“ValueError: codes need to be between -1 and len(c

问题:

In using rpy2 with a built-in dataset from the synthpop R package (SD2011), I get this error:

robjects.r('head(SD2011)')
# ...
# ValueError: codes need to be between -1 and len(categories)-1

I drilled down the problem into a column which has null entries, e.g. I get the same error when doing this, but not adjacent rows or columns:

robjects.r('SD2011[3, 27]')

I confirmed this is a null value with:

robjects.r('is.na(SD2011[, 27])')
# array([0, 0, 1, ..., 0, 0, 0], dtype=int32)

Why is rpy2 not handling this gracefully?

Here's my notebook running through it.

回答1:

Why is rpy2 not handling this gracefully?

This seems like a bug triggered during the conversion of the R factor to pandas with rpy2 versions 2.9.x (the dev branch default, future 3.0.x, does not have this issue). Specifically when doing:

res = pandas.Categorical.from_codes(numpy.asarray(obj) - 1,
                                    categories = obj.do_slot('levels'),
                                    ordered = 'ordered' in obj.rclass)

R "factor" objects are vector of integers, with each integer an index in an associated vector of "levels". The converter is simply subtracting one because R arrays are one-indexed and Python arrays are zero-index, but this is breaking whenever there are missing values (NAs) because R is using a specific integer to encode missing integers (an extreme value) and Python, numpy, and pandas does not have an equivalence for this.

I opened an issue to track this and in the meantime, workarounds can be to replace the NAs on the R side to a level (and call them, say, "missing" or "NA"), change the factors to arrays of strings, or to modify the pandas converter for R factors. For example:

robjects.r("""
  SD2011_nofactor <- SD2011 %>%
    dplyr::mutate_if(is.factor,
                     funs(as.character(.))
""")

(Or use rpy2's Pythonic interface to dplyr)

Note:

Few things are succcessively happening when doing:

robjects.r('SD2011[3, 27]')

1. the R code SD2011[3, 27] is evaluated
2. the result of that evaluation is going through the robjects-level conversion
3. the object resulting from that conversion is shown in your notebook

If unsure, finding which one of the Python statements below is the first to fail can tell it:

1. Evaluate the R code (the added TRUE is to prevent the evaluation from returning x).

   robjects.r('x <- SD2011[3, 27]; TRUE')
   

2. Fetch the object x obtained from the evaluation above and bind it to a Python symbol (the conversion will be aplied).

   x = robjects.r('x')
   

3. Show a text representation of the converted object

   repr(x)