Help understanding the decisions *behind* python?
python at rcn.com
Fri Jul 31 13:32:46 EDT 2009
On Jul 22, 4:55 am, Duncan Booth <duncan.bo... at invalid.invalid> wrote:
> Steven D'Aprano <ste... at REMOVE.THIS.cybersource.com.au> wrote:
> > But that's the wrong solution to the problem. The OP wants the largest
> > (or smallest) item, which he expects to get by sorting, then grabbing
> > the first element:
> > sorted(alist)
> > That requires sorting the entire list, only to throw away everything
> > except the first item. A better solution is to use min() and max(),
> > neither of which sort the list, so they are much faster.
> And if they want the n smallest or largest items (where n is significantly
> less than the length of the list[*]) they might consider using
> heapq.nsmallest() and heapq.nlargest()
> I find it interesting that the heapq functions tell you in the
> documentation that they aren't suitable for use where n==1 or where n is
> near the total size of the sequence whereas random.sample() chooses what it
> thinks is the best algorithm based on the input. At the very least I would
> have thought the heapq functions could check for n==1 and call min/max when
> it is.
The heapq.py code in Py2.7 and Py3.1 already does some automatic
The automatic seletion of alternatives only occurs in clear-cut cases
(such as n==1
or where n==len(iterable) when the iterable has a known length). For
remaining cases, the switchover point from heapq to sorted needs a
programmer's judgment based on whether the input iterable has a known
length, the cost of comparison function, and whether input is already
The advice in the docs helps the reader understand the
relationships between min, max, nsmallest, nlargest, and sorted.
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