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authorRaymond Hettinger <rhettinger@users.noreply.github.com>2019-02-24 19:44:55 (GMT)
committerMiss Islington (bot) <31488909+miss-islington@users.noreply.github.com>2019-02-24 19:44:55 (GMT)
commit9e456bc70e7bc9ee9726d356d7167457e585fd4c (patch)
tree3090fe7c058ff378e35586c1fa0616651f5a8b9a /Lib/test
parenta875ea58b29fbf510f9790ae1653eeaa47dc0de8 (diff)
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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
Diffstat (limited to 'Lib/test')
-rw-r--r--Lib/test/test_statistics.py6
1 files changed, 6 insertions, 0 deletions
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index 9549240..d35cdd8 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -2128,6 +2128,12 @@ class TestNormalDist(unittest.TestCase):
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)