Add harmonic mean and tests.

Lib/statistics.py

@@ -28,6 +28,7 @@ Calculating averages
 Function            Description
 ==================  =============================================
 mean                Arithmetic mean (average) of data.
+harmonic_mean       Harmonic mean of data.
 median              Median (middle value) of data.
 median_low          Low median of data.
 median_high         High median of data.
@@ -95,16 +96,17 @@ 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',
+            'mean', 'mode', 'harmonic_mean',
           ]
 
-
 import collections
+import decimal
 import math
+import numbers
 
 from fractions import Fraction
 from decimal import Decimal
-from itertools import groupby
+from itertools import groupby, chain
 from bisect import bisect_left, bisect_right
 
 
@@ -135,7 +137,8 @@ def _sum(data, start=0):
 
     Some sources of round-off error will be avoided:
 
-    >>> _sum([1e50, 1, -1e50] * 1000)  # Built-in sum returns zero.
+    # Built-in sum returns zero.
+    >>> _sum([1e50, 1, -1e50] * 1000)
     (<class 'float'>, Fraction(1000, 1), 3000)
 
     Fractions and Decimals are also supported:
@@ -291,6 +294,15 @@ def _find_rteq(a, l, x):
         return i-1
     raise ValueError
 
+
+def _fail_neg(values, errmsg='negative value'):
+    """Iterate over values, failing if any are less than zero."""
+    for x in values:
+        if x < 0:
+            raise StatisticsError(errmsg)
+        yield x
+
+
 # === Measures of central tendency (averages) ===
 
 def mean(data):
@@ -319,6 +331,52 @@ def mean(data):
     return _convert(total/n, T)
 
 
+def harmonic_mean(data):
+    """Return the harmonic mean of data.
+
+    The harmonic mean, sometimes called the subcontrary mean, is the
+    reciprocal of the arithmetic mean of the reciprocals of the data,
+    and is often appropriate when averaging quantities which are rates
+    or ratios, for example speeds. Example:
+
+    Suppose an investor purchases an equal value of shares in each of
+    three companies, with P/E (price/earning) ratios of 2.5, 3 and 10.
+    What is the average P/E ratio for the investor's portfolio?
+
+    >>> harmonic_mean([2.5, 3, 10])  # For an equal investment portfolio.
+    3.6
+
+    Using the arithmetic mean would give an average of about 5.167, which
+    is too high.
+
+    If ``data`` is empty, or any element is less than zero,
+    ``harmonic_mean`` will raise ``StatisticsError``.
+    """
+    # For a justification for using harmonic mean for P/E ratios, see
+    # http://fixthepitch.pellucid.com/comps-analysis-the-missing-harmony-of-summary-statistics/
+    # http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2621087
+    if iter(data) is data:
+        data = list(data)
+    errmsg = 'harmonic mean does not support negative values'
+    n = len(data)
+    if n < 1:
+        raise StatisticsError('harmonic_mean requires at least one data point')
+    elif n == 1:
+        x = data[0]
+        if isinstance(x, (numbers.Real, Decimal)):
+            if x < 0:
+                raise StatisticsError(errmsg)
+            return x
+        else:
+            raise TypeError('unsupported type')
+    try:
+        T, total, count = _sum(1/x for x in _fail_neg(data, errmsg))
+    except ZeroDivisionError:
+        return 0
+    assert count == n
+    return _convert(n/total, T)
+
+
 # FIXME: investigate ways to calculate medians without sorting? Quickselect?
 def median(data):
     """Return the median (middle value) of numeric data.

Lib/test/test_statistics.py

@@ -21,6 +21,10 @@ import statistics
 
 # === Helper functions and class ===
 
+def sign(x):
+    """Return -1.0 for negatives, including -0.0, otherwise +1.0."""
+    return math.copysign(1, x)
+
 def _nan_equal(a, b):
     """Return True if a and b are both the same kind of NAN.
 
@@ -264,6 +268,13 @@ class NumericTestCase(unittest.TestCase):
 # === Test the helpers ===
 # ========================
 
+class TestSign(unittest.TestCase):
+    """Test that the helper function sign() works correctly."""
+    def testZeroes(self):
+        # Test that signed zeroes report their sign correctly.
+        self.assertEqual(sign(0.0), +1)
+        self.assertEqual(sign(-0.0), -1)
+
 
 # --- Tests for approx_equal ---
 
@@ -659,7 +670,7 @@ class DocTests(unittest.TestCase):
     @unittest.skipIf(sys.flags.optimize >= 2,
                      "Docstrings are omitted with -OO and above")
     def test_doc_tests(self):
-        failed, tried = doctest.testmod(statistics)
+        failed, tried = doctest.testmod(statistics, optionflags=doctest.ELLIPSIS)
         self.assertGreater(tried, 0)
         self.assertEqual(failed, 0)
 
@@ -971,6 +982,34 @@ class ConvertTest(unittest.TestCase):
             self.assertTrue(_nan_equal(x, nan))
 
 
+class FailNegTest(unittest.TestCase):
+    """Test _fail_neg private function."""
+
+    def test_pass_through(self):
+        # Test that values are passed through unchanged.
+        values = [1, 2.0, Fraction(3), Decimal(4)]
+        new = list(statistics._fail_neg(values))
+        self.assertEqual(values, new)
+
+    def test_negatives_raise(self):
+        # Test that negatives raise an exception.
+        for x in [1, 2.0, Fraction(3), Decimal(4)]:
+            seq = [-x]
+            it = statistics._fail_neg(seq)
+            self.assertRaises(statistics.StatisticsError, next, it)
+
+    def test_error_msg(self):
+        # Test that a given error message is used.
+        msg = "badness #%d" % random.randint(10000, 99999)
+        try:
+            next(statistics._fail_neg([-1], msg))
+        except statistics.StatisticsError as e:
+            errmsg = e.args[0]
+        else:
+            self.fail("expected exception, but it didn't happen")
+        self.assertEqual(errmsg, msg)
+
+
 # === Tests for public functions ===
 
 class UnivariateCommonMixin:
@@ -1082,13 +1121,13 @@ class UnivariateTypeMixin:
     Not all tests to do with types need go in this class. Only those that
     rely on the function returning the same type as its input data.
     """
-    def test_types_conserved(self):
+    def prepare_types_for_conservation_test(self):
-        # Test that functions keeps the same type as their data points.
+        """Return the types which are expected to be conserved."""
-        # (Excludes mixed data types.) This only tests the type of the return
-        # result, not the value.
         class MyFloat(float):
             def __truediv__(self, other):
                 return type(self)(super().__truediv__(other))
+            def __rtruediv__(self, other):
+                return type(self)(super().__rtruediv__(other))
             def __sub__(self, other):
                 return type(self)(super().__sub__(other))
             def __rsub__(self, other):
@@ -1098,9 +1137,14 @@ class UnivariateTypeMixin:
             def __add__(self, other):
                 return type(self)(super().__add__(other))
             __radd__ = __add__
+        return (float, Decimal, Fraction, MyFloat)
 
+    def test_types_conserved(self):
+        # Test that functions keeps the same type as their data points.
+        # (Excludes mixed data types.) This only tests the type of the return
+        # result, not the value.
         data = self.prepare_data()
-        for kind in (float, Decimal, Fraction, MyFloat):
+        for kind in self.prepare_types_for_conservation_test():
             d = [kind(x) for x in data]
             result = self.func(d)
             self.assertIs(type(result), kind)
@@ -1275,12 +1319,16 @@ class AverageMixin(UnivariateCommonMixin):
         for x in (23, 42.5, 1.3e15, Fraction(15, 19), Decimal('0.28')):
             self.assertEqual(self.func([x]), x)
 
+    def prepare_values_for_repeated_single_test(self):
+        return (3.5, 17, 2.5e15, Fraction(61, 67), Decimal('4.9712'))
+
     def test_repeated_single_value(self):
         # The average of a single repeated value is the value itself.
-        for x in (3.5, 17, 2.5e15, Fraction(61, 67), Decimal('4.9712')):
+        for x in self.prepare_values_for_repeated_single_test():
             for count in (2, 5, 10, 20):
-                data = [x]*count
+                with self.subTest(x=x, count=count):
-                self.assertEqual(self.func(data), x)
+                    data = [x]*count
+                    self.assertEqual(self.func(data), x)
 
 
 class TestMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
@@ -1304,7 +1352,7 @@ class TestMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
         self.assertEqual(self.func(data), 22.015625)
 
     def test_decimals(self):
-        # Test mean with ints.
+        # Test mean with Decimals.
         D = Decimal
         data = [D("1.634"), D("2.517"), D("3.912"), D("4.072"), D("5.813")]
         random.shuffle(data)
@@ -1379,6 +1427,97 @@ class TestMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
             self.assertEqual(statistics.mean([tiny]*n), tiny)
 
 
+class TestHarmonicMean(NumericTestCase, AverageMixin, UnivariateTypeMixin):
+    def setUp(self):
+        self.func = statistics.harmonic_mean
+
+    def prepare_data(self):
+        # Override mixin method.
+        values = super().prepare_data()
+        values.remove(0)
+        return values
+
+    def prepare_values_for_repeated_single_test(self):
+        # Override mixin method.
+        return (3.5, 17, 2.5e15, Fraction(61, 67), Decimal('4.125'))
+
+    def test_zero(self):
+        # Test that harmonic mean returns zero when given zero.
+        values = [1, 0, 2]
+        self.assertEqual(self.func(values), 0)
+
+    def test_negative_error(self):
+        # Test that harmonic mean raises when given a negative value.
+        exc = statistics.StatisticsError
+        for values in ([-1], [1, -2, 3]):
+            with self.subTest(values=values):
+                self.assertRaises(exc, self.func, values)
+
+    def test_ints(self):
+        # Test harmonic mean with ints.
+        data = [2, 4, 4, 8, 16, 16]
+        random.shuffle(data)
+        self.assertEqual(self.func(data), 6*4/5)
+
+    def test_floats_exact(self):
+        # Test harmonic mean with some carefully chosen floats.
+        data = [1/8, 1/4, 1/4, 1/2, 1/2]
+        random.shuffle(data)
+        self.assertEqual(self.func(data), 1/4)
+        self.assertEqual(self.func([0.25, 0.5, 1.0, 1.0]), 0.5)
+
+    def test_singleton_lists(self):
+        # Test that harmonic mean([x]) returns (approximately) x.
+        for x in range(1, 101):
+            if x in (49, 93, 98, 99):
+                self.assertApproxEqual(self.func([x]), x, tol=2e-14)
+            else:
+                self.assertEqual(self.func([x]), x)
+
+    def test_decimals_exact(self):
+        # Test harmonic mean with some carefully chosen Decimals.
+        D = Decimal
+        self.assertEqual(self.func([D(15), D(30), D(60), D(60)]), D(30))
+        data = [D("0.05"), D("0.10"), D("0.20"), D("0.20")]
+        random.shuffle(data)
+        self.assertEqual(self.func(data), D("0.10"))
+        data = [D("1.68"), D("0.32"), D("5.94"), D("2.75")]
+        random.shuffle(data)
+        self.assertEqual(self.func(data), D(66528)/70723)
+
+    def test_fractions(self):
+        # Test harmonic mean with Fractions.
+        F = Fraction
+        data = [F(1, 2), F(2, 3), F(3, 4), F(4, 5), F(5, 6), F(6, 7), F(7, 8)]
+        random.shuffle(data)
+        self.assertEqual(self.func(data), F(7*420, 4029))
+
+    def test_inf(self):
+        # Test harmonic mean with infinity.
+        values = [2.0, float('inf'), 1.0]
+        self.assertEqual(self.func(values), 2.0)
+
+    def test_nan(self):
+        # Test harmonic mean with NANs.
+        values = [2.0, float('nan'), 1.0]
+        self.assertTrue(math.isnan(self.func(values)))
+
+    def test_multiply_data_points(self):
+        # Test multiplying every data point by a constant.
+        c = 111
+        data = [3.4, 4.5, 4.9, 6.7, 6.8, 7.2, 8.0, 8.1, 9.4]
+        expected = self.func(data)*c
+        result = self.func([x*c for x in data])
+        self.assertEqual(result, expected)
+
+    def test_doubled_data(self):
+        # Harmonic mean of [a,b...z] should be same as for [a,a,b,b...z,z].
+        data = [random.uniform(1, 5) for _ in range(1000)]
+        expected = self.func(data)
+        actual = self.func(data*2)
+        self.assertApproxEqual(actual, expected)
+
+
 class TestMedian(NumericTestCase, AverageMixin):
     # Common tests for median and all median.* functions.
     def setUp(self):