Some text some message..
Login to get started
Back List Comprehension vs Generator Expression (Object) 26 Jun, 2025
Programming Programming
  • Report Note

🔹 1. List Comprehension

✅ Definition:

A list comprehension creates a full list in memory at once.

📌 Syntax:

[expression for item in iterable if condition]

📘 Example:

squares = [x**2 for x in range(5)]
print(squares)  # [0, 1, 4, 9, 16]

✅ Key Features:

  • Returns a list object

  • Stores all elements in memory

  • Faster for small-to-medium size data

  • Can be nested or filtered

🔹 2. Generator Expression (Object)

✅ Definition:

A generator expression returns a generator object, which generates items one at a time using lazy evaluation.

📌 Syntax:

(expression for item in iterable if condition)

📘 Example:

squares = (x**2 for x in range(5))
print(squares)          # <generator object>
print(list(squares))    # [0, 1, 4, 9, 16]

✅ Key Features:

  • Returns a generator object

  • Doesn’t store all values in memory

  • Efficient for large datasets

  • Generates values on-demand

  • Can be iterated only once

🧠 Key Differences

Feature List Comprehension Generator Expression
Brackets [ ] ( )
Return Type list generator
Memory Usage High (stores all elements) Low (generates one by one)
Performance (small) Faster Slightly slower
Performance (large) May consume more memory More scalable and memory efficient
Reusability Can be reused multiple times Can only be iterated once
Suitable For Small/medium sequences Large data or infinite sequences

✅ Practical Use Case Comparison

📌 List Comprehension

# Store even numbers in a list
evens = [x for x in range(1_000_000) if x % 2 == 0]

📌 Generator Expression

# Iterate even numbers one at a time
evens = (x for x in range(1_000_000) if x % 2 == 0)
for e in evens:
    print(e)

📢 Summary

When to Use Use List Comprehension Use Generator Expression
Need all items at once ✅ Yes ❌ Not ideal
Working with large data ❌ Can crash memory ✅ Ideal due to lazy evaluation
One-time use ✅ Fine ✅ Better