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