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🚀 Data Science Roadmap 2026

📘 Phase 1: Programming Fundamentals

🐍 Topic 8: Python List Comprehensions

Welcome back! 👋

In the previous lesson, you learned about Python's built-in data structures—Lists, Tuples, Sets, and Dictionaries.

Now it's time to learn one of Python's most elegant and frequently used features: List Comprehensions.

List comprehensions provide a concise and readable way to create, filter, and transform lists. They are widely used in Data Science, Machine Learning, data preprocessing, and coding interviews.

🔹 1. What is a List Comprehension?

A list comprehension is a compact way to create a new list by applying an expression to each item in an iterable (such as a list, tuple, or range).

Instead of writing multiple lines with a loop, you can accomplish the same task in a single line.

General Syntax

new_list = [expression for item in iterable]

🔹 2. Creating a List Using a Loop

numbers = []
for i in range(5):
    numbers.append(i)
print(numbers)

Output

[0, 1, 2, 3, 4]

🔹 3. Creating the Same List Using List Comprehension

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

Output

[0, 1, 2, 3, 4]  

Notice how the code is shorter and easier to read.

🔹 4. Performing Calculations

Create a list of squares.

squares = [x ** 2 for x in range(1, 6)]
print(squares)

Output

[1, 4, 9, 16, 25]

🔹 5. Using Conditions

You can filter elements while creating a list.

Example: Even Numbers

even_numbers = [x for x in range(1, 11) if x % 2 == 0]
print(even_numbers)

Output

[2, 4, 6, 8, 10]

🔹 6. Converting Strings

Convert all names to uppercase.

names = ["rahul", "deepak", "anita"]
upper_names = [name.upper() for name in names]
print(upper_names)

Output

['RAHUL', 'DEEPAK', 'ANITA']

🔹 7. Using Conditional Expressions

Replace negative numbers with zero.

numbers = [5, -2, 8, -1, 3]
updated = [0 if x < 0 else x for x in numbers]
print(updated)

Output

[5, 0, 8, 0, 3]

🔹 8. Nested List Comprehension

Create a multiplication table.

table = [[i * j for j in range(1, 6)] for i in range(1, 4)]
print(table)

Output

[[1, 2, 3, 4, 5],
 [2, 4, 6, 8, 10],
 [3, 6, 9, 12, 15]]

🔹 9. Real-World Data Science Example

Suppose you have a list of sales amounts.

sales = [1200, 850, 1500, 600, 2000]
high_sales = [sale for sale in sales if sale > 1000]
print(high_sales)

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)]  # SyntaxError

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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