import numpy as np
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import pytest
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import pandas as pd
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from pandas import Timedelta
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import pandas._testing as tm
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from pandas.core import nanops
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from pandas.core.arrays import TimedeltaArray
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class TestReductions:
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@pytest.mark.parametrize("name", ["std", "min", "max", "median", "mean"])
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reductions_empty(self, name, skipna):
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tdi = pd.TimedeltaIndex([])
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arr = tdi.array
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result = getattr(tdi, name)(skipna=skipna)
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assert result is pd.NaT
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result = getattr(arr, name)(skipna=skipna)
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assert result is pd.NaT
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@pytest.mark.parametrize("skipna", [True, False])
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def test_sum_empty(self, skipna):
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tdi = pd.TimedeltaIndex([])
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arr = tdi.array
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result = tdi.sum(skipna=skipna)
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assert isinstance(result, Timedelta)
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assert result == Timedelta(0)
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result = arr.sum(skipna=skipna)
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assert isinstance(result, Timedelta)
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assert result == Timedelta(0)
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def test_min_max(self):
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arr = TimedeltaArray._from_sequence(["3H", "3H", "NaT", "2H", "5H", "4H"])
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result = arr.min()
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expected = Timedelta("2H")
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assert result == expected
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result = arr.max()
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expected = Timedelta("5H")
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assert result == expected
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result = arr.min(skipna=False)
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assert result is pd.NaT
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result = arr.max(skipna=False)
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assert result is pd.NaT
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def test_sum(self):
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tdi = pd.TimedeltaIndex(["3H", "3H", "NaT", "2H", "5H", "4H"])
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arr = tdi.array
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result = arr.sum(skipna=True)
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expected = Timedelta(hours=17)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = tdi.sum(skipna=True)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = arr.sum(skipna=False)
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assert result is pd.NaT
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result = tdi.sum(skipna=False)
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assert result is pd.NaT
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result = arr.sum(min_count=9)
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assert result is pd.NaT
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result = tdi.sum(min_count=9)
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assert result is pd.NaT
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result = arr.sum(min_count=1)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = tdi.sum(min_count=1)
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assert isinstance(result, Timedelta)
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assert result == expected
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def test_npsum(self):
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# GH#25282, GH#25335 np.sum should return a Timedelta, not timedelta64
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tdi = pd.TimedeltaIndex(["3H", "3H", "2H", "5H", "4H"])
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arr = tdi.array
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result = np.sum(tdi)
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expected = Timedelta(hours=17)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = np.sum(arr)
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assert isinstance(result, Timedelta)
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assert result == expected
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def test_sum_2d_skipna_false(self):
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arr = np.arange(8).astype(np.int64).view("m8[s]").astype("m8[ns]").reshape(4, 2)
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arr[-1, -1] = "Nat"
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tda = TimedeltaArray(arr)
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result = tda.sum(skipna=False)
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assert result is pd.NaT
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result = tda.sum(axis=0, skipna=False)
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expected = pd.TimedeltaIndex([Timedelta(seconds=12), pd.NaT])._values
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tm.assert_timedelta_array_equal(result, expected)
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result = tda.sum(axis=1, skipna=False)
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expected = pd.TimedeltaIndex(
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[
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Timedelta(seconds=1),
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Timedelta(seconds=5),
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Timedelta(seconds=9),
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pd.NaT,
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]
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)._values
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tm.assert_timedelta_array_equal(result, expected)
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# Adding a Timestamp makes this a test for DatetimeArray.std
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@pytest.mark.parametrize(
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"add",
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[
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Timedelta(0),
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pd.Timestamp("2021-01-01"),
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pd.Timestamp("2021-01-01", tz="UTC"),
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pd.Timestamp("2021-01-01", tz="Asia/Tokyo"),
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],
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)
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def test_std(self, add):
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tdi = pd.TimedeltaIndex(["0H", "4H", "NaT", "4H", "0H", "2H"]) + add
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arr = tdi.array
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result = arr.std(skipna=True)
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expected = Timedelta(hours=2)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = tdi.std(skipna=True)
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assert isinstance(result, Timedelta)
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assert result == expected
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if getattr(arr, "tz", None) is None:
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result = nanops.nanstd(np.asarray(arr), skipna=True)
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assert isinstance(result, np.timedelta64)
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assert result == expected
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result = arr.std(skipna=False)
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assert result is pd.NaT
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result = tdi.std(skipna=False)
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assert result is pd.NaT
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if getattr(arr, "tz", None) is None:
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result = nanops.nanstd(np.asarray(arr), skipna=False)
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assert isinstance(result, np.timedelta64)
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assert np.isnat(result)
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def test_median(self):
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tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"])
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arr = tdi.array
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result = arr.median(skipna=True)
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expected = Timedelta(hours=2)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = tdi.median(skipna=True)
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assert isinstance(result, Timedelta)
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assert result == expected
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result = arr.median(skipna=False)
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assert result is pd.NaT
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result = tdi.median(skipna=False)
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assert result is pd.NaT
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def test_mean(self):
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tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"])
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arr = tdi._data
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# manually verified result
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expected = Timedelta(arr.dropna()._ndarray.mean())
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result = arr.mean()
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assert result == expected
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result = arr.mean(skipna=False)
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assert result is pd.NaT
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result = arr.dropna().mean(skipna=False)
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assert result == expected
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result = arr.mean(axis=0)
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assert result == expected
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def test_mean_2d(self):
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tdi = pd.timedelta_range("14 days", periods=6)
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tda = tdi._data.reshape(3, 2)
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result = tda.mean(axis=0)
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expected = tda[1]
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tm.assert_timedelta_array_equal(result, expected)
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result = tda.mean(axis=1)
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expected = tda[:, 0] + Timedelta(hours=12)
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tm.assert_timedelta_array_equal(result, expected)
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result = tda.mean(axis=None)
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expected = tdi.mean()
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assert result == expected
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