Understanding
Map, Filter, and Reduce

What are Map, Filter, and Reduce?

Map, Filter, and Reduce are foundational concepts in functional programming. They are used to process data collections in a declarative way, often replacing the need for explicit loops.

Why use Map, Filter, Reduce?

• They make code shorter and more expressive.
• They allow for a more functional, less imperative approach to programming.
• They help eliminate side effects, leading to more predictable code.

Understanding map()

The map function applies a given transformation to each item in a collection and returns a new collection with the transformed results.

function square(x):
    return x * x

numbers = [1, 2, 3, 4, 5]
squared = map(square, numbers)
print(squared)

Output:

[1, 4, 9, 16, 25]

What's happening here?

The square function is applied to every element of the numbers list. The original list remains unchanged, and a new list is returned with squared values.

Question:

What if you wanted to double every number instead?

Answer: You’d just pass a double function instead of square to map().

Understanding filter()

The filter function selects elements from a collection that satisfy a given condition (i.e., for which a function returns true).

function isEven(x):
    return x % 2 == 0

numbers = [1, 2, 3, 4, 5, 6]
evens = filter(isEven, numbers)
print(evens)

Output:

[2, 4, 6]

Question:

What does filter() return?

Answer: A new collection that only contains elements that passed the test defined by the function.

Understanding reduce()

The reduce function applies a binary function cumulatively to the items of a collection, reducing the collection to a single value.

function add(x, y):
    return x + y

numbers = [1, 2, 3, 4]
sum = reduce(add, numbers)
print(sum)

Output:

10

What’s happening here?

The add function is called like this: add(1, 2) → 3, then add(3, 3) → 6, then add(6, 4) → 10. The result is a single value: 10.

Question:

Can reduce() be used to find the maximum value?

Answer: Yes! Just use a function like max(a, b): return a if a > b else b.

Combining Map, Filter, and Reduce

You can often combine these for powerful one-liners. Let’s say we want to sum the squares of even numbers from a list.

function isEven(x): return x % 2 == 0
function square(x): return x * x
function add(a, b): return a + b

numbers = [1, 2, 3, 4, 5, 6]

result = reduce(add, map(square, filter(isEven, numbers)))
print(result)

Output:

56

This applies filter() to keep evens → [2, 4, 6], then map() to square → [4, 16, 36], then reduce() to sum → 56.

When to Use Map, Filter, Reduce

• map(): when you need to transform each element.
• filter(): when you need to select a subset of elements.
• reduce(): when you need to combine elements into one value.

Conclusion

These three functions make your code cleaner, expressive, and less error-prone. By practicing them with basic use-cases, you can greatly improve your understanding of functional programming principles.