🌱 Junior Track 21 of 21

21. Lambda Functions

Write lightweight, single-expression anonymous functions: master inline key callbacks for `sorted()`, `filter()`, and `map()`, while adhering to PEP 8 guidelines to avoid overusing lambda when `def` is clearer.

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🎓 Junior Python Curriculum — Select Module

1️⃣ Fundamentals 2️⃣ Data Types 3️⃣ Operators 4️⃣ Conditionals 5️⃣ Loops 6️⃣ Collections 7️⃣ Comprehensions 8️⃣ Functions 9️⃣ *args & **kwargs 🔟 Scope & Namespaces 1️⃣1️⃣ OOP 1️⃣2️⃣ Exceptions 1️⃣3️⃣ File Handling 1️⃣4️⃣ Modules & Packages 1️⃣5️⃣ Virtual Environments 1️⃣6️⃣ Standard Library 1️⃣7️⃣ Iterators & Iterables 1️⃣8️⃣ Generators 1️⃣9️⃣ Decorators 2️⃣0️⃣ Context Managers 2️⃣1️⃣ Lambda Functions 2️⃣2️⃣ Functional Programming 2️⃣3️⃣ Type Hints 2️⃣4️⃣ Dataclasses 2️⃣5️⃣ Testing 2️⃣6️⃣ Debugging 2️⃣7️⃣ Logging 2️⃣8️⃣ JSON & Data 2️⃣9️⃣ Regular Expressions 3️⃣0️⃣ Databases

# ==============================================================================
# JUNIOR PYTHON TRACK 21 — LAMBDA FUNCTIONS & HIGHER-ORDER FUNCTIONS
# ==============================================================================

# 1. Custom Sorting with sorted(..., key=lambda)
products = [
    {"id": 101, "name": "Laptop", "price": 1200.0},
    {"id": 102, "name": "Mouse", "price": 25.0},
    {"id": 103, "name": "Monitor", "price": 350.0}
]

# Sort products by price ascending using inline lambda key
by_price = sorted(products, key=lambda p: p["price"])


# 2. Filtering items with filter(lambda, iterable)
numbers = [12, 45, 68, 23, 90, 15]
high_numbers = list(filter(lambda x: x > 40, numbers))


# 3. Transforming items with map(lambda, iterable)
raw_prices = [100.0, 200.0, 500.0]
discounted_prices = list(map(lambda p: p * 0.85, raw_prices))


# 4. PEP 8 Clarity Check: def vs lambda
# Preferred Pythonic comprehension over map/filter + lambda for clarity
clear_high_numbers = [x for x in numbers if x > 40]


if __name__ == "__main__":
    print("Sorted by price:", [p["name"] for p in by_price])
    print("Filtered > 40:", high_numbers)
    print("Discounted prices:", discounted_prices)

$ python3 junior_lambda.py
Sorted by price: ['Mouse', 'Monitor', 'Laptop']
Filtered > 40: [45, 68, 90]
Discounted prices: [85.0, 170.0, 425.0]
Execution completed successfully.

import unittest

class TestJuniorLambda(unittest.TestCase):
    def test_sorted_with_lambda_key(self):
        data = [("B", 3), ("A", 1), ("C", 2)]
        result = sorted(data, key=lambda item: item[1])
        self.assertEqual(result, [("A", 1), ("C", 2), ("B", 3)])

    def test_filter_with_lambda(self):
        nums = [1, 2, 3, 4, 5]
        evens = list(filter(lambda x: x % 2 == 0, nums))
        self.assertEqual(evens, [2, 4])

    def test_map_with_lambda(self):
        nums = [1, 2, 3]
        squared = list(map(lambda x: x ** 2, nums))
        self.assertEqual(squared, [1, 4, 9])

if __name__ == "__main__":
    unittest.main()

🌱 Junior Track 21 Master Reference

21. When to Use Lambda vs Standard `def` Functions

📊 Decision Matrix: `def` vs `lambda` vs Comprehensions

Use Case	Recommended Tool	Example / Syntax	Why?
Custom Sort Key	Inline `lambda`	sorted(items, key=lambda x: x[1])	Concise throwaway callback.
Filtering/Mapping Lists	List Comprehension	[x for x in data if x > 0]	More readable & faster than `filter(lambda...)`.
Reusable / Multi-line Logic	Standard `def` Function	def process(item): ...	Clean tracebacks, docstrings & testability.

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21. Lambda Functions

🎓 Junior Python Curriculum — Select Module

⚡ Key Concept: Lambda Syntax & Clean Usage Guidelines

21. When to Use Lambda vs Standard `def` Functions

📊 Decision Matrix: `def` vs `lambda` vs Comprehensions

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