280 lines
9.0 KiB
Python
280 lines
9.0 KiB
Python
import numpy as np
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import pytest
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import pandas as pd
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from pandas import DataFrame, Index
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import pandas._testing as tm
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@pytest.mark.parametrize(
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"interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
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)
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@pytest.mark.parametrize(
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"a_vals,b_vals",
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[
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# Ints
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([1, 2, 3, 4, 5], [5, 4, 3, 2, 1]),
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([1, 2, 3, 4], [4, 3, 2, 1]),
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([1, 2, 3, 4, 5], [4, 3, 2, 1]),
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# Floats
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([1.0, 2.0, 3.0, 4.0, 5.0], [5.0, 4.0, 3.0, 2.0, 1.0]),
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# Missing data
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([1.0, np.nan, 3.0, np.nan, 5.0], [5.0, np.nan, 3.0, np.nan, 1.0]),
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([np.nan, 4.0, np.nan, 2.0, np.nan], [np.nan, 4.0, np.nan, 2.0, np.nan]),
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# Timestamps
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(
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list(pd.date_range("1/1/18", freq="D", periods=5)),
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list(pd.date_range("1/1/18", freq="D", periods=5))[::-1],
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),
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# All NA
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([np.nan] * 5, [np.nan] * 5),
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],
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)
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@pytest.mark.parametrize("q", [0, 0.25, 0.5, 0.75, 1])
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def test_quantile(interpolation, a_vals, b_vals, q):
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if interpolation == "nearest" and q == 0.5 and b_vals == [4, 3, 2, 1]:
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pytest.skip(
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"Unclear numpy expectation for nearest result with equidistant data"
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)
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a_expected = pd.Series(a_vals).quantile(q, interpolation=interpolation)
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b_expected = pd.Series(b_vals).quantile(q, interpolation=interpolation)
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df = DataFrame(
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{"key": ["a"] * len(a_vals) + ["b"] * len(b_vals), "val": a_vals + b_vals}
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)
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expected = DataFrame(
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[a_expected, b_expected], columns=["val"], index=Index(["a", "b"], name="key")
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)
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result = df.groupby("key").quantile(q, interpolation=interpolation)
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tm.assert_frame_equal(result, expected)
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def test_quantile_array():
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# https://github.com/pandas-dev/pandas/issues/27526
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df = DataFrame({"A": [0, 1, 2, 3, 4]})
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result = df.groupby([0, 0, 1, 1, 1]).quantile([0.25])
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index = pd.MultiIndex.from_product([[0, 1], [0.25]])
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expected = DataFrame({"A": [0.25, 2.50]}, index=index)
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tm.assert_frame_equal(result, expected)
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df = DataFrame({"A": [0, 1, 2, 3], "B": [4, 5, 6, 7]})
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index = pd.MultiIndex.from_product([[0, 1], [0.25, 0.75]])
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result = df.groupby([0, 0, 1, 1]).quantile([0.25, 0.75])
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expected = DataFrame(
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{"A": [0.25, 0.75, 2.25, 2.75], "B": [4.25, 4.75, 6.25, 6.75]}, index=index
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)
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tm.assert_frame_equal(result, expected)
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def test_quantile_array2():
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# https://github.com/pandas-dev/pandas/pull/28085#issuecomment-524066959
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df = DataFrame(
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np.random.RandomState(0).randint(0, 5, size=(10, 3)), columns=list("ABC")
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)
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result = df.groupby("A").quantile([0.3, 0.7])
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expected = DataFrame(
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{
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"B": [0.9, 2.1, 2.2, 3.4, 1.6, 2.4, 2.3, 2.7, 0.0, 0.0],
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"C": [1.2, 2.8, 1.8, 3.0, 0.0, 0.0, 1.9, 3.1, 3.0, 3.0],
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},
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index=pd.MultiIndex.from_product(
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[[0, 1, 2, 3, 4], [0.3, 0.7]], names=["A", None]
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),
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)
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tm.assert_frame_equal(result, expected)
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def test_quantile_array_no_sort():
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df = DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
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result = df.groupby([1, 0, 1], sort=False).quantile([0.25, 0.5, 0.75])
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expected = DataFrame(
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{"A": [0.5, 1.0, 1.5, 1.0, 1.0, 1.0], "B": [3.5, 4.0, 4.5, 4.0, 4.0, 4.0]},
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index=pd.MultiIndex.from_product([[1, 0], [0.25, 0.5, 0.75]]),
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)
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tm.assert_frame_equal(result, expected)
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result = df.groupby([1, 0, 1], sort=False).quantile([0.75, 0.25])
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expected = DataFrame(
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{"A": [1.5, 0.5, 1.0, 1.0], "B": [4.5, 3.5, 4.0, 4.0]},
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index=pd.MultiIndex.from_product([[1, 0], [0.75, 0.25]]),
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)
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tm.assert_frame_equal(result, expected)
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def test_quantile_array_multiple_levels():
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df = DataFrame(
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{"A": [0, 1, 2], "B": [3, 4, 5], "c": ["a", "a", "a"], "d": ["a", "a", "b"]}
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)
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result = df.groupby(["c", "d"]).quantile([0.25, 0.75])
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index = pd.MultiIndex.from_tuples(
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[("a", "a", 0.25), ("a", "a", 0.75), ("a", "b", 0.25), ("a", "b", 0.75)],
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names=["c", "d", None],
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)
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expected = DataFrame(
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{"A": [0.25, 0.75, 2.0, 2.0], "B": [3.25, 3.75, 5.0, 5.0]}, index=index
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("frame_size", [(2, 3), (100, 10)])
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@pytest.mark.parametrize("groupby", [[0], [0, 1]])
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@pytest.mark.parametrize("q", [[0.5, 0.6]])
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def test_groupby_quantile_with_arraylike_q_and_int_columns(frame_size, groupby, q):
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# GH30289
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nrow, ncol = frame_size
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df = DataFrame(np.array([ncol * [_ % 4] for _ in range(nrow)]), columns=range(ncol))
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idx_levels = [list(range(min(nrow, 4)))] * len(groupby) + [q]
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idx_codes = [[x for x in range(min(nrow, 4)) for _ in q]] * len(groupby) + [
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list(range(len(q))) * min(nrow, 4)
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]
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expected_index = pd.MultiIndex(
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levels=idx_levels, codes=idx_codes, names=groupby + [None]
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)
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expected_values = [
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[float(x)] * (ncol - len(groupby)) for x in range(min(nrow, 4)) for _ in q
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]
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expected_columns = [x for x in range(ncol) if x not in groupby]
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expected = DataFrame(
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expected_values, index=expected_index, columns=expected_columns
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)
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result = df.groupby(groupby).quantile(q)
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tm.assert_frame_equal(result, expected)
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def test_quantile_raises():
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df = DataFrame([["foo", "a"], ["foo", "b"], ["foo", "c"]], columns=["key", "val"])
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with pytest.raises(TypeError, match="cannot be performed against 'object' dtypes"):
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df.groupby("key").quantile()
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def test_quantile_out_of_bounds_q_raises():
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# https://github.com/pandas-dev/pandas/issues/27470
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df = DataFrame({"a": [0, 0, 0, 1, 1, 1], "b": range(6)})
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g = df.groupby([0, 0, 0, 1, 1, 1])
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with pytest.raises(ValueError, match="Got '50.0' instead"):
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g.quantile(50)
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with pytest.raises(ValueError, match="Got '-1.0' instead"):
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g.quantile(-1)
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def test_quantile_missing_group_values_no_segfaults():
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# GH 28662
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data = np.array([1.0, np.nan, 1.0])
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df = DataFrame({"key": data, "val": range(3)})
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# Random segfaults; would have been guaranteed in loop
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grp = df.groupby("key")
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for _ in range(100):
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grp.quantile()
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@pytest.mark.parametrize(
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"key, val, expected_key, expected_val",
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[
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([1.0, np.nan, 3.0, np.nan], range(4), [1.0, 3.0], [0.0, 2.0]),
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([1.0, np.nan, 2.0, 2.0], range(4), [1.0, 2.0], [0.0, 2.5]),
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(["a", "b", "b", np.nan], range(4), ["a", "b"], [0, 1.5]),
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([0], [42], [0], [42.0]),
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([], [], np.array([], dtype="float64"), np.array([], dtype="float64")),
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],
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)
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def test_quantile_missing_group_values_correct_results(
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key, val, expected_key, expected_val
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):
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# GH 28662, GH 33200, GH 33569
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df = DataFrame({"key": key, "val": val})
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expected = DataFrame(
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expected_val, index=Index(expected_key, name="key"), columns=["val"]
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)
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grp = df.groupby("key")
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result = grp.quantile(0.5)
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tm.assert_frame_equal(result, expected)
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result = grp.quantile()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"values",
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[
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pd.array([1, 0, None] * 2, dtype="Int64"),
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pd.array([True, False, None] * 2, dtype="boolean"),
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],
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)
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@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
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def test_groupby_quantile_nullable_array(values, q):
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# https://github.com/pandas-dev/pandas/issues/33136
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df = DataFrame({"a": ["x"] * 3 + ["y"] * 3, "b": values})
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result = df.groupby("a")["b"].quantile(q)
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if isinstance(q, list):
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idx = pd.MultiIndex.from_product((["x", "y"], q), names=["a", None])
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true_quantiles = [0.0, 0.5, 1.0]
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else:
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idx = Index(["x", "y"], name="a")
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true_quantiles = [0.5]
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expected = pd.Series(true_quantiles * 2, index=idx, name="b")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
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def test_groupby_quantile_skips_invalid_dtype(q):
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df = DataFrame({"a": [1], "b": [2.0], "c": ["x"]})
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result = df.groupby("a").quantile(q)
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expected = df.groupby("a")[["b"]].quantile(q)
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tm.assert_frame_equal(result, expected)
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def test_groupby_timedelta_quantile():
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# GH: 29485
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df = DataFrame(
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{"value": pd.to_timedelta(np.arange(4), unit="s"), "group": [1, 1, 2, 2]}
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)
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result = df.groupby("group").quantile(0.99)
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expected = DataFrame(
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{
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"value": [
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pd.Timedelta("0 days 00:00:00.990000"),
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pd.Timedelta("0 days 00:00:02.990000"),
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]
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},
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index=Index([1, 2], name="group"),
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)
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tm.assert_frame_equal(result, expected)
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def test_columns_groupby_quantile():
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# GH 33795
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df = DataFrame(
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np.arange(12).reshape(3, -1),
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index=list("XYZ"),
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columns=pd.Series(list("ABAB"), name="col"),
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)
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result = df.groupby("col", axis=1).quantile(q=[0.8, 0.2])
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expected = DataFrame(
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[
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[1.6, 0.4, 2.6, 1.4],
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[5.6, 4.4, 6.6, 5.4],
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[9.6, 8.4, 10.6, 9.4],
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],
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index=list("XYZ"),
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columns=Index(
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[("A", 0.8), ("A", 0.2), ("B", 0.8), ("B", 0.2)], names=["col", None]
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),
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)
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tm.assert_frame_equal(result, expected)
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