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Python vs. Java – Best Programming Language Comparison

There is an old debate regarding Python and Java when it comes to choosing the best programming language. The truth is there isn’t a correct answer. They both have their advantages and disadvantages and it is up to you to choose the one that suits your needs.

Today we are going to analyse all the pros and cons so that it will be easier for you to make a choice. There are plenty of things Python and Java have in common, but there are also numerous features that differentiate these two.

There is plenty of library support for both Java and Python and developers seem to use them both equally. However, there are many things that differentiate these two. There are some clear differences, while others are more subjective.

Types of languages

We should start by mentioning that Python is an interpreted language, while Java is a compiled one. Obviously, each type has its characteristics. It is hard to say which language is faster or better, and most developers have contradicting opinions. Usually, the context in which a language is used influences its performance.

The way you write these languages is also different. When you use Python you use indentation if you want to create a structure. Meanwhile, in Java you will have brackets for that. Some developers seem to believe that indentation makes the code well structured.

Each programmer can choose the structure which appears more accessible. This is usually up to each person, and you decide which structure allows you to type faster. Since many developers also use templates and coding environments, this might not be that big of an issue.

The threading models of these two programming languages is completely different as well. Python can run on a single CPU core at a time and that is because it is single-threaded, unlike Java. This makes it a lot easier to use a CPU with Python.

Popularity

If we take a look at the popularity of these languages, there are some clear differences. Java has been around for more time, so it remains the most popular language. However, Python had an impressive evolution, and if it continues to rise that way, it is very likely that it will overcome Java.

Nonetheless, just because Java is older, this does not mean that it does not evolve. Developers continue to add new features and they are doing their best to make Java faster and more powerful. The JVM is also a great tool when it comes to creating cross-platform Java apps.

If you are thinking about money, you should know that a freeCodeCamp post revealed that programmers who use Python earn more money than those who use Java.

Learning process

When you are choosing a programming language you should also think about how much it will take you to learn it. Obviously, you also need to take into account your current knowledge. If you are a beginner and you have no experience you might want to choose Python. It doesn’t take that much time to get used to it and it has a wide range of techniques. Java can be simple for you if you already know how to program in C++ for example.

You should also think about the way the code is presented. Nowadays, programmers aren’t the only persons who have to work with code, so you must think about the skills of all the persons involved. Python supports the literate programming approach, which can be a big plus. These programs offer explanations, graphs and pictures in a document, while the code remains executable.

Related comparison:

The Best Predictive Analytics Application: R versus Python

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The Best Predictive Analytics Application: R versus Python

Choosing the right language is the first thing you must do when it comes to working on predictive analytics. There are plenty of options, but R and Python appear to be the most popular ones. Most developers choose between these two languages when they develop applications for predictive analytics.

Both programming languages are available on all major operating systems such as Linux, Microsoft Windows, and Mac OS X. While we can’t say that one of them is the right choice, they both have their pros and cons. It is up to you to analyse them and decide which one matches your needs. Nonetheless, we took a look at certain factors in order to make things easier for you.

Data analytics

When it comes to data analysis, R is a more popular in the developer community. That is because it comes with an Integrated Development Environment that was created for data analysis. The R programming language was in fact created for data analytics, and it is also used to teach statistics in high schools and colleges from all around the world. Python can also be used for data analytics, but the obvious choice here remains R.

If you look at its popularity you can also see that R is used more when it comes to data analytics. Nonetheless, Python has its advantages as well. For example, if you want to implement algorithms for production use you might want to choose this one. Additionally, Python appears to be a better choice if your data analysis will be used in production databases or web apps.

Costs

You don’t have to worry about the availability of these two programming languages as they are bot available for free. There are no costs for either one. Python is usually better when it comes to code reliability, while R is recommended for statistics and data analysis.

Learning how to use it

If you are a beginner, you might want to take into account other factors. For example, Python is usually considered more mainstream, and it can represent a better option for amateurs. On the other hand, R can be considered quite complex for those who are not familiar with programming.

Resources

Before you choose a language you might want to see how many resources are available for each one. The community provides assistance for those who want to master these languages, but you need to make sure that there are enough resources for your learning process.

Regarding this topic, a bit of research has already been done. If we take a look at Google search results we can see that there are a lot more results for R compared to the results offered for a Python search. To compare the two, when searching for “linear regression”, there were 6.48 million results for Python and 77.1 million for R.

Python cons

If we are talking about the disadvantages of Python, we must mention the fact that it has its flaws when it comes to mobile development. Additionally, the fact that it is quite simple to learn Python might make it difficult to pick up other programming languages in the future. Python might not be the best when it comes to data analysis.

R cons

One of the biggest cons of the R programming language is the fact that it can be quite difficult to learn. If you need to learn it as fast as possible, you should know that R requires some time. Additionally, its memory management department could be improved. However, if the community continues to grow, this is an issue that will be fixed.

Related comparison:

The Best Predictive Analytics Application: R versus Python

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