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Code Issues Projects Releases Packages Wiki Activity Actions 11

bpo-36018: Address more reviewer feedback (GH-15733)

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Raymond Hettinger 2019-09-08 16:57:58 -07:00 • committed by GitHub
parent 3c87a667bb
commit 4db25d5c39
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3 changed files with 69 additions and 39 deletions
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32
Lib/statistics.py
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@ -624,9 +624,8 @@ def quantiles(data, /, *, n=4, method='exclusive'):
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *data* in to 100 equal sized groups.
The *data* can be any iterable containing sample data or it can be
an instance of a class that defines an inv_cdf() method. For sample
data, the cut points are linearly interpolated between data points.
The *data* can be any iterable containing sample.
The cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the
@ -634,8 +633,6 @@ def quantiles(data, /, *, n=4, method='exclusive'):
"""
if n < 1:
raise StatisticsError('n must be at least 1')
if hasattr(data, 'inv_cdf'):
return [data.inv_cdf(i / n) for i in range(1, n)]
data = sorted(data)
ld = len(data)
if ld < 2:
@ -955,6 +952,17 @@ class NormalDist:
raise StatisticsError('cdf() not defined when sigma at or below zero')
return _normal_dist_inv_cdf(p, self._mu, self._sigma)
def quantiles(self, n=4):
"""Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate the normal distribution in to 100 equal sized groups.
"""
return [self.inv_cdf(i / n) for i in range(1, n)]
def overlap(self, other):
"""Compute the overlapping coefficient (OVL) between two normal distributions.
@ -994,6 +1002,20 @@ class NormalDist:
"Arithmetic mean of the normal distribution."
return self._mu
@property
def median(self):
"Return the median of the normal distribution"
return self._mu
@property
def mode(self):
"""Return the mode of the normal distribution
The mode is the value x where which the probability density
function (pdf) takes its maximum value.
"""
return self._mu
@property
def stdev(self):
"Standard deviation of the normal distribution."