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), ], dtype=index.dtype, ) assert uniques.dtype == index.dtype # 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