bpo-36018: Address more reviewer feedback by rhettinger · Pull Request #15733 · python/cpython · GitHub
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bpo-36018: Address more reviewer feedback #15733

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Merged
rhettinger merged 6 commits into from
Sep 8, 2019
Merged

bpo-36018: Address more reviewer feedback #15733

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ffdef07
Disable inv_cdf() hook
rhettinger Sep 7, 2019
4b16f8d
Move quantiles() a method in NormalDist
rhettinger Sep 7, 2019
6075dcb
Update docs for the quantiles() split
rhettinger Sep 7, 2019
be17ca6
Finish removing the inv_cdf() call
rhettinger Sep 7, 2019
85b0365
Minor edits
rhettinger Sep 7, 2019
9e511a0
Add median and mode properties to make the Dist API more generic (app…
rhettinger Sep 8, 2019
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41 changes: 27 additions & 14 deletions Doc/library/statistics.rst
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32 changes: 27 additions & 5 deletions Lib/statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -624,18 +624,15 @@ 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
maximum value is treated as the 100th percentile.
"""
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:
Expand Down Expand Up @@ -955,6 +952,17 @@ def inv_cdf(self, p):
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.

Expand Down Expand Up @@ -994,6 +1002,20 @@ def mean(self):
"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."
Expand Down
35 changes: 15 additions & 20 deletions Lib/test/test_statistics.py
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