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311 lines
10 KiB
311 lines
10 KiB
from datetime import timedelta
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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.arrays import (
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DatetimeArray,
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TimedeltaArray,
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)
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class TestNonNano:
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@pytest.fixture(params=["s", "ms", "us"])
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def unit(self, request):
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return request.param
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@pytest.fixture
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def tda(self, unit):
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arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
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return TimedeltaArray._simple_new(arr, dtype=arr.dtype)
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def test_non_nano(self, unit):
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arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]")
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tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype)
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assert tda.dtype == arr.dtype
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assert tda[0].unit == unit
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def test_as_unit_raises(self, tda):
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# GH#50616
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with pytest.raises(ValueError, match="Supported units"):
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tda.as_unit("D")
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tdi = pd.Index(tda)
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with pytest.raises(ValueError, match="Supported units"):
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tdi.as_unit("D")
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@pytest.mark.parametrize("field", TimedeltaArray._field_ops)
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def test_fields(self, tda, field):
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as_nano = tda._ndarray.astype("m8[ns]")
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tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
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result = getattr(tda, field)
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expected = getattr(tda_nano, field)
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tm.assert_numpy_array_equal(result, expected)
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def test_to_pytimedelta(self, tda):
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as_nano = tda._ndarray.astype("m8[ns]")
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tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
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result = tda.to_pytimedelta()
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expected = tda_nano.to_pytimedelta()
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tm.assert_numpy_array_equal(result, expected)
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def test_total_seconds(self, unit, tda):
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as_nano = tda._ndarray.astype("m8[ns]")
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tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype)
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result = tda.total_seconds()
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expected = tda_nano.total_seconds()
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tm.assert_numpy_array_equal(result, expected)
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def test_timedelta_array_total_seconds(self):
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# GH34290
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expected = Timedelta("2 min").total_seconds()
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result = pd.array([Timedelta("2 min")]).total_seconds()[0]
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assert result == expected
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def test_total_seconds_nanoseconds(self):
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# issue #48521
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start_time = pd.Series(["2145-11-02 06:00:00"]).astype("datetime64[ns]")
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end_time = pd.Series(["2145-11-02 07:06:00"]).astype("datetime64[ns]")
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expected = (end_time - start_time).values / np.timedelta64(1, "s")
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result = (end_time - start_time).dt.total_seconds().values
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assert result == expected
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@pytest.mark.parametrize(
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"nat", [np.datetime64("NaT", "ns"), np.datetime64("NaT", "us")]
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)
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def test_add_nat_datetimelike_scalar(self, nat, tda):
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result = tda + nat
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assert isinstance(result, DatetimeArray)
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assert result._creso == tda._creso
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assert result.isna().all()
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result = nat + tda
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assert isinstance(result, DatetimeArray)
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assert result._creso == tda._creso
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assert result.isna().all()
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def test_add_pdnat(self, tda):
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result = tda + pd.NaT
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assert isinstance(result, TimedeltaArray)
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assert result._creso == tda._creso
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assert result.isna().all()
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result = pd.NaT + tda
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assert isinstance(result, TimedeltaArray)
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assert result._creso == tda._creso
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assert result.isna().all()
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# TODO: 2022-07-11 this is the only test that gets to DTA.tz_convert
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# or tz_localize with non-nano; implement tests specific to that.
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def test_add_datetimelike_scalar(self, tda, tz_naive_fixture):
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ts = pd.Timestamp("2016-01-01", tz=tz_naive_fixture).as_unit("ns")
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expected = tda.as_unit("ns") + ts
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res = tda + ts
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tm.assert_extension_array_equal(res, expected)
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res = ts + tda
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tm.assert_extension_array_equal(res, expected)
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ts += Timedelta(1) # case where we can't cast losslessly
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exp_values = tda._ndarray + ts.asm8
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expected = (
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DatetimeArray._simple_new(exp_values, dtype=exp_values.dtype)
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.tz_localize("UTC")
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.tz_convert(ts.tz)
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)
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result = tda + ts
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tm.assert_extension_array_equal(result, expected)
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result = ts + tda
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tm.assert_extension_array_equal(result, expected)
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def test_mul_scalar(self, tda):
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other = 2
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result = tda * other
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expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
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tm.assert_extension_array_equal(result, expected)
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assert result._creso == tda._creso
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def test_mul_listlike(self, tda):
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other = np.arange(len(tda))
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result = tda * other
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expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
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tm.assert_extension_array_equal(result, expected)
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assert result._creso == tda._creso
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def test_mul_listlike_object(self, tda):
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other = np.arange(len(tda))
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result = tda * other.astype(object)
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expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype)
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tm.assert_extension_array_equal(result, expected)
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assert result._creso == tda._creso
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def test_div_numeric_scalar(self, tda):
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other = 2
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result = tda / other
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expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
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tm.assert_extension_array_equal(result, expected)
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assert result._creso == tda._creso
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def test_div_td_scalar(self, tda):
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other = timedelta(seconds=1)
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result = tda / other
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expected = tda._ndarray / np.timedelta64(1, "s")
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tm.assert_numpy_array_equal(result, expected)
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def test_div_numeric_array(self, tda):
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other = np.arange(len(tda))
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result = tda / other
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expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype)
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tm.assert_extension_array_equal(result, expected)
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assert result._creso == tda._creso
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def test_div_td_array(self, tda):
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other = tda._ndarray + tda._ndarray[-1]
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result = tda / other
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expected = tda._ndarray / other
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tm.assert_numpy_array_equal(result, expected)
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def test_add_timedeltaarraylike(self, tda):
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tda_nano = tda.astype("m8[ns]")
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expected = tda_nano * 2
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res = tda_nano + tda
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tm.assert_extension_array_equal(res, expected)
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res = tda + tda_nano
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tm.assert_extension_array_equal(res, expected)
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expected = tda_nano * 0
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res = tda - tda_nano
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tm.assert_extension_array_equal(res, expected)
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res = tda_nano - tda
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tm.assert_extension_array_equal(res, expected)
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class TestTimedeltaArray:
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@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
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def test_astype_int(self, dtype):
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arr = TimedeltaArray._from_sequence([Timedelta("1H"), Timedelta("2H")])
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if np.dtype(dtype) != np.int64:
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with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
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arr.astype(dtype)
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return
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result = arr.astype(dtype)
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expected = arr._ndarray.view("i8")
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tm.assert_numpy_array_equal(result, expected)
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def test_setitem_clears_freq(self):
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a = TimedeltaArray(pd.timedelta_range("1H", periods=2, freq="H"))
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a[0] = Timedelta("1H")
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assert a.freq is None
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@pytest.mark.parametrize(
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"obj",
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[
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Timedelta(seconds=1),
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Timedelta(seconds=1).to_timedelta64(),
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Timedelta(seconds=1).to_pytimedelta(),
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],
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)
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def test_setitem_objects(self, obj):
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# make sure we accept timedelta64 and timedelta in addition to Timedelta
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tdi = pd.timedelta_range("2 Days", periods=4, freq="H")
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arr = TimedeltaArray(tdi, freq=tdi.freq)
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arr[0] = obj
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assert arr[0] == Timedelta(seconds=1)
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@pytest.mark.parametrize(
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"other",
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[
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1,
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np.int64(1),
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1.0,
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np.datetime64("NaT"),
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pd.Timestamp("2021-01-01"),
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"invalid",
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np.arange(10, dtype="i8") * 24 * 3600 * 10**9,
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(np.arange(10) * 24 * 3600 * 10**9).view("datetime64[ns]"),
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pd.Timestamp("2021-01-01").to_period("D"),
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],
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)
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@pytest.mark.parametrize("index", [True, False])
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def test_searchsorted_invalid_types(self, other, index):
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data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9
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arr = TimedeltaArray(data, freq="D")
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if index:
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arr = pd.Index(arr)
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msg = "|".join(
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[
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"searchsorted requires compatible dtype or scalar",
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"value should be a 'Timedelta', 'NaT', or array of those. Got",
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]
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)
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with pytest.raises(TypeError, match=msg):
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arr.searchsorted(other)
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class TestUnaryOps:
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def test_abs(self):
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vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
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arr = TimedeltaArray(vals)
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evals = np.array([3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
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expected = TimedeltaArray(evals)
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result = abs(arr)
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tm.assert_timedelta_array_equal(result, expected)
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result2 = np.abs(arr)
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tm.assert_timedelta_array_equal(result2, expected)
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def test_pos(self):
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vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
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arr = TimedeltaArray(vals)
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result = +arr
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tm.assert_timedelta_array_equal(result, arr)
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assert not tm.shares_memory(result, arr)
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result2 = np.positive(arr)
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tm.assert_timedelta_array_equal(result2, arr)
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assert not tm.shares_memory(result2, arr)
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def test_neg(self):
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vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]")
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arr = TimedeltaArray(vals)
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evals = np.array([3600 * 10**9, "NaT", -7200 * 10**9], dtype="m8[ns]")
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expected = TimedeltaArray(evals)
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result = -arr
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tm.assert_timedelta_array_equal(result, expected)
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result2 = np.negative(arr)
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tm.assert_timedelta_array_equal(result2, expected)
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def test_neg_freq(self):
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tdi = pd.timedelta_range("2 Days", periods=4, freq="H")
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arr = TimedeltaArray(tdi, freq=tdi.freq)
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expected = TimedeltaArray(-tdi._data, freq=-tdi.freq)
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result = -arr
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tm.assert_timedelta_array_equal(result, expected)
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result2 = np.negative(arr)
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tm.assert_timedelta_array_equal(result2, expected)
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