Python vs Java has always been a long heated debate, and it still doesn’t have one settled winner. Both are modern-day, in-demand, compelling programming languages used majorly in all types of website development services and stand next to none. Java vs Python, these languages are object-oriented and continuously used for software development purposes. In fact, the two languages have constantly emerged as the top; they’re trendy! But what is the exact difference between Python vs Java?
In technical terms, Python’s syntax is easier to comprehend and can be understood by the masses. Java, however, is based on C/C++ and relies on traditional rules. Java’s code readability is sensitive. Misuse of a mere semicolon can result in a syntax error. At the same time, Python is human-like, based on English. There are countless differences like these; keep reading as we’ll discuss them in further detail.
Both are obviously, high-level programming languages but what distinguishes them is their coding structure. Python is an interpreted language, and developers have to depend on ‘Python’ exclusive interpreters. There are a few nitpicks in-between, but they’re worth it. CPython, an interpreter, requires a .py extension, and you can execute line-by-line code by looking at the file on CPython yourself simultaneously.
While Java is a compiling language and has its own JVM (Java Virtual Machine), the compiler is easy to use and is cross-platform supported. Also, the source code of Java is converted into a bytecode – instruction set for JVM. It’s a simple three-way process for Java. Any compiler converts source code to bytecode. Then, the JVM uses bytecode and compiles it in a machine-readable form. After execution, you have a Java program at your disposal!
Programming paradigms make it easier to classify languages in ‘boxes.’ Is the language imperative? Is it structured? In this case, both are object-oriented languages relying on their own internal (encapsulated) state and public interfaces. Python’s structure is diverse and adaptable to different programming states; imperative, functional, procedural. Use different programming types accordingly.
Java isn’t any less, either. Granted that it started with limited features, the programming language has evolved. On their latest updated version – Java 8, there’s support for important functional programming concepts such as lambda expressions. The update showcased Java’s support for functions. And as always, Java is concurrent, class-based, and object-oriented. In the rivalry of Java vs Python, the latter clearly ones in this case.
From a developer’s perspective, knowing the syntax format is essential before delving into any language. Python, in particular, emphasizes ‘code readability,’ and their IDE (Integrated Development Environment) makes it clear. As mentioned, Python was specifically designed to attract emerging coders – figuring out the syntax does not take much time, unlike Java.
For instance, you can clearly see a side-by-side comparison of the two languages here. Python comes across as a simple, easily comprehensible, language that executes flawlessly. It’s plain English. Even the hashtag on the side symbolizes ‘comments’ and does not have any significance. Hence, Python has always been marketed as a beginner-level language that is easy to understand yet powerful. Python is being taught at middle schools as well. Its syntax makes programming easy for everyone.
With Java, it’s a different story. Before executing a code, you need to be aware of concepts such as class, object, instance variables, and methods. It is case-sensitive software, so you always to be alert. But on Java 9, there are improvements. For a better user experience, they introduced ‘modules.’ With modules, developers can organize the code into categories and create separate packages. Modules ease the compiling process, but organizing the code takes a lot of time nonetheless.
Whitespace refers to typography in the UI of programming languages. And you might have guessed it; Python includes whitespace as a part of its syntax – it plays an integral role. For nesting and loops, you can apply tabs and full colons, respectively. Now, not everyone’s a fan of whitespace in Python. Parsing whitespace is someone’s own aesthetic preference; using black lines isn’t necessary.
Meanwhile, Java does not rely on whitespace at all. Semicolons, parentheses, and curly braces account for Java’s directory.
Duck-typing sets the two languages apart. Now, duck-typing depends on dynamic typing, where the class or object does not have much significance. Fortunately, Python is a dynamic language, and duck typing is a major part of it. Meaning that Python isn’t dependent on variable deceleration.
Interestingly, duck-typing’s concept is applicable to Python. Essentially, duck-typing is based on the philosophy – ‘if it quacks like a duck, it is a duck,’ and metaphorically means that you do not check for attributes in variables.
Frameworks are an abstraction that allows the user to develop applications without dealing with low-level details such as sockets, protocols, etc. It is a well-known fact that writing code or developing an application is a complex and tiring process; however, it becomes a tad bit easier by using this certain feature.
Now, Java Frameworks are, indeed, the templates of pre-written code through which you can add your own code. Java receives praise for its large number of Frameworks. The most popular ones are Spring, Hibernate, etc. However, Python has fewer options for Frameworks as compared to Java. The most popular high-level Frameworks in Python are Django, TurboGears, and Web2py.
If you are planning to become a UI/UX designer soon, then having knowledge of Python vs Java frameworks will help you understand the underlying mechanism of your app or website in a much better manner.
According to experts, both Java and Python lack in the department of speed. The speed required to facilitate high-performance computing and coding is not currently available. Nonetheless, it is important to mention that Java Virtual Machine (JVM) speeds up Java code execution through just-in-time (JIT) compilation. The Bytecode compiles into the Native Machine Code much quicker through the assistance of this JIT compiler. Additionally, through supporting concurrency, Java allows software applications to run much faster as compared to Python.
On the other hand, the coders can quicken Python code execution with the help of several implementations of the programming language. For example, they can use Jython to compile the given Python code into Java bytecode. Similarly, Cython is available for developers to compile Python code into C/C++ code. Although these implementations are an advantage and sometimes optimizes the execution rate for developers, it is essential to mention that it is slower since it uses an interpreter and also determines the data type at run time.
Simply put, Python is the standard language for doing data science today. Enterprises and corporations prefer Python for development. It can vary from scientific computing, big data to artificial intelligence projects. While there are places that exist, such as academia, where R might be more popular, you would be surprised to find out that most data science roles expect you to be experienced in Python instead of languages like Scala and Java. As mentioned above, it is the most preferred programming language for machine learning and data science. A report shows that a large sum of data scientists and machine learning programmers have shown a preference towards Python instead of the usual use of Java while working on sentiment analysis.
Nevertheless, many machine learning programmers opt for Java when working on projects close to network security. This can consist of projects such as cyber-attack prevention and fraud detection.
There has been a continuous increase in enterprises and businesses adopting the so-called agile development methodology to accelerate the deliverance of high-quality software applications. Similarly, numerous organizations nowadays go for DevOps to increase software development along with testing and deployment processes.
Aforementioned, Both Java and Python allow enterprises to embrace new project management methodologies like agile and DevOps. Since Java contains a static type system, it is quite easy for programmers to uncomplicate refactoring. On the other hand, Python helps developers to simplify refactoring by featuring a dynamic type system. Additionally, Python’s easy-to-use and expressive syntax rules give Python programmers access to experiment with diverse ideas.
Python has always had a prominent presence in the agile space. While Python has gained popularity and success for several reasons, one of the main reasons for this popularity is the rise of the DevOps movement.
We can conclude that both Java and Python languages are beneficial on their own. Python is simple and concise, whereas Java is quick and more convenient. While Python coding is dynamic, Java coding is static.
Middle schoolers prefer using Python for writing their first ‘for loop’ or ‘while loop’ and outstanding machine learning engineers. At the same time, users prefer Java for coding more than Python. Both of these seem plausible. Nonetheless, it entirely depends on the user to decide the best language out of these two. It really is up to you to choose a particular language for your next project!