python - from CSV to ndarray, and rpy2, -

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i can make numpy ndarrays rec2csv, 

data = recfromcsv(dataset1, names=true) xvars = ['exp','exp_sqr','wks','occ','ind','south','smsa','ms','union','ed','fem','blk'] y = data['lwage'] x = data[xvars] c = ones_like(data['lwage']) x = add_field(x, 'constant', c)

but, have no idea how take r data frame usable rpy2, 

p = roptim(theta,robjects.r['ols'],method="bfgs",hessian=true ,y= robjects.floatvector(y),x = base.matrix(x))  valueerror: nothing can done type <class 'numpy.core.records.recarray'> @ moment.  p = roptim(theta,robjects.r['ols'],method="bfgs",hessian=true ,y= robjects.floatvector(y),x = base.matrix(array(x)))  valueerror: nothing can done type <type 'numpy.ndarray'> @ moment.

i'm not 100% sure understand issue, couple things:

1) if it's ok, can read csv r directly, is:

robjects.r('name <- read.csv(filename.csv)')

after can refer resulting data frame in later functions.

or 2) can convert numpy array data frame - need import package 'rpy2.robjects.numpy2ri'

then like:

array_ex = np.array([[4,3],[3,2], [1,5]]) rmatrix = robjects.r('matrix') rdf = robjects.r('data.frame') rlm = robjects.r('lm')  mat_ex = rmatrix(array_ex, ncol = 2) df_ex = rdf(mat_ex)  fit_ex = rlm('x1 ~ x2', data = df_ex)

or whatever other functions wanted. there may more direct way - frustrated going between 2 data types , more use option 1) if possible.

would either of these methods need be?


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