person = {'name': 'John', 'age': 30, 'city': 'New York'}

# Keys, values, and items
keys = person.keys()  # dict_keys(['name', 'age', 'city'])
values = person.values()  # dict_values(['John', 30, 'New York'])
items = person.items()  # dict_items([('name', 'John'), ('age', 30), ('city', 'New York')])

# Membership testing
'name' in person  # True
'phone' not in person  # True

# Length
len(person)  # 3

Dictionary Operations

Merging Dictionaries

dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}

# Using update() method
dict1.update(dict2)  # Modifies dict1 in-place

# Using | operator (Python 3.9+)
merged = dict1 | dict2  # Creates new dictionary

# Using ** unpacking
merged = {**dict1, **dict2}

Dictionary Comprehension

# Create dictionary from list
numbers = [1, 2, 3, 4, 5]
squares = {x: x**2 for x in numbers}

# Filter dictionary
person = {'name': 'John', 'age': 30, 'city': 'New York', 'phone': '123-456'}
filtered = {k: v for k, v in person.items() if isinstance(v, str)}

Time Complexity

  • Access: O(1) average case
  • Insert/Update: O(1) average case
  • Delete: O(1) average case
  • Search: O(1) average case
  • Iteration: O(n)

Common Use Cases

  1. Caching/Memoization: Store computed results
  2. Counting: Track frequency of elements
  3. Grouping: Organize data by categories
  4. Configuration: Store settings and parameters
  5. JSON-like Data: Represent structured data