How does R compare to pandas

R vs Python: Which programming language is suitable for data science beginners?

Many beginners in the fields of data science and machine learning are confronted with a weighty and difficult question: Which programming language should one learn in order to get started as effectively as possible and to produce the first results quickly? One of two languages ​​is usually recommended: Python or R.

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In this article, we want to answer the question R vs Python individually for you, including the following points:

  • How beginner-friendly are the languages ​​in each case?
  • What similarities and differences do the languages ​​have?
  • What are the specialties of Python or R?

R vs Python: Where do you get started faster?

When you first step into the world of programming, it's like learning a new language. A lot of unknown vocabulary must be merged into understandable statements under certain rules. It's not called a programming language for nothing. Which of the R vs Python languages ​​is most likely to get you started?

Answers to these and other questions about Python or R can be obtained competently and in detail from Novustat. Contact us without obligation using our inquiry form. We are happy to help!

For Python there is the so-called PEP 8 Style Guide (Python Enhancement Proposal). It suggests stylistic usage to every programmer, all of which aim to make the code easier to read. For Guido van Rossum, the inventor of Python, it is clear that codes are read much more often than they are written. This is of course an advantage for beginners, because there is no such style guide in R. Worse still: even within the programming language R there are contradicting notation rules. This often leads to confusion and in rare cases to programs that do not work. Point for python.

How does the initial setup work with R vs Python?

The initial setup of the programming environments is pretty similar in both cases: First the compiler of the language has to be installed (i.e. the version of R or Python that you want to use) and then, ideally, you install an IDE. IDE stands for Integrated Development Environment. It allows you to write code and translate or execute it in the same program. What is particularly practical about IDEs is that they are often Offer autocompletion that makes a programmer's job easier. For R there is actually only RStudio, which leaves nothing to be desired. There is an abundance of choices for Python: from Atom to Pycharm to Spyder, there is something for everyone.

Not only for beginners, but also for experienced programmers, there is something that makes working with Python or R much easier: Notebooks. With R these are integrated directly into RStudio, for Python you have to install the Jupyter notebooks additionally. The functionality is generally similar, but the notebooks in R work more smoothly and conveniently in many ways. The notebooks are automatically converted into HTML files, which can then be sent to colleagues. The autocomplete in the Jupyter notebooks, however, often does not work or is extremely slow. The point clearly goes to R.

Of course, as a beginner you don't have to commit yourself. The decision R vs Python is not made for life now. But how well can it be changed later? This is difficult to predict, but experience shows that programmers often find it easier to learn Python R than the other way around.

What are the advantages of Python and R?

Both languages ​​were developed and are designed with a specific background in mind. R is a language made by statisticians for statisticians. Even in the basic version it offers a lot of useful functions that can be used to read in data, calculate statistics and regressions and plot them. So it is already highly specialized. In the case of Python, the basic version should remain as narrow and powerful as possible. Therefore, in order to operate data processing effectively, some additional packages have to be loaded. While there are over 10,000 additional function packages for both languages, Python is more dependent on these modules. R also specializes “out of the box” in statistics and, to a certain extent, in data science. However, this does not mean that you cannot still benefit from many packages.

In the following, two speed comparisons with R vs Python are to be carried out. In the first comparison, a 3.5 GB data record is read in and stored in a data frame (a kind of table). Then an overview of the content of the table and the time that the programs needed should be output. (The codes for reading can be found in the appendix of the article, the data set comes from Kaggle and is also linked.) Python (more precisely, the package Pandas) takes an average of 31 seconds to complete the request. R needs much longer with one minute and 40 seconds. Python R dominates here.

What is the performance of the code in simple machine learning?

The second test is a generation of random numbers for which a linear distribution is generated. Then a linear regression should be carried out and its R ^ 2 calculated and saved. The time is compared, each time for six different numbers of particles. In this case, both languages ​​are equal. Both codes took around 60 seconds for 10 million particles. The result can be seen in the following figure.

On the basis of these experiments, two findings can already be drawn: Reading and processing large amounts of data (keyword big data) works faster with Python's ingenious Pandas library than with R. On the other hand, the two languages ​​do not differ significantly in the speed with which basic arithmetic operations be performed.

What is the perspective for R vs Python?

As a budding data scientist, it is of course important to consider which of the two languages ​​is better equipped to develop machine learning models. There are three main aspects:

  • Community support,
  • Development of packages
  • Labor market situation.

When it comes to the community, Python is clearly one step ahead. In a survey of almost 24,000 participants, carried out by in 2018, around 25% of those surveyed stated that they use Python regularly. In contrast, only about 8% use R regularly. The answer to the question of which language they would recommend to data science beginners is even clearer: Almost 60% recommend Python, but R only 10%. Anyone who has any programming experience knows how important the community is when it comes to help. In short: the larger the community, the more effectively the corresponding language can be used.

The larger the community, the more and better packages are developed. Python currently has the better choice here. There are dedicated, well-documented packages that deal with machine learning of all kinds. With R there are also packages of this kind, but some of them seem to be poorly documented and are nowhere near as comprehensive, apart from regression algorithms.

Of course, this is also noticeable on the labor market. Above all, employers are looking for developers who are familiar with Python. The pay for a Python developer also seems, on average, a little higher than for R savvy ones.

Our conclusion

In this article we presented the two programming languages ​​R vs Python and discussed the question of which one is worth learning. After making the comparison, it can be said that Python is the right choice for a budding data scientist. It's actually easier to learn and more powerful at the same time. The handling of huge amounts of data is regulated more effectively, which saves time in later use. This is rounded off by the lively community and the abundance of high-quality and specialized packages.

Even if Python seems to be superior to R, R still has its legitimate areas of application and is popular with many scientists due to its specialization in statistical evaluations. If you are still on the question of R vs Python or have already decided on a language, but are still unclear about the first steps, please contact us and ask for support from our experts without obligation. We look forward to helping you!

Related Links

ZDNet. Python vs R and biggest salaries. Top data science job trends.

2018 Kaggle Machine Learning and Data Science Survey.

Data set used on Kaggle.