code: from datetime import timedelta
  
  import numpy as np
  import pytest
  
  import pandas as pd
  from pandas import (
      DataFrame,
      Index,
      Int64Index,
      Series,
      Timedelta,
      TimedeltaIndex,
      array,
      date_range,
      timedelta_range,
  )
  import pandas._testing as tm
  
  from ..datetimelike import DatetimeLike
  
  randn = np.random.randn
  
  
  class TestTimedeltaIndex(DatetimeLike):
      _holder = TimedeltaIndex
  
      @pytest.fixture
      def indices(self):
          return tm.makeTimedeltaIndex(10)
  
      def create_index(self) -> TimedeltaIndex:
          index = pd.to_timedelta(range(5), unit="d")._with_freq("infer")
          assert index.freq == "D"
          return index + pd.offsets.Hour(1)
  
      def test_numeric_compat(self):
          # Dummy method to override super's version; this test is now done
          # in test_arithmetic.py
          pass
  
      def test_shift(self):
          pass  # this is handled in test_arithmetic.py
  
      def test_pickle_compat_construction(self):
          pass
  
      def test_isin(self):
  
          index = tm.makeTimedeltaIndex(4)
          result = index.isin(index)
          assert result.all()
  
          result = index.isin(list(index))
          assert result.all()
  
          tm.assert_almost_equal(
              index.isin([index[2], 5]), np.array([False, False, True, False])
          )
  
      def test_factorize(self):
          idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"])
  
          exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
          exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"])
  
          arr, idx = idx1.factorize()
          tm.assert_numpy_array_equal(arr, exp_arr)
          tm.assert_index_equal(idx, exp_idx)
  
          arr, idx = idx1.factorize(sort=True)
          tm.assert_numpy_array_equal(arr, exp_arr)
          tm.assert_index_equal(idx, exp_idx)
  
          # freq must be preserved
          idx3 = timedelta_range("1 day", periods=4, freq="s")
          exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
          arr, idx = idx3.factorize()
          tm.assert_numpy_array_equal(arr, exp_arr)
          tm.assert_index_equal(idx, idx3)
  
      def test_sort_values(self):
  
          idx = TimedeltaIndex(["4d", "1d", "2d"])
  
          ordered = idx.sort_values()
          assert ordered.is_monotonic
  
          ordered = idx.sort_values(ascending=False)
          assert ordered[::-1].is_monotonic
  
          ordered, dexer = idx.sort_values(return_indexer=True)
          assert ordered.is_monotonic
  
          tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0]), check_dtype=False)
  
          ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
          assert ordered[::-1].is_monotonic
  
          tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1]), check_dtype=False)
  
      @pytest.mark.parametrize("klass", [list, np.array, array, Series])
      def test_searchsorted_different_argument_classes(self, klass):
          idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
          result = idx.searchsorted(klass(idx))
          expected = np.arange(len(idx), dtype=result.dtype)
          tm.assert_numpy_array_equal(result, expected)
  
          result = idx._data.searchsorted(klass(idx))
          tm.assert_numpy_array_equal(result, expected)
  
      @pytest.mark.parametrize(
          "arg",
          [[1, 2], ["a", "b"], [pd.Timestamp("2020-01-01", tz="Europe/London")] * 2],
      )
      def test_searchsorted_invalid_argument_dtype(self, arg):
          idx = TimedeltaIndex(["1 day", "2 days", "3 days"])
          msg = "searchsorted requires compatible dtype"
          with pytest.raises(TypeError, match=msg):
              idx.searchsorted(arg)
  
      def test_argmin_argmax(self):
          idx = TimedeltaIndex(["1 day 00:00:05", "1 day 00:00:01", "1 day 00:00:02"])
          assert idx.argmin() == 1
          assert idx.argmax() == 0
  
      def test_misc_coverage(self):
  
          rng = timedelta_range("1 day", periods=5)
          result = rng.groupby(rng.days)
          assert isinstance(list(result.values())[0][0], Timedelta)
  
          idx = TimedeltaIndex(["3d", "1d", "2d"])
          assert not idx.equals(list(idx))
  
          non_td = Index(list("abc"))
          assert not idx.equals(list(non_td))
  
      def test_map(self):
          # test_map_dictlike generally tests
  
          rng = timedelta_range("1 day", periods=10)
  
          f = lambda x: x.days
          result = rng.map(f)
          exp = Int64Index([f(x) for x in rng])
          tm.assert_index_equal(result, exp)
  
      def test_pass_TimedeltaIndex_to_index(self):
  
          rng = timedelta_range("1 days", "10 days")
          idx = Index(rng, dtype=object)
  
          expected = Index(rng.to_pytimedelta(), dtype=object)
  
          tm.assert_numpy_array_equal(idx.values, expected.values)
  
      def test_append_numpy_bug_1681(self):
  
          td = timedelta_range("1 days", "10 days", freq="2D")
          a = DataFrame()
          c = DataFrame({"A": "foo", "B": td}, index=td)
          str(c)
  
          result = a.append(c)
          assert (result["B"] == td).all()
  
      def test_fields(self):
          rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
          tm.assert_index_equal(rng.days, Index([1, 1], dtype="int64"))
          tm.assert_index_equal(
              rng.seconds,
              Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype="int64"),
          )
          tm.assert_index_equal(
              rng.microseconds, Index([100 * 1000 + 123, 100 * 1000 + 123], dtype="int64")
          )
          tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype="int64"))
  
          msg = "'TimedeltaIndex' object has no attribute '{}'"
          with pytest.raises(AttributeError, match=msg.format("hours")):
              rng.hours
          with pytest.raises(AttributeError, match=msg.format("minutes")):
              rng.minutes
          with pytest.raises(AttributeError, match=msg.format("milliseconds")):
              rng.milliseconds
  
          # with nat
          s = Series(rng)
          s[1] = np.nan
  
          tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
          tm.assert_series_equal(
              s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
          )
  
          # preserve name (GH15589)
          rng.name = "name"
          assert rng.days.name == "name"
  
      def test_freq_conversion(self):
  
          # doc example
  
          # series
          td = Series(date_range("20130101", periods=4)) - Series(
              date_range("20121201", periods=4)
          )
          td[2] += timedelta(minutes=5, seconds=3)
          td[3] = np.nan
  
          result = td / np.timedelta64(1, "D")
          expected = Series([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan])
          tm.assert_series_equal(result, expected)
  
          result = td.astype("timedelta64[D]")
          expected = Series([31, 31, 31, np.nan])
          tm.assert_series_equal(result, expected)
  
          result = td / np.timedelta64(1, "s")
          expected = Series([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan])
          tm.assert_series_equal(result, expected)
  
          result = td.astype("timedelta64[s]")
          tm.assert_series_equal(result, expected)
  
          # tdi
          td = TimedeltaIndex(td)
  
          result = td / np.timedelta64(1, "D")
          expected = Index([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan])
          tm.assert_index_equal(result, expected)
  
          result = td.astype("timedelta64[D]")
          expected = Index([31, 31, 31, np.nan])
          tm.assert_index_equal(result, expected)
  
          result = td / np.timedelta64(1, "s")
          expected = Index([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan])
          tm.assert_index_equal(result, expected)
  
          result = td.astype("timedelta64[s]")
          tm.assert_index_equal(result, expected)
  
apis: pandas._testing.assert_numpy_array_equal pandas.timedelta_range pandas.Series pandas.offsets.Hour pandas.date_range numpy.timedelta64 pandas._testing.makeTimedeltaIndex pandas.DataFrame pandas.TimedeltaIndex pandas._testing.assert_index_equal pandas._testing.assert_series_equal numpy.array pandas.Index pandas.Timestamp