Scala's name implies that it is a scalable programming language. It was created in 2003 by Martin Odersky and his research team. These days we widely use Scala in Data Science and Machine Learning fields. Scala is a small, fast, and efficient multi-paradigm programming language built on a compiler. The JVM(Java Virtual Machine) is Scala's main advantage . Scala code is first compiled by a Scala compiler, which generates bytecode, which is then transported to the JVM for output generation.
Scala is a high-level programming language that mixes object-oriented and functional programming. Data Science with Python tutorial is a great choice to start learning, and Scala programming for data science problem-solving is an excellent skill to have in your arsenal. Scala was built to implement scalable solutions to crunch big data in order to produce actionable insights. Scala's static types help complicated applications avoid problems, and its JVM and JavaScript runtimes allow you to construct high-performance systems with simple access to a vast library ecosystem.
Why we learn Scala for Data Science:
- Scala has the ability to interact with data that is stored in a distributed manner. It takes advantage of all available resources and allows for parallel data processing.
- It's a language designed to take advantage of big data processing. This language is designed to construct scalable solutions for digesting and grouping large amounts of data in order to generate actionable insights.
- Scala allows you to work with immutable data and higher-order functions, just like we use those concepts frequently in the Python programming paradigm. You can learn more about this from knowledgehut data science with python tutorial.
- Scala is an improved version of Java that was created with the goal of removing redundant code. It supports a variety of libraries and APIs, allowing the programmer to work with less downtime.
- Scala provides various types of Constructs, allowing programmers to easily interact with wrappers and container types.
Here is an article on measures of dispersion.
Scala: A Quick Guide:
Scala programming for data science was created to describe common programming patterns in a concise, expressive, and type-safe manner. It combines the best of object-oriented and functional programming languages.
Scala is object-oriented:
In the sense that every value in Scala is an object, it is a pure object-oriented language. Classes and characteristics explain the types and behavior of objects. Subclassing and a flexible mixing-based composition technique can be used to expand classes as a clean replacement for multiple inheritances.
In the sense that every function is a value, Scala is also a functional language. Scala has a lightweight syntax for defining anonymous functions, as well as support for higher-order functions, nested functions, and currying. The functionality of algebraic types, which are utilized in many functional languages, is provided via Scala's case classes and built-in support for pattern matching. Singleton objects are a simple way to group functions that aren't class members.
Statically Typed Programming Language:
Scala's expressive type system ensures that abstractions are employed safely and consistently at compile time. The type system, in particular, supports:
- Classes that are generic
- Annotations on variance
- Type bounds (upper and lower)
- As object members, inner classes, and abstract type members
- Types of compounds
- Self-references typed explicitly
- Conversions and implicit parameters
- Methods that are polymorphic
In practice, domain-specific language extensions are frequently required when developing domain-specific applications. Scala offers a unique set of language tools that make adding new language constructs in the form of libraries a breeze. In many cases, this can be accomplished without the use of meta-programming tools like macros. Consider the following scenario:
- Implicit classes enable extension methods to be added to existing types.
- With custom interpolators, string interpolation can be extended by the user.
Scala is built to work well with the widely used Java Runtime Environment (JRE). The connection with the popular object-oriented Java programming language, in particular, is as frictionless as feasible. Scala has direct counterparts for newer Java features like SAMs, lambdas, annotations, and generics.
Scala features that don't have Java equivalents, such as default and named parameters, compile as close to Java as possible. Scala uses the same compilation methodology as Java (separate compilation, dynamic class loading) and provides access to hundreds of high-quality libraries already available.
Scala As A Data Science Tool:
Scala is a sophisticated programming language with the ability to support a wide range of tools. At KnowledgeHut, the Data science course duration would be 20+ hours and you will get hands-on experience with more than 100 datasets from real companies. After acquiring this learning experience, Scala programming can be quite beneficial when working with large amounts of data. The following are some of Scala's most important Data Science applications:
- Scala is used by the Spark Framework to handle real-time data streaming. In data analytics, the Spark Framework makes use of Scala.
- Apache Spark MLlib and ML are the libraries for Machine Learning tasks
- Scala has some excellent natural language processing libraries, such as ScalaNLP, Epic, and Puck
- DeepLearning.scala is a great toolkit to do Deep Learning related tasks
- Breeze, Saddle, Scalalab are the data analysis tools available
- Breeze-viz and Vegas are the plotting library on visualization for Data Scientist
- Akka for distributed applications
- Spray and Slick Web Application and web services
Data Types In Scala:
All values in Scala, including numerical values and functions, have a type. A portion of the type hierarchy is depicted in the diagram below -
Any, often known as the top type, is the supertype of all kinds. equals, hashCode, and toString are some of the universal methods defined in it. AnyVal and AnyRef are direct subclasses of Any.
AnyVal is the root class of all value types. There are nine non-nullable predefined value types: Double, Float, Long, Int, Short, Byte, Char, Unit, and Boolean. Unit is a value type that contains no information. There is only one instance of I that can be declared in this way: (). Because all functions must return something, Unit is occasionally a useful return type.
AnyRef is a class that represents reference types. All reference types are declared as non-value types. AnyRef is a subtype of every user-defined type in Scala. AnyRef refers to java.lang.Object when Scala is used in a Java runtime environment. Here's an example of how strings, integers, characters, boolean values, and functions, like everything else, are of the type Any -
val list: List[Any] = List(
"This is a string",
548, // an integer
'c', // a character
true, // a boolean value
() => "an anonymous function returning a string"
)
list.foreach(element => println(element))
output:
This is a string
548
c
true
<function>
The following is how value types can be cast:
For instance:
val x: Long = 6496349
val y: Float = x // 6.4963493E7 (note that some precision is lost in this ca
val face: Char = '
val number: Int = face // 97
Casting is a one-way process. This isn't going to work:
val x: Long = 649634925
val y: Float = x // 6.4963493E7
val z: Long = y // Does not conform
A reference type can also be cast to a subtype.
Nothing and Null:
Nothing, commonly known as the bottom type, is a subtype of all kinds. There isn't a value of type Nothing. Non-termination, such as a thrown exception, program exit, or an infinite loop, is a typical usage (i.e., it is the type of an expression that does not evaluate a value or a method that does not return normally).
All reference types have a subtype called Null (i.e. any subtype of AnyRef). It only has one value, which is denoted by the keyword literal null. Null is primarily given for interoperability with other JVM languages and should be avoided at all costs in Scala programs.
Expressions In Scala:
Expressions are statements that can be computed:
1+1
You can use println to output the results of expressions:
println(7) // 7
println(2 + 2) // 4
println("Hello Universe!") // Hello Universe!
println("Hello," + " Universe!") // Hello, Universe!Values:
The val keyword can be used to name the results of expressions:
val x = 3 + 2
println(x) // 5
Values are named results, such as x in this case. A value is not re-computed when it is referenced.
Re-assigning values is not possible:
x = 7 // This does not compile.
A value's type can be omitted and inferred, or it can be declared explicitly:
val x: Int = 3 + 2
Variables:
var x = 3 + 2
x = 7 // This compiles because x is declared with the var keyword.
println(x * x) // 49
The type of a variable can be ignored and inferred, just like the type of a value, or it can be expressed explicitly:
var x: Int = 3 + 2
Blocks:
You can combine expressions by putting a {} around them. This is referred to as a block.
println({
val x = 3 + 2
x + 5
}) // 10Functions and Methods In Scala:
Functions:
A function is a collection of statements that work together to complete a task. A Scala function declaration has the following form:
def functionName ([list of parameters]) : [return type]
You can write an anonymous function (i.e., a function with no name) that returns a given number plus one :
(x: Int) => x + 1
A list of parameters appears to the left of =>. An expression involving the parameters is shown on the right.
You can also give functions names, such as:
val addOne = (x: Int) => x + 1
println(addOne(1)) // 2
Multiple parameters can be used in a function:
val add = (x: Int, y: Int) => x + y
println(add(3, 2)) // 5
It can also have no parameters:
val getTheAnswer = () => 75
println(getTheAnswer()) // 75
Methods:
Methods and functions are fairly similar in appearance and behavior, but there are a few major differences.
The def keyword is used to define methods. A name, parameter list(s), return type, and body are all followed by def:
def add(x: Int, y: Int): Int = x + y
println(add(3, 2)) // 5
Multiple argument lists can be passed to a method:
def addThenMultiply(x: Int, y: Int)(multiplier: Int): Int = (x + y) * multiplier
println(addThenMultiply(3, 2)(5)) // 25
Alternatively, there are no parameter lists at all:
def name: String = System.getProperty("user.name")
println("Hello, " + name + "!")Methods can also have multiple-line expressions:
def getSquareString(input: Double): String = {
val square = input * input
square.toString
}
println(getSquareString(3)) // 9Main Method:
In a Scala program, the main method is its starting point. The Java Virtual Machine requires a single-parameter main method named main, which takes an array of strings as an input.
The primary focus of modular programming is that it allows us to separate components and partition software into layers in order to create quick, scalable programs that can be readily adjusted later in the development life cycle. You can define the main method using an object like follows:
object Main {
def main(args: Array[String]): Unit =
println("Hello, Scala Learner!")
}Classes And Objects In Scala
Classes:
The class keyword, followed by the class's name and constructor parameters, can be used to define classes
class Greeter(prefix: String, suffix: String) {
def greet(name: String): Unit =
println(prefix + name + suffix)
}The method named greet return type is Unit, indicating that there is nothing useful to return. In Java and C, it is similar to void. (There is one difference: because every Scala expression must have a value, there is a singleton value of type Unit, written as (). It doesn't include any information.)
The new keyword can be used to create a class instance:
val greeter = new Greeter("Hello, ", "!")
greeter.greet("Scala Learner") // Hello, Scala Learner!Case Classes:
A "case" class is a specific sort of class in Scala. Case class objects are immutable by default, and they are compared by value (unlike classes, whose instances are compared by reference). As a result, they're even more beneficial for pattern matching.
The case class keywords can be used to define case classes:
case class Point(x: Int, y: Int)
Case classes can be created without using the new keyword:
val point = Point(1, 2)
val anotherPoint = Point(1, 2)
val yetAnotherPoint = Point(2, 2)
Objects:
Objects are one-of-a-kind manifestations of their own definitions. They can be thought of as singletons in their own classes
The object keyword can be used to define objects:
object IdFactory {
private var counter = 0
def create(): Int = {
counter += 1
counter
}
}You may find out more about an object by looking up its name:
val newId: Int = IdFactory.create()
println(newId) // 1
val newerId: Int = IdFactory.create()
println(newerId) // 2
Packages And Imports:
Creating a Package:
Scala creates namespaces with packages, allowing you to modularize your programs. Packages are defined at the top of a Scala file by stating one or more package names.
package users
class User
One convention is to name the package after the directory in which the Scala file is located. Scala, on the other hand, is unconcerned about file layout. An sbt project's directory structure for package users might look like this:
- ExampleProject
- build.sbt
- project
- src
- main
- scala
- users
User.scala
UserProfile.scala
UserPreferences.scala
- test
Notice how the users directory is contained within the scala directory, and how the package contains numerous Scala files. The package declaration could be the same in every Scala file in the package. The other way to declare packages is by using braces:
package users {
package administrators {
class NormalUser
}
package normalusers {
class NormalUser
}
}As you can see, this enables package nesting and gives you more scope and encapsulation control.
If the code is being created within an organization that has a website, the package name should be all lower case, and the format convention should be <top-level-domain>.<domain-name>.<project-name>. If Google had a project called SelfDrivingCar, for example, the package name would be:
package com.google.selfdrivingcar.camera
class Lens
This could be equivalent to the directory structure below:
SelfDrivingCar/src/main/scala/com/google/selfdrivingcar/camera/Lens.scala
Imports:
Import clauses are used to get access to other packages' members (classes, traits, functions, and so on). When accessing members of the same package, an import clause is not necessary. Import clauses are limited in scope:
import users._ // import everything from the users package
import users.User // import the class User
import users.{User, UserPreferences} // Only imports selected members
import users.{UserPreferences => UPrefs} // import and rename for convenienceImports can be used everywhere in Scala, which is one of the ways it differs from Java:
def sqrtplus1(x: Int) = {
import scala.math.sqrt
sqrt(x) + 1.0
}If you need to import something from the project's root because of a naming issue, prefix the package name with _root_:
package accounts
import _root_.users._
To summarize imports and packages in one example:
package com.acme.myapp.model
class Person ...
import users.* // import everything from the `users` package
import users.User // import only the `User` class
import users.{User, UserPreferences} // import only two selected members
import users.{UserPreferences as UPrefs} // rename a member as you import it
Note: By default, the scala and java.lang packages, as well as object Predef, are imported.
Parallel Collection In Scala:
Parallel collections are intended to be utilized in the same way that sequential collections are. The only difference being how a parallel collection is obtained. In general, there are two ways to make a parallel collection. To begin, use the new term in conjunction with a correct
import statement:
import scala.collection.parallel.immutable.ParVector
val pv = new ParVector[Int]
Second, by converting from a sequential collection:
val pv = Vector(1,2,3,4,5,6,7,8,9).par
These conversion methods are worth elaborating on: sequential collections can be converted to parallel collections by invoking the par method of the sequential collection, and parallel collections can be converted to sequential collections by invoking the seq method of the parallel collection.
Semantics:
While the parallel collections abstraction resembles typical sequential collections in appearance, it's crucial to note that its semantics differ, particularly in terms of side effects and non-associative operations. Parallel collections' concurrent and "out-of-order" semantics have two implications:
1. Side-effecting operations can lead to non-determinism: Given the parallel collections framework's concurrent execution semantics, operations on a collection that create side-effects should be avoided in order to maintain determinism. For instance, using an accessor method like foreach to increment a var declared outside of the closure and supplied to foreach is a basic example.
scala> var sum = 0
sum: Int = 0
scala> val list = (1 to 1000).toList.par
list: scala.collection.parallel.immutable.ParSeq[Int] = ParVector(1, 2, 3,...
scala> list.foreach(sum += _); sum
res01: Int = 524896
scala> var sum = 0
sum: Int = 0
scala> list.foreach(sum += _); sum
res02: Int = 365489
scala> var sum = 0
sum: Int = 0
scala> list.foreach(sum += _); sum
res03: Int = 756821
2. Non-associative operations lead to non-determinism: Because of the "out-of-order" semantics, it's also important to avoid non-determinism by only performing associative operations. That is, when invoking a higher-order function on pcoll, such as pcoll.reduce(func), the order in which func is applied to the items of pcoll should be random. A non-associative operation like subtraction is a simple yet apparent example:
scala> val list = (1 to 1000).toList.par
list: scala.collection.parallel.immutable.ParSeq[Int] = ParVector(1, 2, 3,...
scala> list.reduce(_-_)
res01: Int = -546589
scala> list.reduce(_-_)
res02: Int = -51357
scala> list.reduce(_-_)
res03: Int = -651278
In the example above, we take a ParVector[Int], call reduce, and pass it _-_, which simply takes two unnamed items and subtracts one from the other. The outcome of two runs of reduce( _-_) on the same collection will not be the same because the parallel collections framework creates threads that, in effect, perform reduce( _-_) on separate regions of the collection independently.
Scala’s Benefits:
- In Data Science perspective, Scala is nearly ten times faster than Python in terms of performance.
- Most JVM libraries can be used with Scala, allowing it to become firmly ingrained in enterprise programming.
- This language combines functions within class declarations and shares some legible syntax aspects with popular languages like Ruby.
- It has several functional features including string comparison advances and pattern matching, among others.
Scala’s Drawbacks:
- Because of the combination of functional and object-oriented characteristics of this language, type-information can be difficult to comprehend at times.
- The community of developers for this language is relatively small.
More than a Small Wonder
Scala is a multi-stream wonder of the twentieth century. It has experienced phenomenal growth since its inception, and it is without a doubt one of the most in-demand programming languages. So that brings us to the end of this article. I hope this article has shed some light on Scala, its characteristics, and the numerous types of operations that may be conducted with it.
