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)