Hi all,
I have a python list of lists (each sublist is a row of data), plus a list
of column names. Something like this ...
>>> d = [['S80', 'C', 137.5, 0],
['S82', 'C', 155.1, 1],
['S83', 'T', 11.96, 0],
['S84', 'T', 47, 1]]
['S85', 'T', numpy.nan, 1]]
>>> colnames = ['code','pop','score','flag']
I'm looking for the /fastest/ way to create an R dataframe (via rpy2)
using these two variables. It could be via dictionaries, numpy object
arrays, whatever, it just needs to be fast. Note that the data has mixed
types (some columns are strings, some are floats, some are ints), and
there are missing values which I'd like R to interpret as NA. I can
pre-transform the elements of the d variable as required to facilitate
this.
I need to do this step several hundred thousand times (yes, different data
each time) on up to ~10,000 element datasets, so any speedup suggestions
are welcome.
-best
Gary
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