[Python-checkins] bpo-36546: Clean-up comments (GH-14857) (#14859)

[Raymond Hettinger]

https://github.com/python/cpython/commit/e5bfd1ce9da51b64d157392e0a831637f7335ff5
commit: e5bfd1ce9da51b64d157392e0a831637f7335ff5
branch: 3.8
author: Miss Islington (bot) <31488909+miss-islington at users.noreply.github.com>
committer: Raymond Hettinger <rhettinger at users.noreply.github.com>
date: 2019-07-19T02:17:53-07:00
summary:

bpo-36546:  Clean-up comments (GH-14857) (#14859)

(cherry picked from commit eed5e9a9562d4dcd137e9f0fc7157bc3373c98cc)

Co-authored-by: Raymond Hettinger <rhettinger at users.noreply.github.com>

files:
M Lib/statistics.py

diff --git a/Lib/statistics.py b/Lib/statistics.py
index 79b65a29183c..f09f7be354c2 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -596,12 +596,9 @@ def multimode(data):
 # intervals, and exactly 100p% of the intervals lie to the left of
 # Q7(p) and 100(1 - p)% of the intervals lie to the right of Q7(p)."
 
-# If the need arises, we could add method="median" for a median
-# unbiased, distribution-free alternative.  Also if needed, the
-# distribution-free approaches could be augmented by adding
-# method='normal'.  However, for now, the position is that fewer
-# options make for easier choices and that external packages can be
-# used for anything more advanced.
+# If needed, other methods could be added.  However, for now, the
+# position is that fewer options make for easier choices and that
+# external packages can be used for anything more advanced.
 
 def quantiles(dist, /, *, n=4, method='exclusive'):
     '''Divide *dist* into *n* continuous intervals with equal probability.
@@ -620,9 +617,6 @@ def quantiles(dist, /, *, n=4, method='exclusive'):
     data.  The minimum value is treated as the 0th percentile and the
     maximum value is treated as the 100th percentile.
     '''
-    # Possible future API extensions:
-    #     quantiles(data, already_sorted=True)
-    #     quantiles(data, cut_points=[0.02, 0.25, 0.50, 0.75, 0.98])
     if n < 1:
         raise StatisticsError('n must be at least 1')
     if hasattr(dist, 'inv_cdf'):