3 ms·

This looks really well done. I wonder what the motivation is to do this when R is so mature (especially in the availability of specialized packages), and avail

by ims 15y ago

This looks really well done. I wonder what the motivation is to do this when R is so mature (especially in the availability of specialized packages), and available through RPy.

wesm 15y ago
Here's an article I wrote about some of my motivations on the Python side of things: http://news.ycombinator.com/item?id=2790762 http://news.ycombinator.com/item?id=2790762 The bigger picture reason "why not R" is that R is not very suitable for building production systems. I started building this library while working for AQR, a quant hedge fund, and needed to have statistical computing building blocks integrated with a much larger system. R is a mediocre programming language and has very weak general purpose libraries. But amazingly good data visualization and mature statistics libraries indeed. Using R as a black box (e.g. via RPy, Rcpp, or RJava) is a good idea in theory, but recovering from and dealing with errors/exceptions with real world data is a very thorny problem. Plus maintaining a big pile of R code is kind of a nightmare (believe me, been there, done that!).
- Estragon 15y ago
  Exactly. I use scipy, and only use R when forced to, mainly because I want to know what's going on, and write my analyses in a real language.
- pmiller2 15y ago
  On a related note, I've said exactly the same thing about Matlab to people before. It (Matlab) is good for getting the algorithms and calculations correct, but as a programming language, it's pretty terrible.
- joshu 15y ago
  I've had to do this with SAS and perl. Looking forward to checking this out.
wesm 15y ago
I want to also point out that doing statistics in Python is currently a chicken-and-egg problem. You need the foundation of statistical data structures and algorithms to enable people to implement their models and research. NumPy and SciPy by themselves just don't compare with base R. So I've been trying to a) fill that gap and b) build tools that are quite a lot superior to R's. I use R myself but I have no intention of "copying" or "replicating" R but rather figuring out what what are the fundamental data manipulation / statistical computing challenges and building tools that address them.

by ims 15y ago

This looks really well done. I wonder what the motivation is to do this when R is so mature (especially in the availability of specialized packages), and available through RPy.

wesm 15y ago

Here's an article I wrote about some of my motivations on the Python side of things: http://news.ycombinator.com/item?id=2790762 http://news.ycombinator.com/item?id=2790762 The bigger picture reason "why not R" is that R is not very suitable for building production systems. I started building this library while working for AQR, a quant hedge fund, and needed to have statistical computing building blocks integrated with a much larger system. R is a mediocre programming language and has very weak general purpose libraries. But amazingly good data visualization and mature statistics libraries indeed. Using R as a black box (e.g. via RPy, Rcpp, or RJava) is a good idea in theory, but recovering from and dealing with errors/exceptions with real world data is a very thorny problem. Plus maintaining a big pile of R code is kind of a nightmare (believe me, been there, done that!).

Estragon 15y ago
Exactly. I use scipy, and only use R when forced to, mainly because I want to know what's going on, and write my analyses in a real language.
pmiller2 15y ago
On a related note, I've said exactly the same thing about Matlab to people before. It (Matlab) is good for getting the algorithms and calculations correct, but as a programming language, it's pretty terrible.
joshu 15y ago
I've had to do this with SAS and perl. Looking forward to checking this out.

wesm 15y ago

I want to also point out that doing statistics in Python is currently a chicken-and-egg problem. You need the foundation of statistical data structures and algorithms to enable people to implement their models and research. NumPy and SciPy by themselves just don't compare with base R. So I've been trying to a) fill that gap and b) build tools that are quite a lot superior to R's. I use R myself but I have no intention of "copying" or "replicating" R but rather figuring out what what are the fundamental data manipulation / statistical computing challenges and building tools that address them.