<html><head/><body><html><head></head><body>For the naming, how about changing median(callable) to median.regular? That way, we don't have to deal with a callable namespace.<br><br><div class="gmail_quote">Steven D'Aprano <steve@pearwood.info> wrote:<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<pre style="white-space: pre-wrap; word-wrap:break-word; font-family: sans-serif; margin-top: 0px">On 15/08/13 21:42, Mark Dickinson wrote:<br /><blockquote class="gmail_quote" style="margin: 0pt 0pt 1ex 0.8ex; border-left: 1px solid #729fcf; padding-left: 1ex;">The PEP and code look generally good to me.<br /><br />I think the API for median and its variants deserves some wider discussion:<br />the reference implementation has a callable 'median', and variant callables<br />'median.low', 'median.high', 'median.grouped'. The pattern of attaching<br />the variant callables as attributes on the main callable is unusual, and<br />isn't something I've seen elsewhere in the standard library. I'd like to<br />see some explanation in the PEP for why it's done this way. (There was<br />already some discussion of this on the issue, but that was more centered<br />around the implementation than the API.)<br /><br />I'd propose two alternatives for this: either have separate functions<br
/>'median', 'median_low', 'median_high', etc., or have a single function<br />'median' with a "method" argument that takes a string specifying<br />computation using a particular method. I don't see a really good reason to<br />deviate from standard patterns here, and fear that users would find the<br />current API surprising.</blockquote><br />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:<br /><br /><br />def _singleton(cls):<br />return cls()<br /><br /><br />@_singleton<br />class median:<br />def __call__(self, data):<br />...<br />def low(self, data):<br />...<br />...<br /><br /><br />In my earlier stats module, I had a single median function that took a argument to choose between alternatives. I called it "scheme":<br /><br />median(data, scheme="low")<br /><br />R uses parameter
called "type" to choose between alternate calculations, not for median as we are discussing, but for quantiles:<br /><br />quantile(x, probs ... type = 7, ...).<br /><br />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:<br /><br />- 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.<br /><br />- The API doesn't really feel very Pythonic to me. For example, we write:<br /><br />mystring.rjust(width)<br />dict.items()<br /><br />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.<br /><br /><br />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.<br /><br />(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.)<br /><br /><br
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