>Or are the R and Python Computational libraries close to the performance of C/C++ that it's a viable compromise?
That's a big part of it. Python's data analysis tooling is generally written on top of Numpy, which is insanely optimised code that an average C/C++ developer couldn't compete with in terms of perf. Numpy will completely smoke naive C code to do common data manipulation tasks. So it's a double win, really. You get very good perf out of the box, and you get a high level dynamic typed language which lets you focus on high level logic and iterate quickly.
The other part is the ecosystem. For a variety of reasons, Python has become the premier language for Big Data™, and over the past 5 years has accreted a huge collection of libraries for analytics, visualization, ML, distributed compute, etc. A single developer can now click these libraries together to achieve stuff that could only be done with huge teams before. You can build a distributed training cluster for a deep learning algorithm and deploy it to Amazon in maybe 2k lines of code. C++? I don't even know where to begin.
This. Python's stength as a language IMO comes from it's ability to interface with more efficient code. You can write the meat of your library at near C levels of efficiency, and have python treat it like a "black box" essentially. That seems to be how NN code works often as well.
Given that the R interpreter is written in C and admits trivial FFI bindings, as demonstrated by libraries like glmnet or gbm calling out to it, I don't see how this is an inherent advantage of Python.
While it doesn't have an inherent advantage, it has the mindshare and momentum of a community that has these tools now.
R could be just as capable as Python, but I think Python has largely won the race to be the most popular language for data analysis which in turn encourage more developers to commit to it, cementing Python's advantage.
R still has solid lead in statistics and a good mindshare amongst academics.
> R could be just as capable as Python, but I think Python has largely won the race to be the most popular language for data analysis which in turn encourage more developers to commit to it, cementing Python's advantage.
Your comparing Apples and Oranges. R is a domain specific language and will never be a general purpose language.
Let alone in industry investment coming from Microsoft and other major players.
R is above Python in Statistics in momentum and numbers. Python is a good choice but Python is still playing catch up to R due to the speed at which R is developing. R with data.table and Hadleyverse (https://www.r-bloggers.com/welcome-to-the-hadleyverse/) and RStudio the momentum has been clearly on the side of R.
R just 5 years ago was a fraction of the users it has today.
Python and R are both good choices with equal speed but the difference is that R is a domain specific language that has a lot of positive ecco system.
R is a LISP. I would disagree heavily with it being domain-specific. It is as capable and Turing complete as any language. The only argument you can create is about performance and the judiciousness of putting stats functions in the base library, as opposed to Common Lisp which ships with even less. Not only "will" it be a general purpose programming language, it already is.
That's a big part of it. Python's data analysis tooling is generally written on top of Numpy, which is insanely optimised code that an average C/C++ developer couldn't compete with in terms of perf. Numpy will completely smoke naive C code to do common data manipulation tasks. So it's a double win, really. You get very good perf out of the box, and you get a high level dynamic typed language which lets you focus on high level logic and iterate quickly.
The other part is the ecosystem. For a variety of reasons, Python has become the premier language for Big Data™, and over the past 5 years has accreted a huge collection of libraries for analytics, visualization, ML, distributed compute, etc. A single developer can now click these libraries together to achieve stuff that could only be done with huge teams before. You can build a distributed training cluster for a deep learning algorithm and deploy it to Amazon in maybe 2k lines of code. C++? I don't even know where to begin.