On 24/02/11 11:26, Artur Wroblewski wrote:
> On Thu, Feb 24, 2011 at 07:57:19AM +0100, Laurent wrote:
>> On 24/02/11 00:12, Artur Wroblewski wrote:
>>> On Wed, Feb 23, 2011 at 8:00 PM, Laurent Gautier<lgaut...@gmail.com>
>>> wrote:
>>> [...]
>>>> Rpy2 aims at doing very little hidden magic or second guessing of the
>>>> user's
>>>> intention (R is already enough).
>>>
>>> Probably "magic" is bad word.
>>>
>>>> I have considered the option of having an "on_fail" (or other name)
>>>> parameter to specify a value to use is conversion to the required type
>>>> fails, but chose to keep the interface simple unless need.
>>>
>>> What I would expect from rpy in case of FloatVector (or other vector)
>>> - accept any Python iterable as argument (not only lists and sequences)
>>
>> I considered that but there was no performance gain from doing so so
>> left it out: to create an R vector the length must be known from the
>> beginning, and the iterator must be unwind into a sequence before
>> anything else.
>> Thinking further what can be added is a test to rescue non-sequences
>> that happen to be iterables and internally unwind them into a tuple.
>
> It is less about performance but more about convenience. Every Python
> container (tuple, list, set, dict) and related functions (zip) accept
> iterables at the moment. Unwinding is OK for me in such case.
Point taken. If every container does so, it definitely makes sense that
<Whatever>Vectors do so as well.
> Also, some iterable might implement __len__ operator, but not being tuple
> or list.
That's an extra condition to add in order to keep performances when
possible (if iterator /but/ __len__, the iterator can be unwound without
creating a temporary internal tuple).
>>> - accept anything what Python's "float" function accepts, which is string,
>>> integer and float as item of above iterable, i.e. (1, "1", 1.0)
>>> shall be accepted
>>
>> I'd prefer sticking to what array.array accepts since it is
>> conceptually closer.
>> (Long) experience tells that those seemingly convenience functions
>> are bug nests.
>>
>> You can still tweak it the way you prefer by subclassing the
>> existing classes, and you also have R's as.numeric().
>> import rpy2.interactive as r
>> my_iterable = (x for x in (1, "1", 1.0))
>> r.packages.base.as_numeric(*tuple(my_iterable))
>
> OK.
>
>>> - by default, convert Python's None to R's NA; add optional vector
>>> constructor
>>> parameter to allow conversion of None to R's NULL (or maybe even
>>> other value), i.e. FloatVector(iterable, none=R.NA)
>>
>> I am somewhat reluctant. Python's None is closer to R's NULL. The
>> multiple parameter thing seems like complication that would not
>> benefit the greatest number.
>
> In Python
>
> >>> (1, None, 2, None, 3)
> (1, None, 2, None, 3)
>
> In R
>
> > c(1, NULL, 2, NULL, 3)
> [1] 1 2 3<- NULL is removed, by default
>
> > c(1, NA, 2, NA, 3)
> [1] 1 NA 2 NA 3<- NA is kept, by default
There are places where some people can see R as counter-intuitive, and I
think that this is one such place. Although Python-None seems closer to
C-Null and R-NULL, the behaviour you describe is what made me have None
in the list passed to the constructor raise an exception: better
explicit than implicit when confusion can occur.
Rather than None, one should be using one of the
rpy2.rinterface.NA_<whatever>.
> Above is problematic with data frames (vectors need to be the same length)
>
> a = [1, None, 2]
> b = range(3) # in Python 3 this is iterable implementing __len__
>
> df = ro.DataFrame({
> 'a': ro.FloatVector(a),
> 'b': ro.FloatVector(tuple(b)),
> })
>
>
> Best regards,
>
> w
>
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