Day 18 – Lambda Functions (Anonymous Functions)
Project: Build a “Quick Data Transformer” using lambda, map, filter, and reduce.
01. Learning Goal
By the end of this lesson, you will be able to:
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.
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.
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.
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]
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]
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]
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
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
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']
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]
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]
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)
09. Reflection
You have learned how to:
Next → Day 19 – Modules and Packages
Learn how to organize your code into reusable files and folders.
More...
Project: Build a “Quick Data Transformer” using lambda, map, filter, and reduce.
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
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.
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.
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.
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]
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]
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]
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
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
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']
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]
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]
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)
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.
More...