Lesson 6.4: List Comprehensions

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By the end of this lesson, you will be able to:

Write list comprehensions to create lists concisely
Add conditions to filter elements in comprehensions
Convert between traditional for-loops and list comprehensions
Know when to use comprehensions vs. traditional loops

What is a List Comprehension?

List Comprehension: A concise, one-line way to create a new list by applying an expression to each item in an existing sequence. Syntax: [expression for item in iterable]

List comprehensions let you create lists in a single line of code. They are one of Python's most popular features because they make code shorter and more readable for simple transformations.

Traditional Loop vs. List Comprehension

# Traditional for-loop approach
squares = []
for n in range(1, 6):
    squares.append(n ** 2)
print(squares)

# Same thing with a list comprehension
squares = [n ** 2 for n in range(1, 6)]
print(squares)

[1, 4, 9, 16, 25] [1, 4, 9, 16, 25]

Breaking Down the Syntax

A list comprehension has three parts:

[expression for item in iterable]

expression -- what to do with each item (e.g., n ** 2)
for item in iterable -- the loop that generates items

Basic List Comprehensions

# Convert temperatures from Celsius to Fahrenheit
celsius = [0, 10, 20, 30, 40]
fahrenheit = [c * 9/5 + 32 for c in celsius]
print(fahrenheit)

# Make all names uppercase
names = ["alice", "bob", "charlie"]
upper_names = [name.upper() for name in names]
print(upper_names)

# Get the length of each word
words = ["hello", "world", "python"]
lengths = [len(word) for word in words]
print(lengths)

[32.0, 50.0, 68.0, 86.0, 104.0] ['ALICE', 'BOB', 'CHARLIE'] [5, 5, 6]

List Comprehensions with Conditions

You can add an if condition to filter which items are included in the new list.

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

# Get only even numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
evens = [n for n in numbers if n % 2 == 0]
print(evens)

# Get words longer than 4 characters
words = ["hi", "hello", "hey", "howdy", "greetings"]
long_words = [w for w in words if len(w) > 4]
print(long_words)

# Get passing scores
scores = [45, 82, 93, 67, 55, 78, 91]
passing = [s for s in scores if s >= 70]
print(passing)

[2, 4, 6, 8, 10] ['hello', 'howdy', 'greetings'] [82, 93, 78, 91]

Combining Transformation and Filtering

# Square only the even numbers
numbers = list(range(1, 11))
even_squares = [n ** 2 for n in numbers if n % 2 == 0]
print(even_squares)

[4, 16, 36, 64, 100]

Comprehensions with if/else

To use an if/else expression (where you want different values based on a condition), the syntax changes -- the condition goes before the for.

# Label numbers as "even" or "odd"
numbers = [1, 2, 3, 4, 5]
labels = ["even" if n % 2 == 0 else "odd" for n in numbers]
print(labels)

# Pass or fail
scores = [85, 42, 91, 67, 55]
results = ["Pass" if s >= 60 else "Fail" for s in scores]
print(results)

['odd', 'even', 'odd', 'even', 'odd'] ['Pass', 'Fail', 'Pass', 'Pass', 'Fail']

Syntax Difference: Filtering vs. Transforming

Filtering (if only): [x for x in list if condition] -- condition goes at the END
Transforming (if/else): [A if condition else B for x in list] -- condition goes at the BEGINNING

Practical Examples

Example 1: Processing a List of Names

names = ["  alice  ", "BOB", "  Charlie", "diana  "]

# Clean up: strip whitespace and capitalize
clean_names = [name.strip().title() for name in names]
print(clean_names)

['Alice', 'Bob', 'Charlie', 'Diana']

Example 2: Extracting Data

# Extract first letters
words = ["Python", "Is", "Fun"]
initials = [word[0] for word in words]
print(initials)
print("".join(initials))

['P', 'I', 'F'] PIF

Example 3: Working with Numbers

# Create a multiplication table row
row_5 = [5 * i for i in range(1, 11)]
print("5x table:", row_5)

5x table: [5, 10, 15, 20, 25, 30, 35, 40, 45, 50]

When to Use List Comprehensions

Rule of Thumb: Use list comprehensions when the logic is simple and fits on one line. Use traditional for-loops when the logic is complex, involves multiple steps, or when readability suffers.

Good Uses

# Simple transformation
doubled = [x * 2 for x in range(10)]

# Simple filter
positives = [x for x in numbers if x > 0]

# Simple string operation
lower_names = [name.lower() for name in names]

When to Stick with For-Loops

# Complex logic -- use a regular loop instead
results = []
for item in data:
    if item > 0:
        processed = item ** 2 + 1
        if processed < 100:
            results.append(processed)

# Side effects (printing, writing files) -- use a loop
for name in names:
    print(f"Hello, {name}!")

Try It Yourself

Write a list comprehension that creates a list of the first 10 cube numbers (1, 8, 27, 64, ...). Then write another that filters this list to only include cubes less than 100.

Check Your Understanding

Rewrite this loop as a list comprehension: result = []; for x in range(10): result.append(x * 3)
What does [x for x in range(20) if x % 5 == 0] produce?
Write a comprehension that converts ["hello", "world"] to ["HELLO", "WORLD"].

result = [x * 3 for x in range(10)]
[0, 5, 10, 15] (multiples of 5 from 0 to 19)
[word.upper() for word in ["hello", "world"]]

Key Takeaways

List comprehensions provide a concise way to create lists: [expr for item in iterable]
Add a filter with if: [expr for item in iterable if condition]
Use if/else for transformations: [A if cond else B for item in iterable]
Comprehensions are best for simple, one-line operations
Use traditional loops for complex logic or side effects
Readability matters -- if a comprehension is hard to read, use a loop instead

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