An insightful comment below about “pyper vs rpy2” was made by Dr Xiao-Qin Xia, the developer of pyper. 1) While Rpy2 package and objects has many methods to learn, R interpreter is wrapped as a Python object in PypeR without special methods, that means the user can learn to use PypeR in minutes. 2) While […]
Within Python, there are two packages allowing pythonians to call R functions from a Python program. Below is a comparison between RPY2 and PYPER. The design of this study is very simple: 1) read a 100 by 100,000 data matrix into a pandas DataFrame 2) convert the pandas DataFrame to a R data frame 3) […]
For a statistical analyst, the first step to start a data analysis project is to import the data into the program and then to screen the descriptive statistics of the data. In python, we can easily do so with pandas package. Tonight, I’d like to add some spice to my python learning experience and do […]
Poisson and Negative Binomial regressions are two popular approaches to model frequency measures in the operational loss and can be implemented in Python with the statsmodels package as below: Although Quasi-Poisson regressions is not currently supported by the statsmodels package, we are still able to estimate the model with the rpy2 package by using R […]
Rmagic (http://ipython.org/ipython-doc/dev/config/extensions/rmagic.html) is the ipython extension that utilizes rpy2 in the back-end and provides a convenient interface accessing R from ipython. Compared with the generic use of rpy2, the rmagic extension allows users to exchange objects between ipython and R in a more flexible way and to run a single R function or a block […]
Different from RPy2, PypeR provides another simple way to access R from Python through pipes (http://www.jstatsoft.org/v35/c02/paper). This handy feature enables data analysts to do the data munging with python and the statistical analysis with R by passing objects interactively between two computing systems. Below is a simple demonstration on how to call R within Python […]
Below is an example showing how to run R code within python, which is an extremely attractive feature for hardcore R programmers.
Yet Another Blog in Statistical Computing 