How to Use List Comprehensions vs Generator Expressions

Table Of Contents

• Problem
• Solution
• Explanation

Problem

You need to process sequences efficiently but don't know when to use list comprehensions [] versus generator expressions ().

Solution

# List comprehension - creates entire list in memory
numbers = [1, 2, 3, 4, 5]
squares_list = [x**2 for x in numbers]
print(squares_list)  # [1, 4, 9, 16, 25]

# Generator expression - creates iterator, lazy evaluation
squares_gen = (x**2 for x in numbers)
print(list(squares_gen))  # [1, 4, 9, 16, 25]

# Memory comparison
import sys
list_comp = [x for x in range(1000)]
gen_exp = (x for x in range(1000))
print(sys.getsizeof(list_comp))  # ~9000 bytes
print(sys.getsizeof(gen_exp))    # ~120 bytes

# Use list comprehension when you need the full list
filtered_data = [x for x in range(100) if x % 2 == 0]
print(len(filtered_data))  # Need immediate access to all items

# Use generator when processing large datasets
large_gen = (x**2 for x in range(1000000))
for item in large_gen:
    if item > 1000:
        break  # Only compute what's needed

# Generator for file processing
def read_lines():
    with open('large_file.txt') as f:
        return (line.strip().upper() for line in f)

# Only loads one line at a time

Explanation

List comprehensions create the entire list in memory immediately. Generator expressions create an iterator that computes values on-demand, using less memory.

Use list comprehensions when you need immediate access to all elements. Use generators for large datasets or when you only need to iterate once.