def test_std(self):
tdi = pd.TimedeltaIndex(["0H", "4H", "NaT", "4H", "0H", "2H"])
arr = tdi.array
result = arr.std(skipna=True)
expected = pd.Timedelta(hours=2)
assert isinstance(result, pd.Timedelta)
assert result == expected
result = tdi.std(skipna=True)
assert isinstance(result, pd.Timedelta)
assert result == expected
result = nanops.nanstd(np.asarray(arr), skipna=True)
assert isinstance(result, pd.Timedelta)
assert result == expected
result = arr.std(skipna=False)
assert result is pd.NaT
result = tdi.std(skipna=False)
assert result is pd.NaT
result = nanops.nanstd(np.asarray(arr), skipna=False)
assert result is pd.NaT
def test_std(self, add):
tdi = pd.TimedeltaIndex(["0H", "4H", "NaT", "4H", "0H", "2H"]) + add
arr = tdi.array
result = arr.std(skipna=True)
expected = Timedelta(hours=2)
assert isinstance(result, Timedelta)
assert result == expected
result = tdi.std(skipna=True)
assert isinstance(result, Timedelta)
assert result == expected
if getattr(arr, "tz", None) is None:
result = nanops.nanstd(np.asarray(arr), skipna=True)
assert isinstance(result, Timedelta)
assert result == expected
result = arr.std(skipna=False)
assert result is pd.NaT
result = tdi.std(skipna=False)
assert result is pd.NaT
if getattr(arr, "tz", None) is None:
result = nanops.nanstd(np.asarray(arr), skipna=False)
assert result is pd.NaT
def test_ground_truth(self):
# Test against values that were precomputed with Numpy.
samples = np.empty((4, 4))
samples[:3, :3] = np.array([[0.97303362, 0.21869576, 0.55560287],
[0.72980153, 0.03109364, 0.99155171],
[0.09317602, 0.60078248, 0.15871292]])
samples[3] = samples[:, 3] = np.nan
# Actual variances along axis=0, 1 for ddof=0, 1, 2
variance = np.array([[[0.13762259, 0.05619224, 0.11568816],
[0.20643388, 0.08428837, 0.17353224],
[0.41286776, 0.16857673, 0.34706449]],
[[0.09519783, 0.16435395, 0.05082054],
[0.14279674, 0.24653093, 0.07623082],
[0.28559348, 0.49306186, 0.15246163]]])
# Test nanvar.
for axis in range(2):
for ddof in range(3):
var = nanops.nanvar(samples, skipna=True, axis=axis, ddof=ddof)
np.testing.assert_array_almost_equal(var[:3], variance[axis,
ddof])
np.testing.assert_equal(var[3], np.nan)
# Test nanstd.
for axis in range(2):
for ddof in range(3):
std = nanops.nanstd(samples, skipna=True, axis=axis, ddof=ddof)
np.testing.assert_array_almost_equal(std[:3],
variance[axis, ddof]**0.5)
np.testing.assert_equal(std[3], np.nan)
def std(self, axis=None, dtype=None, out=None, ddof=1, keepdims=False,
skipna=True):
nv.validate_stat_ddof_func((), dict(dtype=dtype, out=out,
keepdims=keepdims),
fname='std')
return nanops.nanstd(self._ndarray, axis=axis, skipna=skipna,
ddof=ddof)
def test_ground_truth(self):
# Test against values that were precomputed with Numpy.
samples = np.empty((4, 4))
samples[:3, :3] = np.array([[0.97303362, 0.21869576, 0.55560287
], [0.72980153, 0.03109364, 0.99155171],
[0.09317602, 0.60078248, 0.15871292]])
samples[3] = samples[:, 3] = np.nan
# Actual variances along axis=0, 1 for ddof=0, 1, 2
variance = np.array([[[0.13762259, 0.05619224, 0.11568816
], [0.20643388, 0.08428837, 0.17353224],
[0.41286776, 0.16857673, 0.34706449]],
[[0.09519783, 0.16435395, 0.05082054
], [0.14279674, 0.24653093, 0.07623082],
[0.28559348, 0.49306186, 0.15246163]]])
# Test nanvar.
for axis in range(2):
for ddof in range(3):
var = nanops.nanvar(samples, skipna=True, axis=axis, ddof=ddof)
tm.assert_almost_equal(var[:3], variance[axis, ddof])
self.assertTrue(np.isnan(var[3]))
# Test nanstd.
for axis in range(2):
for ddof in range(3):
std = nanops.nanstd(samples, skipna=True, axis=axis, ddof=ddof)
tm.assert_almost_equal(std[:3], variance[axis, ddof] ** 0.5)
self.assertTrue(np.isnan(std[3]))
def std(
self, *, axis=None, dtype=None, out=None, ddof=1, keepdims=False, skipna=True
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
)
return nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
def std(
self, *, axis=None, dtype=None, out=None, ddof=1, keepdims=False, skipna=True
):
nv.validate_stat_ddof_func(
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
)
result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
return self._wrap_reduction_result(axis, result)
def test_nanstd_nans(self):
samples = np.nan * np.ones(2 * self.samples.shape[0])
samples[::2] = self.samples
actual_std = nanops.nanstd(samples, skipna=True)
tm.assert_almost_equal(actual_std, self.variance**0.5, rtol=1e-2)
actual_std = nanops.nanvar(samples, skipna=False)
tm.assert_almost_equal(actual_std, np.nan, rtol=1e-2)
def test_nanstd_nans(self):
samples = np.nan * np.ones(2 * self.samples.shape[0])
samples[::2] = self.samples
actual_std = nanops.nanstd(samples, skipna=True)
tm.assert_almost_equal(actual_std, self.variance ** 0.5, check_less_precise=2)
actual_std = nanops.nanvar(samples, skipna=False)
tm.assert_almost_equal(actual_std, np.nan, check_less_precise=2)
def test_nanstd_nans(self):
samples = np.nan * np.ones(2 * self.samples.shape[0])
samples[::2] = self.samples
actual_std = nanops.nanstd(samples, skipna=True)
np.testing.assert_almost_equal(actual_std, self.variance ** 0.5, decimal=2)
actual_std = nanops.nanvar(samples, skipna=False)
np.testing.assert_almost_equal(actual_std, np.nan, decimal=2)
def std(
self,
axis=None,
dtype=None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
)
result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
if axis is None or self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)
def std(
self,
*,
axis: int | None = None,
dtype: NpDtype | None = None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func(
(), {"dtype": dtype, "out": out, "keepdims": keepdims}, fname="std"
)
result = nanops.nanstd(self._ndarray, axis=axis, skipna=skipna, ddof=ddof)
if axis is None or self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)
def std(
self,
axis=None,
dtype=None,
out=None,
ddof: int = 1,
keepdims: bool = False,
skipna: bool = True,
):
nv.validate_stat_ddof_func(
(), dict(dtype=dtype, out=out, keepdims=keepdims), fname="std"
)
if not len(self):
return NaT
if not skipna and self._hasnans:
return NaT
result = nanops.nanstd(self._data, axis=axis, skipna=skipna, ddof=ddof)
return Timedelta(result)