diff options
| -rw-r--r-- | Lib/statistics.py | 30 | ||||
| -rw-r--r-- | Lib/test/test_statistics.py | 12 | ||||
| -rw-r--r-- | Misc/NEWS.d/next/Library/2023-12-08-11-17-17.gh-issue-112540.Pm5egX.rst | 2 |
3 files changed, 35 insertions, 9 deletions
diff --git a/Lib/statistics.py b/Lib/statistics.py index 4da0688..83aaedb 100644 --- a/Lib/statistics.py +++ b/Lib/statistics.py @@ -527,8 +527,10 @@ def fmean(data, weights=None): def geometric_mean(data): """Convert data to floats and compute the geometric mean. - Raises a StatisticsError if the input dataset is empty, - if it contains a zero, or if it contains a negative value. + Raises a StatisticsError if the input dataset is empty + or if it contains a negative value. + + Returns zero if the product of inputs is zero. No special efforts are made to achieve exact results. (However, this may change in the future.) @@ -536,11 +538,25 @@ def geometric_mean(data): >>> round(geometric_mean([54, 24, 36]), 9) 36.0 """ - try: - return exp(fmean(map(log, data))) - except ValueError: - raise StatisticsError('geometric mean requires a non-empty dataset ' - 'containing positive numbers') from None + n = 0 + found_zero = False + def count_positive(iterable): + nonlocal n, found_zero + for n, x in enumerate(iterable, start=1): + if x > 0.0 or math.isnan(x): + yield x + elif x == 0.0: + found_zero = True + else: + raise StatisticsError('No negative inputs allowed', x) + total = fsum(map(log, count_positive(data))) + if not n: + raise StatisticsError('Must have a non-empty dataset') + if math.isnan(total): + return math.nan + if found_zero: + return math.nan if total == math.inf else 0.0 + return exp(total / n) def harmonic_mean(data, weights=None): diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index b24fc3c..bf2c254 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -2303,10 +2303,12 @@ class TestGeometricMean(unittest.TestCase): with self.assertRaises(StatisticsError): geometric_mean([]) # empty input with self.assertRaises(StatisticsError): - geometric_mean([3.5, 0.0, 5.25]) # zero input - with self.assertRaises(StatisticsError): geometric_mean([3.5, -4.0, 5.25]) # negative input with self.assertRaises(StatisticsError): + geometric_mean([0.0, -4.0, 5.25]) # negative input with zero + with self.assertRaises(StatisticsError): + geometric_mean([3.5, -math.inf, 5.25]) # negative infinity + with self.assertRaises(StatisticsError): geometric_mean(iter([])) # empty iterator with self.assertRaises(TypeError): geometric_mean(None) # non-iterable input @@ -2328,6 +2330,12 @@ class TestGeometricMean(unittest.TestCase): with self.assertRaises(ValueError): geometric_mean([Inf, -Inf]) + # Cases with zero + self.assertEqual(geometric_mean([3, 0.0, 5]), 0.0) # Any zero gives a zero + self.assertEqual(geometric_mean([3, -0.0, 5]), 0.0) # Negative zero allowed + self.assertTrue(math.isnan(geometric_mean([0, NaN]))) # NaN beats zero + self.assertTrue(math.isnan(geometric_mean([0, Inf]))) # Because 0.0 * Inf -> NaN + def test_mixed_int_and_float(self): # Regression test for b.p.o. issue #28327 geometric_mean = statistics.geometric_mean diff --git a/Misc/NEWS.d/next/Library/2023-12-08-11-17-17.gh-issue-112540.Pm5egX.rst b/Misc/NEWS.d/next/Library/2023-12-08-11-17-17.gh-issue-112540.Pm5egX.rst new file mode 100644 index 0000000..263b13d --- /dev/null +++ b/Misc/NEWS.d/next/Library/2023-12-08-11-17-17.gh-issue-112540.Pm5egX.rst @@ -0,0 +1,2 @@ +The statistics.geometric_mean() function now returns zero for datasets +containing a zero. Formerly, it would raise an exception. |