Functional Programming #

Filter #

Filters elements of a sequence based on a function that returns True or False.

# Example: Filtering even numbers from a list
numbers = [1, 2, 3, 4, 5, 6]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4, 6]

The list around filter is there so we don’t have to deal with generators, which is one of the more advanced topics in Python.

Map #

Applies a function to every item in a sequence.

# Example: Squaring each number in a list
numbers = [1, 2, 3, 4]
squared_numbers = list(map(lambda x: x**2, numbers))
print(squared_numbers)  # Output: [1, 4, 9, 16]

Reduce #

Reduces a sequence to a single value using a function.

from functools import reduce

# Example: Finding the product of a list of numbers
numbers = [1, 2, 3, 4]
product = reduce(lambda x, y: x * y, numbers)
print(product)  # Output: 24

List Comprehension #

A compact way to generate or transform lists. Example: Creating a list of squares:

numbers = [1, 2, 3, 4]
squared_numbers = [x**2 for x in numbers]
print(squared_numbers)  # Output: [1, 4, 9, 16]

Example: Filtering even numbers:

even_numbers = [x for x in numbers if x % 2 == 0]
print(even_numbers)  # Output: [2, 4]

List comprehension can be used to generate sets and dictionaries.

Lambda Function #

Anonymous, single-expression functions. Handy if you know the function will be short and will not be reused.

# Example: Lambda function to add two numbers
add = lambda x, y: x + y
print(add(5, 3))  # Output: 8

# Example: Using lambda with map
numbers = [1, 2, 3, 4]
cubed_numbers = list(map(lambda x: x**3, numbers))
print(cubed_numbers)  # Output: [1, 8, 27, 64]