diff options
Diffstat (limited to 'Lib')
| -rw-r--r-- | Lib/statistics.py | 72 | ||||
| -rw-r--r-- | Lib/test/test_statistics.py | 27 |
2 files changed, 59 insertions, 40 deletions
diff --git a/Lib/statistics.py b/Lib/statistics.py index e85aaa9..97f1543 100644 --- a/Lib/statistics.py +++ b/Lib/statistics.py @@ -17,6 +17,7 @@ median_low Low median of data. median_high High median of data. median_grouped Median, or 50th percentile, of grouped data. mode Mode (most common value) of data. +multimode List of modes (most common values of data) ================== ============================================= Calculate the arithmetic mean ("the average") of data: @@ -79,10 +80,9 @@ A single exception is defined: StatisticsError is a subclass of ValueError. __all__ = [ 'StatisticsError', 'NormalDist', 'pstdev', 'pvariance', 'stdev', 'variance', 'median', 'median_low', 'median_high', 'median_grouped', - 'mean', 'mode', 'harmonic_mean', 'fmean', + 'mean', 'mode', 'multimode', 'harmonic_mean', 'fmean', ] -import collections import math import numbers import random @@ -92,8 +92,8 @@ from decimal import Decimal from itertools import groupby from bisect import bisect_left, bisect_right from math import hypot, sqrt, fabs, exp, erf, tau, log, fsum - - +from operator import itemgetter +from collections import Counter # === Exceptions === @@ -249,20 +249,6 @@ def _convert(value, T): raise -def _counts(data): - # Generate a table of sorted (value, frequency) pairs. - table = collections.Counter(iter(data)).most_common() - if not table: - return table - # Extract the values with the highest frequency. - maxfreq = table[0][1] - for i in range(1, len(table)): - if table[i][1] != maxfreq: - table = table[:i] - break - return table - - def _find_lteq(a, x): 'Locate the leftmost value exactly equal to x' i = bisect_left(a, x) @@ -334,9 +320,9 @@ def fmean(data): nonlocal n n += 1 return x - total = math.fsum(map(count, data)) + total = fsum(map(count, data)) else: - total = math.fsum(data) + total = fsum(data) try: return total / n except ZeroDivisionError: @@ -523,19 +509,38 @@ def mode(data): >>> mode(["red", "blue", "blue", "red", "green", "red", "red"]) 'red' - If there is not exactly one most common value, ``mode`` will raise - StatisticsError. + If there are multiple modes, return the first one encountered. + + >>> mode(['red', 'red', 'green', 'blue', 'blue']) + 'red' + + If *data* is empty, ``mode``, raises StatisticsError. + """ - # Generate a table of sorted (value, frequency) pairs. - table = _counts(data) - if len(table) == 1: - return table[0][0] - elif table: - raise StatisticsError( - 'no unique mode; found %d equally common values' % len(table) - ) - else: - raise StatisticsError('no mode for empty data') + data = iter(data) + try: + return Counter(data).most_common(1)[0][0] + except IndexError: + raise StatisticsError('no mode for empty data') from None + + +def multimode(data): + """ Return a list of the most frequently occurring values. + + Will return more than one result if there are multiple modes + or an empty list if *data* is empty. + + >>> multimode('aabbbbbbbbcc') + ['b'] + >>> multimode('aabbbbccddddeeffffgg') + ['b', 'd', 'f'] + >>> multimode('') + [] + + """ + counts = Counter(iter(data)).most_common() + maxcount, mode_items = next(groupby(counts, key=itemgetter(1)), (0, [])) + return list(map(itemgetter(0), mode_items)) # === Measures of spread === @@ -836,6 +841,7 @@ if __name__ == '__main__': from math import isclose from operator import add, sub, mul, truediv from itertools import repeat + import doctest g1 = NormalDist(10, 20) g2 = NormalDist(-5, 25) @@ -893,3 +899,5 @@ if __name__ == '__main__': S = NormalDist.from_samples([x - y for x, y in zip(X.samples(n), Y.samples(n))]) assert_close(X - Y, S) + + print(doctest.testmod()) diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py index a63e4bf..26b22a1 100644 --- a/Lib/test/test_statistics.py +++ b/Lib/test/test_statistics.py @@ -1769,7 +1769,7 @@ class TestMode(NumericTestCase, AverageMixin, UnivariateTypeMixin): def test_range_data(self): # Override test from UnivariateCommonMixin. data = range(20, 50, 3) - self.assertRaises(statistics.StatisticsError, self.func, data) + self.assertEqual(self.func(data), 20) def test_nominal_data(self): # Test mode with nominal data. @@ -1790,13 +1790,14 @@ class TestMode(NumericTestCase, AverageMixin, UnivariateTypeMixin): # Test mode with bimodal data. data = [1, 1, 2, 2, 2, 2, 3, 4, 5, 6, 6, 6, 6, 7, 8, 9, 9] assert data.count(2) == data.count(6) == 4 - # Check for an exception. - self.assertRaises(statistics.StatisticsError, self.func, data) + # mode() should return 2, the first encounted mode + self.assertEqual(self.func(data), 2) - def test_unique_data_failure(self): - # Test mode exception when data points are all unique. + def test_unique_data(self): + # Test mode when data points are all unique. data = list(range(10)) - self.assertRaises(statistics.StatisticsError, self.func, data) + # mode() should return 0, the first encounted mode + self.assertEqual(self.func(data), 0) def test_none_data(self): # Test that mode raises TypeError if given None as data. @@ -1809,8 +1810,18 @@ class TestMode(NumericTestCase, AverageMixin, UnivariateTypeMixin): # Test that a Counter is treated like any other iterable. data = collections.Counter([1, 1, 1, 2]) # Since the keys of the counter are treated as data points, not the - # counts, this should raise. - self.assertRaises(statistics.StatisticsError, self.func, data) + # counts, this should return the first mode encountered, 1 + self.assertEqual(self.func(data), 1) + + +class TestMultiMode(unittest.TestCase): + + def test_basics(self): + multimode = statistics.multimode + self.assertEqual(multimode('aabbbbbbbbcc'), ['b']) + self.assertEqual(multimode('aabbbbccddddeeffffgg'), ['b', 'd', 'f']) + self.assertEqual(multimode(''), []) + class TestFMean(unittest.TestCase): |