This post is part of a series that will teach you to use Python if you come from Java. I highly recommend you start from here if you don’t know o only has a little bit of Python knowledge. Let’s begin.

Before to continue, I like to mention that none of them is better than the other. Python and Java has its own strengths and specialization. The choice between Java and Python depends on the specific needs of the project.

Here’s a detailed comparison between Java and Python across various aspects:

Aspect	Java	Python
Typing System	Statically typed (types are declared explicitly, when a type is declared the type can’t be changed, even using var)	Dynamically typed (types are inferred at runtime. This means that you can declare a variable without indicating the data type, and also you can change the data type after assigning a new value)
Performance	Typically faster due to Just-In-Time (JIT) compilation	Slower in raw execution speed (interpreted language. Python is primarily an interpreted language by CPython, meaning that code is executed line by line at runtime by CPython. This introduces overhead, as the interpreter must read and execute each line of code sequentially).
Syntax	Verbose, requires explicit syntax and structure.	Concise and readable, often considered easier to learn because is simple and similar to English.
Compilation	Compiled to bytecode, runs on JVM	Interpreted (bytecode generated but executed directly by CPython)
Memory Management	Automatic garbage collection via JVM, using the Mark-and-Sweep Algorithm.	Automatic garbage collection (via reference counting and garbage collection. This means that every object in Python maintains a count of the number of references pointing to it. When a new reference to an object is created, its reference count is incremented. Conversely, when a reference is deleted or goes out of scope, the reference count is decremented. If the reference count of an object drops to zero, it means there are no more references to that object, and it can be safely deallocated (freed from memory).
Concurrency	Strong support with multithreading and concurrency libraries	Concurrency available but Global Interpreter Lock (GIL) limits true multithreading. Python imitates or simulates multithreading and parallelism due to that Python is interpreted.
Object Orientation	Strictly object-oriented (everything must be inside a class)	Flexible, supports both object-oriented and procedural programming
Built-in Data Types	Primitive types (int, double, char, etc.) and objects	Everything is an object, even primitive types like int
Use Cases	Common in large-scale, enterprise-level applications, Android development, and backend systems.	Popular for web development, data science, machine learning, automation, and scripting
Development Speed	Slower due to explicit type declarations and syntax	Faster due to simple syntax and dynamic typing
Exception Handling	Must explicitly declare exceptions (checked exceptions)	Simpler, no checked exceptions, only runtime exceptions. In Python, all exceptions are unchecked. There are no checked exceptions, meaning that developers do not have to declare exceptions that might be thrown by functions. This simplifies the code because you don’t have to handle or declare every possible exception that could occur. Python primarily uses runtime exceptions to indicate errors that occur during the execution of a program using the try-except.
Standard Library	Extensive, especially for networking, file I/O, and concurrency	Rich standard library, but particularly strong in areas like data science and web development
Ecosystem	Mature, with frameworks like Spring, Hibernate, and Java EE	Extensive, with popular libraries like Django (web), NumPy, Pandas (data science), and Flask (web)
Code Readability	Code is more verbose and structured, which can be harder to read	Emphasizes simplicity and readability (e.g., enforced indentation)
Learning Curve	Steeper, especially for beginners due to its verbose syntax and strict type system	Gentler, often recommended for beginners due to its readable and concise syntax
Lambda Functions	Available but more limited in flexibility	Fully supported, with functions as first-class citizens (can be passed around like objects)
Error Handling	Explicit error handling with try-catch blocks and checked exceptions	try-except for exceptions, no checked exceptions simplifies error handling
Popular Frameworks	Spring, Hibernate, Java EE, Apache Struts	Django, Flask, FastAPI, TensorFlow, PyTorch (ML), SciPy
Cross-Platform	Write Once, Run Anywhere via the JVM	Cross-platform, runs on any system with an appropriate Python interpreter. This means that, to ensure compatibility and the proper functioning of your code, you need to make sure it runs on the interpreter for which it was developed or that is designed to handle it.
Versioning	Relatively stable with long-term support releases	More frequent updates; some breaking changes between major versions (e.g., Python 2 to Python 3)
Community Support	Large, well-established, particularly for enterprise development	Massive, growing community with strong support in web development, data science, and AI
Popularity	Popular for large-scale enterprise applications and Android development	Extremely popular for web development, automation, data science, and AI
Error Detection	Errors are caught at compile time due to static typing	Errors are caught at runtime because of dynamic typing
Cost of Development	Higher upfront due to longer development times	Generally faster development, which can reduce costs

This are the strengths of Java against Python:

• Performance: Faster due to JIT compilation and optimized execution on the JVM.
• Enterprise Development: Strong ecosystem for building large, scalable enterprise systems (e.g., Spring, Hibernate).
• Type Safety: Statically typed language reduces the likelihood of certain types of bugs by catching them at compile time.
• Concurrency: Better built-in concurrency support with multithreading.
• Cross-Platform: Write once, run anywhere using the JVM.

This are the strengths of Python against Java:

• Ease of Learning: Simplified syntax and dynamic typing make it more accessible to beginners.
• Rapid Development: Concise syntax and dynamic typing allow for faster prototyping and development.
• Versatility: Used for web development, data science, machine learning, automation, and scripting.
• Rich Libraries: Extensive support for tasks like data analysis, scientific computing, and machine learning (e.g., Pandas, TensorFlow, NumPy).
• Community Support: Huge community and ecosystem, especially in areas like AI and data science.

Remember this is an Aspect Comparison of both languages. I strongly disagree with the internet discussion about the Python is better than Java or vice versa. So, here are a few points to remember:

• Java is ideal for building large-scale, high-performance, and enterprise-level applications, particularly where security, scalability, and strict typing are important.
• Python excels in fast development, readability, and versatility, making it a great choice for web development, data science, and automation.

Happy Learning!!!