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 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()