[Python-Dev] PEP 450 adding statistics module

[Mark Dickinson]

On Thu, Aug 15, 2013 at 6:48 PM, Ryan <rymg19 at gmail.com> wrote:

> For the naming, how about changing median(callable) to median.regular?
> That way, we don't have to deal with a callable namespace.
>

Hmm.  That sounds like a step backwards to me:  whatever the API is, a
simple "from statistics import median; m = median(my_data)" should still
work in the simple case.

Mark





>
> Steven D'Aprano <steve at pearwood.info> wrote:
>
>> On 15/08/13 21:42, Mark Dickinson wrote:
>>
>>> The PEP and code look generally good to me.
>>>
>>> I think the API for median and its variants deserves some wider discussion:
>>> the reference implementation has a callable 'median', and variant callables
>>> 'median.low', 'median.high', 'median.grouped'.  The pattern of attaching
>>> the variant callables as attributes on the main callable is unusual, and
>>> isn't something I've seen elsewhere in the standard library.  I'd like to
>>> see some explanation in the PEP for why it's done this way.  (There was
>>> already some discussion of this on the issue, but that was more centered
>>> around the implementation than the API.)
>>>
>>> I'd propose two alternatives for this:  either have separate functions
>>> 'median', 'median_low', 'median_high', etc., or have a single function
>>> 'median' with a "method" argument that takes a string specifying
>>> computation using a particular method.  I don't see a really good reason to
>>> deviate from standard patterns here, and fear that users would find the
>>> current API surprising.
>>
>>
>> Alexander Belopolsky has convinced me (off-list) that my current implementation is better changed to a more conservative one of a callable singleton instance with methods implementing the alternative computations. I'll have something like:
>>
>>
>> def _singleton(cls):
>> return cls()
>>
>>
>> @_singleton
>> class median:
>> def __call__(self, data):
>> ...
>> def low(self, data):
>> ...
>> ...
>>
>>
>> In my earlier stats module, I had a single median function that took a argument to choose between alternatives. I called it "scheme":
>>
>> median(data, scheme="low")
>>
>> R uses parameter
>> called "type" to choose between alternate calculations, not for median as we are discussing, but for quantiles:
>>
>> quantile(x, probs ... type = 7, ...).
>>
>> SAS also uses a similar system, but with different numeric codes. I rejected both "type" and "method" as the parameter name since it would cause confusion with the usual meanings of those words. I eventually decided against this system for two reasons:
>>
>> - Each scheme ended up needing to be a separate function, for ease of both implementation and testing. So I had four private median functions, which I put inside a class to act as namespace and avoid polluting the main namespace. Then I needed a "master function" to select which of the methods should be called, with all the additional testing and documentation that entailed.
>>
>> - The API doesn't really feel very Pythonic to me. For example, we write:
>>
>> mystring.rjust(width)
>> dict.items()
>>
>> rather than mystring.justify(width,
>> "right") or dict.iterate("items"). So I think individual methods is a better API, and one which is more familiar to most Python users. The only innovation (if that's what it is) is to have median a callable object.
>>
>>
>> As far as having four separate functions, median, median_low, etc., it just doesn't feel right to me. It puts four slight variations of the same function into the main namespace, instead of keeping them together in a namespace. Names like median_low merely simulates a namespace with pseudo-methods separated with underscores instead of dots, only without the advantages of a real namespace.
>>
>> (I treat variance and std dev differently, and make the sample and population forms separate top-level functions rather than methods, simply because they are so well-known from scientific calculators that it is unthinkable to me to do differently. Whenever I use numpy, I am surprised all over again that it has only a single variance function.)
>>
>>
>>
> --
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>
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