List Comprehensions

Intermediate ~25 min read

List comprehensions are one of Python's most beloved features - they let you create new lists by transforming and filtering existing iterables in a single, elegant line of code. Once you learn them, you'll wonder how you ever lived without them!

Basic List Comprehension

The basic syntax is [expression for item in iterable]. It's equivalent to a for loop that appends to a list, but more concise and often faster.

# Basic List Comprehensions

# The syntax: [expression for item in iterable]

# Traditional for loop approach
numbers = [1, 2, 3, 4, 5]
squares_loop = []
for n in numbers:
    squares_loop.append(n ** 2)
print(f"Using loop: {squares_loop}")

# List comprehension approach - same result, one line!
squares_comp = [n ** 2 for n in numbers]
print(f"Using comprehension: {squares_comp}")
print()

# More examples
print("=== More Examples ===")

# Double each number
doubles = [x * 2 for x in range(5)]
print(f"Doubles of 0-4: {doubles}")

# Convert to strings
str_nums = [str(x) for x in range(1, 6)]
print(f"Numbers as strings: {str_nums}")

# Uppercase words
words = ["hello", "world", "python"]
upper_words = [word.upper() for word in words]
print(f"Uppercase: {upper_words}")

# Get lengths
lengths = [len(word) for word in words]
print(f"Lengths: {lengths}")
print()

# Using different expressions
print("=== Various Expressions ===")
nums = [1, 2, 3, 4, 5]

# Expression can be any valid Python expression
results = [
    [x for x in nums],           # Identity
    [x + 10 for x in nums],      # Add 10
    [x ** 2 for x in nums],      # Square
    [x * x * x for x in nums],   # Cube
    [-x for x in nums],          # Negate
]

labels = ["Identity", "Add 10", "Square", "Cube", "Negate"]
for label, result in zip(labels, results):
    print(f"  {label}: {result}")

Output

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Why Use List Comprehensions? They're not just shorter - they're often faster than equivalent for loops because Python optimizes them internally. They're also considered more "Pythonic" - the preferred Python style for creating lists.

Filtering with Conditions

Add an if clause at the end to filter items: [expr for item in iterable if condition]. Only items that satisfy the condition are included in the result.

# List Comprehensions with Conditions (Filtering)

# Syntax: [expression for item in iterable if condition]

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(f"Original: {numbers}")
print()

# Filter even numbers
print("=== Basic Filtering ===")
evens = [x for x in numbers if x % 2 == 0]
print(f"Even numbers: {evens}")

odds = [x for x in numbers if x % 2 != 0]
print(f"Odd numbers: {odds}")

greater_than_5 = [x for x in numbers if x > 5]
print(f"Greater than 5: {greater_than_5}")
print()

# Combining transformation and filtering
print("=== Transform + Filter ===")
# Square of even numbers only
even_squares = [x ** 2 for x in numbers if x % 2 == 0]
print(f"Squares of evens: {even_squares}")
print()

# Filtering strings
print("=== String Filtering ===")
fruits = ["apple", "banana", "cherry", "date", "elderberry", "fig"]
print(f"Fruits: {fruits}")

# Words containing 'a'
with_a = [f for f in fruits if 'a' in f]
print(f"Contains 'a': {with_a}")

# Words longer than 5 characters
long_words = [f for f in fruits if len(f) > 5]
print(f"Longer than 5: {long_words}")

# Words starting with vowel
vowels = 'aeiou'
start_vowel = [f for f in fruits if f[0] in vowels]
print(f"Starts with vowel: {start_vowel}")
print()

# Multiple conditions (AND)
print("=== Multiple Conditions ===")
# Even AND greater than 4
both = [x for x in numbers if x % 2 == 0 and x > 4]
print(f"Even AND > 4: {both}")

# Contains 'a' AND longer than 5
both_str = [f for f in fruits if 'a' in f and len(f) > 5]
print(f"Has 'a' AND len > 5: {both_str}")

Output

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Pro Tip: The filtering if goes at the END of the comprehension (after for). This filters which items to include. Don't confuse it with the if-else ternary expression, which goes at the BEGINNING and transforms values.

If-Else Transformation

Use the ternary expression true_expr if condition else false_expr as your expression to transform items differently based on a condition. This goes BEFORE the for.

# List Comprehensions with If-Else (Ternary)

# Syntax: [true_expr if condition else false_expr for item in iterable]
# Note: if-else goes BEFORE 'for', filtering 'if' goes AFTER

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
print(f"Numbers: {numbers}")
print()

# 1. Basic if-else in comprehension
print("=== If-Else Transformation ===")

# Label as even or odd
labels = ["even" if x % 2 == 0 else "odd" for x in numbers]
print(f"Even/Odd labels: {labels}")

# Replace negatives with 0
mixed = [-2, -1, 0, 1, 2]
non_negative = [x if x >= 0 else 0 for x in mixed]
print(f"Original: {mixed}")
print(f"Non-negative: {non_negative}")
print()

# 2. Conditional transformation
print("=== Conditional Transform ===")

# Double evens, triple odds
transformed = [x * 2 if x % 2 == 0 else x * 3 for x in numbers]
print(f"Double evens, triple odds: {transformed}")

# Grade labels
scores = [95, 82, 67, 78, 90, 55]
grades = ["Pass" if s >= 60 else "Fail" for s in scores]
print(f"Scores: {scores}")
print(f"Grades: {grades}")
print()

# 3. String transformations
print("=== String If-Else ===")
words = ["Hello", "WORLD", "Python", "CODE"]

# Normalize case: lowercase if upper, uppercase if lower
normalized = [w.lower() if w.isupper() else w.upper() for w in words]
print(f"Original: {words}")
print(f"Swapped case: {normalized}")
print()

# 4. Combining if-else with filter
print("=== Combined: Transform + Filter ===")
# Only process positive numbers, double evens, triple odds
nums = [-2, -1, 0, 1, 2, 3, 4, 5]
result = [x * 2 if x % 2 == 0 else x * 3 for x in nums if x > 0]
print(f"Original: {nums}")
print(f"Positive only, double evens/triple odds: {result}")

Output

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Position Matters! Filtering if (no else) goes at the END: [x for x in nums if x > 0]. Ternary if-else goes at the START: [x if x > 0 else 0 for x in nums]. Mixing them up is a common source of syntax errors!

Nested List Comprehensions

You can nest comprehensions to work with 2D data. Read them left-to-right: the outer loop comes first, then the inner loop.

# Nested List Comprehensions

# 1. Flattening a 2D list
print("=== Flattening a Matrix ===")
matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]
print(f"Matrix: {matrix}")

# Traditional nested loop
flat_loop = []
for row in matrix:
    for num in row:
        flat_loop.append(num)
print(f"Flattened (loop): {flat_loop}")

# List comprehension - read left to right
flat_comp = [num for row in matrix for num in row]
print(f"Flattened (comp): {flat_comp}")
print()

# 2. Creating a 2D list
print("=== Creating a Matrix ===")
# 3x3 matrix of zeros
zeros = [[0 for col in range(3)] for row in range(3)]
print(f"3x3 zeros: {zeros}")

# Multiplication table
mult_table = [[i * j for j in range(1, 4)] for i in range(1, 4)]
print(f"Mult table:")
for row in mult_table:
    print(f"  {row}")
print()

# 3. Nested with conditions
print("=== Nested with Filter ===")
# Get all even numbers from matrix
even_nums = [num for row in matrix for num in row if num % 2 == 0]
print(f"Even from matrix: {even_nums}")

# Get diagonal elements (where row index == col index)
diagonal = [matrix[i][i] for i in range(len(matrix))]
print(f"Diagonal: {diagonal}")
print()

# 4. All combinations
print("=== All Combinations ===")
colors = ["red", "green"]
sizes = ["S", "M", "L"]

# Cartesian product
combinations = [(c, s) for c in colors for s in sizes]
print(f"Color-Size combos: {combinations}")

# As formatted strings
combo_strings = [f"{c}-{s}" for c in colors for s in sizes]
print(f"As strings: {combo_strings}")

Output

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Common Mistakes

1. Wrong position for if vs if-else

# Wrong - filter if with else
evens = [x if x % 2 == 0 for x in nums]  # SyntaxError!

# Correct - filter if (no else) goes at end
evens = [x for x in nums if x % 2 == 0]

# Correct - ternary if-else goes at start
labels = [x if x % 2 == 0 else "odd" for x in nums]

2. Overcomplicating with too much nesting

# Hard to read - too complex
result = [[y*2 for y in x if y > 0] for x in matrix if sum(x) > 5]

# Better - break into steps or use regular loops
filtered_rows = [row for row in matrix if sum(row) > 5]
result = [[y*2 for y in row if y > 0] for row in filtered_rows]

3. Forgetting that comprehensions create NEW lists

# Wrong - expecting in-place modification
numbers = [1, 2, 3]
[x * 2 for x in numbers]  # Creates new list, thrown away!
print(numbers)  # Still [1, 2, 3]

# Correct - assign the result
numbers = [x * 2 for x in numbers]
print(numbers)  # [2, 4, 6]

4. Variable scope leaking (Python 2 issue)

# In Python 3, comprehension variables are scoped
x = 10
squares = [x**2 for x in range(5)]
print(x)  # Still 10 in Python 3!

# This was a bug in Python 2 where x would become 4
# Python 3 fixed this - comprehension variables stay local

Exercise: Filtering and Transforming

Task: Use list comprehensions to process a list of words.

Requirements:

Create a list of the length of each word
Create a list of words longer than 5 characters

Output

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Show Solution

words = ["apple", "banana", "cherry", "date", "elderberry"]

# List of lengths
lengths = [len(word) for word in words]
print(f"Lengths: {lengths}")  # [5, 6, 6, 4, 10]

# Words longer than 5 characters
long_words = [word for word in words if len(word) > 5]
print(f"Long words: {long_words}")  # ['banana', 'cherry', 'elderberry']

Summary

Basic: [expr for item in iterable]
Filter: [expr for item in iterable if condition]
Transform: [true_expr if cond else false_expr for item in iterable]
Nested: [expr for outer in iterable1 for inner in iterable2]
Position: Filter if at END, ternary if-else at START
Benefits: More concise, often faster, more Pythonic
Readability: If it's too complex, use a regular loop instead

What's Next?

Now let's learn about Tuples - Python's immutable sequence type. They're like lists that can't be changed, which makes them perfect for data that shouldn't be modified!

Previous List Methods

Next Tuples

Basic List Comprehension

Filtering with Conditions

If-Else Transformation

Nested List Comprehensions

Common Mistakes

1. Wrong position for if vs if-else

2. Overcomplicating with too much nesting

3. Forgetting that comprehensions create NEW lists

4. Variable scope leaking (Python 2 issue)

Exercise: Filtering and Transforming

Summary

What's Next?

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