Best Practices for Writing Python List Comprehensions

List comprehensions are one of Python’s most elegant features—a way to write clear, concise code when generating lists. But with great power comes great responsibility. Used improperly, they can get messy fast. Below are my favorite best practices for keeping them readable and effective.

1. Keep Your Python Code Readable

Just because you can write it in one line doesn’t mean you should. A short, clean list comprehension is great—but if logic gets too complex, use a regular loop.

# Good
squares = [x * x for x in range(10)]

# Better as a loop
result = []
for user in users:
    if user.is_active and user.last_login < 30:
        result.append(user.name.upper())

2. Use Simple Filters

If you’re filtering items, use an if condition after the for clause:

even = [x for x in numbers if x % 2 == 0]

For conditional values, use a ternary operator inside:

labels = ["even" if x % 2 == 0 else "odd" for x in numbers]

3. Avoid Side Effects

Don’t use list comprehensions just to call a function with side effects:

# Don’t do this
[print(x) for x in data]

# Use a loop
for x in data:
    print(x)

4. Keep Nesting Minimal

Comprehensions with two levels of iteration are OK, but beyond that, switch to loops.

# Flatten a list of lists
flat = [item for sublist in matrix for item in sublist]

5. Use When You Need a List

If you’re not actually creating a list, consider a generator or loop:

# Better as generator
squares = (x * x for x in range(10))

6. Comment or Split if Needed

If your comprehension gets complex, comment it—or break it into parts:

# Get uppercase names of active users
names = [user.name.upper() for user in users if user.is_active]