import numpy as np import pytest import pandas as pd from pandas import Timedelta import pandas._testing as tm from pandas.core import nanops from pandas.core.arrays import TimedeltaArray class TestReductions: @pytest.mark.parametrize("name", ["std", "min", "max", "median", "mean"]) @pytest.mark.parametrize("skipna", [True, False]) def test_reductions_empty(self, name, skipna): tdi = pd.TimedeltaIndex([]) arr = tdi.array result = getattr(tdi, name)(skipna=skipna) assert result is pd.NaT result = getattr(arr, name)(skipna=skipna) assert result is pd.NaT @pytest.mark.parametrize("skipna", [True, False]) def test_sum_empty(self, skipna): tdi = pd.TimedeltaIndex([]) arr = tdi.array result = tdi.sum(skipna=skipna) assert isinstance(result, Timedelta) assert result == Timedelta(0) result = arr.sum(skipna=skipna) assert isinstance(result, Timedelta) assert result == Timedelta(0) def test_min_max(self): arr = TimedeltaArray._from_sequence(["3H", "3H", "NaT", "2H", "5H", "4H"]) result = arr.min() expected = Timedelta("2H") assert result == expected result = arr.max() expected = Timedelta("5H") assert result == expected result = arr.min(skipna=False) assert result is pd.NaT result = arr.max(skipna=False) assert result is pd.NaT def test_sum(self): tdi = pd.TimedeltaIndex(["3H", "3H", "NaT", "2H", "5H", "4H"]) arr = tdi.array result = arr.sum(skipna=True) expected = Timedelta(hours=17) assert isinstance(result, Timedelta) assert result == expected result = tdi.sum(skipna=True) assert isinstance(result, Timedelta) assert result == expected result = arr.sum(skipna=False) assert result is pd.NaT result = tdi.sum(skipna=False) assert result is pd.NaT result = arr.sum(min_count=9) assert result is pd.NaT result = tdi.sum(min_count=9) assert result is pd.NaT result = arr.sum(min_count=1) assert isinstance(result, Timedelta) assert result == expected result = tdi.sum(min_count=1) assert isinstance(result, Timedelta) assert result == expected def test_npsum(self): # GH#25282, GH#25335 np.sum should return a Timedelta, not timedelta64 tdi = pd.TimedeltaIndex(["3H", "3H", "2H", "5H", "4H"]) arr = tdi.array result = np.sum(tdi) expected = Timedelta(hours=17) assert isinstance(result, Timedelta) assert result == expected result = np.sum(arr) assert isinstance(result, Timedelta) 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) # Adding a Timestamp makes this a test for DatetimeArray.std @pytest.mark.parametrize( "add", [ Timedelta(0), pd.Timestamp("2021-01-01"), pd.Timestamp("2021-01-01", tz="UTC"), pd.Timestamp("2021-01-01", tz="Asia/Tokyo"), ], ) 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, np.timedelta64) 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 isinstance(result, np.timedelta64) assert np.isnat(result) def test_median(self): tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"]) arr = tdi.array result = arr.median(skipna=True) expected = Timedelta(hours=2) assert isinstance(result, Timedelta) assert result == expected result = tdi.median(skipna=True) assert isinstance(result, Timedelta) assert result == expected result = arr.median(skipna=False) assert result is pd.NaT result = tdi.median(skipna=False) assert result is pd.NaT def test_mean(self): tdi = pd.TimedeltaIndex(["0H", "3H", "NaT", "5H06m", "0H", "2H"]) arr = tdi._data # manually verified result expected = Timedelta(arr.dropna()._ndarray.mean()) result = arr.mean() assert result == expected result = arr.mean(skipna=False) assert result is pd.NaT result = arr.dropna().mean(skipna=False) assert result == expected result = arr.mean(axis=0) assert result == expected 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