🧪 Python List Comprehension – Compact, Pythonic, and Powerful

🧲 Introduction – What Is List Comprehension?

List comprehension is a concise and readable way to create new lists from existing iterables (like ranges, lists, or strings). It replaces verbose for loop constructions with one-liner expressions that are often more efficient and Pythonic.

List comprehensions are ideal for mapping, filtering, and transforming data—core tasks in Python development, data science, and web apps.

🎯 In this guide, you’ll learn:

• The syntax and structure of list comprehensions
• How to use conditions in comprehensions
• Nested comprehensions for 2D data
• Best practices and performance tips

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📐 Basic List Comprehension Syntax

new_list = [expression for item in iterable]

✅ Explanation:

• expression is the value to include in the list.
• item is a variable representing each element from iterable.

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🔁 Example 1: Square Numbers

squares = [x**2 for x in range(5)]
print(squares)

✅ Explanation:

• Iterates over x from 0 to 4.
• Computes x**2 (square) for each value.
• Output: [0, 1, 4, 9, 16]

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🔍 Example 2: Filter Even Numbers

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

✅ Explanation:

• Loops through numbers from 0 to 9.
• Includes only numbers divisible by 2 (x % 2 == 0).
• Output: [0, 2, 4, 6, 8]

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🔄 Example 3: Convert to Uppercase

words = ["apple", "banana", "cherry"]
upper_words = [word.upper() for word in words]
print(upper_words)

✅ Explanation:

• Iterates over word in words.
• Converts each word to uppercase using .upper().
• Output: ['APPLE', 'BANANA', 'CHERRY']

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🔁 Example 4: Nested List Comprehension (2D Lists)

matrix = [[1, 2], [3, 4], [5, 6]]
flattened = [num for row in matrix for num in row]
print(flattened)

✅ Explanation:

• Loops through each row in matrix, then each num in that row.
• Collects all numbers into a single flat list.
• Output: [1, 2, 3, 4, 5, 6]

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🧪 Example 5: Conditional Expression (if–else)

labels = ["even" if x % 2 == 0 else "odd" for x in range(5)]
print(labels)

✅ Explanation:

• Uses inline if–else to assign "even" or "odd" to each value.
• Output: ['even', 'odd', 'even', 'odd', 'even']

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⚠️ When Not to Use List Comprehensions

Avoid list comprehensions when:

• The logic is too complex (e.g., nested conditions or multiple function calls).
• You’re performing operations that don’t return values (like logging or file writing).
• The code becomes unreadable—clarity is more important than brevity.

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💡 Best Practices

• ✅ Keep it readable: use simple expressions and conditions.
• ✅ Avoid deeply nested comprehensions—prefer regular loops for clarity.
• ✅ Use list comprehensions for filtering, transformation, and mapping.
• ✅ Pair with functions like .upper(), .strip(), or math operations for transformations.

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📌 Summary – Recap & Next Steps

List comprehensions are elegant and efficient tools for transforming and filtering data. They help you replace loops with compact, readable code, making your Python scripts cleaner and faster.

🔍 Key Takeaways:

• ✅ Basic syntax: [expression for item in iterable]
• ✅ Add filtering with if and mapping with functions like upper()
• ✅ Use inline if–else for condition-based expressions
• ✅ Nest comprehensions carefully for multi-dimensional lists

⚙️ Real-World Relevance:
Used in data pipelines, API response parsing, matrix transformations, and text processing, list comprehensions make Python code cleaner and more expressive.

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❓ FAQ Section – Python List Comprehension

❓ What is a list comprehension in Python?

✅ It’s a compact way to create lists using a for loop inside square brackets:

[x**2 for x in range(5)]

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❓ Can I use an if statement in list comprehensions?

✅ Yes. You can filter items using if:

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

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❓ How do I use if–else in a list comprehension?

✅ Place the condition before the for:

["even" if x % 2 == 0 else "odd" for x in range(5)]

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❓ Are list comprehensions faster than loops?

✅ Usually yes. They are optimized and run faster than equivalent for loops in most cases.

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❓ Can I use nested loops in list comprehensions?

✅ Yes. Example for flattening 2D lists:

[num for row in matrix for num in row]

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