map()

The map() function applies a given function to each item of an iterable (like a list) and returns a map object (an iterator).

Syntax:

map(function, iterable)

Example:

# Doubling the numbers in a list
numbers = [1, 2, 3, 4, 5]
doubled = list(map(lambda x: x * 2, numbers))
print(doubled)  # Output: [2, 4, 6, 8, 10]

filter()

The filter() function filters elements from an iterable based on a function that returns True or False.

Syntax:

filter(function, iterable)

Example:

# Filtering even numbers from a list
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
print(even_numbers)  # Output: [2, 4, 6, 8, 10]

reduce()

The reduce() function applies a rolling computation to sequential pairs of elements in an iterable, reducing them to a single value.

Note: In Python 3, reduce() is part of the functools module.

Syntax:

from functools import reduce
reduce(function, iterable)

Example:

from functools import reduce

# Adding all numbers in a list
numbers = [1, 2, 3, 4, 5]
sum_numbers = reduce(lambda x, y: x + y, numbers)
print(sum_numbers)  # Output: 15 (1 + 2 + 3 + 4 + 5 = 15)

These functions are powerful when used appropriately. They enhance code readability and efficiency, especially when dealing with transformations, filtering, or aggregations on iterable data structures.