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767 lines
25 KiB
767 lines
25 KiB
import numpy as np
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import pytest
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from pandas.compat.numpy import np_percentile_argname
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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Timestamp,
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)
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import pandas._testing as tm
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class TestDataFrameQuantile:
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@pytest.mark.parametrize(
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"df,expected",
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[
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[
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DataFrame(
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{
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0: Series(pd.arrays.SparseArray([1, 2])),
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1: Series(pd.arrays.SparseArray([3, 4])),
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}
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),
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Series([1.5, 3.5], name=0.5),
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],
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[
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DataFrame(Series([0.0, None, 1.0, 2.0], dtype="Sparse[float]")),
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Series([1.0], name=0.5),
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],
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],
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)
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def test_quantile_sparse(self, df, expected):
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# GH#17198
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# GH#24600
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result = df.quantile()
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tm.assert_series_equal(result, expected)
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def test_quantile(self, datetime_frame):
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from numpy import percentile
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df = datetime_frame
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q = df.quantile(0.1, axis=0)
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assert q["A"] == percentile(df["A"], 10)
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tm.assert_index_equal(q.index, df.columns)
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q = df.quantile(0.9, axis=1)
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assert q["2000-01-17"] == percentile(df.loc["2000-01-17"], 90)
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tm.assert_index_equal(q.index, df.index)
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# test degenerate case
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q = DataFrame({"x": [], "y": []}).quantile(0.1, axis=0)
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assert np.isnan(q["x"]) and np.isnan(q["y"])
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# non-numeric exclusion
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df = DataFrame({"col1": ["A", "A", "B", "B"], "col2": [1, 2, 3, 4]})
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rs = df.quantile(0.5)
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with tm.assert_produces_warning(FutureWarning, match="Select only valid"):
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xp = df.median().rename(0.5)
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tm.assert_series_equal(rs, xp)
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# axis
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df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
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result = df.quantile(0.5, axis=1)
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expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5)
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tm.assert_series_equal(result, expected)
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result = df.quantile([0.5, 0.75], axis=1)
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expected = DataFrame(
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{1: [1.5, 1.75], 2: [2.5, 2.75], 3: [3.5, 3.75]}, index=[0.5, 0.75]
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)
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tm.assert_frame_equal(result, expected, check_index_type=True)
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# We may want to break API in the future to change this
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# so that we exclude non-numeric along the same axis
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# See GH #7312
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df = DataFrame([[1, 2, 3], ["a", "b", 4]])
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result = df.quantile(0.5, axis=1)
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expected = Series([3.0, 4.0], index=[0, 1], name=0.5)
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tm.assert_series_equal(result, expected)
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def test_quantile_date_range(self):
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# GH 2460
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dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
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ser = Series(dti)
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df = DataFrame(ser)
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result = df.quantile(numeric_only=False)
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expected = Series(
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["2016-01-02 00:00:00"], name=0.5, dtype="datetime64[ns, US/Pacific]"
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)
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tm.assert_series_equal(result, expected)
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def test_quantile_axis_mixed(self):
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# mixed on axis=1
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df = DataFrame(
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{
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"A": [1, 2, 3],
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"B": [2.0, 3.0, 4.0],
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"C": pd.date_range("20130101", periods=3),
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"D": ["foo", "bar", "baz"],
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}
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)
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result = df.quantile(0.5, axis=1)
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expected = Series([1.5, 2.5, 3.5], name=0.5)
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tm.assert_series_equal(result, expected)
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# must raise
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msg = "'<' not supported between instances of 'Timestamp' and 'float'"
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with pytest.raises(TypeError, match=msg):
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df.quantile(0.5, axis=1, numeric_only=False)
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def test_quantile_axis_parameter(self):
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# GH 9543/9544
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df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
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result = df.quantile(0.5, axis=0)
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expected = Series([2.0, 3.0], index=["A", "B"], name=0.5)
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tm.assert_series_equal(result, expected)
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expected = df.quantile(0.5, axis="index")
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tm.assert_series_equal(result, expected)
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result = df.quantile(0.5, axis=1)
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expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5)
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tm.assert_series_equal(result, expected)
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result = df.quantile(0.5, axis="columns")
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tm.assert_series_equal(result, expected)
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msg = "No axis named -1 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.quantile(0.1, axis=-1)
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msg = "No axis named column for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.quantile(0.1, axis="column")
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def test_quantile_interpolation(self):
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# see gh-10174
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# interpolation method other than default linear
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df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3])
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result = df.quantile(0.5, axis=1, interpolation="nearest")
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expected = Series([1, 2, 3], index=[1, 2, 3], name=0.5)
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tm.assert_series_equal(result, expected)
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# cross-check interpolation=nearest results in original dtype
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exp = np.percentile(
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np.array([[1, 2, 3], [2, 3, 4]]),
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0.5,
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axis=0,
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**{np_percentile_argname: "nearest"},
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)
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expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="int64")
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tm.assert_series_equal(result, expected)
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# float
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df = DataFrame({"A": [1.0, 2.0, 3.0], "B": [2.0, 3.0, 4.0]}, index=[1, 2, 3])
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result = df.quantile(0.5, axis=1, interpolation="nearest")
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expected = Series([1.0, 2.0, 3.0], index=[1, 2, 3], name=0.5)
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tm.assert_series_equal(result, expected)
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exp = np.percentile(
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np.array([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]),
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0.5,
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axis=0,
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**{np_percentile_argname: "nearest"},
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)
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expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="float64")
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tm.assert_series_equal(result, expected)
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# axis
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result = df.quantile([0.5, 0.75], axis=1, interpolation="lower")
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expected = DataFrame(
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{1: [1.0, 1.0], 2: [2.0, 2.0], 3: [3.0, 3.0]}, index=[0.5, 0.75]
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)
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tm.assert_frame_equal(result, expected)
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# test degenerate case
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df = DataFrame({"x": [], "y": []})
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q = df.quantile(0.1, axis=0, interpolation="higher")
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assert np.isnan(q["x"]) and np.isnan(q["y"])
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# multi
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df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], columns=["a", "b", "c"])
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result = df.quantile([0.25, 0.5], interpolation="midpoint")
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# https://github.com/numpy/numpy/issues/7163
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expected = DataFrame(
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[[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]],
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index=[0.25, 0.5],
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columns=["a", "b", "c"],
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)
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tm.assert_frame_equal(result, expected)
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def test_quantile_interpolation_datetime(self, datetime_frame):
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# see gh-10174
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# interpolation = linear (default case)
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df = datetime_frame
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q = df.quantile(0.1, axis=0, interpolation="linear")
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assert q["A"] == np.percentile(df["A"], 10)
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def test_quantile_interpolation_int(self, int_frame):
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# see gh-10174
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df = int_frame
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# interpolation = linear (default case)
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q = df.quantile(0.1)
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assert q["A"] == np.percentile(df["A"], 10)
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# test with and without interpolation keyword
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q1 = df.quantile(0.1, axis=0, interpolation="linear")
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assert q1["A"] == np.percentile(df["A"], 10)
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tm.assert_series_equal(q, q1)
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def test_quantile_multi(self):
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df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], columns=["a", "b", "c"])
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result = df.quantile([0.25, 0.5])
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expected = DataFrame(
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[[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]],
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index=[0.25, 0.5],
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columns=["a", "b", "c"],
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)
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tm.assert_frame_equal(result, expected)
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# axis = 1
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result = df.quantile([0.25, 0.5], axis=1)
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expected = DataFrame(
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[[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], index=[0.25, 0.5], columns=[0, 1, 2]
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)
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# empty
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result = DataFrame({"x": [], "y": []}).quantile([0.1, 0.9], axis=0)
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expected = DataFrame(
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{"x": [np.nan, np.nan], "y": [np.nan, np.nan]}, index=[0.1, 0.9]
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)
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tm.assert_frame_equal(result, expected)
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def test_quantile_datetime(self):
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df = DataFrame({"a": pd.to_datetime(["2010", "2011"]), "b": [0, 5]})
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# exclude datetime
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result = df.quantile(0.5)
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expected = Series([2.5], index=["b"])
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# datetime
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result = df.quantile(0.5, numeric_only=False)
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expected = Series(
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[Timestamp("2010-07-02 12:00:00"), 2.5], index=["a", "b"], name=0.5
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)
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tm.assert_series_equal(result, expected)
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# datetime w/ multi
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result = df.quantile([0.5], numeric_only=False)
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expected = DataFrame(
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[[Timestamp("2010-07-02 12:00:00"), 2.5]], index=[0.5], columns=["a", "b"]
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)
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tm.assert_frame_equal(result, expected)
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# axis = 1
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df["c"] = pd.to_datetime(["2011", "2012"])
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result = df[["a", "c"]].quantile(0.5, axis=1, numeric_only=False)
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expected = Series(
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[Timestamp("2010-07-02 12:00:00"), Timestamp("2011-07-02 12:00:00")],
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index=[0, 1],
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name=0.5,
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)
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tm.assert_series_equal(result, expected)
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result = df[["a", "c"]].quantile([0.5], axis=1, numeric_only=False)
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expected = DataFrame(
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[[Timestamp("2010-07-02 12:00:00"), Timestamp("2011-07-02 12:00:00")]],
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index=[0.5],
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columns=[0, 1],
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)
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tm.assert_frame_equal(result, expected)
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# empty when numeric_only=True
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result = df[["a", "c"]].quantile(0.5)
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expected = Series([], index=[], dtype=np.float64, name=0.5)
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tm.assert_series_equal(result, expected)
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result = df[["a", "c"]].quantile([0.5])
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expected = DataFrame(index=[0.5])
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tm.assert_frame_equal(result, expected)
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def test_quantile_invalid(self, datetime_frame):
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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_frame.quantile(invalid)
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def test_quantile_box(self):
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df = DataFrame(
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{
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"A": [
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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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"B": [
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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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"C": [
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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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],
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}
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)
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res = df.quantile(0.5, numeric_only=False)
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exp = Series(
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[
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timedelta("2 days"),
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],
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name=0.5,
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index=["A", "B", "C"],
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)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5], numeric_only=False)
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exp = DataFrame(
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[
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[
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timedelta("2 days"),
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]
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],
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index=[0.5],
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columns=["A", "B", "C"],
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)
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tm.assert_frame_equal(res, exp)
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# DatetimeLikeBlock may be consolidated and contain NaT in different loc
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df = DataFrame(
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{
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"A": [
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Timestamp("2011-01-01"),
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pd.NaT,
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Timestamp("2011-01-02"),
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Timestamp("2011-01-03"),
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],
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"a": [
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Timestamp("2011-01-01"),
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Timestamp("2011-01-02"),
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pd.NaT,
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Timestamp("2011-01-03"),
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],
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"B": [
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Timestamp("2011-01-01", tz="US/Eastern"),
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pd.NaT,
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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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"b": [
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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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pd.NaT,
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Timestamp("2011-01-03", tz="US/Eastern"),
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],
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"C": [
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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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"c": [
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pd.NaT,
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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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],
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},
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columns=list("AaBbCc"),
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)
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res = df.quantile(0.5, numeric_only=False)
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exp = Series(
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[
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timedelta("2 days"),
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pd.Timedelta("2 days"),
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],
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name=0.5,
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index=list("AaBbCc"),
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)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5], numeric_only=False)
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exp = DataFrame(
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[
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[
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timedelta("2 days"),
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pd.Timedelta("2 days"),
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]
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],
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index=[0.5],
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columns=list("AaBbCc"),
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)
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tm.assert_frame_equal(res, exp)
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def test_quantile_nan(self):
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# GH 14357 - float block where some cols have missing values
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df = DataFrame({"a": np.arange(1, 6.0), "b": np.arange(1, 6.0)})
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df.iloc[-1, 1] = np.nan
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res = df.quantile(0.5)
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exp = Series([3.0, 2.5], index=["a", "b"], name=0.5)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5, 0.75])
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exp = DataFrame({"a": [3.0, 4.0], "b": [2.5, 3.25]}, index=[0.5, 0.75])
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tm.assert_frame_equal(res, exp)
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res = df.quantile(0.5, axis=1)
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exp = Series(np.arange(1.0, 6.0), name=0.5)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5, 0.75], axis=1)
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exp = DataFrame([np.arange(1.0, 6.0)] * 2, index=[0.5, 0.75])
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tm.assert_frame_equal(res, exp)
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# full-nan column
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df["b"] = np.nan
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res = df.quantile(0.5)
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exp = Series([3.0, np.nan], index=["a", "b"], name=0.5)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5, 0.75])
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exp = DataFrame({"a": [3.0, 4.0], "b": [np.nan, np.nan]}, index=[0.5, 0.75])
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tm.assert_frame_equal(res, exp)
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def test_quantile_nat(self):
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# full NaT column
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df = DataFrame({"a": [pd.NaT, pd.NaT, pd.NaT]})
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res = df.quantile(0.5, numeric_only=False)
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exp = Series([pd.NaT], index=["a"], name=0.5)
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tm.assert_series_equal(res, exp)
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res = df.quantile([0.5], numeric_only=False)
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exp = DataFrame({"a": [pd.NaT]}, index=[0.5])
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tm.assert_frame_equal(res, exp)
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|
|
# mixed non-null / full null column
|
|
df = DataFrame(
|
|
{
|
|
"a": [
|
|
Timestamp("2012-01-01"),
|
|
Timestamp("2012-01-02"),
|
|
Timestamp("2012-01-03"),
|
|
],
|
|
"b": [pd.NaT, pd.NaT, pd.NaT],
|
|
}
|
|
)
|
|
|
|
res = df.quantile(0.5, numeric_only=False)
|
|
exp = Series([Timestamp("2012-01-02"), pd.NaT], index=["a", "b"], name=0.5)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
res = df.quantile([0.5], numeric_only=False)
|
|
exp = DataFrame(
|
|
[[Timestamp("2012-01-02"), pd.NaT]], index=[0.5], columns=["a", "b"]
|
|
)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_quantile_empty_no_rows_floats(self):
|
|
|
|
# floats
|
|
df = DataFrame(columns=["a", "b"], dtype="float64")
|
|
|
|
res = df.quantile(0.5)
|
|
exp = Series([np.nan, np.nan], index=["a", "b"], name=0.5)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
res = df.quantile([0.5])
|
|
exp = DataFrame([[np.nan, np.nan]], columns=["a", "b"], index=[0.5])
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.quantile(0.5, axis=1)
|
|
exp = Series([], index=[], dtype="float64", name=0.5)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
res = df.quantile([0.5], axis=1)
|
|
exp = DataFrame(columns=[], index=[0.5])
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_quantile_empty_no_rows_ints(self):
|
|
# ints
|
|
df = DataFrame(columns=["a", "b"], dtype="int64")
|
|
|
|
res = df.quantile(0.5)
|
|
exp = Series([np.nan, np.nan], index=["a", "b"], name=0.5)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
def test_quantile_empty_no_rows_dt64(self):
|
|
# datetimes
|
|
df = DataFrame(columns=["a", "b"], dtype="datetime64[ns]")
|
|
|
|
res = df.quantile(0.5, numeric_only=False)
|
|
exp = Series(
|
|
[pd.NaT, pd.NaT], index=["a", "b"], dtype="datetime64[ns]", name=0.5
|
|
)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
# Mixed dt64/dt64tz
|
|
df["a"] = df["a"].dt.tz_localize("US/Central")
|
|
res = df.quantile(0.5, numeric_only=False)
|
|
exp = exp.astype(object)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
# both dt64tz
|
|
df["b"] = df["b"].dt.tz_localize("US/Central")
|
|
res = df.quantile(0.5, numeric_only=False)
|
|
exp = exp.astype(df["b"].dtype)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
def test_quantile_empty_no_columns(self):
|
|
# GH#23925 _get_numeric_data may drop all columns
|
|
df = DataFrame(pd.date_range("1/1/18", periods=5))
|
|
df.columns.name = "captain tightpants"
|
|
result = df.quantile(0.5)
|
|
expected = Series([], index=[], name=0.5, dtype=np.float64)
|
|
expected.index.name = "captain tightpants"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.quantile([0.5])
|
|
expected = DataFrame([], index=[0.5], columns=[])
|
|
expected.columns.name = "captain tightpants"
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_quantile_item_cache(self, using_array_manager):
|
|
# previous behavior incorrect retained an invalid _item_cache entry
|
|
df = DataFrame(np.random.randn(4, 3), columns=["A", "B", "C"])
|
|
df["D"] = df["A"] * 2
|
|
ser = df["A"]
|
|
if not using_array_manager:
|
|
assert len(df._mgr.blocks) == 2
|
|
|
|
df.quantile(numeric_only=False)
|
|
ser.values[0] = 99
|
|
|
|
assert df.iloc[0, 0] == df["A"][0]
|
|
|
|
|
|
class TestQuantileExtensionDtype:
|
|
# TODO: tests for axis=1?
|
|
# TODO: empty case?
|
|
|
|
@pytest.fixture(
|
|
params=[
|
|
pytest.param(
|
|
pd.IntervalIndex.from_breaks(range(10)),
|
|
marks=pytest.mark.xfail(reason="raises when trying to add Intervals"),
|
|
),
|
|
pd.period_range("2016-01-01", periods=9, freq="D"),
|
|
pd.date_range("2016-01-01", periods=9, tz="US/Pacific"),
|
|
pd.timedelta_range("1 Day", periods=9),
|
|
pd.array(np.arange(9), dtype="Int64"),
|
|
pd.array(np.arange(9), dtype="Float64"),
|
|
],
|
|
ids=lambda x: str(x.dtype),
|
|
)
|
|
def index(self, request):
|
|
# NB: not actually an Index object
|
|
idx = request.param
|
|
idx.name = "A"
|
|
return idx
|
|
|
|
@pytest.fixture
|
|
def obj(self, index, frame_or_series):
|
|
# bc index is not always an Index (yet), we need to re-patch .name
|
|
obj = frame_or_series(index).copy()
|
|
|
|
if frame_or_series is Series:
|
|
obj.name = "A"
|
|
else:
|
|
obj.columns = ["A"]
|
|
return obj
|
|
|
|
def compute_quantile(self, obj, qs):
|
|
if isinstance(obj, Series):
|
|
result = obj.quantile(qs)
|
|
else:
|
|
result = obj.quantile(qs, numeric_only=False)
|
|
return result
|
|
|
|
def test_quantile_ea(self, obj, index):
|
|
|
|
# result should be invariant to shuffling
|
|
indexer = np.arange(len(index), dtype=np.intp)
|
|
np.random.shuffle(indexer)
|
|
obj = obj.iloc[indexer]
|
|
|
|
qs = [0.5, 0, 1]
|
|
result = self.compute_quantile(obj, qs)
|
|
|
|
# expected here assumes len(index) == 9
|
|
expected = Series(
|
|
[index[4], index[0], index[-1]], dtype=index.dtype, index=qs, name="A"
|
|
)
|
|
expected = type(obj)(expected)
|
|
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_quantile_ea_with_na(self, obj, index):
|
|
|
|
obj.iloc[0] = index._na_value
|
|
obj.iloc[-1] = index._na_value
|
|
|
|
# result should be invariant to shuffling
|
|
indexer = np.arange(len(index), dtype=np.intp)
|
|
np.random.shuffle(indexer)
|
|
obj = obj.iloc[indexer]
|
|
|
|
qs = [0.5, 0, 1]
|
|
result = self.compute_quantile(obj, qs)
|
|
|
|
# expected here assumes len(index) == 9
|
|
expected = Series(
|
|
[index[4], index[1], index[-2]], dtype=index.dtype, index=qs, name="A"
|
|
)
|
|
expected = type(obj)(expected)
|
|
tm.assert_equal(result, expected)
|
|
|
|
# TODO(GH#39763): filtering can be removed after GH#39763 is fixed
|
|
@pytest.mark.filterwarnings("ignore:Using .astype to convert:FutureWarning")
|
|
def test_quantile_ea_all_na(
|
|
self, obj, index, frame_or_series, using_array_manager, request
|
|
):
|
|
if (
|
|
using_array_manager
|
|
and frame_or_series is DataFrame
|
|
and index.dtype == "m8[ns]"
|
|
):
|
|
mark = pytest.mark.xfail(
|
|
reason="obj.astype fails bc obj is incorrectly dt64 at this point"
|
|
)
|
|
request.node.add_marker(mark)
|
|
|
|
obj.iloc[:] = index._na_value
|
|
|
|
# TODO(ArrayManager): this casting should be unnecessary after GH#39763 is fixed
|
|
obj[:] = obj.astype(index.dtype)
|
|
assert np.all(obj.dtypes == index.dtype)
|
|
|
|
# result should be invariant to shuffling
|
|
indexer = np.arange(len(index), dtype=np.intp)
|
|
np.random.shuffle(indexer)
|
|
obj = obj.iloc[indexer]
|
|
|
|
qs = [0.5, 0, 1]
|
|
result = self.compute_quantile(obj, qs)
|
|
|
|
expected = index.take([-1, -1, -1], allow_fill=True, fill_value=index._na_value)
|
|
expected = Series(expected, index=qs, name="A")
|
|
expected = type(obj)(expected)
|
|
tm.assert_equal(result, expected)
|
|
|
|
def test_quantile_ea_scalar(self, obj, index):
|
|
# scalar qs
|
|
|
|
# result should be invariant to shuffling
|
|
indexer = np.arange(len(index), dtype=np.intp)
|
|
np.random.shuffle(indexer)
|
|
obj = obj.iloc[indexer]
|
|
|
|
qs = 0.5
|
|
result = self.compute_quantile(obj, qs)
|
|
|
|
expected = Series({"A": index[4]}, dtype=index.dtype, name=0.5)
|
|
if isinstance(obj, Series):
|
|
expected = expected["A"]
|
|
assert result == expected
|
|
else:
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dtype, expected_data, expected_index, axis",
|
|
[
|
|
["float64", [], [], 1],
|
|
["int64", [], [], 1],
|
|
["float64", [np.nan, np.nan], ["a", "b"], 0],
|
|
["int64", [np.nan, np.nan], ["a", "b"], 0],
|
|
],
|
|
)
|
|
def test_empty_numeric(self, dtype, expected_data, expected_index, axis):
|
|
# GH 14564
|
|
df = DataFrame(columns=["a", "b"], dtype=dtype)
|
|
result = df.quantile(0.5, axis=axis)
|
|
expected = Series(
|
|
expected_data, name=0.5, index=Index(expected_index), dtype="float64"
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dtype, expected_data, expected_index, axis, expected_dtype",
|
|
[
|
|
pytest.param(
|
|
"datetime64[ns]",
|
|
[],
|
|
[],
|
|
1,
|
|
"datetime64[ns]",
|
|
marks=pytest.mark.xfail(reason="#GH 41544"),
|
|
),
|
|
["datetime64[ns]", [pd.NaT, pd.NaT], ["a", "b"], 0, "datetime64[ns]"],
|
|
],
|
|
)
|
|
def test_empty_datelike(
|
|
self, dtype, expected_data, expected_index, axis, expected_dtype
|
|
):
|
|
# GH 14564
|
|
df = DataFrame(columns=["a", "b"], dtype=dtype)
|
|
result = df.quantile(0.5, axis=axis, numeric_only=False)
|
|
expected = Series(
|
|
expected_data, name=0.5, index=Index(expected_index), dtype=expected_dtype
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"expected_data, expected_index, axis",
|
|
[
|
|
[[np.nan, np.nan], range(2), 1],
|
|
[[], [], 0],
|
|
],
|
|
)
|
|
def test_datelike_numeric_only(self, expected_data, expected_index, axis):
|
|
# GH 14564
|
|
df = DataFrame(
|
|
{
|
|
"a": pd.to_datetime(["2010", "2011"]),
|
|
"b": [0, 5],
|
|
"c": pd.to_datetime(["2011", "2012"]),
|
|
}
|
|
)
|
|
result = df[["a", "c"]].quantile(0.5, axis=axis)
|
|
expected = Series(
|
|
expected_data, name=0.5, index=Index(expected_index), dtype=np.float64
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|