Functional Programming in Scala

Functional Programming in Scala link

Scala’s support for functional programming (FP) emphasizes writing software by composing pure functions, avoiding shared state, mutable data, and side-effects.

Understanding Functional Programming

Functional programming treats computation as the evaluation of mathematical functions. It emphasizes the use of immutable data and functions as first-class citizens.

• Immutable Collections: Scala provides a rich set of immutable collections, making code safer and easier to understand.
• Higher-Order Functions: Higher-order functions can take functions as parameters or return functions, and they are critical in FP.

Immutable Collections

Scala provides several immutable collections:

• List: An immutable sequence.

    val numbers = List(1, 2, 3, 4, 5)
    

• Vector: An immutable indexed sequence.

    val vector = Vector(1, 2, 3, 4, 5)
    

• Map: An immutable map of key-value pairs.

    val map = Map("Alice" -> 25, "Bob" -> 30)
    

Higher-Order Functions

Scala’s higher-order functions include map, filter, and reduce.

• map: Transforms each element in a collection.

    val doubled = numbers.map(_ * 2)
    

• filter: Returns elements of a collection that meet a condition.

    val evenNumbers = numbers.filter(_ % 2 == 0)
    

• reduce: Combines all elements of a collection using a binary function.

    val sum = numbers.reduce(_ + _)
    

Code Example: Scala Functional Programming

Here’s a Scala program demonstrating functional programming concepts:

  object FunctionalApp {
  def main(args: Array[String]): Unit = {
    val numbers = List(1, 2, 3, 4, 5)
    val doubled = numbers.map(_ * 2)
    println(s"Doubled Numbers: $doubled")
    val evenNumbers = numbers.filter(_ % 2 == 0)
    println(s"Even Numbers: $evenNumbers")
    val sum = numbers.reduce(_ + _)
    println(s"Sum of Numbers: $sum")
  }
}
  

This program showcases the use of map, filter, and reduce functions on a list of numbers.

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