Java vs Python: What are the differences?
Developers describe Java as "A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible". Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!. On the other hand, Python is detailed as "A clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java". Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
Java and Python can be categorized as "Languages" tools.
"Great libraries", "Widely used" and "Excellent tooling" are the key factors why developers consider Java; whereas "Great libraries", "Readable code" and "Beautiful code" are the primary reasons why Python is favored.
Python is an open source tool with 25.3K GitHub stars and 10.5K GitHub forks. Here's a link to Python's open source repository on GitHub.
According to the StackShare community, Python has a broader approval, being mentioned in 2826 company stacks & 3632 developers stacks; compared to Java, which is listed in 2399 company stacks and 2723 developer stacks.
Python has become the most popular language for machine learning right now since almost all machine learning tools provide service for this language, and it is really to use since it has many build-in objects like Hashtable. In C, you need to implement everything by yourself.
C++ is one of the most popular programming languages in graphics. It has many fancy libraries like eigen to help us process matrix. I have many previous projects about graphics based on C++ and this time, we also need to deal with graphics since we need to analyze movements of the human body. C++ has much more advantages than Java. C++ uses only compiler, whereas Java uses compiler and interpreter in both. C++ supports both operator overloading and method overloading whereas Java only supports method overloading. C++ supports manual object management with the help of new and delete keywords whereas Java has built-in automatic garbage collection.
Go is a way faster than both Python and PHP, which is pretty understandable, but we were amazed at how good we adapted to use it. Go was a blessing for a team , since strict typing is making it very easy to develop and control everything inside team, so the quality was really good. We made huge leap forward in dev speed because of it.
I am working in the domain of big data and machine learning. I am helping companies with bringing their machine learning models to the production. In many projects there is a tendency to port Python, PySpark code to Scala and Scala Spark.
This yields to longer time to market and a lot of mistakes due to necessity to understand and re-write the code. Also many libraries/apis that data scientists/machine learning practitioners use are not available in jvm ecosystem.
Simply, refactoring (if necessary) and organising the code of the data scientists by following best practices of software development is less error prone and faster comparing to re-write in Scala.
Pipeline orchestration tools such as Luigi/Airflow is python native and fits well to this picture.
I have heard some arguments against Python such as, it is slow, or it is hard to maintain due to its dynamically typed language. However cost/benefit of time consumed porting python code to java/scala alone would be enough as a counter-argument. ML pipelines rarerly contains a lot of code (if that is not the case, such as complex domain and significant amount of code, then scala would be a better fit).
In terms of performance, I did not see any issues with Python. It is not the fastest runtime around but ML applications are rarely time-critical (majority of them is batch based).
I still prefer Scala for developing APIs and for applications where the domain contains complex logic.
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