List comprehensions provide a concise way to create lists in Python. They’re more readable and often faster than traditional loops.

Basic Syntax

# Traditional loop
squares = []
for x in range(10):
    squares.append(x ** 2)

# List comprehension
squares = [x ** 2 for x in range(10)]
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

With Conditionals

Filter elements while creating the list:

# Get only even numbers
evens = [x for x in range(20) if x % 2 == 0]
print(evens)  # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

# Filter and transform
even_squares = [x ** 2 for x in range(10) if x % 2 == 0]
print(even_squares)  # [0, 4, 16, 36, 64]

If-Else in List Comprehensions

Use conditional expressions (ternary operator):

# Label numbers as even or odd
labels = ["even" if x % 2 == 0 else "odd" for x in range(5)]
print(labels)  # ['even', 'odd', 'even', 'odd', 'even']

# Replace negative numbers with zero
numbers = [-3, -1, 0, 2, 5]
positive = [x if x > 0 else 0 for x in numbers]
print(positive)  # [0, 0, 0, 2, 5]

Nested List Comprehensions

Work with nested data structures:

# Flatten a 2D list
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat = [num for row in matrix for num in row]
print(flat)  # [1, 2, 3, 4, 5, 6, 7, 8, 9]

# Create a matrix
matrix = [[i * j for j in range(1, 4)] for i in range(1, 4)]
print(matrix)  # [[1, 2, 3], [2, 4, 6], [3, 6, 9]]

Working with Strings

# Convert to uppercase
words = ["hello", "world", "python"]
upper = [word.upper() for word in words]
print(upper)  # ['HELLO', 'WORLD', 'PYTHON']

# Extract first letters
first_letters = [word[0] for word in words]
print(first_letters)  # ['h', 'w', 'p']

# Filter by length
long_words = [word for word in words if len(word) > 4]
print(long_words)  # ['hello', 'world', 'python']

Dictionary and Set Comprehensions

Similar syntax for other data structures:

# Dictionary comprehension
squares_dict = {x: x ** 2 for x in range(5)}
print(squares_dict)  # {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

# Set comprehension (removes duplicates)
nums = [1, 2, 2, 3, 3, 3, 4]
unique_squares = {x ** 2 for x in nums}
print(unique_squares)  # {1, 4, 9, 16}

Practical Examples

# Parse CSV-like data
data = "1,2,3,4,5"
numbers = [int(x) for x in data.split(",")]
print(numbers)  # [1, 2, 3, 4, 5]

# Filter files by extension
files = ["doc.txt", "image.png", "data.csv", "script.py"]
python_files = [f for f in files if f.endswith(".py")]
print(python_files)  # ['script.py']

# Extract values from dictionaries
users = [{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}]
names = [user["name"] for user in users]
print(names)  # ['Alice', 'Bob']

Summary

Use [expression for item in iterable] for basic transformations
Add if condition to filter elements
Use if-else in the expression for conditional values
Nest comprehensions for multi-dimensional data
Use {} for dict/set comprehensions

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