https://github.com/python/cpython/commit/ef17fdbc1c274dc84c2f611c40449ab8482... commit: ef17fdbc1c274dc84c2f611c40449ab84824607e branch: master author: Raymond Hettinger <rhettinger@users.noreply.github.com> committer: Miss Islington (bot) <31488909+miss-islington@users.noreply.github.com> date: 2019-02-28T09:16:25-08:00 summary: bpo-36018: Add special value tests and make minor tweaks to the docs (GH-12096) 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 a0d4d3910220..8d961b7ca5b1 100644 --- a/Doc/library/statistics.rst +++ b/Doc/library/statistics.rst @@ -482,9 +482,9 @@ of applications in statistics, including simulations and hypothesis testing. .. class:: NormalDist(mu=0.0, sigma=1.0) Returns a new *NormalDist* object where *mu* represents the `arithmetic - mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ of data and *sigma* + mean <https://en.wikipedia.org/wiki/Arithmetic_mean>`_ and *sigma* represents the `standard deviation - <https://en.wikipedia.org/wiki/Standard_deviation>`_ of the data. + <https://en.wikipedia.org/wiki/Standard_deviation>`_. If *sigma* is negative, raises :exc:`StatisticsError`. @@ -579,7 +579,7 @@ of applications in statistics, including simulations and hypothesis testing. :class:`NormalDist` Examples and Recipes ---------------------------------------- -A :class:`NormalDist` readily solves classic probability problems. +:class:`NormalDist` readily solves classic probability problems. For example, given `historical data for SAT exams <https://blog.prepscholar.com/sat-standard-deviation>`_ showing that scores diff --git a/Lib/statistics.py b/Lib/statistics.py index bab585750d91..e917a5dddd8b 100644 --- a/Lib/statistics.py +++ b/Lib/statistics.py @@ -735,7 +735,7 @@ def pdf(self, x): return exp((x - self.mu)**2.0 / (-2.0*variance)) / sqrt(tau * variance) def cdf(self, x): - 'Cumulative density function: P(X <= x)' + 'Cumulative distribution function: P(X <= x)' if not self.sigma: raise StatisticsError('cdf() not defined when sigma is zero') return 0.5 * (1.0 + erf((x - self.mu) / (self.sigma * sqrt(2.0)))) diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index d35cdd8420a3..4adc5e4cbf4a 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -2113,6 +2113,10 @@ def test_pdf(self): Y = NormalDist(100, 0) with self.assertRaises(statistics.StatisticsError): Y.pdf(90) + # Special values + self.assertEqual(X.pdf(float('-Inf')), 0.0) + self.assertEqual(X.pdf(float('Inf')), 0.0) + self.assertTrue(math.isnan(X.pdf(float('NaN')))) def test_cdf(self): NormalDist = statistics.NormalDist @@ -2127,6 +2131,10 @@ def test_cdf(self): Y = NormalDist(100, 0) with self.assertRaises(statistics.StatisticsError): Y.cdf(90) + # Special values + self.assertEqual(X.cdf(float('-Inf')), 0.0) + self.assertEqual(X.cdf(float('Inf')), 1.0) + self.assertTrue(math.isnan(X.cdf(float('NaN')))) def test_properties(self): X = statistics.NormalDist(100, 15)