def f(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_quantile(x,
window,
q,
min_periods=min_periods,
freq=freq,
center=center)
def test_expanding_quantile(self):
result = mom.expanding_quantile(self.series, 0.5)
rolling_result = mom.rolling_quantile(self.series,
len(self.series),
0.5, min_periods=1)
assert_almost_equal(result, rolling_result)
def test_expanding_quantile(self):
result = mom.expanding_quantile(self.series, 0.5)
rolling_result = mom.rolling_quantile(self.series,
len(self.series),
0.5, min_periods=1)
assert_almost_equal(result, rolling_result)
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_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 f(x, window, min_periods=None, freq=None):
return mom.rolling_quantile(x,
window,
q,
min_periods=min_periods,
freq=freq)
def f(x, window, min_periods=None, time_rule=None):
return moments.rolling_quantile(x, window, q,
min_periods=min_periods,
time_rule=time_rule)
def f(x, window, min_periods=None, freq=None, center=False):
return mom.rolling_quantile(x, window, q,
min_periods=min_periods,
freq=freq,
center=center)
def f(x, window, min_periods=None, freq=None):
return mom.rolling_quantile(x, window, q,
min_periods=min_periods,
freq=freq)
def f(x, window, min_periods=None, time_rule=None):
return mom.rolling_quantile(x,
window,
q,
min_periods=min_periods,
time_rule=time_rule)