""" generic datetimelike tests """ import numpy as np import pytest import pandas as pd import pandas._testing as tm from pandas.tests.indexes.common import Base class DatetimeLike(Base): def test_isin(self, simple_index): index = simple_index[:4] result = index.isin(index) assert result.all() result = index.isin(list(index)) assert result.all() result = index.isin([index[2], 5]) expected = np.array([False, False, True, False]) tm.assert_numpy_array_equal(result, expected) def test_argsort_matches_array(self, simple_index): idx = simple_index idx = idx.insert(1, pd.NaT) result = idx.argsort() expected = idx._data.argsort() tm.assert_numpy_array_equal(result, expected) def test_can_hold_identifiers(self, simple_index): idx = simple_index key = idx[0] assert idx._can_hold_identifiers_and_holds_name(key) is False def test_shift_identity(self, simple_index): idx = simple_index tm.assert_index_equal(idx, idx.shift(0)) def test_shift_empty(self, simple_index): # GH#14811 idx = simple_index[:0] tm.assert_index_equal(idx, idx.shift(1)) def test_str(self, simple_index): # test the string repr idx = simple_index idx.name = "foo" assert not (f"length={len(idx)}" in str(idx)) assert "'foo'" in str(idx) assert type(idx).__name__ in str(idx) if hasattr(idx, "tz"): if idx.tz is not None: assert idx.tz in str(idx) if isinstance(idx, pd.PeriodIndex): assert f"dtype='period[{idx.freqstr}]'" in str(idx) else: assert f"freq='{idx.freqstr}'" in str(idx) def test_view(self, simple_index): idx = simple_index idx_view = idx.view("i8") result = self._index_cls(idx) tm.assert_index_equal(result, idx) idx_view = idx.view(self._index_cls) result = self._index_cls(idx) tm.assert_index_equal(result, idx_view) def test_map_callable(self, simple_index): index = simple_index expected = index + index.freq result = index.map(lambda x: x + x.freq) tm.assert_index_equal(result, expected) # map to NaT result = index.map(lambda x: pd.NaT if x == index[0] else x) expected = pd.Index([pd.NaT] + index[1:].tolist()) tm.assert_index_equal(result, expected) @pytest.mark.parametrize( "mapper", [ lambda values, index: {i: e for e, i in zip(values, index)}, lambda values, index: pd.Series(values, index, dtype=object), ], ) def test_map_dictlike(self, mapper, simple_index): index = simple_index expected = index + index.freq # don't compare the freqs if isinstance(expected, (pd.DatetimeIndex, pd.TimedeltaIndex)): expected = expected._with_freq(None) result = index.map(mapper(expected, index)) tm.assert_index_equal(result, expected) expected = pd.Index([pd.NaT] + index[1:].tolist()) result = index.map(mapper(expected, index)) tm.assert_index_equal(result, expected) # empty map; these map to np.nan because we cannot know # to re-infer things expected = pd.Index([np.nan] * len(index)) result = index.map(mapper([], [])) tm.assert_index_equal(result, expected) def test_getitem_preserves_freq(self, simple_index): index = simple_index assert index.freq is not None result = index[:] assert result.freq == index.freq def test_where_cast_str(self, simple_index): index = simple_index mask = np.ones(len(index), dtype=bool) mask[-1] = False result = index.where(mask, str(index[0])) expected = index.where(mask, index[0]) tm.assert_index_equal(result, expected) result = index.where(mask, [str(index[0])]) tm.assert_index_equal(result, expected) expected = index.astype(object).where(mask, "foo") result = index.where(mask, "foo") tm.assert_index_equal(result, expected) result = index.where(mask, ["foo"]) tm.assert_index_equal(result, expected)