def test_expanding_apply_args_kwargs(self):
def mean_w_arg(x, const):
return np.mean(x) + const
df = DataFrame(np.random.rand(20, 3))
expected = mom.expanding_apply(df, np.mean) + 20.0
assert_frame_equal(mom.expanding_apply(df, mean_w_arg, args=(20,)), expected)
assert_frame_equal(mom.expanding_apply(df, mean_w_arg, kwargs={"const": 20}), expected)
def test_expanding_apply_args_kwargs(self):
def mean_w_arg(x, const):
return np.mean(x) + const
df = DataFrame(np.random.rand(20, 3))
expected = mom.expanding_apply(df, np.mean) + 20.
assert_frame_equal(mom.expanding_apply(df, mean_w_arg, args=(20, )),
expected)
assert_frame_equal(
mom.expanding_apply(df, mean_w_arg, kwargs={'const': 20}),
expected)
def test_expanding_apply(self):
ser = Series([])
assert_series_equal(ser, mom.expanding_apply(ser, lambda x: x.mean()))
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x, lambda x: x.mean(), min_periods=min_periods, freq=freq)
self._check_expanding(expanding_mean, np.mean)
def test_expanding_apply(self):
ser = Series([])
assert_series_equal(ser, mom.expanding_apply(ser, lambda x: x.mean()))
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x,
lambda x: x.mean(),
min_periods=min_periods,
freq=freq)
self._check_expanding(expanding_mean, np.mean)
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x,
lambda x: x.mean(),
min_periods=min_periods,
freq=freq)
def expanding_mean(x, min_periods=1, freq=None):
return mom.expanding_apply(x,
lambda x: x.mean(),
min_periods=min_periods,
freq=freq)
def getPerc(X):
res1 = pd.Series(X.shift(-1).Close / X.Close)
res2 = expanding_apply(
res1, lambda p: functools.reduce(operator.mul, p, 1)).shift(1)
return res2.dropna()
def getPerc(X):
res1=pd.Series(X.shift(-1).Close/X.Close)
res2=expanding_apply(res1,lambda p:functools.reduce(operator.mul, p, 1)).shift(1)
return res2.dropna()
