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author | Miss Islington (bot) <31488909+miss-islington@users.noreply.github.com> | 2019-07-19 09:17:53 (GMT) |
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committer | Raymond Hettinger <rhettinger@users.noreply.github.com> | 2019-07-19 09:17:53 (GMT) |
commit | e5bfd1ce9da51b64d157392e0a831637f7335ff5 (patch) | |
tree | 2801cc7490194fbc4f6eafe21f1115ca28d19bb6 | |
parent | a50a6225a06e5a83ce2a880a7eb4496043fdbb55 (diff) | |
download | cpython-e5bfd1ce9da51b64d157392e0a831637f7335ff5.zip cpython-e5bfd1ce9da51b64d157392e0a831637f7335ff5.tar.gz cpython-e5bfd1ce9da51b64d157392e0a831637f7335ff5.tar.bz2 |
bpo-36546: Clean-up comments (GH-14857) (#14859)
(cherry picked from commit eed5e9a9562d4dcd137e9f0fc7157bc3373c98cc)
Co-authored-by: Raymond Hettinger <rhettinger@users.noreply.github.com>
-rw-r--r-- | Lib/statistics.py | 12 |
1 files changed, 3 insertions, 9 deletions
diff --git a/Lib/statistics.py b/Lib/statistics.py index 79b65a2..f09f7be 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'): |