Creating an R data.frame in python with low level rpy2
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I am using the rpy2 package to bring some R functionality to python.
The functions I'm using in R need a data.frame object, and by using rlike.TaggedList and then robjects.DataFrame I am able to make this work. 

However I'm having performance issues, when comparing to the exact same R functions with the exact same data, which led me to try and use the rpy2 low level interface as mentioned here - http://rpy.sourceforge.net/rpy2/doc-2.3/html/performances.html

So far I have tried:

1. Using TaggedList with FloatSexpVector objects instead of numpy arrays, and the DataFrame object.
2. Dumping the TaggedList and DataFrame classes by using a dictionary like this:

        d = dict((var_name, var_sexp_vector) for ...)
        dataframe = robjects.r('data.frame')(**d)

Both did not get me any noticeable speedup.

I have noticed that DataFrame objects can get a rinterface.SexpVector in their constructor , so I have thought of creating a such a named vector, but I have no idea on how to put in the names (in R I know its just names(vec) = c('a','b'...)).

How do I do that? Is there another way?
And is there an easy way to profile rpy itself, so I could know where the bottleneck is? 
