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 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 _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
