Python vs Java: Differences and Similarities in 9 Key Areas

Comparing programming languages is always a touchy subject among developers. While you could have a relatively balanced discussion about the differences between R and Python, that may not be the case for Python and Java. Both languages have rich and similar growth histories and are often the first programming language that many developers learn.

When these two or any other languages are concerned, it is hard to find unbiased opinions. And, as my first language is Python, you may not agree with some of the differences I share in terms of ease of use, syntax, and readability. But other than that, prepare for a comprehensive fact-based rundown of the differences between Python and Java in nine key areas.

1. Python vs Java: Use Cases and Industry Adoption

Let’s start by comparing where and why both languages are used because that’s probably the first question beginners ask before deciding between the two. Choosing one may decide what kind of a developer they will eventually be.

First of all, both are general-purpose languages, meaning you can build nearly anything in Java or Python. The big question is, “How good are they at each thing?”

If you want to develop AI applications and machine learning models or generate beautiful-looking plots of your data, then Python is the king. For example, most algorithms you interact with daily on Instagram, TikTok, or LinkedIn are probably written in Python.

Outside of AI and data, Python is also popular in backend web development with frameworks like Django and Flask. It is also widely used in academia for scientific computing as it is easy for researchers with no background in programming to pick up.

On the other hand, you will see Java used in enterprise massive-scale applications due to its robustness and security. If you use Android, many of the apps on your phone may have Java brains.

Java’s security and stability make it an ideal choice for banks and other financial institutions for their backend systems. I mentioned Python was the king in the world of AI and data, but some key big data technologies like Hadoop and Apache Spark are written in Java because it is much faster.

While there’s significant overlap in their capabilities, the choice between Python and Java often comes down to the specific requirements of the project, the existing technology stack of the organization, and the available expertise in the development team.

2. Learning Curve and Ease of Use of Python and Java

Most people you talk to will say that Python is much easier to learn for beginners because it reads like English and uses fewer words, so your code will be shorter and more readable. Meanwhile, Java enforces strict syntax and OOP concepts from the very beginning, which may provide a highly unpleasant experience for newcomers. So, the clear winner in terms of the learning curve is Python. But how about ease of use?

One way to look at this is the trade-off between immediate and delayed gratification. In Python, you start enjoying programming straight away because its syntax is intuitive and short. But you don’t realize that this fun and easy syntax comes at the cost of sacrificing good programming practices (the type system comes to mind, but more on that later).

If you start your programming journey with Java, each of your learning sessions will feel like a brain workout. To put on programming muscle and develop skills, you have to sweat (and swear) a lot because Java forces you to use static typing and follow OOP principles, which are concepts Python developers learn (or start caring about) at the advanced level.

So, in Python, you have an easy time in the beginning but may struggle with bad habits developed early in your journey in serious projects. Java’s complexity may lead to a steeper initial learning curve, but it will definitely provide you with a deeper understanding of programming concepts in the long run.

3. Java vs Python: Syntax and Readability

It is at this point that anything I might have said in Java’s favor loses all meaning, and you go running toward Python. Consider this:

Source: https://python-scripts.com/

The above example shows how to perform a very common operation in both languages: reading a text file. While Python needs only four lines of code, Java requires a whopping 18 lines. So, why is its syntax so verbose?

The number one contributor to word count in Java code is Java’s strict type system. Every variable, method parameter, and return value must be explicitly declared with its type. This verbosity, while sometimes seen as cumbersome, provides clarity and helps catch type-related errors at compile-time rather than runtime.

In contrast, Python uses dynamic typing, which allows for more concise code but can sometimes lead to type-related bugs that only surface during execution. This is a huge problem because you might have a complex script run for an hour and at line 246, you encounter a type error, breaking the whole execution (it happens).

Java also uses a lot of curly braces and semi-colons, further contributing to its unreadability. The purpose of curly braces is the declaration of blocks of code. Every function, class, loop, and conditional statement in Java is enclosed in curly braces. While this explicit structure can help organize code, it can make the code appear cluttered, especially to beginners.

Semi-colons are used to terminate statements, which adds another layer of syntax that Python doesn’t require. These elements, combined with the verbose type declarations, make Java code generally longer and potentially more challenging to read at a glance compared to Python’s cleaner, more concise syntax, which relies on white space to define code block hierarchy.

4. Type System in Python and Java

Python’s dynamic type system is the aspect of the language that most programmers complain about. In dynamic typing:

• Variable types are determined at runtime
• Variables can hold different data types throughout the program’s execution
• Type checking occurs during run time
• No need to declare variable types explicitly

This makes Python code more concise, readable, and flexible at the cost of more runtime errors and slower performance. Type hints were introduced in Python 3.5, allowing developers to follow static typing principles like in the example below:

text: str = "DataCamp"
times: int = 5
def echo(text: str, times: int) -> str:
   return text * times

Unfortunately, these are just hints — the interpreter doesn’t enforce them at runtime. This means I can pass whatever values I want to echo:

# A list is passed instead of a string
result2 = echo([1, 2, 3], 3)
>>> print(result2)
[1, 2, 3, 1, 2, 3, 1, 2, 3]

Now, Java’s static typing is the real deal. The variable types are known at compile-time because all variable, function input, and output types must be declared before use. Once a variable is declared, its type cannot change. Many type-checking errors are caught before the program type. The code becomes more verbose, but performance is significantly faster.

It is again a trade-off between immediate gratification and long-term struggle. Since Python developers don’t have to specify types every time, they are more productive at an early stage but may have to spend more time during testing fixing simple bugs that could have been avoided with good static typing principles. Java developers spend a lot of time writing types but they don’t deal with a lot of fixing type-related bugs as the compiler catches most of them early on.

5. Python vs Java: Performance and Execution

Now comes the Python deal-breaker for a lot of developers: its slow performance. In fact, Python is among the turtles of programming languages. Take a look at this table:

Source

It compares more than 25 programming languages in terms of speed and energy consumption and Python is at the bottom in both criteria while Java is in the top five. So, what is the reason for such a huge discrepancy in the performances of these languages?

Well, one of the main reasons for Python’s slow execution is its use of an interpreter rather than a compiler. An interpreted language executes code line by line at runtime, while a compiled language translates the entire program into machine code before execution. This interpretation process adds overhead to Python’s execution, making it slower compared to compiled languages like Java.

Also, Python’s dynamic typing, while offering flexibility, also contributes to its slower performance. The interpreter needs to check types at runtime, which adds extra processing time. In contrast, Java’s static typing allows for more optimizations at compile time, resulting in faster execution.

Another factor is Python’s Global Interpreter Lock (GIL), which limits true multi-threading capabilities in CPython (the standard Python implementation). This can hinder performance in multi-core systems, whereas Java can more effectively use multiple cores.

However, it’s important to note that Python’s ease of use and rapid development capabilities often outweigh its performance drawbacks for many applications, especially when raw processing speed isn’t the primary concern.

6. Object-Oriented Programming (OOP) Features

Both Python and Java are object-oriented programming languages (OOP is a programming paradigm that organizes software design around data or objects rather than functions and logic) but they implement OOP concepts in different ways. Let’s explore the key differences.

Class definition and object creation

In Python, classes are defined using the class keyword, and the constructor is defined using __init__. Objects are created by writing the class name with brackets (obj = Car()). Java, on the other hand, requires classes to be defined in separate files (one public class per file). Constructors in Java have the same name as their class, and objects are created using the new keyword.

Inheritance

Python supports multiple inheritance and uses the super() function to call parent class methods. It employs a Method Resolution Order (MRO) to determine method calling in complex inheritance hierarchies. Java only supports single inheritance for classes using the extends keyword but allows multiple interface implementation. It similarly calls super to call parent class methods.

Encapsulation

Python doesn’t have strict access modifiers (no surprises here) like public, private, or protected. Instead, it uses a convention-based privacy system with underscores (e.g. _private_var). It relies on the goodwill of developers to respect this convention (which they mostly do). Getter and setter methods are optional and often replaced by properties.

Java is, again, more strict. It enforces access modifies (public, private, protected, package-private) and typically implements encapsulation through private fields with public getter/setter methods.

Polymorphism

Python achieves polymorphism through duck typing, and method overriding is implicit. Java implements polymorphism through method overriding and interfaces, using the @Override annotation for explicit method overriding.

Here is what duck typing looks like in Python:

class Duck:
   def quack(self):
       print("Quack!")

class Person:
   def quack(self):
       print("I'm pretending to be a duck!")

def make_it_quack(thing):
   thing.quack()

# Create instances
duck = Duck()
person = Person()

# Both can be passed to the same function
make_it_quack(duck)    # Output: Quack!
make_it_quack(person)  # Output: I'm pretending to be a duck!

# This demonstrates duck typing:
# If it looks like a duck and quacks like a duck, it's a duck.
# Both Duck and Person have a quack() method, so they can both be used where a "duck" is expected.

Output:
Quack!
I'm pretending to be a duck!

Static members

Python uses @staticmethod and @classmethod decorators for static methods, and class variables are shared among all instances. Java uses the static keyword for both methods and variables and provides static initializer blocks for complex static initialization.

7. Features of Python and Java Not Seen in Other Languages

Individual idiosyncrasies and quirks of both languages have resulted in some delightful features that set trends for other languages to follow (or envy).

For example, Python’s indentation-based block structure eliminates the need for curly braces or keywords, making Python code the prettiest and most readable among others. Python also supports list or dictionary comprehensions, which are just awesome to use:

# List comprehension
squares = [x**2 for x in range(10)]
print("Squares:", squares)

# Dictionary comprehension
word_lengths = {word: len(word) for word in ["Python", "Java", "Comprehension"]}
print("Word lengths:", word_lengths)

# List comprehension with conditional
even_squares = [x**2 for x in range(10) if x % 2 == 0]
print("Even squares:", even_squares)

# Dictionary comprehension with conditional
long_word_lengths = {word: len(word) for word in ["Python", "Java", "Comprehension"] if len(word) > 4}
print("Long word lengths:", long_word_lengths)

Output:
Squares: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
Word lengths: {'Python': 6, 'Java': 4, 'Comprehension': 13}
Even squares: [0, 4, 16, 36, 64]
Long word lengths: {'Python': 6, 'Comprehension': 13}

Python’s decorators allow modifying or enhancing functions and methods without directly changing their code. While the Global Interpreter Lock (GIL) is not a feature per se, this unique implementation detail significantly affects how Python handles concurrency. Finally, Python’s with statement provides a clean way to manage resources, ensuring proper setup and cleanup (a tedious process in other languages).

Java is not so bad, too. Its JVM (Java Virtual Machine), while not unique to Java anymore, enabled the “write once, run anywhere” philosophy, which was revolutionary when introduced.

Java also offers checked exceptions where Java requires certain exceptions to be explicitly caught or declared in method signatures. Java 8 introduced interface default methods, allowing adding new methods to interfaces without breaking existing implementations.

Java’s annotation processing is a powerful metaprogramming feature for generating code at compile time based on annotations.

As languages evolve and influence each other, features that were once unique often get adopted by other languages. For example, many languages now have some form of list comprehensions or decorators inspired by Python.

8. Python vs Java: Standard Library And Ecosystem

The standard library and ecosystem of a programming language are essential for its usefulness and adoption. Both Python and Java offer rich standard libraries and vibrant ecosystems, but they differ in their focus and strengths.

Standard Library

Python follows a “batteries included” philosophy, providing a comprehensive standard library that includes modules for file I/O, networking, web services, data processing, and more. This rich standard library allows developers to execute many tasks without having to install third-party packages. Key modules include json, datetime, sqlite3, csv, unittest, and re (regular expressions).

Java’s extensive Class Library also provides utilities for common tasks, including data structures (java.util), I/O (java.io), networking (java.net), and concurrency (java.util.concurrent). It also excels in enterprise development with Java EE specifications for building large-scale, secure network applications.

Ecosystem and third-party libraries

Python dominates in data science and machine learning with libraries like NumPy, Pandas, Tensorflow, and PyTorch. It is strong in web development as well, with frameworks like Flask or Django. The Python Package Index (PyPI) and pip make package management straightforward.

Java’s ecosystem is more focused on enterprise software, with frameworks like Spring and Hibernate. It’s also prominent in big data (Hadoop, Spark) and Android development. Maven and Gradle facilitate dependency management, with Maven Central Repository as a key resource.

9. Development Tools and IDE Support

IDEs

The choice of an IDE is highly personal to each developer but both languages are integrated into editors of various flavors to satisfy every taste.

If you want the ability to customize every aspect of your coding environment, Visual Studio Code with Python extension is the way to go. The equivalent in Java is Eclipse, which is the second most popular Java IDE.

For a more reliable, everything-included experience, PyCharm is the best Python choice with advanced features like intelligent code completion, refactoring, and debugging. As for Java, IntelliJ IDEA is the most popular and is offered by the same company that released PyCharm — JetBrains.

For scientific scenarios and interactive code execution, we can’t forget Jupyter Notebooks for Python. It has been my IDE of choice for more than four years, until I switched to Cursor for its great AI integrations.

Development tools

Python uses pip and Conda for package management and virtual environments for project isolation. Tools like Poetry are gaining popularity for more comprehensive project management.

Java relies on Maven or Gradle for build automation and dependency management. These tools offer more complex but powerful project configuration options.

Python vs Java: A Brief Summary

We’ve covered a lot of ground comparing these two titans of programming languages. To give you a quick overview, here is a comparison table showing the key similarities and differences: 

Conclusion

Python and Java, while both powerful and versatile programming languages, cater to different needs and philosophies in the world of software development. Python excels in simplicity, readability, and rapid development, making it a favorite for data science, AI, and scripting tasks. Its dynamic typing and concise syntax allow for quick prototyping and ease of learning but may present challenges in large-scale applications.

Java, with its strict typing and verbose syntax, offers robustness and scalability that shine in enterprise-level applications and Android development. Its performance and strong typing provide advantages in building complex, mission-critical systems.

If you want to start learning Python, check out our Python Fundamentals skill track. We also have a few Java resources if you decide to go down this path:

• Introduction to Java course
• Introduction to OOP with Java course
• Getting started with Java for Data webinar