243 lines
7.6 KiB
Python
243 lines
7.6 KiB
Python
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_integer
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import pandas as pd
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from pandas import (
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Index,
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Series,
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)
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import pandas._testing as tm
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from pandas.core.indexes.datetimes import Timestamp
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class TestSeriesQuantile:
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def test_quantile(self, datetime_series):
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q = datetime_series.quantile(0.1)
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assert q == np.percentile(datetime_series.dropna(), 10)
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q = datetime_series.quantile(0.9)
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assert q == np.percentile(datetime_series.dropna(), 90)
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# object dtype
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q = Series(datetime_series, dtype=object).quantile(0.9)
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assert q == np.percentile(datetime_series.dropna(), 90)
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# datetime64[ns] dtype
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dts = datetime_series.index.to_series()
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q = dts.quantile(0.2)
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assert q == Timestamp("2000-01-10 19:12:00")
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# timedelta64[ns] dtype
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tds = dts.diff()
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q = tds.quantile(0.25)
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assert q == pd.to_timedelta("24:00:00")
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# GH7661
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result = Series([np.timedelta64("NaT")]).sum()
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assert result == pd.Timedelta(0)
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msg = "percentiles should all be in the interval \\[0, 1\\]"
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for invalid in [-1, 2, [0.5, -1], [0.5, 2]]:
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with pytest.raises(ValueError, match=msg):
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datetime_series.quantile(invalid)
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def test_quantile_multi(self, datetime_series):
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qs = [0.1, 0.9]
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result = datetime_series.quantile(qs)
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expected = Series(
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[
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np.percentile(datetime_series.dropna(), 10),
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np.percentile(datetime_series.dropna(), 90),
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],
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index=qs,
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name=datetime_series.name,
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)
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tm.assert_series_equal(result, expected)
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dts = datetime_series.index.to_series()
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dts.name = "xxx"
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result = dts.quantile((0.2, 0.2))
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expected = Series(
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[Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")],
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index=[0.2, 0.2],
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name="xxx",
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)
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tm.assert_series_equal(result, expected)
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result = datetime_series.quantile([])
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expected = Series(
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[], name=datetime_series.name, index=Index([], dtype=float), dtype="float64"
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)
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tm.assert_series_equal(result, expected)
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def test_quantile_interpolation(self, datetime_series):
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# see gh-10174
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# interpolation = linear (default case)
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q = datetime_series.quantile(0.1, interpolation="linear")
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assert q == np.percentile(datetime_series.dropna(), 10)
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q1 = datetime_series.quantile(0.1)
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assert q1 == np.percentile(datetime_series.dropna(), 10)
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# test with and without interpolation keyword
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assert q == q1
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def test_quantile_interpolation_dtype(self):
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# GH #10174
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# interpolation = linear (default case)
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q = Series([1, 3, 4]).quantile(0.5, interpolation="lower")
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assert q == np.percentile(np.array([1, 3, 4]), 50)
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assert is_integer(q)
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q = Series([1, 3, 4]).quantile(0.5, interpolation="higher")
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assert q == np.percentile(np.array([1, 3, 4]), 50)
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assert is_integer(q)
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def test_quantile_nan(self):
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# GH 13098
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s = Series([1, 2, 3, 4, np.nan])
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result = s.quantile(0.5)
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expected = 2.5
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assert result == expected
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# all nan/empty
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s1 = Series([], dtype=object)
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cases = [s1, Series([np.nan, np.nan])]
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for s in cases:
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res = s.quantile(0.5)
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assert np.isnan(res)
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res = s.quantile([0.5])
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tm.assert_series_equal(res, Series([np.nan], index=[0.5]))
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res = s.quantile([0.2, 0.3])
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tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3]))
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@pytest.mark.parametrize(
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"case",
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[
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[
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Timestamp("2011-01-01"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-03"),
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],
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[
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Timestamp("2011-01-01", tz="US/Eastern"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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Timestamp("2011-01-03", tz="US/Eastern"),
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],
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[pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")],
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# NaT
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[
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Timestamp("2011-01-01"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-03"),
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pd.NaT,
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],
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[
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Timestamp("2011-01-01", tz="US/Eastern"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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Timestamp("2011-01-03", tz="US/Eastern"),
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pd.NaT,
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],
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[
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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pd.Timedelta("3 days"),
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pd.NaT,
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],
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],
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)
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def test_quantile_box(self, case):
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s = Series(case, name="XXX")
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res = s.quantile(0.5)
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assert res == case[1]
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res = s.quantile([0.5])
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exp = Series([case[1]], index=[0.5], name="XXX")
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tm.assert_series_equal(res, exp)
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def test_datetime_timedelta_quantiles(self):
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# covers #9694
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assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5))
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assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5))
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def test_quantile_nat(self):
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res = Series([pd.NaT, pd.NaT]).quantile(0.5)
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assert res is pd.NaT
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res = Series([pd.NaT, pd.NaT]).quantile([0.5])
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tm.assert_series_equal(res, Series([pd.NaT], index=[0.5]))
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@pytest.mark.parametrize(
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"values, dtype",
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[([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")],
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)
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def test_quantile_sparse(self, values, dtype):
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ser = Series(values, dtype=dtype)
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result = ser.quantile([0.5])
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expected = Series(np.asarray(ser)).quantile([0.5])
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tm.assert_series_equal(result, expected)
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def test_quantile_empty(self):
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# floats
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s = Series([], dtype="float64")
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res = s.quantile(0.5)
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assert np.isnan(res)
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res = s.quantile([0.5])
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exp = Series([np.nan], index=[0.5])
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tm.assert_series_equal(res, exp)
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# int
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s = Series([], dtype="int64")
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res = s.quantile(0.5)
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assert np.isnan(res)
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res = s.quantile([0.5])
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exp = Series([np.nan], index=[0.5])
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tm.assert_series_equal(res, exp)
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# datetime
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s = Series([], dtype="datetime64[ns]")
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res = s.quantile(0.5)
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assert res is pd.NaT
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res = s.quantile([0.5])
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exp = Series([pd.NaT], index=[0.5])
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tm.assert_series_equal(res, exp)
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@pytest.mark.parametrize("dtype", [int, float, "Int64"])
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def test_quantile_dtypes(self, dtype):
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result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25))
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expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25))
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if dtype == "Int64":
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expected = expected.astype("Float64")
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tm.assert_series_equal(result, expected)
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def test_quantile_all_na(self, any_int_ea_dtype):
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# GH#50681
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ser = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype)
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with tm.assert_produces_warning(None):
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result = ser.quantile([0.1, 0.5])
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expected = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype, index=[0.1, 0.5])
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tm.assert_series_equal(result, expected)
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def test_quantile_dtype_size(self, any_int_ea_dtype):
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# GH#50681
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ser = Series([pd.NA, pd.NA, 1], dtype=any_int_ea_dtype)
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result = ser.quantile([0.1, 0.5])
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expected = Series([1, 1], dtype=any_int_ea_dtype, index=[0.1, 0.5])
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tm.assert_series_equal(result, expected)
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