def test_rolling_kurt_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = mom.rolling_kurt(d, window=5)
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = mom.rolling_kurt(d, window=3)
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 1.224307, 2.671499]
d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
x = mom.rolling_kurt(d, window=4)
assert_series_equal(expected, x)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=['A', 'B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index,
major_axis=df.columns,
minor_axis=df.columns)
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(
x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(
x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [
lambda x: mom.rolling_cov(
x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(
x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5
),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)
def test_rolling_kurt_edge_cases(self):
all_nan = Series([np.NaN] * 5)
# yields all NaN (0 variance)
d = Series([1] * 5)
x = mom.rolling_kurt(d, window=5)
assert_series_equal(all_nan, x)
# yields all NaN (window too small)
d = Series(np.random.randn(5))
x = mom.rolling_kurt(d, window=3)
assert_series_equal(all_nan, x)
# yields [NaN, NaN, NaN, 1.224307, 2.671499]
d = Series(
[-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401])
expected = Series([np.NaN, np.NaN, np.NaN, 1.224307, 2.671499])
x = mom.rolling_kurt(d, window=4)
assert_series_equal(expected, x)
def test_rolling_functions_window_non_shrinkage(self):
# GH 7764
s = Series(range(4))
s_expected = Series(np.nan, index=s.index)
df = DataFrame([[1,5], [3, 2], [3,9], [-1,0]], columns=['A','B'])
df_expected = DataFrame(np.nan, index=df.index, columns=df.columns)
df_expected_panel = Panel(items=df.index, major_axis=df.columns, minor_axis=df.columns)
functions = [lambda x: mom.rolling_cov(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=False, window=10, min_periods=5),
lambda x: mom.rolling_max(x, window=10, min_periods=5),
lambda x: mom.rolling_min(x, window=10, min_periods=5),
lambda x: mom.rolling_sum(x, window=10, min_periods=5),
lambda x: mom.rolling_mean(x, window=10, min_periods=5),
lambda x: mom.rolling_std(x, window=10, min_periods=5),
lambda x: mom.rolling_var(x, window=10, min_periods=5),
lambda x: mom.rolling_skew(x, window=10, min_periods=5),
lambda x: mom.rolling_kurt(x, window=10, min_periods=5),
lambda x: mom.rolling_quantile(x, quantile=0.5, window=10, min_periods=5),
lambda x: mom.rolling_median(x, window=10, min_periods=5),
lambda x: mom.rolling_apply(x, func=sum, window=10, min_periods=5),
lambda x: mom.rolling_window(x, win_type='boxcar', window=10, min_periods=5),
]
for f in functions:
try:
s_result = f(s)
assert_series_equal(s_result, s_expected)
df_result = f(df)
assert_frame_equal(df_result, df_expected)
except (ImportError):
# scipy needed for rolling_window
continue
functions = [lambda x: mom.rolling_cov(x, x, pairwise=True, window=10, min_periods=5),
lambda x: mom.rolling_corr(x, x, pairwise=True, window=10, min_periods=5),
# rolling_corr_pairwise is depracated, so the following line should be deleted
# when rolling_corr_pairwise is removed.
lambda x: mom.rolling_corr_pairwise(x, x, window=10, min_periods=5),
]
for f in functions:
df_result_panel = f(df)
assert_panel_equal(df_result_panel, df_expected_panel)