[Python-checkins] bpo-36018: Add properties for mean and stdev (GH-12022)

[Miss Islington (bot)]

https://github.com/python/cpython/commit/9e456bc70e7bc9ee9726d356d7167457e585fd4c
commit: 9e456bc70e7bc9ee9726d356d7167457e585fd4c
branch: master
author: Raymond Hettinger <rhettinger at users.noreply.github.com>
committer: Miss Islington (bot) <31488909+miss-islington at users.noreply.github.com>
date: 2019-02-24T11:44:55-08:00
summary:

bpo-36018: Add properties for mean and stdev (GH-12022)



Responding to suggestions on the tracker and some off-line suggestions.

Davin suggested that english named accessors instead of greek letters would result in more intelligible user code. Steven suggested that the parameters still need to be *mu* and *theta* which are used elsewhere (and I noted those parameter names are used in linked-to resources). 

Michael suggested proving-out the API by seeing whether it generalized to *Lognormal*.  I did so and found that Lognormal distribution parameters *mu* and *sigma*  do not represent the mean and standard deviation of the lognormal distribution (instead, they are for the underlying regular normal distribution).

Putting these ideas together, we have NormalDist parameterized by *mu* and *sigma* but offering English named properties for accessors.  That gives lets us match other API that access mu and sigma, it matches the external resources on the topic, gives us clear english names in user code. The API extends nicely to LogNormal where the parameters and the summary statistic accessors are not the same.


https://bugs.python.org/issue36018

files:
M Doc/library/statistics.rst
M Lib/statistics.py
M Lib/test/test_statistics.py

diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
index c1be295cbf95..a0d4d3910220 100644
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -488,13 +488,17 @@ of applications in statistics, including simulations and hypothesis testing.
 
     If *sigma* is negative, raises :exc:`StatisticsError`.
 
-    .. attribute:: mu
+    .. attribute:: mean
 
-        The mean of a normal distribution.
+       A read-only property representing the `arithmetic mean
+       <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ of a normal
+       distribution.
 
-    .. attribute:: sigma
+    .. attribute:: stdev
 
-        The standard deviation of a normal distribution.
+       A read-only property representing the `standard deviation
+       <https://en.wikipedia.org/wiki/Standard_deviation>`_ of a normal
+       distribution.
 
     .. attribute:: variance
 
@@ -566,8 +570,8 @@ of applications in statistics, including simulations and hypothesis testing.
         >>> birth_weights = NormalDist.from_samples([2.5, 3.1, 2.1, 2.4, 2.7, 3.5])
         >>> drug_effects = NormalDist(0.4, 0.15)
         >>> combined = birth_weights + drug_effects
-        >>> f'mu={combined.mu :.1f}   sigma={combined.sigma :.1f}'
-        'mu=3.1   sigma=0.5'
+        >>> f'mean: {combined.mean :.1f}   standard deviation: {combined.stdev :.1f}'
+        'mean: 3.1   standard deviation: 0.5'
 
     .. versionadded:: 3.8
 
diff --git a/Lib/statistics.py b/Lib/statistics.py
index bf10e19c0d41..bab585750d91 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -740,6 +740,16 @@ def cdf(self, x):
             raise StatisticsError('cdf() not defined when sigma is zero')
         return 0.5 * (1.0 + erf((x - self.mu) / (self.sigma * sqrt(2.0))))
 
+    @property
+    def mean(self):
+        'Arithmetic mean of the normal distribution'
+        return self.mu
+
+    @property
+    def stdev(self):
+        'Standard deviation of the normal distribution'
+        return self.sigma
+
     @property
     def variance(self):
         'Square of the standard deviation'
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 9549240f9092..d35cdd8420a3 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2128,6 +2128,12 @@ def test_cdf(self):
         with self.assertRaises(statistics.StatisticsError):
             Y.cdf(90)
 
+    def test_properties(self):
+        X = statistics.NormalDist(100, 15)
+        self.assertEqual(X.mean, 100)
+        self.assertEqual(X.stdev, 15)
+        self.assertEqual(X.variance, 225)
+
     def test_unary_operations(self):
         NormalDist = statistics.NormalDist
         X = NormalDist(100, 12)