The Data Science and Analytics community bears an exceptional likeness for Python and Scala, and rightly so. Both Python and Scala are excellent tools that can cater to various programming and Data Science needs. From designing small-scale projects to building complex ML projects, Python and Scala show outstanding agility and flexibility.
While both these programming languages are great for developing innovative projects on new-age technologies, there are significant differences between Python and Scala.
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Python vs. Scala
Python is a high-level, general-purpose language that supports multiple paradigms, including functional, procedural, and object-oriented programming. It is one of the most popular and top-ranking programming languages with an easy learning curve. Python’s English-like syntax and user-friendly features make it the go-to tool for software development projects and Data Science projects.
Python’s dynamic typing, coupled with its interpreted nature, makes it the perfect choice for scripting and speedy application development. Furthermore, the Python interpreter and its standard library are freely available on and compatible with all major platforms, including Windows, macOS, and Linux.
Unlike Python’s dynamic typic, Scala has strong support for static typing. This particular feature allows developers to eliminate the possibility of bugs in software applications.
Python vs. Scala: The key differences
Below are the most significant differences between Python and Scala:
Both Python and Scala share the similarities of functional and object-oriented paradigms, resulting in a similar syntax. Despite this, Scala may be a tad complex for beginners since it packs many high-level functional features. However, Python boasts of having an intuitive logic and a comprehensive suite of libraries and is thus the best choice for beginners.
When it comes to performance, Scala is almost ten times faster than Python. Scala’s reliance on the Java Virtual Machine (JVM) during runtime imparts speed to it. Generally, compiled languages perform faster than interpreted languages. Since Python is dynamically typed, the development speed reduces.
Python has a massive community of followers and users who continually contribute to improving and extending Python’s abilities. The community hosts frequent meetups like conferences, webinars, coding competitions, etc. In fact, Python enjoys the largest programming communities in the world. According to a 2019 report, Python held the third rank after Java and C language, whereas Scala secured the 30th position among the 50 trending programming languages.
Scala comes with many standard libraries and multiple cores that facilitate the quick integration of databases in Big Data ecosystems. With Scala, you can write code with multiple concurrency primitives, which allows for better memory management and data processing. Contrary to this, Python lacks support for concurrency, which means only one thread can be active at a time. Thus, when you deploy a new code, you must restart the running processes, which inevitably increases the memory load.
As Scala is statically typed, it is easier to find compile-time errors. However, Python is a dynamically typed language and hence, is more prone to bugs, especially when you modify existing code. Naturally, it is much easier to refactor or restore Scala code than Python code.
Data Science application
Presently, Python is the most preferred language of the Data Science community, thanks to its easy learning curve and an extensive network of libraries and tools. In the Data Science domain, Python has several libraries like Pandas, SciPy, NumPy, Matplolib, Keras, Pytorch, and TensorFlow. These are excellent for building ML and Deep Learning projects. Coming to Scala, its easy integration with Apache Spark makes it a useful tool for handling Big Data and developing ML models.
Scala is perfectly compatible with the Hadoop ecosystem because it is built on top of Hadoop’s filesystem HDFS. It can interact with Hadoop via Hadoop’s native API in Java. This allows developers to write native Hadoop applications in Scala. Python cannot integrate or interact with Hadoop as smoothly as Scala.
To conclude, both Python and Scala have their distinct advantages and limitations. While both the languages are great for software development and building Data Science applications, their performance and practicality largely depend on their use cases.
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