How to use if, else & elif in Python Lambda Functions

Python lambda functions are anonymous, single-line functions that provide a concise way to define small operations. When combined with conditional statements like if, else, and elif, they become powerful tools for inline decision-making logic.

In this tutorial, we will explore how to incorporate conditional statements within lambda functions, demonstrating their syntax and practical applications through working examples.

Basic Syntax

A lambda function with conditional statements follows this general structure ?

lambda arguments: expression_if_true if condition else expression_if_false

The key components are ?

• lambda ? keyword to define the anonymous function

• arguments ? input parameters for the function

• condition ? logical expression that evaluates to True or False

• expressions ? values returned based on the condition result

Simple If-Else Example

Let's create a lambda function that determines if a number is even or odd ?

check_even = lambda x: "Even" if x % 2 == 0 else "Odd"

print(check_even(4))
print(check_even(7))
print(check_even(0))

Even
Odd
Even

Multiple Conditions Using Chained If-Else

For multiple conditions, we can chain if-else statements to simulate elif behavior ?

get_grade = lambda score: "A" if score >= 90 else "B" if score >= 80 else "C" if score >= 70 else "D" if score >= 60 else "F"

print(get_grade(95))
print(get_grade(85))
print(get_grade(75))
print(get_grade(65))
print(get_grade(45))

A
B
C
D
F

Nested Conditional Logic

Lambda functions can handle nested conditions for more complex decision-making ?

categorize_number = lambda num: "Positive" if num > 0 else "Negative" if num < 0 else "Zero"

print(categorize_number(5))
print(categorize_number(-2))
print(categorize_number(0))

Positive
Negative
Zero

Practical Applications

Lambda functions with conditionals are commonly used with built-in functions like map(), filter(), and sorted() ?

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

# Using with map() to categorize numbers
categories = list(map(lambda x: "Small" if x <= 3 else "Medium" if x <= 7 else "Large", numbers))
print(categories)

# Using with filter() to get specific values
filtered_nums = list(filter(lambda x: "Even" if x % 2 == 0 else None, numbers))
print([x for x in numbers if (lambda y: True if y % 2 == 0 else False)(x)])

['Small', 'Small', 'Small', 'Medium', 'Medium', 'Medium', 'Medium', 'Large', 'Large', 'Large']
[2, 4, 6, 8, 10]

Comparison of Approaches

Conclusion

Lambda functions with conditional statements provide a powerful way to create concise, inline decision-making logic in Python. While they excel at simple conditions, complex logic may be better suited to regular functions for improved readability.