def test_floats(self):
# Test mean with floats.
data = [17.25, 19.75, 20.0, 21.5, 21.75, 23.25, 25.125, 27.5]
random.shuffle(data)
assert statistics.mean(data) == 22.015625
assert Math.mean(data) == 22.015625
assert statistics.mean(data) == Math.mean(data)
def test_ints(self):
# Test mean with ints.
data = [0, 1, 2, 3, 3, 3, 4, 5, 5, 6, 7, 7, 7, 7, 8, 9]
random.shuffle(data)
assert statistics.mean(data) == 4.8125
assert Math.mean(data) == 4.8125
assert statistics.mean(data) == Math.mean(data)
def test_regression_20561(self):
# Regression test for issue 20561.
# See http://bugs.python.org/issue20561
d = Decimal('1e4')
assert statistics.mean([d]) == d
assert Math.mean([d]) == d
assert Math.mean([d]) == statistics.mean([d])
def test_doubled_data(self):
# Mean of [a,b,c...z] should be same as for [a,a,b,b,c,c...z,z].
data = [random.uniform(-3, 5) for _ in range(1000)]
expected = statistics.mean(data)
assert statistics.mean(data * 2) == expected
assert Math.mean(data * 2) == expected
assert statistics.mean(data * 2) == Math.mean(data * 2)
def test_big_data(self):
# Test adding a large constant to every data point.
c = 1e9
data = [3.4, 4.5, 4.9, 6.7, 6.8, 7.2, 8.0, 8.1, 9.4]
expected = statistics.mean(data) + c
assert expected != c
assert statistics.mean([x + c for x in data]) == expected
assert Math.mean([x + c for x in data]) == expected
assert statistics.mean([x + c for x in data
]) == Math.mean([x + c for x in data])
def test_inf(self):
# Test mean with infinities.
raw = [1, 3, 5, 7, 9] # Use only ints, to avoid TypeError later.
for kind in (float, Decimal):
for sign in (1, -1):
inf = kind("inf") * sign
data = raw + [inf]
assert math.isinf(statistics.mean(data))
assert statistics.mean(data) == inf
assert math.isinf(Math.mean(data))
assert Math.mean(data) == inf
assert statistics.mean(data) == Math.mean(data)
def test_decimals(self):
# Test mean with Decimals.
data = [
Decimal("1.634"),
Decimal("2.517"),
Decimal("3.912"),
Decimal("4.072"),
Decimal("5.813")
]
random.shuffle(data)
assert statistics.mean(data) == Decimal("3.5896")
assert Math.mean(data) == Decimal("3.5896")
assert statistics.mean(data) == Math.mean(data)
def test_fractions(self):
# Test mean with Fractions.
data = [
Fraction(1, 2),
Fraction(2, 3),
Fraction(3, 4),
Fraction(4, 5),
Fraction(5, 6),
Fraction(6, 7),
Fraction(7, 8)
]
random.shuffle(data)
assert statistics.mean(data) == Fraction(1479, 1960)
assert Math.mean(data) == Fraction(1479, 1960)
assert statistics.mean(data) == Math.mean(data)
def test_nan(self):
# Test mean with NANs.
raw = [1, 3, 5, 7, 9] # Use only ints, to avoid TypeError later.
for kind in (float, Decimal):
inf = kind("nan")
data = raw + [inf]
assert math.isnan(statistics.mean(data))
assert math.isnan(Math.mean(data))
def test_regression_25177(self):
# Regression test for issue 25177.
# Ensure very big and very small floats don't overflow.
# See http://bugs.python.org/issue25177.
assert statistics.mean([8.988465674311579e+307, 8.98846567431158e+307
]) == 8.98846567431158e+307
assert Math.mean([8.988465674311579e+307,
8.98846567431158e+307]) == 8.98846567431158e+307
assert statistics.mean([
8.988465674311579e+307, 8.98846567431158e+307
]) == Math.mean([8.988465674311579e+307, 8.98846567431158e+307])
big = 8.98846567431158e+307
tiny = 5e-324
for n in (2, 3, 5, 200):
assert statistics.mean([big] * n) == big
assert statistics.mean([tiny] * n) == tiny
assert Math.mean([big] * n) == big
assert Math.mean([tiny] * n) == tiny
assert statistics.mean([big] * n) == Math.mean([big] * n)
assert statistics.mean([tiny] * n) == Math.mean([tiny] * n)
def test_mismatched_infs(self):
# Test mean with infinities of opposite sign.
data = [2, 4, 6, float('inf'), 1, 3, 5, float('-inf')]
assert math.isnan(statistics.mean(data))
assert math.isnan(Math.mean(data))
def test_torture_pep(self):
# "Torture Test" from PEP-450.
assert statistics.mean([1e100, 1, 3, -1e100]) == 1
assert Math.mean([1e100, 1, 3, -1e100]) == 1
assert statistics.mean([1e100, 1, 3,
-1e100]) == Math.mean([1e100, 1, 3, -1e100])