Scala vs Python – Difference between Python and Scala
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1. Scala vs Python
To get the best of your time and efforts, you must choose wisely what tools you use. For this purpose, today, we compare two major languages, Scala vs Python for data science and other uses to understand which of python vs Scala for spark is best option for learning.
But before we begin with the comparison, we must make small talk about Scala.
An object-oriented programming language, Scala was designed by Martin Odersky in 2004. Scala gets its name as a portmanteau of ‘scalable’ and ‘language’, in that it can scale according to the number of users. In Scala, everything is an expression. Today, it finds application in data analytics using Apaché Spark. Understand difference between java vs scala for more learning.
For an introduction to Python, if you haven’t begun your Python journey yet, read up on our Introduction to Python.
Now that we’re through the introductions, let’s begin comparing scala vs python.
The first factor that we’ll use for comparison is performance. We’ve talked earlier about how being a dynamically-typed language creates extra work for the interpreter at run time. It has to decide the types of data at run time. Scala, however, uses the JVM, and is therefore 10 times faster than Python. When there’s a lot to process, you should consider going with Scala instead.
We couldn’t be clearer when we say Python is perfect for rookies. Its extremely easy and English-like syntax contributes to its popularity. Although bundled with a bunch of syntactic sugars, Scala isn’t as easy to master. However, for concurrent and scalable systems like SoundCloud and Twitter, Python falls short. This is the main Point in Scala vs Python.
With its list of asynchronous libraries and reactive cores, is a great choice when you want to implement concurrency. Python, on the other hand, does not support true multithreading. Although, it does support heavyweight process forking. With it, only one thread is active at a time. So whenever a new code is deployed, more processes must be restarted, which increases the memory overhead.
6. Type Safety
We’ve often said this- Python is a dynamically-typed language. This means you don’t need to declare the data type in python while declaring it. It follows the duck-typing principle. “If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck”. While this is easy on the programmers, it slows the applications down. Contrarily, Scala appears to be dynamically-typed, but is statically-typed. The compiler will detect errors at compile time.
We see that refactoring Scala code is easier, whereas doing that to Python code may create more bugs than it solves. So, while Python is a good choice for smaller ad-hoc experiments, Scala fares better for large products.
Winner– It’s a tie.
7. Productivity and Ease of Use
While Scala isn’t as verbose as Java, it definitely isn’t as concise as Python. Python is a clear winner in this case with its user-friendliness and expressivity.
8. Advanced Features
While Scala has several existential types, macros, and implicits, its syntax may make it difficult to experiment with them. Frameworks and libraries, however, allow you to make good use of these features.
Python, on the other hand, has enough data science tools and libraries for Machine Learning and Natural Language Processing. SparkMLib is one such library for machine learning on big data.
Winner– It’s a tie.
This was all on Scala vs Python.
In comparing Python vs Scala, we measured them over a range of factors. These included performance, simplicity, concurrency, type safety, productivity, and advanced features. Both languages fared better twice, and we had a tie twice. So concluding this venture, which one you choose is entirely up to you and the requirements of your project.
But before you leave us for today, we want you to glance over our opinion on Python vs Java. Signing off.
Refer Top Python books and top Scala books for deep learning.