Both Python and R are not the fastest of languages but the integration with one of the fastest, namely C++, is so much better in R (via Rcpp by Dirk Eddelbuettel) than in Python that it can by now be considered a standard approach.A concrete example can be found here, where it is explained why the function any(df2 = 1) gives the wrong result and you have to use e.g. That it is not only me can be seen in this illuminating discussion where people scramble to find criteria when to use which. The reason for this problem is what I stated above: Python wants to be everybody’s darling and tries to achieve everything at the same time. There is no general rule when to use a function and when to use a method on an object.To give you just one example: whereas vectorized code is supported by NumPy and pandas it is not supported in base Python and you have to use good old loops instead. That in itself is not the problem but the inconsistencies that this brings. For example, you need the NumPy package for vectors and the pandas package for data frames. More sophisticated data science data structures are not part of the core language. ![]() And even if you want to use Jupyter notebooks, you can do this with R too. There are several GUIs out there and admittedly it is also a matter of taste which one to use but in my opinion when it comes to data scientific tasks – where you need a combination of online work and scripts – there is no better GUI than RStudio.There are now nearly 18,000 R packages in the official repository CRAN alone! The same is true for more sophisticated visualizations: it is no coincidence that all of the renowned news organizations create their impressive infographics with R! Many of the standard statistical techniques are adequately covered by Python but try more unconventional stuff and you are quickly lost. It is an indisputable fact that nothing comes even close to R in this respect. Talking of packages: the basis of much of data science is statistics and visualizations.To just give you a taste, have a look at the official documentation: – eight (!) pages for what is basically one command in R: install.packages() (I know, this is not entirely fair, but you get the idea). One of the reasons for this is that the whole package system in Python is a mess. The most well known for data science is Anaconda. The next thing is which distribution to choose! What seems like a joke to R users is a sad reality for Python users: there are all kinds of different distributions out there.Even the syntax of the print command got changed! The problem is that there is no backward compatibility. It starts with which version to use! The current version has release number 3 and is gaining traction but there is still a lot of code based on the former version number 2.No, really, it is a great language to learn programming but I think it has some really serious flaws. I think one of the problems is that Python tries to be everybody’s darling. I dug deep into the language and some of its extensions. I have to make a confession: I really wanted to like Python. Now, why is this blog about R and not Python? ![]() In the area of data science, there are two big contenders: R and Python. the whole alphabet of one letter programming languages is taken. There are literally hundreds of programming languages out there, e.g.
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