Gary,
The "wrong" order (transposed) is for the creation of a data.frame,
which is distinct from reading the information needed to create a
data.frame from a file in which each row is a represented by a line.
In R, the functions read.table, read.csv, read.delim, etc... are doing
the transposit
Very helpful, thanks!
As for having data in the "wrong" order, it's a little odd that a datafile
that's perfect for loading into R as a dataframe (via read.table), is
inherently in the "wrong" order for dataframe creation after reading it
into python (using numpy.genfromtext(), or f.readlines(
On Tue, Sep 29, 2009 at 4:21 AM, Gary Strangman
wrote:
> Without benchmarking, that seems mighty inefficient. Nathaniel Smith's
> rnumpy mostly allows the following:
>
> df = rnumpy.r.data_frame(numpy.array(d,np.object))
>
> ... which is 2 conversions (rather than 4), but I haven't been able to ge
That's the problem ... I don't have the data in R format to start, nor is
there a simple way of getting it there (except through python, of course,
in which case I have it in python, not R ;-) I did actually use the
read.table method for a while, but with several hundred thousand disk
hits eac
Great. Thanks for the jump-start!
On Tue, 29 Sep 2009, Laurent Gautier wrote:
> Gary Strangman wrote:
>>
>> Hi Laurent,
>>
>> The only way to reduce the number of transformations is to add an
>> equivalent number of columns to the dataframe (so that instead of several
>> hundred thousand con
Gary Strangman wrote:
>
> Hi Laurent,
>
> The only way to reduce the number of transformations is to add an
> equivalent number of columns to the dataframe (so that instead of
> several hundred thousand conversions, I need several hundred thousand
> columns), and then passing this beast back-a
On Tue, Sep 29, 2009 at 12:21 PM, Gary Strangman wrote:
>
> Hi Laurent,
>
> The only way to reduce the number of transformations is to add an
> equivalent number of columns to the dataframe (so that instead of several
> hundred thousand conversions, I need several hundred thousand columns),
> and t
Hi Laurent,
The only way to reduce the number of transformations is to add an
equivalent number of columns to the dataframe (so that instead of several
hundred thousand conversions, I need several hundred thousand columns),
and then passing this beast back-and-forth between python and R for
r
Gary,
Two things come to my mind:
- Try having an initial Python data structure that requires less
transformations than your current one in order to become a DataFrame.
- Use rpy2.rinterface when speed matters. This can already get you
faster than R.
http://rpy.sourceforge.net/rpy2/doc-dev/htm
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]
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