On 2012-02-24 09:55, Luca Beltrame wrote:
Hello,
in an effort to create a conversion between pandas objects[1] and R's
data.frames,
This would be handy. A common interface ("protocol" in the Python lingo)
would allow duck typing.
I'm trying to add columns to an empty DataFrame.
Python code:
from rpy2.robjects.packages import importr
base = importr("base")
my_row_names =
empty = base.data_frame(row_names=[item for item in range(1000)] )
Problems arise when I try to add a column through the rx2 accessor:
empty.rx2["New"] = [str(item) for item in range(len(empty.rownames))]
RRuntimeError: Error in `[[<-.data.frame`(list(), "New", list("0", "1", "2",
"3", "4", : argument "value" is missing, with no default
Anything I'm doing wrong?
Not necessarily; I think that you are reaching less "battle-tested"
corners of rpy2 (may be missing unit tests here and this went unnoticed).
I am intrigued by the line:
empty = base.data_frame(row_names=[item for item in range(1000)] )
Wouldn't a simpler alternative be
empty = base.data_frame(row_names = range(1000))
?... and may be specifying the vector type be needed
empty = base.data_frame(row_names = IntVector(range(1000)))
?
Or is there a more efficient way?
Appending to R vector is generally not the most efficient.
Try using R's cbind() (column-bind); is not more efficient but it states
more clearly what is happening.
(For reference,
I'm trying to *avoid* creating taggedlists and so on because they involve
intermediates, and with large objects that would mean performance issues).
[1]http://pandas.pydata.org
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Cloud computing makes use of virtualization - but cloud computing
also focuses on allowing computing to be delivered as a service.
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