Python Comprehensions: List, Dictionary & Set Comprehensions Masterclass

One of Python's most celebrated and elegant features is comprehensions. Comprehensions offer a compact, declarative syntax to construct new lists, dictionaries, and sets from existing iterables in a single line of code, replacing boilerplate multi-line for loops and .append() calls.

Figure 34.1: Anatomy of a list comprehension: Output Expression < Iteration Loop < Conditional Filter.

1. List Comprehensions: Syntax & Comparison

# Task: Get squares of even numbers from 1 to 10

# Traditional for loop:
even_squares = []
for x in range(1, 11):
    if x % 2 == 0:
        even_squares.append(x ** 2)

# Pythonic List Comprehension:
even_squares_comp = [x ** 2 for x in range(1, 11) if x % 2 == 0]

print("Loop result:         ", even_squares)       # [4, 16, 36, 64, 100]
print("Comprehension result:", even_squares_comp)  # [4, 16, 36, 64, 100]

2. Dictionary Comprehensions ({k: v for ...})

Construct dictionaries dynamically from sequences:

# Create a student score lookup dictionary:
names = ["Aman", "Rhea", "Vikram", "Pooja"]
marks = [85, 92, 78, 95]

# Zip into dictionary with comprehension:
student_scores = {name: mark for name, mark in zip(names, marks) if mark >= 80}
print("Honors Students:", student_scores)
# {'Aman': 85, 'Rhea': 92, 'Pooja': 95}

3. Set Comprehensions ({x for ...})

Extract unique, deduplicated elements in one step:

text = "veewom python programming masterclass"
# Extract unique vowels:
unique_vowels = {char for char in text if char in "aeiou"}
print("Unique Vowels in Text:", unique_vowels)  # {'a', 'e', 'i', 'o'}

4. Flattening a 2D Matrix (Nested Comprehensions)

matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

# Flatten 2D grid into a 1D list:
flattened = [val for row in matrix for val in row]
print("Flattened List:", flattened)
# [1, 2, 3, 4, 5, 6, 7, 8, 9]

Frequently Asked Questions (FAQ)