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- from datetime import (
- datetime,
- timedelta,
- )
- from pandas import (
- DatetimeIndex,
- NaT,
- Timestamp,
- )
- import pandas._testing as tm
- def test_unique(tz_naive_fixture):
- idx = DatetimeIndex(["2017"] * 2, tz=tz_naive_fixture)
- expected = idx[:1]
- result = idx.unique()
- tm.assert_index_equal(result, expected)
- # GH#21737
- # Ensure the underlying data is consistent
- assert result[0] == expected[0]
- def test_index_unique(rand_series_with_duplicate_datetimeindex):
- dups = rand_series_with_duplicate_datetimeindex
- index = dups.index
- uniques = index.unique()
- expected = DatetimeIndex(
- [
- datetime(2000, 1, 2),
- datetime(2000, 1, 3),
- datetime(2000, 1, 4),
- datetime(2000, 1, 5),
- ]
- )
- assert uniques.dtype == "M8[ns]" # sanity
- tm.assert_index_equal(uniques, expected)
- assert index.nunique() == 4
- # GH#2563
- assert isinstance(uniques, DatetimeIndex)
- dups_local = index.tz_localize("US/Eastern")
- dups_local.name = "foo"
- result = dups_local.unique()
- expected = DatetimeIndex(expected, name="foo")
- expected = expected.tz_localize("US/Eastern")
- assert result.tz is not None
- assert result.name == "foo"
- tm.assert_index_equal(result, expected)
- def test_index_unique2():
- # NaT, note this is excluded
- arr = [1370745748 + t for t in range(20)] + [NaT._value]
- idx = DatetimeIndex(arr * 3)
- tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
- assert idx.nunique() == 20
- assert idx.nunique(dropna=False) == 21
- def test_index_unique3():
- arr = [
- Timestamp("2013-06-09 02:42:28") + timedelta(seconds=t) for t in range(20)
- ] + [NaT]
- idx = DatetimeIndex(arr * 3)
- tm.assert_index_equal(idx.unique(), DatetimeIndex(arr))
- assert idx.nunique() == 20
- assert idx.nunique(dropna=False) == 21
- def test_is_unique_monotonic(rand_series_with_duplicate_datetimeindex):
- index = rand_series_with_duplicate_datetimeindex.index
- assert not index.is_unique
|
