def test_mad_dataframe_apply():
x = [1, 2, 3]
y = [1, 10, 5]
z = [1, 8, 0.6]
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
out = list(df.apply(lambda x: stats.mad(x), axis=1).values)
assert isinstance(out, list)
assert isinstance(out[0], float)
assert list(out)[0] == 0.0
def test_mad_returns_correct_answer():
data_in = [1, 1, 2, 2, 4, 6, 9]
correct = 1.0
assert stats.mad(data_in) == correct
def test_mad_dataframe_row():
x = [1, 2, 3]
y = [4, 10, 5]
z = [0.4, 8, 0.6]
df = pd.DataFrame(list(zip(x, y, z)), columns=["x", "y", "z"])
assert isinstance(stats.mad(df.ix[0]), float)
def test_mad_negatives():
data_in = [-1, -1, -1, -1, -1]
correct = 0.0
assert stats.mad(data_in) == correct
def test_mad_skewed():
data_in = [1, 1, 1, 1, 1, 1, 999]
correct = 0.0
assert stats.mad(data_in) == correct
def test_mad_all_ones():
data_in = [1, 1, 1, 1, 1, 1]
correct = 0.0
assert stats.mad(data_in) == correct