⚡ Dictionary Methods and Patterns — Effective Dictionary Usage

Master dictionary methods and practical patterns for real-world data manipulation.

🎯 Essential Dictionary Methods

`.get()` — Safe Access

person = {"name": "Alice", "age": 30, "city": "NYC"}

# Regular access - raises KeyError if missing
print(person["country"])  # KeyError!

# Safe access with .get()
print(person.get("country"))  # None (no error)
print(person.get("country", "Unknown"))  # "Unknown" (default)

`.keys()`, `.values()`, `.items()` — Iteration

person = {"name": "Alice", "age": 30, "city": "NYC"}

# Get all keys
print(list(person.keys()))    # ["name", "age", "city"]

# Get all values
print(list(person.values()))  # ["Alice", 30, "NYC"]

# Get key-value pairs
for key, value in person.items():
    print(f"{key}: {value}")

`.update()` — Merge Dictionaries

person = {"name": "Alice", "age": 30}
updates = {"age": 31, "city": "NYC"}

person.update(updates)
print(person)  # {"name": "Alice", "age": 31, "city": "NYC"}

💡 Practical Dictionary Patterns

Pattern 1: Grouping Data

# Organize students by grade
students = ["Alice", "Bob", "Carol", "David"]
grades = {"Alice": "A", "Bob": "B", "Carol": "A", "David": "C"}

by_grade = {}
for student, grade in grades.items():
    if grade not in by_grade:
        by_grade[grade] = []
    by_grade[grade].append(student)

print(by_grade)
# {"A": ["Alice", "Carol"], "B": ["Bob"], "C": ["David"]}

# Cleaner: use .setdefault()
by_grade = {}
for student, grade in grades.items():
    by_grade.setdefault(grade, []).append(student)

Pattern 2: Counting Occurrences

text = "hello world"
letter_counts = {}

for letter in text:
    if letter in letter_counts:
        letter_counts[letter] += 1
    else:
        letter_counts[letter] = 1

print(letter_counts)  # {"h": 1, "e": 1, "l": 3, "o": 2, "w": 1, "r": 1, "d": 1}

# Better: use .get() with default
letter_counts = {}
for letter in text:
    letter_counts[letter] = letter_counts.get(letter, 0) + 1

# Best: use collections.Counter (specialized tool)
from collections import Counter
letter_counts = Counter(text)

Pattern 3: Dictionary Comprehension

# Create dict from list
numbers = [1, 2, 3, 4, 5]
squares = {n: n**2 for n in numbers}
print(squares)  # {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

# Filter while creating
evens = {n: n**2 for n in numbers if n % 2 == 0}
print(evens)  # {2: 4, 4: 16}

# Transform values
prices = {"apple": 1.50, "banana": 0.75}
discounted = {item: price * 0.9 for item, price in prices.items()}

🎨 Real-World Example: User Profile System

# Store and manipulate user data
users = {
    "alice": {"email": "alice@example.com", "posts": 5, "followers": 100},
    "bob": {"email": "bob@example.com", "posts": 3, "followers": 50}
}

# Add new user
new_user = {"email": "carol@example.com", "posts": 0, "followers": 0}
users["carol"] = new_user

# Update user
users["alice"]["posts"] += 1

# Find users with most followers
most_popular = max(users.items(), key=lambda item: item[1]["followers"])
print(most_popular)  # ("alice", {...})

# Count total posts
total_posts = sum(user["posts"] for user in users.values())
print(total_posts)  # 8

📊 Common Dictionary Methods

🔑 Key Takeaways

• ✅ Use `.get()` for safe key access

• ✅ Iterate with `.items()` for key-value pairs

• ✅ Use `.setdefault()` to initialize missing keys

• ✅ Dictionary comprehensions create dicts elegantly

• ✅ Pattern: group, count, transform, filter

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