def test_neg_freq(self):
tdi = pd.timedelta_range("2 Days", periods=4, freq="H")
arr = TimedeltaArray(tdi, freq=tdi.freq)
expected = TimedeltaArray(-tdi._data, freq=-tdi.freq)
result = -arr
tm.assert_timedelta_array_equal(result, expected)
def test_neg(self):
vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
arr = TimedeltaArray(vals)
evals = np.array([3600 * 10**9, "NaT", -7200 * 10**9], dtype="m8[ns]")
expected = TimedeltaArray(evals)
result = -arr
tm.assert_timedelta_array_equal(result, expected)
def test_pos(self):
vals = np.array([-3600 * 10 ** 9, "NaT", 7200 * 10 ** 9], dtype="m8[ns]")
arr = TimedeltaArray(vals)
result = +arr
tm.assert_timedelta_array_equal(result, arr)
result2 = np.positive(arr)
tm.assert_timedelta_array_equal(result2, arr)
def test_mean_2d(self):
tdi = pd.timedelta_range("14 days", periods=6)
tda = tdi._data.reshape(3, 2)
result = tda.mean(axis=0)
expected = tda[1]
tm.assert_timedelta_array_equal(result, expected)
result = tda.mean(axis=1)
expected = tda[:, 0] + Timedelta(hours=12)
tm.assert_timedelta_array_equal(result, expected)
result = tda.mean(axis=None)
expected = tdi.mean()
assert result == expected
def test_sum_2d_skipna_false(self):
arr = np.arange(8).astype(
np.int64).view("m8[s]").astype("m8[ns]").reshape(4, 2)
arr[-1, -1] = "Nat"
tda = TimedeltaArray(arr)
result = tda.sum(skipna=False)
assert result is pd.NaT
result = tda.sum(axis=0, skipna=False)
expected = pd.TimedeltaIndex([Timedelta(seconds=12), pd.NaT])._values
tm.assert_timedelta_array_equal(result, expected)
result = tda.sum(axis=1, skipna=False)
expected = pd.TimedeltaIndex([
Timedelta(seconds=1),
Timedelta(seconds=5),
Timedelta(seconds=9),
pd.NaT,
])._values
tm.assert_timedelta_array_equal(result, expected)
