@@ -596,12 +596,9 @@ def multimode(data):
596596# intervals, and exactly 100p% of the intervals lie to the left of
597597# Q7(p) and 100(1 - p)% of the intervals lie to the right of Q7(p)."
598598
599- # If the need arises, we could add method="median" for a median
600- # unbiased, distribution-free alternative. Also if needed, the
601- # distribution-free approaches could be augmented by adding
602- # method='normal'. However, for now, the position is that fewer
603- # options make for easier choices and that external packages can be
604- # used for anything more advanced.
599+ # If needed, other methods could be added. However, for now, the
600+ # position is that fewer options make for easier choices and that
601+ # external packages can be used for anything more advanced.
605602
606603def quantiles (dist , / , * , n = 4 , method = 'exclusive' ):
607604 '''Divide *dist* into *n* continuous intervals with equal probability.
@@ -620,9 +617,6 @@ def quantiles(dist, /, *, n=4, method='exclusive'):
620617 data. The minimum value is treated as the 0th percentile and the
621618 maximum value is treated as the 100th percentile.
622619 '''
623- # Possible future API extensions:
624- # quantiles(data, already_sorted=True)
625- # quantiles(data, cut_points=[0.02, 0.25, 0.50, 0.75, 0.98])
626620 if n < 1 :
627621 raise StatisticsError ('n must be at least 1' )
628622 if hasattr (dist , 'inv_cdf' ):
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