Name	Size	Mode	Actions
base_class/	-	0755	rm
categorical/	-	0755	rm
datetimes/	-	0755	rm
interval/	-	0755	rm
multi/	-	0755	rm
numeric/	-	0755	rm
period/	-	0755	rm
ranges/	-	0755	rm
timedeltas/	-	0755	rm
__pycache__/	-	0755	rm
common.py	32027	0644	editdlrm
conftest.py	726	0644	editdlrm
datetimelike.py	3259	0644	editdlrm
test_any_index.py	2375	0644	editdlrm
test_base.py	93126	0644	editdlrm
test_common.py	14453	0644	editdlrm
test_engines.py	8656	0644	editdlrm
test_frozen.py	3069	0644	editdlrm
test_indexing.py	2856	0644	editdlrm
test_index_new.py	4117	0644	editdlrm
test_numeric.py	22982	0644	editdlrm
test_numpy_compat.py	3362	0644	editdlrm
test_setops.py	3608	0644	editdlrm
__init__.py	0	0644	editdlrm

""" generic datetimelike tests """ import numpy as np import pytest import pandas as pd import pandas._testing as tm from .common import Base class DatetimeLike(Base): def test_argmax_axis_invalid(self): # GH#23081 msg = r"`axis` must be fewer than the number of dimensions \(1\)" rng = self.create_index() with pytest.raises(ValueError, match=msg): rng.argmax(axis=1) with pytest.raises(ValueError, match=msg): rng.argmin(axis=2) with pytest.raises(ValueError, match=msg): rng.min(axis=-2) with pytest.raises(ValueError, match=msg): rng.max(axis=-3) def test_can_hold_identifiers(self): idx = self.create_index() key = idx[0] assert idx._can_hold_identifiers_and_holds_name(key) is False def test_shift_identity(self): idx = self.create_index() tm.assert_index_equal(idx, idx.shift(0)) def test_str(self): # test the string repr idx = self.create_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 hasattr(idx, "freq"): assert f"freq='{idx.freqstr}'" in str(idx) def test_view(self): i = self.create_index() i_view = i.view("i8") result = self._holder(i) tm.assert_index_equal(result, i) i_view = i.view(self._holder) result = self._holder(i) tm.assert_index_equal(result, i_view) def test_map_callable(self): index = self.create_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): index = self.create_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): index = self.create_index() assert index.freq is not None result = index[:] assert result.freq == index.freq