383 lines
12 KiB
Python
383 lines
12 KiB
Python
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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 import nanops
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from pandas.core.arrays import TimedeltaArray
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class TestTimedeltaArrayConstructor:
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def test_only_1dim_accepted(self):
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# GH#25282
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arr = np.array([0, 1, 2, 3], dtype="m8[h]").astype("m8[ns]")
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with pytest.raises(ValueError, match="Only 1-dimensional"):
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# 3-dim, we allow 2D to sneak in for ops purposes GH#29853
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TimedeltaArray(arr.reshape(2, 2, 1))
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with pytest.raises(ValueError, match="Only 1-dimensional"):
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# 0-dim
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TimedeltaArray(arr[[0]].squeeze())
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def test_freq_validation(self):
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# ensure that the public constructor cannot create an invalid instance
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arr = np.array([0, 0, 1], dtype=np.int64) * 3600 * 10 ** 9
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msg = (
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"Inferred frequency None from passed values does not "
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"conform to passed frequency D"
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)
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with pytest.raises(ValueError, match=msg):
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TimedeltaArray(arr.view("timedelta64[ns]"), freq="D")
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def test_non_array_raises(self):
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with pytest.raises(ValueError, match="list"):
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TimedeltaArray([1, 2, 3])
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def test_other_type_raises(self):
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with pytest.raises(ValueError, match="dtype bool cannot be converted"):
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TimedeltaArray(np.array([1, 2, 3], dtype="bool"))
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def test_incorrect_dtype_raises(self):
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# TODO: why TypeError for 'category' but ValueError for i8?
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with pytest.raises(
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ValueError, match=r"category cannot be converted to timedelta64\[ns\]"
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):
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TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype="category")
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with pytest.raises(
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ValueError, match=r"dtype int64 cannot be converted to timedelta64\[ns\]"
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):
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TimedeltaArray(np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64"))
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def test_copy(self):
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data = np.array([1, 2, 3], dtype="m8[ns]")
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arr = TimedeltaArray(data, copy=False)
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assert arr._data is data
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arr = TimedeltaArray(data, copy=True)
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assert arr._data is not data
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assert arr._data.base is not data
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class TestTimedeltaArray:
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# TODO: de-duplicate with test_npsum below
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def test_np_sum(self):
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# GH#25282
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vals = np.arange(5, dtype=np.int64).view("m8[h]").astype("m8[ns]")
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arr = TimedeltaArray(vals)
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result = np.sum(arr)
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assert result == vals.sum()
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result = np.sum(pd.TimedeltaIndex(arr))
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assert result == vals.sum()
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def test_from_sequence_dtype(self):
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msg = "dtype .*object.* cannot be converted to timedelta64"
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with pytest.raises(ValueError, match=msg):
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TimedeltaArray._from_sequence([], dtype=object)
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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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result = arr.astype(dtype)
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if np.dtype(dtype).kind == "u":
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expected_dtype = np.dtype("uint64")
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else:
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expected_dtype = np.dtype("int64")
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expected = arr.astype(expected_dtype)
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assert result.dtype == expected_dtype
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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.now(),
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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.now().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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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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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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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#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.now(),
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pd.Timestamp.now("UTC"),
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pd.Timestamp.now("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, Timedelta)
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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 result is pd.NaT
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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)
|
||
|
|
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
|