def test_nanmean(self, tz): dti = pd.date_range('2016-01-01', periods=3, tz=tz) expected = dti[1] for obj in [dti, DatetimeArray(dti), Series(dti)]: result = nanops.nanmean(obj) assert result == expected dti2 = dti.insert(1, pd.NaT) for obj in [dti2, DatetimeArray(dti2), Series(dti2)]: result = nanops.nanmean(obj) assert result == expected
def test_nanmean(self, tz): dti = pd.date_range("2016-01-01", periods=3, tz=tz) expected = dti[1] for obj in [dti, DatetimeArray(dti), Series(dti)]: result = nanops.nanmean(obj) assert result == expected dti2 = dti.insert(1, pd.NaT) for obj in [dti2, DatetimeArray(dti2), Series(dti2)]: result = nanops.nanmean(obj) assert result == expected
def test_nanmean_skipna_false(self, dtype): arr = np.arange(12).astype(np.int64).view(dtype).reshape(4, 3) arr[-1, -1] = "NaT" result = nanops.nanmean(arr, skipna=False) assert result is pd.NaT result = nanops.nanmean(arr, axis=0, skipna=False) expected = np.array([4, 5, "NaT"], dtype=arr.dtype) tm.assert_numpy_array_equal(result, expected) result = nanops.nanmean(arr, axis=1, skipna=False) expected = np.array([arr[0, 1], arr[1, 1], arr[2, 1], arr[-1, -1]]) tm.assert_numpy_array_equal(result, expected)
def mean(self, axis=None, dtype=None, out=None, keepdims=False, skipna=True): nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims)) return nanops.nanmean(self._ndarray, axis=axis, skipna=skipna)
def mean(self, *, axis=None, dtype=None, out=None, keepdims=False, skipna=True): nv.validate_mean((), dict(dtype=dtype, out=out, keepdims=keepdims)) result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)
def mean( self, *, axis=None, dtype: Optional[NpDtype] = None, out=None, keepdims=False, skipna=True, ): nv.validate_mean((), {"dtype": dtype, "out": out, "keepdims": keepdims}) result = nanops.nanmean(self._ndarray, axis=axis, skipna=skipna) return self._wrap_reduction_result(axis, result)
def get_seasonality(row, window_size): """ Returns seasonality component of a time series :param row: pandas series containing the time series to extract seasonality from :param window_size: length of moving window :return: pandas series of seasonality component """ averages = np.array([nanmean(row[i::window_size], axis=0) for i in range (window_size)]) averages -= np.mean(averages, axis=0) season = np.tile(averages.T, len(row)//window_size+1).T[:len(row)] return pd.Series(season)
def _nanmean_wrap(self, value, *args, **kwargs): dtype = value.dtype res = nanops.nanmean(value, *args, **kwargs) if dtype.kind == 'O': res = np.round(res, decimals=13) return res