155 lines
5.1 KiB
Python
155 lines
5.1 KiB
Python
import numpy as np
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import pytest
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import pandas as pd
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from pandas.core.arrays import TimedeltaArray
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import pandas.util.testing as tm
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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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# 2-dim
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TimedeltaArray(arr.reshape(2, 2))
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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 " r"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,
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match=r"dtype int64 cannot be converted " r"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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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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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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@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([pd.Timedelta("1H"), pd.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] = pd.Timedelta("1H")
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assert a.freq is None
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class TestReductions:
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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 = pd.Timedelta("2H")
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assert result == expected
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result = arr.max()
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expected = pd.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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@pytest.mark.parametrize("skipna", [True, False])
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def test_min_max_empty(self, skipna):
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arr = TimedeltaArray._from_sequence([])
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result = arr.min(skipna=skipna)
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assert result is pd.NaT
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result = arr.max(skipna=skipna)
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assert result is pd.NaT
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