146 lines
4.4 KiB
Python
146 lines
4.4 KiB
Python
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 (
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Index,
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NaT,
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Series,
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Timedelta,
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TimedeltaIndex,
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timedelta_range,
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)
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import pandas._testing as tm
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from pandas.core.indexes.api import Int64Index
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from pandas.tests.indexes.datetimelike import DatetimeLike
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randn = np.random.randn
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class TestTimedeltaIndex(DatetimeLike):
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_index_cls = TimedeltaIndex
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@pytest.fixture
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def simple_index(self) -> TimedeltaIndex:
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index = pd.to_timedelta(range(5), unit="d")._with_freq("infer")
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assert index.freq == "D"
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ret = index + pd.offsets.Hour(1)
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assert ret.freq == "D"
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return ret
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@pytest.fixture
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def index(self):
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return tm.makeTimedeltaIndex(10)
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def test_numeric_compat(self):
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# Dummy method to override super's version; this test is now done
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# in test_arithmetic.py
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pass
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def test_shift(self):
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pass # this is handled in test_arithmetic.py
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def test_misc_coverage(self):
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rng = timedelta_range("1 day", periods=5)
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result = rng.groupby(rng.days)
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assert isinstance(list(result.values())[0][0], Timedelta)
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def test_map(self):
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# test_map_dictlike generally tests
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rng = timedelta_range("1 day", periods=10)
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f = lambda x: x.days
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result = rng.map(f)
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exp = Int64Index([f(x) for x in rng])
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tm.assert_index_equal(result, exp)
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def test_pass_TimedeltaIndex_to_index(self):
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rng = timedelta_range("1 days", "10 days")
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idx = Index(rng, dtype=object)
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expected = Index(rng.to_pytimedelta(), dtype=object)
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tm.assert_numpy_array_equal(idx.values, expected.values)
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def test_fields(self):
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rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
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tm.assert_index_equal(rng.days, Index([1, 1], dtype="int64"))
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tm.assert_index_equal(
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rng.seconds,
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Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype="int64"),
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)
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tm.assert_index_equal(
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rng.microseconds, Index([100 * 1000 + 123, 100 * 1000 + 123], dtype="int64")
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)
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tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype="int64"))
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msg = "'TimedeltaIndex' object has no attribute '{}'"
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with pytest.raises(AttributeError, match=msg.format("hours")):
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rng.hours
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with pytest.raises(AttributeError, match=msg.format("minutes")):
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rng.minutes
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with pytest.raises(AttributeError, match=msg.format("milliseconds")):
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rng.milliseconds
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# with nat
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s = Series(rng)
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s[1] = np.nan
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tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
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tm.assert_series_equal(
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s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
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)
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# preserve name (GH15589)
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rng.name = "name"
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assert rng.days.name == "name"
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def test_freq_conversion_always_floating(self):
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# even if we have no NaTs, we get back float64; this matches TDA and Series
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tdi = timedelta_range("1 Day", periods=30)
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res = tdi.astype("m8[s]")
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expected = Index((tdi.view("i8") / 10**9).astype(np.float64))
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tm.assert_index_equal(res, expected)
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# check this matches Series and TimedeltaArray
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res = tdi._data.astype("m8[s]")
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tm.assert_numpy_array_equal(res, expected._values)
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res = tdi.to_series().astype("m8[s]")
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tm.assert_numpy_array_equal(res._values, expected._values)
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def test_freq_conversion(self, index_or_series):
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# doc example
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scalar = Timedelta(days=31)
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td = index_or_series(
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[scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT],
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dtype="m8[ns]",
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)
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result = td / np.timedelta64(1, "D")
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expected = index_or_series(
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[31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan]
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)
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tm.assert_equal(result, expected)
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result = td.astype("timedelta64[D]")
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expected = index_or_series([31, 31, 31, np.nan])
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tm.assert_equal(result, expected)
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result = td / np.timedelta64(1, "s")
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expected = index_or_series(
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[31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]
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)
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tm.assert_equal(result, expected)
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result = td.astype("timedelta64[s]")
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tm.assert_equal(result, expected)
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