⚙️

Intermediate

List · Dict · Set · Generator expressions · yield

Week 2 — Comprehensions & Generators

Write concise, readable data transformations with list, dict, and set comprehensions. Then step into generators and yield for memory-efficient iteration.

comprehensiongeneratoryieldlazy evaluation

Duration

⏱ 2.5 hours

Level

📊 Intermediate

Prerequisite

🎯 Basic Week 6–7

OUTCOME

Replace verbose for-loops with comprehensions and write a generator pipeline

What you'll learn

1Write list, dict, and set comprehensions with filtering
2Use nested comprehensions for 2D data
3Explain the difference between a list comprehension and a generator expression
4Write generator functions with yield
5Chain generators for memory-efficient pipelines

1. List Comprehensions

python

# Basic: [expression for item in iterable if condition]
squares = [x**2 for x in range(1, 11)]
evens   = [x for x in range(20) if x % 2 == 0]
matrix  = [[row*col for col in range(1,4)] for row in range(1,4)]

print(squares)
print(evens)
print(matrix)

💡

If the resulting list is only iterated once, prefer a generator expression () instead of [] to save memory.

2. Dict & Set Comprehensions

python

words = ["apple", "banana", "cherry"]
lengths = {w: len(w) for w in words}
print(lengths)  # {'apple': 5, 'banana': 6, 'cherry': 6}

unique_lengths = {len(w) for w in words}
print(unique_lengths)  # {5, 6}

3. Generator Expressions & yield

python

# Generator expression (lazy — doesn't compute all at once)
gen = (x**2 for x in range(1_000_000))
print(next(gen))  # 0
print(next(gen))  # 1

# Generator function
def countdown(n):
    while n > 0:
        yield n
        n -= 1

for v in countdown(5):
    print(v, end=' ')  # 5 4 3 2 1

4. Common Mistakes

Deeply nested comprehensions hurt readability — if you need three levels, use a regular loop.
Generators are exhausted after one pass. If you need to iterate twice, convert to list first.
Using a walrus operator (:=) in comprehensions is valid but can make code hard to read.

💻 Examples

Run these examples and check the output yourself.

01_comprehensions.py— List / dict / set comprehensions

CODE

numbers = list(range(1, 21))
squares = [n**2 for n in numbers]
even_sq = [n**2 for n in numbers if n % 2 == 0]
sq_dict = {n: n**2 for n in range(1, 6)}
uniq     = {n % 5 for n in range(20)}
print(squares[:5])
print(even_sq)
print(sq_dict)
print(uniq)

▶ Output

[1, 4, 9, 16, 25]
[4, 16, 36, 64, 100, 144, 196, 256, 324, 400]
{1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
{0, 1, 2, 3, 4}

02_flatten.py— Flatten a nested list with a nested comprehension

CODE

nested = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]
flat = [item for sublist in nested for item in sublist]
print(flat)

▶ Output

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

03_generator_pipeline.py— Lazy pipeline for large data

CODE

def read_lines(n):
    for i in range(n):
        yield f"log line {i}: value={i*10}"

def parse_value(lines):
    for line in lines:
        yield int(line.split('=')[1])

def filter_large(values, threshold):
    for v in values:
        if v > threshold:
            yield v

result = filter_large(parse_value(read_lines(20)), 100)
print(list(result))

▶ Output

[110, 120, 130, 140, 150, 160, 170, 180, 190]

📝 Exercises

Try them yourself first, then open the solution to compare.

Exercise 1

Matrix Transpose

Goal: Transpose a 3×3 matrix using a list comprehension.

Requirements

Input: [[1,2,3],[4,5,6],[7,8,9]]
Output: [[1,4,7],[2,5,8],[3,6,9]]
Use a single comprehension expression

▶Toggle solution

SOLUTION

matrix = [[1,2,3],[4,5,6],[7,8,9]]
transposed = [[row[i] for row in matrix] for i in range(len(matrix[0]))]
for row in transposed:
    print(row)

▶ Output

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

Exercise 2

Infinite Fibonacci Generator

Goal: Write fib_gen() that yields Fibonacci numbers indefinitely.

Requirements

Yields 0, 1, 1, 2, 3, 5, 8, ... infinitely
Use islice from itertools to take first N values

▶Toggle solution

SOLUTION

from itertools import islice
def fib_gen():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

print(list(islice(fib_gen(), 10)))

▶ Output

[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Example code / lecture materials

All lecture materials and example code are openly available on GitHub.

View on GitHub ↗