An Introduction to Map, Filter, Reduce

Higher order functions are a powerful tool for functional programmers. The idea of higher order functions/functions as data has given rise to three especially useful functions. Enter map, filter, and reduce.

Map: Transform Data

  • Input: Colletion and unary function
  • Output: A collection, equal in size to the original collection
  • Uses: One-to-one transformation of a collection

In Clojure, map is used to transform each element in a collection. Map takes a unary (one argument) function and a collection as input.

Given a unary function, f, and a collection C = [v0, v1, ..., vN], (map f C) will return [(f v0), (f v1), ..., (f vN)].

For instance, lets pretend we have to apply a curve to exam grades,

(def grades [72 68 52 70 81 63])

(defn curve
  [grades extra-points]
  (map #(+ extra-points %) grades))

(curve grades 10)
;;=> [82 78 62 80 91 73]

; Awful color scheme

Filter: Filter Data

  • Input: Collection and predicate function
  • Output: A subset of the original collection
  • Uses: To remove unwanted elements of a collection

Filtering a collection is a common operation in any kind of programming. Clojure (any many other languages) provide to you a function, filter, to do just this. As inputs, you provide a predicate function and a collection. The return will always be a subset of the original collection.

Lets say we have a list of names but we only want names starting with “S”,

(def names ["Andy" "Jimmy" "Sarah" "Sam" "Zach"])

(def s-names
  (filter #(clojure.string/starts-with? names "S")))
(prn s-names)
;;=> ["Sarah" "Sam"]

Filter is very straight forward in what it’s intended use is and how it’s used. As long as you understand the idea of higher order functions, filter comes easy.

Reduce: Accumulate Data

  • Input: Collection and combining function
  • Output: Recombination of original data
  • Uses: To Aggregate, accumulate, or otherwise transform data

Reduce, also known as fold, is primarily used to recursively recombine a collection of data. Reduce is the most flexible of of the three functions in this article. In fact, map and filter can be defined in terms of reduce.

(def my-map
  [f col]
  (reduce (fn [acc v]
            (conj acc (f v)))

(defn refilter 
  [f col]
  (reduce (fn [acc v]
            (if (f v)
              (conj acc v)

If you have a keen eye for laziness, you’ll notice that something is off. While map and filter are both known to be lazy, reduce isn’t. Our new my-map and my-filter functions are both eager. This isn’t necessarily a bad thing in all problem domains. However, since map and filter are typically used on large data sets, laziness is good. I’ll explain why in a later post.

But what do we do with this thing?

(defn my-sum
  (reduce + nums))

(def costs [100.10 45.70 255.45])

(my-sum costs)
;;=> 401.25

Fun fact, this is how + is defined in Clojure This is actually how many mathematical, collection related functions are defined. (i.e. +, -, *, max, min, etc…) Not quite this simplistic, but kind of close.

We can also use reduce to


  1. Map, filter, and reduce are staple, higher order functions.
  2. Reduce can be used to define a wide variety of data related functions.
  3. Map excels at surjective (onto) transformations.
  4. Filter is a very useful when you need to refine a set of data based on a predicate like odd?.
Written on January 18, 2017