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Difference between Java and Python – An In-depth Comparison

Java and Python are the two most popular programming languages at this time. Java and Python are both object-oriented programming languages that support real-world-like objects. Because they offer different benefits, features, and use cases, developers frequently need help deciding between them. Many libraries are available for each of these widely used languages, making selection tough.

Python’s simplicity is making it more and more popular, but Java has been around for a lot longer and is consequently more widely used than Python. Python is an interpreted and dynamically typed language, whereas Java is a compiled and statically typed language. This one main difference between the two languages allows us to differentiate them and learn more about them in detail.

In this guide, we will compare Java and Python in depth and see how they both differ in features, syntax, performance, design, etc. As both Java and Python are very popular languages, we will understand them both in detail along with code examples and many other things.

What is Java?

Java is a high-level, object-oriented programming language developed by James Gosling at Sun Microsystems. The first release of Java was in 1995 and was acquired by Oracle from Sun Microsystems. Java’s syntax is low-level and resembles those of C and C++. The code does not need to be recompiled. One of the most popular languages for client-server applications these days is Java.

No matter the computer architecture, Java code is converted to bytecode that the Java Virtual Machine (JVM) may execute. Because of Java’s reputation for being platform-independent, compiled Java code can execute on any computer having a Java Virtual Machine (JVM). “Write once, run anywhere” (WORA) is a common way to describe this capability of platform independence. Learning Java is, therefore, more difficult than learning Python.

Java programming language is used in building many applications, such as:

• Large-scale enterprise applications,
• Web applications
• Android app development
• Big data technologies
• Gaming applications
• Mobile Applications
• Artificial intelligence
• Business applications

Features

Java is the most popular object-oriented programming language that comes with various features and characteristics, and it includes:

1. When Java code is generated, it produces a bytecode that can run on any computer that has the Java Virtual Machine installed (JVM), thereby it is a platform-independent programming language.
2. Java applications are very portable.
3. Java adheres to the object-oriented programming (OOP) paradigm, which emphasises ideas such as inheritance, polymorphism, encapsulation, and abstraction.
4. Java supports the use of classes and objects while writing a program.
5. Strong memory management, exception handling, and security features like bytecode verification and an integrated security manager are all elements that make Java robust and secure.
6. Java allows for the simultaneous execution of two or more threads. Because of its extremely effective multithreading capabilities, it can be used in applications where high performance is required.
7. Java has a rich standard library and allows it to perform various tasks like file I/O, networking, data structures, algorithms, and graphical user interface (GUI) development.
8. Java offers an extensive collection of APIs.
9. Java is an interpreted language in which the Java code compiles and runs simultaneously.
10. In Java, the memory allocation occurs during runtime; therefore, a Java application can be constructed without a main method.

Syntax

The given below is a simple syntax to write a Java program:

public class MyClass {

public static void main(String[] args) {

System.out.println(“Hello, HeroVide!”);

}

}

In the given syntax, a simple message is displayed using the “System.out.println” statement in Java. The statement is used inside the main method “public static void main()” and is inside a class known as MyClass.

What is Python?

Python is a high-level, easy-to-learn programming language. It is a powerful programming language that works as a general-purpose language and provides different features to be combined with other libraries. Python’s interpreted nature, dynamic typing, and beautiful syntax make it a perfect language for scripting and quick application development across various platforms.

Python was discovered in the 1980s and was implemented in 1989. Python has evolved since its first implementation by introducing various great features like garbage collection, list comprehension, etc. Compared to languages like C++ or Java, Python emphasises readability and simplicity of code, enabling programmers to express concepts in fewer lines of code. Writing rational and understandable code for both small and large-scale tasks is encouraged by Python’s design ethos.

As Python is an interpreted language, therefore it consists of an interpreter that is extended with various data types and functions like C++. Python is extensible in C or C++ and offers interfaces to numerous system calls, libraries, and window systems. It can also be used as an extension language for programs that require an interface that can be programmed.

Python programming language is used in building many applications, such as:

• Large-scale enterprise applications,
• Web applications
• Machine learning
• Artificial intelligence
• Big Data Analytics
• Data Science
• Data Analytics
• Research
• Image processing (including OpenCV, Pillow)
• Web scraping (including BeautifulSoup and Selenium)
• Testing frameworks
• Multimedia usage
• Scientific computing
• Text processing and generation

Features

Python is a language that helps you work more quickly and integrate the systems effectively. To work effectively, it provides different features that you can use to build applications. Below are some of the key features of Python:

1. Python is an open-source language, meaning the code is available to everyone online and can also contribute to making it a better programming language with great features.
2. Python is an easy-to-read language as its syntax is very simple like English. Being a straightforward language, it makes it simple to learn a programming language.
3. Python does not use curly braces; rather, it uses indentation for defining the code blocks.
4. It is a dynamically typed language, meaning the type of variables declared in the program are determined at the runtime.
5. Unlike Java where it supports only object-oriented paradigms, Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.
6. Python is used in different applications including Machine Learning, Artificial Intelligence, data science, Data analysis, Web application development, etc.
7. Python is an object-oriented language, meaning you can work like a real-world object in a program.
8. Less code writing and more dependence on third-party libraries. Python has a very vast ecosystem to choose from various third-party libraries to integrate into your application.
9. A vast standard library that supports a wide range of typical programming activities is included with Python.
10. Python is cross-platform, meaning it can run on various operating systems such as Windows, macOS, Linux, and more.

Syntax

The given below is a simple syntax to write a Python program:

print(‘Hello, HeroVide!’)

In this example, the print statement is used to print a “Hello, HeroVide!” text. The syntax is very simple and straightforward, like English.

Design Philosophy

Java

Java’s design philosophy is guided by several key principles that emphasise simplicity, reliability, and platform independence. The key principles include:

• Java was designed to be easy to use and accessible to a broad audience of developers.
• Everything in Java is an object, therefore real-world entities can be used to represent in a Java program.
• Java emphasises strong memory management, error handling, and runtime checking.
• It allowed multithreading in Java.
• Using the Just-in-compilation (JIT), Java performance gets increased.

Python

According to Guido van Rossum, grouping using indentation is quite elegant and makes an average Python program far more readable. Most users eventually grow to adore this function. Python was built to make the language more readable, concise, simple, and much more usable. The key principles include:

• Simple is better than complex, therefore Python was aimed to have an easier syntax.
• Support multiple programming paradigms, including procedural, object-oriented, and functional programming.
• Explicit is better than implicit.
• Focus on solving problems rather than dealing with syntax.
• Python is dynamically typed, meaning that variable types are determined at runtime

Python was designed with a focus on increasing the programmer’s productivity, readability, etc. It was also prioritised for clean and concise code that was useful for scripting, automation, data analytics, and rapid prototyping.

Performance

Java

Java is faster and quicker than Python due to compile time. The Java Virtual Machine (JVM) interprets the bytecode produced by the compilation of Java code. Java programs operate more smoothly because the JVM optimises and compiles the bytecode into machine code during runtime.

One part of the runtime environment that helps Java programs run faster is the Just-In-Time (JIT) compiler, which translates bytecode into native machine code when the application is running.

Example: The below example demonstrates the performance test using start and end time in Java.

public class TestExample {

public static void main(String[] args) {

//Use the nanoTime method to get the starting nano time

long startingTime = System.nanoTime();

// Perform a computationally intensive task

long sum = 0;

for (long i = 0; i < 100000000L; i++) {

sum = sum + i;

}

// Use the nanoTime method to get the ending nano time

long endingTime = System.nanoTime();

// Calculate the time duration

long performanceDuration = (endingTime – startingTime) / 100000; // Convert to milliseconds

// print the time taken

System.out.println(“Time taken to compute the sum is: ” + performanceDuration + ” ms”);

}

}

Output:

Time taken to compute the sum is: 779 ms

Python

Python is an interpreted language, which means it is slower than compiled languages like Java. Due to its interpreted or runtime nature, it makes it slower as here it requires the runtime code translation, and that too line-by-line. This performance issue creates an overhead for Python.

However, Python’s performance can be improved using tools like PyPy, a just-in-time compiler, or by integrating with C/C++ libraries.

Example: The below example demonstrates the performance test using start and end time in Python.

import time

# use the time method to get the starting time

startingTime = time.time()

# Perform a computationally intensive task

sum = 0

for k in range(100000000):

sum = sum + k

# use the time method to get the ending time

endingTime = time.time()

# calculate the time duration

performanceDuration = (endingTime – startingTime) * 1000  # Convert to milliseconds

# print the duration of the performance test

print(f”Time taken to compute the sum is: {performanceDuration} ms”)

Output:

Time taken to compute the sum is: 9414.913415908813 ms

Memory Management

Java

Memory management in Java refers to a process of allocating new objects and removing unused objects from the memory so that new object allocations after having space. Java objects reside in an area called the heap. Java manages memory using automatic garbage collection. Deallocating memory that is no longer in use is done automatically by the JVM, helping to avoid memory leaks and other related problems.

The elimination of the necessity for manual memory management by developers can lower errors and boost output. Note that JVM consumes more memory than the heap.

Example: The example below demonstrates memory management concerning memory usage when running a basic program in Java.

public class TestExample {

public static void main(String[] args) {

Runtime rT = Runtime.getRuntime();

// current memory usage

System.out.println(“Initial memory usage on your system: ” + (rT.totalMemory() – rT.freeMemory()) + ” bytes”);

// using a large array of 5 mil to increase memory usage

int[] arr = new int[5000000];

// Print memory usage after allocation

System.out.println(“Memory usage after allocation on your system: ” + (rT.totalMemory() – rT.freeMemory()) + ” bytes”);

// Explicitly request garbage collection

arr = null;

rT.gc();

// Memory usage after garbage collection through Java

System.out.println(“Memory usage after garbage collection on your system: ” + (rT.totalMemory() – rT.freeMemory()) + ” bytes”);

}

}

Output:

Initial memory usage on your system: 905720 bytes

Memory usage after allocation on your system: 20701968 bytes

Memory usage after garbage collection on your system: 703424 bytes

Python

Python manages memory by using a private heap that holds all of the program’s objects and data structures. Python makes use of automatic waste collection as well. Reference counting and a cyclic garbage collector control memory in Python. Although Python’s memory management works well in most situations, memory-intensive applications may have problems. If necessary, developers can manually manage the trash collection process with tools like the ‘gc’ module.

The Python memory manager is in charge of maintaining this private heap internally. Different parts of the Python memory manager, such as sharing, segmentation, preallocation, and caching, handle different elements of dynamic storage management.

Example: The example below demonstrates memory management concerning memory usage when running a basic program in Python.

# import the sys and gc for performing the memory usage

import sys as s

import gc as g

# Print initial memory usage

print(f”Current initial memory usage on your system: {s.getsizeof(g.get_objects())} bytes”)

# Create a large list to increase memory usage

num_list = [i for i in range(5000000)]

# Print memory usage after allocation

print(f”Memory usage after allocation on your system: {s.getsizeof(g.get_objects())} bytes”)

# Explicitly request garbage collection using gc to collect

del num_list

g.collect()

# Print memory usage after garbage collection

print(f”Memory usage after garbage collection: {s.getsizeof(g.get_objects())} bytes”)

Output:

Current initial memory usage on your system: 41880 bytes

Memory usage after allocation on your system: 41880 bytes

Memory usage after garbage collection: 41880 bytes

Java

Java has its collection framework called Java Collections Framework. An object such as the traditional Vector class that represents a collection of objects is called a collection. Java collection allows collections to be handled without regard to implementation details by providing a consistent architecture for representing and working with collections.

Java’s collection is like C++ STL (standard template library), often referred to as the Java Development Kit (JDK), is extensive and provides a wide array of classes and interfaces to support various programming tasks. The collections framework consists of collection interfaces, legacy implementations, wrapper classes, array utils, and many more things.

Here are some of the common and most used Java collections components:

• lang (include classes and interfaces like functions, strings, threads, etc.)
• util (include data structures like lists, sets, maps, etc)
• io (include input and output through data streams)
• net (include classes to use HTTP, URLs, etc.)
• nio (include new input and output)
• sql (include APIs related to JDBC (java database connectivity))
• swing (for creating graphical user interfaces)
• time (include LocalDate, LocalTime, etc.)

Example: The below example demonstrates the performance of Java when using the classes and objects.

// Define a Parent class vehicle

class Vehicle {

String speed;

Vehicle(String speed) {

this.speed = speed;

}

void drives() {

System.out.println(“Vehicle makes a sound”);

}

}

// Define a child class using extends

class Car extends Vehicle {

Car(String speed) {

super(speed);

}

@Override

void drives() {

System.out.println(“Car makes crackling sound!”);

}

}

// Define a child class using extends

class Bike extends Vehicle {

Bike(String speed) {

super(speed);

}

@Override

void drives() {

System.out.println(“Bike makes crackle sound!”);

}

}

public class TestExample {

public static void main(String[] args) {

Vehicle carSpeed = new Car(“Mercedes”);

Vehicle bikeSpeed = new Bike(“Kawasaki”);

carSpeed.drives();

bikeSpeed.drives();

}

}

Output:

Car makes crackling sound!

Bike makes crackle sound!

Python

Python has a very vast and extensive standard library. It supports numerous tasks including data science, data analysis, artificial intelligence, machine learning, etc. The library includes modules written in Python that offer standardised solutions for many common problems encountered in everyday programming, as well as built-in (originally written in C) modules that give programmers access to system capabilities like file I/O, and more.

Python libraries can be installed and used using the famous package manager pip. Python internal library contains various components that can be used for individual programs, building complete applications using frameworks, and more. These include:

• Built-in-Types (Comparisons, Numeric Types — int, float, complex, Boolean Type – bool Iterator, Types, etc.)
• Collections (include dict, set, list, and tuple)
• Text Processing Services (string, re (for math ops), difflib, etc.)
• Data Types (datetime, zoneinfo, calendar)
• Databases (sqlite3)
• File and Directory Access (pathlib, os.path, etc.)
• File formats (csv, tomlib, etc.)
• Generic Operating System Services (os, io, time, etc.)
• Internet Protocols and Support (smtplib, urllib, poplib, etc.)

These are some of the common packages and modules that Python gives you to use for building various applications and programs. Apart from this, there are various other third-party popular libraries also like:

• NumPy and SciPy (scientific computing)
• Pandas (data manipulation)
• TensorFlow and PyTorch (machine learning)
• Django and Flask (web development)

Example: The below example demonstrates the performance of Python when using the classes and objects.

# Define a Parent class vehicle

class Vehicle:

def __init__(self, speed):

self.speed = speed

def drives(self):

print(“Vehicle runs at faster speed.”)

# Define a child class

class Car(Vehicle):

def drives(self):

print(“Car makes crackling sound!”)

# Define a child class

class Bike(Vehicle):

def drives(self):

print(“Bike makes crackling sound!”)

# main function to create objects of classes derived from parent classes

if __name__ == “__main__”:

carSpeed = Car(“Mercedes”)

bikeSpeed = Bike(“kawasaki”)

carSpeed.drives()

bikeSpeed.drives()

Output:

Car makes crackling sound!

Bike makes crackling sound!

Use Cases

Java

Java is used to build enterprise-level applications as it provides a high level of security, integrity, etc. Java is used in tasks such as building large-scale enterprise desktop applications using frameworks like SpringBoot, android development, and web development (using Java Servlet, Java Swing, etc).

Python

Python is used for various tasks including machine learning, artificial intelligence, data science, data analysis, and much more. Apart from this, Python is used in automation and scripting, and web application development using Django and Flask frameworks.

Community Support

Java

There are numerous methods to become engaged with the millions of individuals that make up the Java community. You may help Oracle by joining groups, writing technical papers, and other means to strengthen Java. Here are some of the common ways to get engaged in the Java community:

• Active Forums and Communities by joining Java’s Stack Overflow, Reddit’s r/java, and Java Ranch.
• Participate in Java conferences and events like JavaOne, and Devoxx.
• Contribute to Java’s educational resources, including its extensive documentation, Oracle’s official resources, numerous tutorials, and courses.

Python

Python has great community support for programmers and developers. Being an open-source language, new programmers are regularly contributing to it to make it a better, more efficient, and optimised language. There is a thriving Python community with a wealth of tutorials, forums, and documentation.

Python’s community can help support the beginner and the expert and adds to the ever-increasing open-source knowledgebase. Community support has been further reinforced by the language’s growing use in cutting-edge sectors like AI/ML, data science, application development, etc. Because of its community-driven development approach, Python offers developers a bunch of tools and ongoing enhancements.

Interoperability

The ability of Python and Java to communicate and cooperate within a single application is referred to as interoperability. This can be quite helpful in utilising the benefits of both languages. To accomplish Java and Python compatibility, several tools and techniques are available such as:

• Jython

The Jython offers Java versions of Python, giving Python access to Java classes and the advantages of running on the JVM. It allows Python code to be compiled into Java bytecode and executed on the Java Virtual Machine (JVM). With Jython, you can seamlessly integrate Python and Java, enabling the use of Java libraries in Python code and vice versa.

Example:

from java.util import ArrayList

# Creating a Java ArrayList in Python

myList = ArrayList()

myList.add(“List item 1”)

myList.add(“List item 2”)

myList.add(“List item 3”)

print(myList)

A simple example of using Java’s ArrayList to implement in Python using the Jython language.

• JPype

A Python module called JPype gives users complete access to Java from within Python. It makes it possible for Python to run Java code in Python with ease and to leverage Java-specific libraries. It allows Python code to interact with Java code using the Java Native Interface (JNI). It enables calling Java functions, creating Java objects, and handling Java exceptions directly from Python.

Example:

import jpype

import jpype.imports

from jpype.types import *

# start off the JVM

jpype.startJVM()

# Import specific Java class

from java.util import ArrayList

# Creating a Java ArrayList in Python

myList = ArrayList()

myList.add(“List item 1”)

myList.add(“List item 2”)

myList.add(“List item 3”)

print(myList)

# Shut down the JVM

jpype.shutdownJVM()

A simple example of using Java’s ArrayList to implement in Python using the Jpype module.

• Py4J

A Java package called Py4J allows Python programs to dynamically access Java objects in a Java Virtual Machine while they are executing in a Python interpreter. It makes it possible for Python code to access Java fields, call Java methods, create Java objects, and run the Java code the same as Python.

Example:

from py4j.java_gateway import JavaGateway

# Connect to the JVM

myGateway = JavaGateway()

myList = myGateway.jvm.java.util.ArrayList()

myList.add(“Item 1”)

myList.add(“Item 2”)

print(myList)

This example uses the Py4J package to use Java classes and methods to print an ArrayList in Java.

Key Differences Between Java and Python

The given below are some of the key differences between Java and Python programming languages:

Conclusion

The Java and Python programming languages are powerful in their own right, but each has its advantages and drawbacks. In this article, we have learned in-depth about the differences between Java and Python. Java is a strong and high-quality language that can be used in large-scale applications with enterprise-level features. Its strong typing, comprehensive concurrency support, and extensive ecosystem make it reliable even when developing complex high-performance systems at scale.

While Python is dynamic and easy to learn — making it ideal for rapid development plus scripting and data-centric applications where ease of use is paramount over performance considerations. Both first-timers and seasoned developers find Python appealing due to its readability as well as the many libraries available for use.

The selection among these two depends on several different factors: the specific use case, performance requirements, the development speed, and other choices including personal preferences. Java and Python both have established themselves as a powerful programming language in the software development field— each with its unique strengths and weaknesses that developers will need to look at when deciding which tool best suits their needs.