Output

[1200, 1500, 2000]

This technique is commonly used while cleaning and filtering datasets before analysis.

🔹 10. Benefits of List Comprehensions

✅ Shorter code

✅ Easier to read

✅ Faster than traditional loops in many cases

✅ Widely used in Data Science and Machine Learning

🔹 11. Common Mistakes

❌ Forgetting the Expression

numbers = [for i in range(5)] # SyntaxError

Correct:

numbers = [i for i in range(5)]

❌ Incorrect Order of "if"

numbers = [if x % 2 == 0 x for x in range(10)]

Correct:

numbers = [x for x in range(10) if x % 2 == 0]

🎯 Practice Questions

1. Create a list of numbers from 1 to 20.

2. Create a list containing the squares of numbers from 1 to 10.

3. Create a list containing only odd numbers from 1 to 20.

4. Convert a list of names to lowercase.

5. Replace all negative values in a list with zero using a list comprehension.

🎯 Key Takeaways

✅ List comprehensions provide a concise way to create lists.

✅ They combine loops and expressions into a single line.

✅ You can filter data using "if" conditions.

✅ Conditional expressions allow values to be modified during list creation.

✅ List comprehensions are widely used in data cleaning, feature engineering, and machine learning workflows.

Mastering list comprehensions will help you write cleaner, more Pythonic code and prepare you for technical interviews and real-world Data Science projects.

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