python · miss-islington · Feb 24, 2019 · Feb 24, 2019
diff --git 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
@@ -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
@@ -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)