Finding subsets for a robust regression

[Gerard flanagan]

tkpmep at hotmail.com wrote:

> 
>     x1 = []                 #unique instances of x and y
>     y1 = []                 #median(y) for each unique value of x
>     for xx,yy in d.iteritems():
>         x1.append(xx)
>         l = len(yy)
>         if l == 1:
>             y1.append(yy[0])
>         else:
>             yy.sort()
>             y1.append( (yy[l//2-1] + yy[l//2])/2.0 if l % 2 == 0 else
> yy[l//2] )
> --

Not tested, but is this equivalent?

x1 = []
y1 = []
for xx, yy in d.iteritems():
     L = len(yy) // 2
     yy.sort()
     y1.append(yy[L])
     if not L & 1:
         y1[-1] = (y1[-1] + yy[L-1]) / 2.0

It means that you have a pointless 'sort' when len(yy) == 1, but then 
you save an 'if' per-iteration.

G.