Dict & Set Comprehensions

Overview

Just like list comprehensions, Dictionary and Set Comprehensions provide a clean one-line syntax to build dictionaries and sets dynamically from iterables.

Learning Objectives

• Use {key_expr: val_expr for item in iterable} to build dictionaries.
• Use {expr for item in iterable} to build unique sets.
• Filter key-value pairs using conditional logic.

Concept Explanation

1. Dictionary Comprehension Syntax:
   python

   new_dict = {key_func(item): val_func(item) for item in iterable if condition}
   

2. Set Comprehension Syntax:
   python

   new_set = {func(item) for item in iterable if condition}
   

Code Examples

Example 1 — Dictionary Comprehension

names = ["Alice", "Bob", "Charlie"]

# Create a dictionary mapping name -> length
name_lengths = {name: len(name) for name in names}
print("Name Lengths:", name_lengths) # {'Alice': 5, 'Bob': 3, 'Charlie': 7}

# Swapping keys and values in a dictionary
original = {"a": 1, "b": 2, "c": 3}
inverted = {v: k for k, v in original.items()}
print("Inverted Dict:", inverted) # {1: 'a', 2: 'b', 3: 'c'}

Example 2 — Set Comprehension

words = ["apple", "BANANA", "cherry", "APPLE", "banana"]

# Extract unique lowercase word lengths
unique_word_lengths = {len(w) for w in words}
print("Unique Word Lengths:", unique_word_lengths) # {5, 6}

# Extract unique uppercase words
clean_words = {w.upper() for w in words}
print("Unique Words:", clean_words)

Common Mistakes

• Overwriting Duplicate Keys in Dict Comprehensions: If the key expression produces identical keys for different items, later items overwrite earlier ones.

Best Practices

• Use dict comprehensions to transpose or filter configuration mappings.
• Use set comprehensions when transforming data that requires uniqueness guarantees.