List Comprehensions in Python

A list comprehension is a compact way to build a list in one line. Instead of writing a loop with .append(), you describe the list directly:

# The long way
squares = []
for n in range(1, 6):
    squares.append(n * n)

# The same thing as a list comprehension
squares = [n * n for n in range(1, 6)]

print(squares)    # [1, 4, 9, 16, 25]

Both produce the same list, but the comprehension is shorter and, once you're used to it, easier to read. Let's learn the pattern.

The basic pattern

A list comprehension has the shape:

[ expression for item in sequence ]

Read it as: "give me expression for each item in sequence".

# Double every number
nums = [1, 2, 3, 4]
doubled = [n * 2 for n in nums]
print(doubled)    # [2, 4, 6, 8]

# Uppercase every word
words = ["apple", "banana"]
caps = [w.upper() for w in words]
print(caps)       # ['APPLE', 'BANANA']

Adding a condition (filtering)

You can add an if at the end to keep only items that match:

[ expression for item in sequence if condition ]

nums = range(1, 11)

# Keep only even numbers
evens = [n for n in nums if n % 2 == 0]
print(evens)      # [2, 4, 6, 8, 10]

# Square only the numbers greater than 5
big_squares = [n * n for n in nums if n > 5]
print(big_squares)  # [36, 49, 64, 81, 100]

Transforming with if-else

If you want to transform every item (not filter), put the if/else before the for — this is a conditional expression:

nums = [1, 2, 3, 4, 5]

Label each number even or odd

labels = ["even" if n % 2 == 0 else "odd" for n in nums] print(labels) # ['odd', 'even', 'odd', 'even', 'odd']

The key difference: a filtering `if` goes at the *end*; a transforming `if`/`else` goes at the *front*.

## Dictionary and set comprehensions

The same idea works for dictionaries and sets — just change the brackets:

```python
# Dictionary comprehension: number -> its square
squares = {n: n * n for n in range(1, 5)}
print(squares)    # {1: 1, 2: 4, 3: 9, 4: 16}

# Set comprehension: unique word lengths
words = ["hi", "hello", "hey", "world"]
lengths = {len(w) for w in words}
print(lengths)    # {2, 5, 3} — unique lengths only

A practical example

# Extract and clean a list of valid email usernames

emails = ["[email protected]", "[email protected]", "invalid-email", "[email protected]"]

# Keep only proper emails, then take the part before the @
usernames = [e.split("@")[0] for e in emails if "@" in e]
print(usernames)    # ['asha', 'ravi', 'meena']

Common mistakes

• Putting the filter if in the wrong place: A filtering if goes after the for. An if/else that transforms goes before the for. Mixing these up causes syntax errors.
• Making comprehensions too complex: If you nest several loops and conditions, the line becomes unreadable. At that point, a normal loop is clearer — readability wins.
• Forgetting it returns a new list: A comprehension creates a new list; it doesn't modify the original. Assign it to a variable to keep the result.
• Using {} and expecting a list: {x for x in ...} is a set comprehension; [x for x in ...] is a list. Watch your brackets.
• Side effects inside comprehensions: Don't use a comprehension just to call print() for each item — use a plain loop. Comprehensions are for building collections.

FAQ

Are comprehensions faster than loops? Usually slightly faster, because they're optimised internally. But the main benefit is clarity for simple transformations.

Can I nest loops in a comprehension? Yes: [x * y for x in a for y in b]. It works, but more than one nested loop quickly gets hard to read.

Is there a tuple comprehension? Not directly — (x for x in ...) creates a generator, not a tuple. Wrap it in tuple(...) if you need a tuple.

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Comprehensions build on Lists in Python and Loops in Python, so make sure those feel comfortable first. More topics on the Python learning hub.

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