This content originally appeared on DEV Community and was authored by Rahul Gupta
Welcome to Day 28 of the 100 Days of Python series!
Today, we’re diving into one of Python’s most elegant and powerful features: List Comprehensions.
If you’ve ever written a loop just to create a list, Python has a much shorter — and cleaner — way of doing it. List comprehensions let you generate lists with less code and more readability.
What You’ll Learn
- What list comprehensions are
- Basic syntax and examples
- How to add conditions (if/else)
- Nested list comprehensions
- Real-world use cases
What is a List Comprehension?
A list comprehension is a concise way to create lists using a single line of code.
Basic Syntax:
[expression for item in iterable]
This is equivalent to:
result = []
for item in iterable:
result.append(expression)
Example 1: Squaring Numbers
With loop:
squares = []
for i in range(5):
squares.append(i ** 2)
With list comprehension:
squares = [i ** 2 for i in range(5)]
Example 2: Convert Strings to Uppercase
names = ["alice", "bob", "charlie"]
upper_names = [name.upper() for name in names]
print(upper_names) # ['ALICE', 'BOB', 'CHARLIE']
Why Use List Comprehensions?
- Shorter and cleaner syntax
- Faster performance
- More readable for simple transformations
Adding Conditions
Syntax:
[expression for item in iterable if condition]
Example: Even Numbers Only
evens = [i for i in range(10) if i % 2 == 0]
print(evens) # [0, 2, 4, 6, 8]
With if-else in Expression
labels = ["even" if i % 2 == 0 else "odd" for i in range(5)]
print(labels) # ['even', 'odd', 'even', 'odd', 'even']
Nested List Comprehensions
You can even nest comprehensions, especially useful for 2D lists or matrices.
Example: Flatten a 2D List
matrix = [[1, 2], [3, 4], [5, 6]]
flattened = [num for row in matrix for num in row]
print(flattened) # [1, 2, 3, 4, 5, 6]
Real-World Examples
1. Extract Digits from String
text = "Age: 24, Score: 89"
digits = [char for char in text if char.isdigit()]
print(digits) # ['2', '4', '8', '9']
2. Filter Valid Emails
emails = ["a@gmail.com", "b@site", "c@yahoo.com"]
valid = [email for email in emails if "@" in email and "." in email]
print(valid) # ['a@gmail.com', 'c@yahoo.com']
3. Remove Duplicates from List
data = [1, 2, 2, 3, 4, 4]
unique = list({x for x in data})
print(unique) # [1, 2, 3, 4]
Tips & Best Practices
Use list comprehensions for simple transformations
Avoid making them too complex or nested too deeply — use loops for readability
Clean and readable comprehensions can improve performance and clarity
Bonus: Dictionary & Set Comprehensions
Python also supports:
Dictionary Comprehension
squares = {x: x ** 2 for x in range(5)}
# {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
Set Comprehension
unique = {char for char in "hello"}
# {'h', 'e', 'l', 'o'}
Recap
Today you learned:
- What list comprehensions are
- How to use them with conditions
- When to use
if,if-else, and nested comprehensions - Real-world practical examples
- Bonus: dictionary and set comprehensions
This content originally appeared on DEV Community and was authored by Rahul Gupta