Python basics - Day 18

Day 18 – Lambda Functions (Anonymous Functions)

Project: Build a “Quick Data Transformer” using lambda, map, filter, and reduce.

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01. Learning Goal

By the end of this lesson, you will be able to:

• Understand what lambda functions are and when to use them
• Replace small def functions with concise lambda expressions
• Use lambda with map(), filter(), and reduce()
• Apply lambda for sorting and conditional expressions

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02. Problem Scenario

Imagine you’re working on a data processing app that needs to perform quick operations

— squaring numbers, filtering values, or sorting strings — all in one line.

Instead of defining a new function each time, you can use lambda functions to simplify your code.

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03. Step 1 – What is a Lambda Function?

A lambda function is a small anonymous function written in one line.

Syntax:

lambda arguments: expression

add = lambda x, y: x + y
print(add(3, 5))   # 8

Use lambda when you need a simple function for short-term use.

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04. Step 2 – Regular vs Lambda Function

# Regular function
def square(x):
    return x * x

# Lambda function
square2 = lambda x: x * x

print(square(4))   # 16
print(square2(4))  # 16

Both work the same — but lambda makes your code shorter and cleaner.

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05. Step 3 – Common Uses of Lambda Functions

1️⃣ Sorting with key

nums = [3, 1, 5, 2, 4]
nums.sort(key=lambda x: -x)
print(nums)   # [5, 4, 3, 2, 1]

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2️⃣ Using map() – Apply a Function to All Elements

numbers = [1, 2, 3, 4]
squares = list(map(lambda x: x**2, numbers))
print(squares)   # [1, 4, 9, 16]

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3️⃣ Using filter() – Keep Only Matching Items

numbers = [10, 15, 20, 25]
even = list(filter(lambda x: x % 2 == 0, numbers))
print(even)   # [10, 20]

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4️⃣ Using reduce() – Combine All Elements

from functools import reduce

numbers = [1, 2, 3, 4]
product = reduce(lambda x, y: x * y, numbers)
print(product)   # 24

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06. Step 4 – Lambda with Conditions

You can use conditional expressions directly inside lambda.

max_num = lambda a, b: a if a > b else b
print(max_num(10, 20))   # 20

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07. Step 5 – Practice Examples

Example 1: Sort Words by Length

words = ["apple", "banana", "kiwi"]
words.sort(key=lambda w: len(w))
print(words)   # ['kiwi', 'apple', 'banana']

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Example 2: Filter Odd Numbers

nums = [1, 2, 3, 4, 5]
odds = list(filter(lambda n: n % 2 == 1, nums))
print(odds)   # [1, 3, 5]

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Example 3: Convert Celsius to Fahrenheit

temps_c = [0, 10, 20, 30]
temps_f = list(map(lambda c: (c * 9/5) + 32, temps_c))
print(temps_f)   # [32.0, 50.0, 68.0, 86.0]

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08. Step 6 – Mini Project: Quick Data Transformer

Let’s combine what you’ve learned into a simple data processing tool.

from functools import reduce

numbers = [2, 4, 6, 8, 10]

# Square numbers
squares = list(map(lambda x: x**2, numbers))

# Filter values greater than 20
filtered = list(filter(lambda x: x > 20, squares))

# Sum them all
total = reduce(lambda x, y: x + y, filtered)

print("Original:", numbers)
print("Squares:", squares)
print("Filtered (>20):", filtered)
print("Total:", total)

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09. Reflection

You have learned how to:

• Write lambda functions as one-line anonymous functions
• Combine lambda with map, filter, and reduce
• Use lambda for sorting, filtering, and transforming data
• Build a Quick Data Transformer that processes lists efficiently

Next → Day 19 – Modules and Packages
Learn how to organize your code into reusable files and folders.