What Is the Best Programming Language for You?

Java vs. Python: Which Programming Language Is Best for You?

However, as two of the most widely used programming languages, their similarities, differences, benefits, drawbacks, and ideal use cases are worth considering. ​

First and foremost, despite the Instagram joke, it’s vital to recognize that Java and Python have a lot in common and some significant differences. Both are powerful programming languages with enormous, devoted communities and a vast library of libraries supported by legions of developers, for example. If you can’t do something using your native language, you’ll almost certainly be able to find a library that can help. No one could know the full breadth of library support for either language because no one could try all of them. Some libraries are more well-known than others, while some appear in almost every significant application at some point.

However, from various views, Java and Python are significantly different. Some of these discrepancies are empirical and unassailable, while others are based on personal preference, usage, or programming environment.

Java, for instance, is a compiled language, but Python is an interpreted language. Because of this distinction, each language has its advantages and disadvantages. While debates boil about whether compiled code is faster to execute than interpreted code, the truth is usually more subtle. Whether one language is more rapid than another is influenced by various factors, including the setting in which they are used. Python, for example, excels at running in massively parallel mode on GPUs.

In addition, the two languages are written differently. When establishing a structure in Java, brackets are used to encapsulate it. Indentation is used in Python to fulfill the same tasks. Python code is described as “neat, readable, and well structured” by FreeCodeCamp. Indentation is essential not only for aesthetic reasons but also for code execution.”

These structural variations can impact how programmers perceive languages and how quickly they can type them. Theoretically, they affect the level of proficiency required to master the language. However, in most cases, the issue boils down to personal preference. More to the point, many developers rely on IDEs, coding environments, and templates, making the problem less of a concern.

In other aspects, programmers debate the advantages of the two languages incessantly. Some argue that a developer can be five to ten times more productive in Python since it allows dynamic typing, among other features. Others hold opposing viewpoints on the subject of productivity. Most of these arguments have diverse ways to establish their points, making apples-to-oranges comparisons useless. The developer’s familiarity with the language, coding style, and application-development requirements influence which language is ultimately more productive.

Remember that language tools finally convert anything programmers generate into machine code. Choosing a language is thus a matter of meeting a developer’s needs for clearly conveying a task to the machine in words that the developer understands, rather than a matter of which one the machine prefers. 

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Java and Python Trends

Although not as popular as it once was, Java remains the most popular programming language by almost any measure. On the other hand, Python’s rise has been phenomenal, particularly in developed, high-income countries. Python, according to some sources, will someday surpass Java in popularity. This incredible rise can be attributed to developer productivity, language flexibility, library support, community support, and ease of learning. Python is also frequently used in domains like data science and artificial intelligence and web apps, desktop apps, network servers, and media tools.

Meanwhile, Java’s popularity may have been harmed by its poor security image. The Register and The Hill both love to trash Java, with The Hill claiming that 88 percent of Java apps had security flaws. Of course, Java’s negative rep stems primarily from issues with the Java browser plug-in; otherwise, the language’s security issues are comparable to those of other languages. While Python’s security record is far from perfect, it has benefited from increased assistance in this area, and Python’s ease of use can help less-experienced developers safeguard their code.

However, thinking of Java as a “has been” language is a mistake. Java developers constantly introduce new capabilities and make Java smaller, faster, and more versatile when it comes to large-scale development. The powerful Java Virtual Machine (JVM) makes creating cross-platform compatible Java programs simple. Java continues to excel at constructing large traditional applications that represent the type of coding that most organizations do today—utilized by 90% of the Fortune 500! The massive installed base of Java application code—and Java programmer jobs—isn’t going away anytime soon, no matter how you slice it.

The threading models used by Java and Python are also highly different. Python’s GIL, or Global Interpreter Lock, implies that, unlike Java, it is functionally single-threaded, meaning that it can only operate on one CPU core at a time. Using a GPU using Python, on the other hand, is relatively simple compared to accomplishing the same thing with Java. A Python application running in massively parallel mode on a 5,120-core GPU will surely smoke a properly optimized Java application running on an 8-core CPU, even though it isn’t yet a standard implementation outside of scientific applications.

Python programmers make higher money than Java developers, according to a recent freeCodeCamp post: $116,000 vs. $102,000 on Indeed and $99,000 vs. $96,000 on StackOverflow. It could be owing to the vast range of Java-development positions.

The problem of “literate programming.”

The length of time it takes to learn a programming language is highly dependent on your prior knowledge, intended language use, and learning environment. Learning Java, for example, can be straightforward if you already know how to program in C, C++, or JavaScript, especially if you only want to build application code. On the other hand, Python may be a better choice if you’ve never written before and plan to utilize the language to develop output for the scientific community. Python is the most popular programming language in schools because it teaches students how to code quickly and has many applications.

The distinction between the two languages in code display is becoming increasingly essential. Previously, developers primarily used code to create applications. Other developers—and machines—read the code, and for a long time, Java’s simple grammar held the upper hand. However, now that people with various views and talents use programming languages to achieve multiple goals (not only application development), you must also consider their requirements and abilities. Python may have an advantage in this area because it fully supports the literate programming style developed by Stanford computer scientist Donald Knuth.

When using literate programming approaches, a single document can contain code, explanatory prose, graphs, graphics, and other elements, all entirely executable within the environment. It enables a presenter or non-programmer to interact with the environment in ways that few coders would consider “coding.”

Literate programming shines in a variety of ways:

• Demonstration
• sCollaboration
• sResearch
• sTeaching
• sPresentation 

Python provides direct support for such settings with IDEs like Leo and Jupyter Notebook. You can also use special instructions to add this functionality to other editors, such as Atom. By comparison, while working in a similar context using Java, the environment can feel tacked on, and the learning curve is usually steeper.

Comparison of results

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Some programmers feel that “interpreted” is synonymous with “slow.” “Sure, write it in Python or any programming language you like.,” Java users often remark, “but when you need your program to scale, you’ll have to redo it in Java.”

The trouble with comparing the speed of one language to another is that it often comes down to the environment—you may develop a test in a lab setting that favors one language over another. Still, the results that matter are the ones you observe in the field. You must also evaluate the libraries utilized and the coding style used in the application’s development (at least with Python). It is due to Python’s support for various coding paradigms (later on). When comparing the performance of Python apps created in the functional paradigm vs. those written in the object-oriented paradigm, the results are likely to differ in unpredictable ways.

It’s also crucial to think about how different languages affect performance. It’s not always about comparing the latest versions in the actual world. Most Java applications are two to three versions behind the current version. Python 3. x is often quicker than Python 2. x, but only with libraries that are particularly optimized for Python 3. x. While it may appear counterproductive, sure data science programmers (and others) continue to choose Python 2. x over Python 3. x to use specific libraries. It’s worth noting that the Python community has struggled to shift from 2x to 3x and is still maintaining both forks. Still, with Python 2. x nearing its end of life, you’d have to have a compelling reason to use it for new applications.

Performance is a bewilderingly complicated metric. The environment—how code is written, utilized, and run and the influence of libraries and other external contributors—usually determines which language runs the fastest.

Share of the market and community

Importantly, huge and active user groups support both languages. You can find Java User Groups (JUGs) all over the world. (You can use this map application to discover a JUG near you.) Java programmers can also participate in large-scale, high-profile events like JavaOne. The Python community is substantial, with over 860,000 members in 1,637 user groups in 191 cities and 37 countries. PyLadies gatherings, where women can meet and code together, to PyCon and other Python events are just a few examples.

If your main goal is to build a marketable talent, learning Java or Python (or C, C++, or any other commonly used language) is a good choice. Knowing any of these languages will assist you in finding work. Even better, it’s a good idea to become familiar with a few of the most popular selections.

As previously said, the primary purpose of a programming language is to assist a developer in communicating a specific task to a computer most plainly and transparently possible. For some engineers, simplicity means writing the fewest lines of code or developing the fastest program possible. However, the problem is much larger than either of these issues. Python, for example, might be the perfect choice if you’re a data scientist working on a machine-learning project. (Java is ranked third in this group, although it is still reasonably high.)

Flexibility is also essential. Working with Python gives you access to various programming paradigms that you can combine inside a single application. Java only supports one programming paradigm: object-oriented programming. Python, interestingly, allows you to utilize numerous programming paradigms in a single application, allowing you to choose the paradigm that best matches a subtask within the application rather than relying on a single paradigm regardless of whether it meets the need.

The idea is that there is no such thing as a one-size-fits-all programming language; instead, you must choose the wording that best suits your needs at each given time and for every given project. Developers should ideally know various languages so that they aren’t forced to choose one that isn’t well suited to their objectives. Learning additional languages makes it easier to adapt to different scenarios, such as joining an application upgrade team, even if the project language isn’t one you would choose if you were starting from zero.

In the end, though, some generalizations regarding programming languages are achievable. Python is my go-to language for programming applications involving data science, artificial intelligence, and machine learning. On the other hand, Java is likely to be at the top of my list when I need to build programs for end-users, particularly embedded and cross-platform apps. Java is also an excellent language for writing server-side programming. Whether or not those preferences apply to you, maybe this comparison will assist you in determining your own programming language decisions.

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