Python vs Java: Making the Right Choice for Your Software Development Project

The choice of the programming language for your software product should align with the business goals, be able to handle the needed performance levels, and support the potential growth of your app. The language should also ensure robust security, integration with other systems and tools, and adoption of future industry trends.

This article offers clear guidance on selecting between Python and Java based on your project requirements. It also provides insights into each language’s cost, performance, and scalability implications.

We’ll also explore use cases and share our expertise in providing top-tier developers, but let’s start with an overview of the two languages.

Introduction to Python and Java

According to the Stack Overflow 2024 Survey, Python and Java are among the seven most popular programming languages for software solutions used by seasoned software developers during the past year.

What Is Python?

Python is a dynamically typed, high-level, object-oriented programming language recognized for its simplicity and use in various fields. Businesses hire Python developers as this language has gained popularity in data science, AI, web development, automation, and scripting. Given its clear syntax, integration capabilities, extensive libraries with pre-built modules, and cross-platform compatibility, it has remained at the top for fast development, scalability, and versatility.

What Is Java?

Java is a general-purpose, object-oriented programming language designed for portability, performance, and reliability. It’s widely used in enterprise applications, Android development, backend systems, and large-scale distributed computing.

Java is a statically typed language, it enforces strict type safety, making it more structured and suitable for long-term, maintainable development of IT solutions. With an extensive framework ecosystem, Java dominates enterprise and cloud-based applications.

Key Differences Between Java and Python

The design philosophy and core principles, coding format and structure, and Java and Python use cases and code examples will help you identify their fundamental distinctions.

The Comparison of the Design Philosophy and Core Principles

Understanding the development approach and fundamental concepts of Python and Java will help you check which programming language better fits your project goals and adapts to trends.

Python follows the “Zen of Python” guiding principles, which focus on clarity and simplicity. These principles stress the need to make the Python code easy to understand and maintain.

The syntax of this programming language is smooth and clean, which is good for quicker development and prototyping. Therefore, Python is highly useful for scripting, data analysis, and web development where rapid changes are needed.

Java is recognized for its reliability, mobility, and efficiency, especially in large enterprises. The “Write Once, Run Anywhere” (WORA) concept enables developers to write Java programs that will run on any system with a Java Virtual Machine (JVM).

Knowing the key differences between Python and Java, organizations hire Java developers to work on enterprise solutions, mobile app development, and extensive distributed networks. Java’s strength, security, and outstanding efficiency allow applications created with it to manage heavy workloads and essential tasks with ease.

Syntax and Structure

Clear syntax and structure in a programming language lead to faster onboarding of programmers, a manageable codebase that can be improved over time, lower maintenance costs, more predictable application behavior, and more. Let’s learn which characteristics each of the languages has.

Since Python is dynamically typed, its variable types are determined at runtime. This reduces code verbosity, as developers don’t have to state variable types explicitly. However, this dynamic nature can lead to runtime errors if variable types are not handled correctly, requiring careful testing and debugging.

The indentation-based syntax also enhances the readability of Python programs and makes them visually appealing. For businesses, this means lower maintenance costs, faster development cycles, and an overall streamlined coding process.

Java is statically typed, meaning variables have types and are checked at compile time. The syntax of Java is more complex compared to Python, resulting in codebases that are more rigid and structured while inferring the rules reduces the possibility of a large number of errors at runtime.

Static typing allows errors to be caught at an earlier stage of software creation, which helps improve safety within the system. The verbosity of Java’s syntax aids in maintaining consistency and is crucial for larger and complex projects.

This structured approach ensures the codebase is well-organized and manageable over a longer period of time, supporting long-term maintainability.

Error Handling Approaches

The approach to handling code errors impacts the development speed and cost, reliability, data integrity, compliance, security, and other core features of business applications. That is why exploring this element within each programming language is essential.

Python relies on exceptions and try-except blocks to handle errors. If an error occurs, an exception is raised, allowing developers to control it and sustain the program’s flow.

When the Python interpreter encounters an error during runtime, it terminates with an automatic or customized error message unless it finds the try-except block that explains how to treat this exception.

Automatic error message:

>>> result = 10 / 0
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero

Customized error message:

age = -1
if age < 0:
    raise ValueError("Age cannot be negative")

The try block contains the exception(s) raising code, while the except block explains how to handle it (them), e.g., printing the error message or taking action:

try:
    result = 10 / 0
except ZeroDivisionError as e:
    print(f"Error occurred: {e}")  # Signifying the error

try:
    result = 10 / 0
except ZeroDivisionError:
    result = 1  # Taking corrective action
    print("Cannot divide by zero, defaulting result to 1")

The finally block will ensure the stated code execution regardless of whether the exception was raised or not. For example, it can close files or database and network connections, preventing resource leaks.

try:
    file = open("example.txt", "r")
    # Perform file operations
except IOError:
    print("An I/O error occurred")
finally:
    file.close()
    print("File closed")

Java

Java employs structured exception handling with try-catch blocks, similar to Python. However, Java enforces explicit exception declarations, requiring developers to specify which exceptions to throw. This approach ensures that potential errors are documented and handled appropriately, promoting more rigorous error management.

In this example, several catch blocks identify the exceptions, then the program prints a message and continues:

public class MultipleCatchExample {
    public static void main(String[] args) {
        try {
            int[] numbers = {1, 2, 3};
            System.out.println(numbers[5]); // This will throw an ArrayIndexOutOfBoundsException
        } catch (ArrayIndexOutOfBoundsException e) {
            System.out.println("Array index is out of bounds: " + e.getMessage());
        } catch (ArithmeticException e) {
            System.out.println("Arithmetic error: " + e.getMessage());
        }
        System.out.println("Program continues running");
    }
}

In addition to try-catch blocks, Java provides a finally block that is executed regardless of the stated exceptions.

public class FinallyExample {
    public static void main(String[] args) {
        try {
            int result = 10 / 0; // This will throw an ArithmeticException
        } catch (ArithmeticException e) {
            System.out.println("An error occurred: " + e.getMessage());
        } finally {
            System.out.println("This will always be executed");
        }
        System.out.println("Program continues running");
    }
}

Java also differentiates between checked and unchecked exceptions. Checked exceptions require explicit handling using try-catch or declared with throws, while unchecked ones are ignored at compile time and don’t have to be explicitly handled.

Handling checked exceptions:

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;

public class CheckedExceptionExample {
    public static void main(String[] args) {
        try {
            BufferedReader reader = new BufferedReader(new FileReader("example.txt"));
            String line = reader.readLine();
            System.out.println(line);
            reader.close();
        } catch (IOException e) {
            System.out.println("An I/O error occurred: " + e.getMessage());
        }
    }
}

Handling unchecked exceptions:

public class UncheckedExceptionExample {
    public static void main(String[] args) {
        try {
            int result = 10 / 0; // This will throw an ArithmeticException
        } catch (ArithmeticException e) {
            System.out.println("An arithmetic error occurred: " + e.getMessage());
        }
    }
}

When comparing Python vs Java, we see that they both utilize similar mechanisms for detecting, managing, and recovering from bugs. Python’s try-except blocks enable developers to catch and handle errors dynamically without predefined exceptions, making this language highly adaptable. Unlike Python, Java’s try-catch blocks enforce explicit exception declarations, requiring programmers to specify potential issues upfront, leading to more predictable error handling.

Java vs Python: Performance and Speed

The program execution time of a language impacts the optimization of computing resources, scalability of apps, real-time data processing, and the time of delivering services.

Let’s check these parameters for Java and Python.

Compilation vs Interpretation

These criteria impact the execution speed, development efficiency, number of bugs in production, solution scalability, and operational costs.

Python is an interpreted language, so during execution it translates and processes the code in real-time, being slower than compiled languages like Java.

However, Python offers faster debugging and testing, since developers can spot changes immediately without recompilation. Python’s interpretive nature makes it more suited for rapid prototyping and iterative development compared to Java.

Java’s program is first compiled into bytecode and then executed by the Java Virtual Machine. Java is faster than Python due to the JVM’s optimizations, which significantly enhance processing efficiency.

Java offers superior computational and resource efficiency, thanks to the JVM’s capability to optimize bytecode during execution, for example with runtime compilation or Just-In-Time (JIT) compilation.

The Roles of the Java Virtual Machine and Python’s Interpreter

Knowing the operational limitations of Java and Python’s working environments helps businesses optimize code execution, improve security, and implement new features.

The JVM is crucial for enhancing Java’s computational efficiency, as it executes Java code on any platform. The JVM further supports advanced optimizations, including adaptive compilation and garbage collection, making Java applications excel in code execution time and scalability.

Python relies on an interpreter to execute programs. This tool processes code line-by-line, simplifying development and debugging but slowing down execution compared to compiled languages.

While Python’s interpreter may introduce bottlenecks in computational efficiency, the language’s ease of use and extensive library support often outweigh these limitations.

Comparison of the Code Execution Speed

Java programming language generally outperforms Python in computational-heavy tasks, such as image processing, large-scale system simulations, and financial transactions. Java’s optimized memory management and JIT compiler enable it to handle large-scale processing tasks efficiently.

Despite its slower execution speed, Python excels in AI and data science domains. Its optimized libraries, such as NumPy and TensorFlow, leverage C and C++ backend operations to mitigate some code processing differences.

While fast development, ease of use, and efficiency for data science and rapid prototyping are among the advantages of Python, excelling in performance-critical and scalable applications and faster code execution are the advantages of Java. The choice between the two depends on the specific project requirements.

Common Misconceptions Around Java and Python

Despite their popularity, Python and Java are often subject to misconceptions that can mislead project owners. Let’s debunk two myths about these powerful programming languages.

Performance Myths

One common misconception is that Java performance surpasses Python’s. While Java’s compiled nature and JVM optimizations provide significant code execution advantages in computationally heavy tasks, this language doesn’t always win.

Python’s performance, particularly in data science and AI, benefits from optimized libraries like NumPy and TensorFlow, which leverage C and C++ for heavy-lifting computational tasks. Consequently, Python can be as efficient as Java in terms of AI and data analytics projects.

The difference in computational efficiency is more noticeable in large-scale systems or applications requiring intense computational power, where Java’s robustness shines.

Use Case Limitations

Another myth is that Python can be used only for scripting or web development, while Java is better for enterprise-level applications. In reality, both languages are highly versatile.

Python also excels in automation and building web applications, Data Science, ML, and even developing IoT systems. Java, known for enterprise applications, thrives in Android development, large-scale distributed systems, and high-performance computing.

Both languages offer extensive libraries and frameworks that enable them to adapt to a wide range of domains beyond their stereotypes.

Popular Libraries and Frameworks

Java and Python support a rich ecosystem of frameworks and libraries, including pre-built tools for streamlining the coding process. In this section, we’ll check the most popular and powerful tools for these languages and start with Python.

Java

Despite several disadvantages of Java, including memory consumption, verbose syntax, and garbage collection delays, this language remains a cornerstone in the world of software creation. Here are some of the most popular libraries, frameworks, and tools in the Java ecosystem, including their use cases.

Using popular libraries, frameworks, and tools in Python and Java can significantly enhance your software engineering projects, ensuring security, scalability, and optimal task execution for diverse application areas. Sometimes, you might not even have to choose but to leverage the benefits of both languages, like Netflix.

Future Trends and Advancements

Java continues to adapt and thrive as technology evolves, just like Python does. Let’s explore the emerging trends and advancements that will shape the evolution of these two powerhouse programming languages.

Trends for Python

Python remains at the forefront of software development due to its broad range of applications and adaptability. Let’s examine the trends that have defined its evolution.

AI and Machine Learning: Python remains the go-to language for AI and ML projects due to its simplicity and extensive library support. Libraries like TensorFlow and PyTorch continue to evolve, offering faster and more efficient model training.

Cybersecurity and Ethical Hacking: Python is widely used in security analysis, penetration testing, and automation of security processes. Libraries like Scapy and PyCrypto help strengthen cybersecurity measures.

Automation and DevOps: Python is a powerful tool for automating repetitive tasks in building software, DevOps, and IT operations. It integrates with tools like Terraform and Ansible to automate cloud infrastructure management.

Web Development: Python’s frameworks like Django and Flask continue to be popular choices for web engineering, allowing businesses to create robust and scalable web applications.

Integration with Emerging Technologies: Python’s versatility allows it to integrate seamlessly with cutting-edge technologies like IoT and quantum computing, making it a valuable language for upcoming innovations.

Trends for Java

Let’s learn the latest trends shaping the evolution of Java, highlighting key developments and advancements that keep this language relevant and powerful.

AI and Machine Learning: Java’s strong emphasis on security and portability makes it a popular choice for AI and ML applications. Libraries like Deeplearning4j and RapidMiner are gaining popularity.

Microservices Architecture: Java frameworks like Spring Boot and Eclipse MicroProfile simplify the creation and deployment of microservices, enabling flexible and scalable applications.

Cloud Computing and Serverless Architecture: Java’s platform independence and scalability make it ideal for cloud computing environments. It supports seamless operation across various systems and hardware configurations.

DevOps Integration: Java’s strong tooling support and large ecosystem make it well-suited for DevOps practices, improving the efficiency of software delivery processes.

Enhanced Concurrency and Performance: Projects like Loom and Panama enhance Java’s concurrency and foreign function interfacing capabilities, making it more performant for modern applications.

As an offshore/nearshore tech talent provider, we can confirm businesses select these programming languages for their software creation projects. Our statistics show that Mobilunity’s clients hire nearly 2.5 times more remote Java engineers than Python experts for back-end development.

Comparison of Python vs Java Engineers’ Salaries

Stack Overflow shows that in 2024, the average annual salary of a Python developer equaled $68,000 and Java’s — $61,000 (globally). Below, we’ll estimate the approximate cost of hiring these developers in popular offshore and onshore locations according to talent.com and DOU.

Costs for developers based in Latin America, Asia, and Eastern Europe are on average 30%-50% lower than in the USA or Western Europe.

This is achievable due to lower living costs and local hiring expenses. However, developers based in Ukraine, Poland, and the Baltic countries can be a more suitable option due to their strong backgrounds, business-focused work attitude, and cultural fit.

Our partners successfully launch their own products with our dedicated development teams based in Ukraine.

For instance, a Canadian agency, 3e Joueur, turned to us after not quite favorable cooperation with a few tech team providers. They needed a seasoned Python engineer with expertise in banking. Our recruiters vetted highly qualified candidates from Ukraine and onboarded one of them in just two weeks.

In three years, the company decided to launch its own fintech product, FinX. It asked for their team extension to 5-6 people while offering the CTO role to the first developer.

Python versus Java: Decision-Making Framework

Choosing Python over Java or vice versa can be challenging. Here’s a mini guide to help you make an informed decision based on your project’s needs.

By evaluating these factors, you can select the language that aligns with your project’s requirements and decide which one is better suited for your goals.

Wrapping Up

While Python is more suited for AI, automation tasks, and fast development, organizations prefer Java for large-scale, high-capacity enterprise apps. Both languages continue to evolve, remaining essential tools for modern software development.By understanding each language’s strengths and limitations, businesses can select the technology that better suits their project scope, functional requirements, and team expertise. Companies can build scalable, maintainable, optimized applications with the right tech stack or benefit from both languages. And with the right IT staffing partner they can find top-tier Python or Java developers to do that.

Written by:

Kristina Stepanova

Market Researcher