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- """ 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
- rng = self.create_index()
- with pytest.raises(ValueError):
- rng.argmax(axis=1)
- with pytest.raises(ValueError):
- rng.argmin(axis=2)
- with pytest.raises(ValueError):
- rng.min(axis=-2)
- with pytest.raises(ValueError):
- 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 "length={}".format(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 "freq='{idx.freqstr}'".format(idx=idx) 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):
- expected._data.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)
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