Java vs Python: Code examples and comparison

| 15 min. (3113 words)

As two of the most popular and practical languages out there, should you choose Java or Python for your next project? Is one of these languages a clear-cut better option?

The answer is a long one. According to GitHub’s annual Octoverse report, Python has now climbed to the second most popular language in usage, pushing Java down to third place. Python also remains one of the top ten fastest-growing languages, but despite Python’s recent meteoric rise, it has actually been around longer than Java.

The most recent TIOBE index also places Python at the top of their list, with Java falling to number three. You’ll find both languages consistently appearing in the top 3-4 of various ranking lists around the internet.

So, the two languages are clearly developer favorites, and neither of them is going anywhere anytime soon. But which is your best choice?

In this post:

An overview of Java

Java was originally created by Oracle in 1995, and is a high-level, class-based, object-oriented programming language. One of the chief priorities for the designers of Java was to have as few implementation dependencies as possible. It’s a general-purpose language, used frequently for desktop computing, mobile computing, games, and numerical computing.

Features of Java

  • Write Once, Run Anywhere: this is fairly self-explanatory (and more common these days), but put Java on the map. One version of Java code will run on any machine.
  • Backwards compatible: In one of the most powerful and durable Java features, the newest versions of Java are still (mostly) compatible with even the oldest, making migration painless.
  • Java’s popularity has endured for decades, so there’s a huge ecosystem of frameworks, libraries, and community support.

Disadvantages of Java

  • Backwards compatibility can be controversial; teams have complained that migrations still sometimes throw unexpected incompatibilities, and others argue that the principle is taken too far, to the point of extending the life of outdated and flawed features that should be retired.
  • Arguably the biggest Java disadvantage is that it’s a bit greedy with memory and is a relatively verbose language, especially compared to Python.

An overview of Python

Created by Guido van Rossum in 1991, Python is an interpreted high-level general-purpose programming language. It prioritizes developer experience and was designed to make code easier to read than alternative languages through significant indentation. It’s most commonly used for data analytics and machine learning, but has a wide range of uses.

Features of Python

  • Unrivaled readability and flexibility make Python suitable for a huge range of applications.
  • Dynamic typing and asynchronous code are much-loved Python features that help to speed up the development process.
  • Can be learned very quickly by newbie developers.

Disadvantages of Python

  • Executes a single thread at a time because of Python’s GIL.
  • Limited performance owing to prioritizing developer convenience.
  • No native compatibility with iOS or Android is a major Python disadvantage for mobile developers.


Python and Java are both highly versatile and found in a huge range of applications, which is a major factor in the popularity of both languages. But within this, the most popular applications of Python are in data science, finance, and machine learning. Python even has several major libraries, like PyTorch and Tensorflow, specifically built for machine learning applications. Some of the biggest and most prominent programs written (partially) in Python include Youtube, Instagram, and Dropbox.

Java is incredibly multi-purpose, but common applications of Java are in big, enterprise applications, like Amazon, Netflix, Spotify, and Zoom. Other popular uses include gaming apps, and increasingly, cloud development and IoT. Java is a favorite for mobile development, owing to cross-platform support and native mobile development tools. It’s also a popular choice in the finance world, notably with enterprise banking applications through Java Enterprise Edition (Java EE).

Java vs. Python code examples

While we’re focused on the differences, Java and Python have many similarities. Both languages have strong cross-platform support and extensive standard libraries. They both treat (nearly) everything as objects. Both languages compile to bytecode, but Python is usually compiled at runtime. They are both members of the Algol family, although Python deviates further from C/C++ than Java does.

At the time of writing, Python 3.11 is the most recent version, with only 3.x versions still supported.

The third and most recent LTS, Java 17, was released September 2021. Oracle Java SE Support Roadmap states that versions 17, 11 and 8 are currently supported long-term support (LTS) versions, where Oracle Customers will receive Oracle Premier Support. Java 7 is no longer publicly supported, and Oracle will not provide long-term support for Java 11, but this is anticipated to be picked up the broader OpenJDK community. Oracle is now rolling out new Java versions twice annually.

Let’s take a closer look at the similarities and differences between some code snippets in Java vs. Python.

Java vs. Python typing

Python and Java are both object-oriented languages, but Java uses static types, while Python is dynamic. This is the most significant difference and fundamentally affects how you design, write, and troubleshoot programs. Let’s look at two code examples.

First, in Python, we’ll create an array with some data in it, and print it to the console.

stuff = ["Hello, World!", "Hi there, Everyone!", 6]
for i in stuff:

Next, let’s try it in Java.

public class Test {
    public static void main(String args[]) {
        String array[] = {"Hello, World", "Hi there, Everyone", "6"};
        for (String i : array) {

In Python, we put two strings and an integer in the same array, and then printed the contents. For Java, we declared a List of Strings and put three string values in it.

We can’t mix types in a Java array. The code won’t compile.

 String array[] = {"Hello, World", "Hi there, Everyone", 6};

We could declare the array as containing Object instead of String, and override Java’s type system. But that’s not how any experienced Java developer uses the language.

In Python, we don’t have to provide a type when we declare the array and can put whatever we want in it. It’s up to us to make sure we don’t try to misuse the contents.

For example, what if we modified the code above to do this?

stuff = ["Hello, World!", "Hi there, Everyone!", 6]
for i in stuff:
    print(i + " Foobar!")

The above code will throw an error when we try to run it since we can’t append an integer with a string.

What are advantages and disadvantages to dynamic typing and static typing?

Static typing catches type errors at compile time. So, if mixing strings and integers wasn’t what you wanted to do, the Java compiler catches the mistake. How much of an advantage compile-time checks is up for debate in some circles. But static typing does enforce a discipline that some developers appreciate.

Whether static typing prevents errors or not, it does make code run faster. A compiler working on statically-typed code can optimize better for the target platform. Also, you avoid runtime type errors, adding another performance boost.

Code that’s written with dynamic types tends to be less verbose than static languages. Variables aren’t declared with types, and the type can change. This saves a copy or type conversion to new variable declarations.

The question of code readability comes up often when debating Java vs. Python. Let’s take a look at that next.

Code readability and formatting

Let’s compare examples from Java and Python. In this example, we need to open a large text file and collect each line into sets of 50 comma-separated records. Here is the Python code:

def get_symbols(file_name):
    with open(file_name, "r") as in_file:
        records = []
        count = 0
        symbol_set = ""
        for line in in_file:
            symbol_set = symbol_set + line[:-1] + ','
            count = count + 1
            if count % 50 == 0:
                symbol_set = ""

        return records

Here’s the Java code:

List<String> getSymbols(String filename) throws IOException {
  List<String> records = new ArrayList<>();
  try (BufferedReader reader = new BufferedReader(new FileReader(filename))) {
    String line;
    int count = 0;
    StringBuilder symbol_set = new StringBuilder();
    while ((line = reader.readLine()) != null) {
      if ((count % 50) == 0) {
    return records;

Let’s break down the readability components, based on the above examples.


Whitespace is part of Python’s syntax, while Java ignores it. Python uses tabs for nesting and a full colon to start loops and conditional blocks. Java ignores whitespace and uses semicolons, parentheses, and curly braces. Arguments over which code is easier to read, like the debate over static vs. dynamic typing, are subjective. Some say Python code is more concise and uniform than Java because your formatting choices are more limited. Python’s use of whitespace ends debates over how to format code. The only option you have left is how to use blank lines.

The Python snippet is a few lines shorter than the Java snippet, a difference that adds up fast in larger programs. Much of the difference is because there are no closing braces. But Python’s brevity — when compared to Java — goes deeper.


Let’s look at how the two languages handle files.

Here’s Python again:

with open(file_name, "r") as in_file:

Here’s Java:

try (BufferedReader reader = new BufferedReader(new FileReader(filename))) {

In both cases, the declaration creates a block. The file resource remains in scope, and the languages close it when the code exits the block.

In Python, we’re opening a file and reading from it. When the loop reaches the end of the file, the loop exits.

for line in in_file:

Java is more complicated. We’re opening a BufferedReader by passing it a FileReader. We consume lines from the reader. It’s our responsibility to check for null when the file ends.

while ((line = reader.readLine()) != null) {

This only demonstrates that it’s easier to handle text files in Python. But it shows how Java tends to be more verbose than Python. “Pythonic” constructs are more concise and less demanding. Java has evolved over the past few releases, with the introduction of try-with-resources in Java 7 and lambdas in Java 8, but it’s still a verbose language.

Let’s revisit our first example.

Here’s the Python again:

stuff = ["Hello, World!", "Hi there, Everyone!", 6]
    for i in stuff:

Here is the Java:

public class Test {
    public static void main(String args[]) {
        String array[] = {"Hello, World", "Hi there, Everyone", "6"};
        for (String i : array) {

Both of these snippets will build and run as is. Python will run a script from beginning to end of a file. Java requires at least one entry point, a static method named main. The JVM (Java Virtual Machine) runs this method in the class passed to it on the command line.

Putting together a Python program tends to be faster and easier than in Java. This is especially true of utility programs for manipulating files or retrieving data from web resources.

Key differences

Interpreted vs compiled languages

When a language is compiled, the machine directly translates the code. It’s initially converted into machine-native code by a compiler, requiring an explicit build step before execution. For interpreted languages, the source code isn’t translated by the target machine, but read and executed by an interpreter. Python is an interpreted language, whereas Java is termed as primarily a compiled language, though is sometimes considered as both compiled and interpreted because its source code is first compiled into a binary byte-code.

Interpreted languages were once considered slower than compiled languages. However, with the development of just-in-time (JIT) compilation, interpreted languages can be just as fast, or have a less significant performance lag.

It’s generally easier in an interpreted language to implement platform independence and dynamic typing and perform debugging.


Java has long been a learner favorite owing to its simple syntax relative to languages like C, with many of Java’s processes running automatically, including memory allocation.

However, Python’s syntax is more concise and closer to plain English, making it even faster to learn and simple to incrementally test your code. It’s also much more human-readable, with plentiful whitespace and no need to declare data disruptive types, and none of the braces, semicolons, or rigid naming conventions.


Both Java and Python compile to bytecode and run on virtual machines. This means the code transcends differences between operating systems, making the languages cross-platform. But there’s a critical difference. Python usually compiles code at runtime, while Java compiles it in advance, and distributes the bytecode.

Most JVMs perform just-in-time compilation to all or part of programs to native code, which significantly improves performance. Mainstream Python doesn’t do this, but a few variants such as PyPy do.

The difference in performance between Java and Python is significant in some cases. A simple binary tree test runs ten times faster in Java than in Python.

Java’s new virtual threads make it possible for Java developers to improve performance by reducing the RAM demands of Java’s standard multithreaded request model, while Python 3.11 made significant performance gains on start-up through the pycache directory and runtime via an adaptive interpreter.


Java’s backwards compatibility supports stability with the introduction of new versions, and Oracle’s structured release schedule and careful introduction of experimental features can also grant Java developers peace of mind. Because Java is so stable and backwards-compatible, the huge archive of troubleshooting and knowledge sharing built up by the Java community over the decades remains applicable even to new Java code.

Python has evolved drastically over the years, and some version migrations have been criticized for their messiness. With Python’s widespread use by casual coders in the data science and ML worlds, layers of code imports often lead to unstable builds, though this is more a function of Python’s accessibility and popularity among “amateurs”.


Java and the JVM were designed with portability in mind, introducing the WORA principle, so almost all major platforms can run Java code. To facilitate this, the Java Virtual Machine compiles Java source code into bytecode, a machine language native to the JVM. The JVM interprets and executes this code at runtime, and is customized for each platform, acting as an intermediary. Python has matched Java’s run-anywhere approach, and also has the TcI GUI toolkit to implement portable GUIs.


Python may have a slight edge over Java on this front, with reported security vulnerabilities in Python declining while its peers, including Java, are seeing a slight increase. This isn’t a great indicator though, as “reported” vulnerabilities are inevitably higher in a language like Java that has been so widely used for so long. Java does, however, exhibit an unusual vulnerability in the form of deserialization issues, which aren’t seen across other popular languages (however, it’s important to note that Java 17 added features to combat this problem).

Java’s built-in security package offers encryption, PKI, XML signatures, and authentication, and by nature, Java has some advantages such as strong data typing, which Python lacks. Java employs both static analysis and dynamic checking to keep all executing programs in line with security protocols. The JVM’s bytecode compilation process also tests for viruses and malware at the point of compilation. Python lacks much of this enforced discipline, relying on good habits from the developer themselves.

Mobile app development compatibility

Java is an obvious choice for mobile applications owing to its ubiquity and compatibility. It’s long been accepted as the “go-to” for Android development in particular.

On the other hand, Python is an interpreted language, which isn’t supported by Android or iOS. But Android app development or iOS app development is still possible with the help of a Python framework to translate code before deploying it natively. You’ve got a few different GUI frameworks to choose from, bridging Python’s object-oriented web framework and native mobile application requirements. So while Java makes more direct sense, mobile development with Python is still possible.

Final thoughts on Java vs. Python

Which is better for the future - Java or Python?

Both languages are stable and well-established, and are set to be reliably supported for the foreseeable future. It’s safe to say that both will continue to be relevant, though Python’s growth trajectory is definitely the steeper of the two. Python has also announced ambitious plans to achieve as much as 5x performance gains, eliminating one of Java’s chief advantages. However, Java has the endurance factor, and is deeply ingrained in the structures and habits of thousands of development teams.

Will Python replace Java?

While Python has added many new capabilities that help it to equal or exceed Java in various ways, Java has also progressed considerably, especially since Oracle’s introduction of bi-annual releases. At the same time, there are fundamental differences, strengths and weaknesses of each language that are intrinsic, and each has its areas of application. So, the short answer is no. Java is so entrenched that it’s unlikely to be “replaced” by anything in the short term, and Python’s stratospheric growth is likely to reach a plateau soon, while remaining the undisputed champ for ML and data science.

So, which language is your best choice?

Both languages offer plenty confidence in their solid community support, stability, and longevity. In most cases, either language is a safe and smart choice.

Whether Python’s dynamic typing is better than Java’s static approach is subjective. The debate between the two models pre-dates both of them, and it’s a question of what’s best for you and your team. Python’s more concise syntax and shorter learning curve might be appealing for your situation, or you might be more invested in the superior performance and security offered by Java. While neither language is suitable for latency-sensitive applications, Java is still a great deal faster than Python.

All things considered, for mobile or enterprise applications, or projects with stringent security standards, Java is still your best choice. Python is the clear winner for ML, data science, or sheer velocity of learning and coding.

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