summaryrefslogtreecommitdiffstats
path: root/Lib
diff options
context:
space:
mode:
authorRaymond Hettinger <rhettinger@users.noreply.github.com>2019-02-21 23:06:29 (GMT)
committerGitHub <noreply@github.com>2019-02-21 23:06:29 (GMT)
commit47d9987247bcc45983a6d51fd1ae46d5d356d0f8 (patch)
tree16b7e88590f9a28ff47e8a0e041510c4a2d86756 /Lib
parentf36f89257b30e0bf88e8aaff6da14a9a96f57b9e (diff)
downloadcpython-47d9987247bcc45983a6d51fd1ae46d5d356d0f8.zip
cpython-47d9987247bcc45983a6d51fd1ae46d5d356d0f8.tar.gz
cpython-47d9987247bcc45983a6d51fd1ae46d5d356d0f8.tar.bz2
bpo-35904: Add statistics.fmean() (GH-11892)
Diffstat (limited to 'Lib')
-rw-r--r--Lib/statistics.py29
-rw-r--r--Lib/test/test_statistics.py45
2 files changed, 73 insertions, 1 deletions
diff --git a/Lib/statistics.py b/Lib/statistics.py
index 47c2bb4..8ecb906 100644
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -79,7 +79,7 @@ A single exception is defined: StatisticsError is a subclass of ValueError.
__all__ = [ 'StatisticsError',
'pstdev', 'pvariance', 'stdev', 'variance',
'median', 'median_low', 'median_high', 'median_grouped',
- 'mean', 'mode', 'harmonic_mean',
+ 'mean', 'mode', 'harmonic_mean', 'fmean',
]
import collections
@@ -312,6 +312,33 @@ def mean(data):
assert count == n
return _convert(total/n, T)
+def fmean(data):
+ """ Convert data to floats and compute the arithmetic mean.
+
+ This runs faster than the mean() function and it always returns a float.
+ The result is highly accurate but not as perfect as mean().
+ If the input dataset is empty, it raises a StatisticsError.
+
+ >>> fmean([3.5, 4.0, 5.25])
+ 4.25
+
+ """
+ try:
+ n = len(data)
+ except TypeError:
+ # Handle iterators that do not define __len__().
+ n = 0
+ def count(x):
+ nonlocal n
+ n += 1
+ return x
+ total = math.fsum(map(count, data))
+ else:
+ total = math.fsum(data)
+ try:
+ return total / n
+ except ZeroDivisionError:
+ raise StatisticsError('fmean requires at least one data point') from None
def harmonic_mean(data):
"""Return the harmonic mean of data.
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
index b577433..e351446 100644
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -1810,6 +1810,51 @@ class TestMode(NumericTestCase, AverageMixin, UnivariateTypeMixin):
# counts, this should raise.
self.assertRaises(statistics.StatisticsError, self.func, data)
+class TestFMean(unittest.TestCase):
+
+ def test_basics(self):
+ fmean = statistics.fmean
+ D = Decimal
+ F = Fraction
+ for data, expected_mean, kind in [
+ ([3.5, 4.0, 5.25], 4.25, 'floats'),
+ ([D('3.5'), D('4.0'), D('5.25')], 4.25, 'decimals'),
+ ([F(7, 2), F(4, 1), F(21, 4)], 4.25, 'fractions'),
+ ([True, False, True, True, False], 0.60, 'booleans'),
+ ([3.5, 4, F(21, 4)], 4.25, 'mixed types'),
+ ((3.5, 4.0, 5.25), 4.25, 'tuple'),
+ (iter([3.5, 4.0, 5.25]), 4.25, 'iterator'),
+ ]:
+ actual_mean = fmean(data)
+ self.assertIs(type(actual_mean), float, kind)
+ self.assertEqual(actual_mean, expected_mean, kind)
+
+ def test_error_cases(self):
+ fmean = statistics.fmean
+ StatisticsError = statistics.StatisticsError
+ with self.assertRaises(StatisticsError):
+ fmean([]) # empty input
+ with self.assertRaises(StatisticsError):
+ fmean(iter([])) # empty iterator
+ with self.assertRaises(TypeError):
+ fmean(None) # non-iterable input
+ with self.assertRaises(TypeError):
+ fmean([10, None, 20]) # non-numeric input
+ with self.assertRaises(TypeError):
+ fmean() # missing data argument
+ with self.assertRaises(TypeError):
+ fmean([10, 20, 60], 70) # too many arguments
+
+ def test_special_values(self):
+ # Rules for special values are inherited from math.fsum()
+ fmean = statistics.fmean
+ NaN = float('Nan')
+ Inf = float('Inf')
+ self.assertTrue(math.isnan(fmean([10, NaN])), 'nan')
+ self.assertTrue(math.isnan(fmean([NaN, Inf])), 'nan and infinity')
+ self.assertTrue(math.isinf(fmean([10, Inf])), 'infinity')
+ with self.assertRaises(ValueError):
+ fmean([Inf, -Inf])
# === Tests for variances and standard deviations ===