1260 lines
43 KiB
Python
1260 lines
43 KiB
Python
import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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Series,
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Timestamp,
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date_range,
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to_datetime,
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)
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import pandas._testing as tm
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from pandas.api.indexers import BaseIndexer
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from pandas.core.groupby.groupby import get_groupby
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@pytest.fixture
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def times_frame():
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"""Frame for testing times argument in EWM groupby."""
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return DataFrame(
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{
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"A": ["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"],
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"B": [0, 0, 0, 1, 1, 1, 2, 2, 2, 3],
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"C": to_datetime(
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[
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"2020-01-01",
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"2020-01-01",
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"2020-01-01",
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"2020-01-02",
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"2020-01-10",
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"2020-01-22",
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"2020-01-03",
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"2020-01-23",
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"2020-01-23",
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"2020-01-04",
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]
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),
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}
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)
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@pytest.fixture
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def roll_frame():
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return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
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class TestRolling:
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def test_mutated(self, roll_frame):
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msg = r"groupby\(\) got an unexpected keyword argument 'foo'"
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with pytest.raises(TypeError, match=msg):
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roll_frame.groupby("A", foo=1)
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g = roll_frame.groupby("A")
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assert not g.mutated
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g = get_groupby(roll_frame, by="A", mutated=True)
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assert g.mutated
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def test_getitem(self, roll_frame):
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g = roll_frame.groupby("A")
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g_mutated = get_groupby(roll_frame, by="A", mutated=True)
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expected = g_mutated.B.apply(lambda x: x.rolling(2).mean())
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result = g.rolling(2).mean().B
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tm.assert_series_equal(result, expected)
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result = g.rolling(2).B.mean()
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tm.assert_series_equal(result, expected)
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result = g.B.rolling(2).mean()
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tm.assert_series_equal(result, expected)
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result = roll_frame.B.groupby(roll_frame.A).rolling(2).mean()
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tm.assert_series_equal(result, expected)
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def test_getitem_multiple(self, roll_frame):
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# GH 13174
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g = roll_frame.groupby("A")
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r = g.rolling(2, min_periods=0)
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g_mutated = get_groupby(roll_frame, by="A", mutated=True)
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expected = g_mutated.B.apply(lambda x: x.rolling(2, min_periods=0).count())
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result = r.B.count()
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tm.assert_series_equal(result, expected)
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result = r.B.count()
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"f",
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[
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"sum",
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"mean",
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"min",
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"max",
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pytest.param(
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"count",
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marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
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),
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"kurt",
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"skew",
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],
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)
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def test_rolling(self, f, roll_frame):
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g = roll_frame.groupby("A", group_keys=False)
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r = g.rolling(window=4)
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.rolling(4), f)())
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f", ["std", "var"])
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def test_rolling_ddof(self, f, roll_frame):
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g = roll_frame.groupby("A", group_keys=False)
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r = g.rolling(window=4)
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result = getattr(r, f)(ddof=1)
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expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
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)
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def test_rolling_quantile(self, interpolation, roll_frame):
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g = roll_frame.groupby("A", group_keys=False)
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r = g.rolling(window=4)
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result = r.quantile(0.4, interpolation=interpolation)
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expected = g.apply(
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lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation)
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)
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f, expected_val", [["corr", 1], ["cov", 0.5]])
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def test_rolling_corr_cov_other_same_size_as_groups(self, f, expected_val):
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# GH 42915
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df = DataFrame(
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{"value": range(10), "idx1": [1] * 5 + [2] * 5, "idx2": [1, 2, 3, 4, 5] * 2}
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).set_index(["idx1", "idx2"])
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other = DataFrame({"value": range(5), "idx2": [1, 2, 3, 4, 5]}).set_index(
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"idx2"
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)
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result = getattr(df.groupby(level=0).rolling(2), f)(other)
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expected_data = ([np.nan] + [expected_val] * 4) * 2
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expected = DataFrame(
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expected_data,
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columns=["value"],
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index=MultiIndex.from_arrays(
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[
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[1] * 5 + [2] * 5,
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[1] * 5 + [2] * 5,
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list(range(1, 6)) * 2,
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],
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names=["idx1", "idx1", "idx2"],
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),
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f", ["corr", "cov"])
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def test_rolling_corr_cov_other_diff_size_as_groups(self, f, roll_frame):
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g = roll_frame.groupby("A")
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r = g.rolling(window=4)
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result = getattr(r, f)(roll_frame)
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def func(x):
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return getattr(x.rolling(4), f)(roll_frame)
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expected = g.apply(func)
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# GH 39591: The grouped column should be all np.nan
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# (groupby.apply inserts 0s for cov)
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expected["A"] = np.nan
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f", ["corr", "cov"])
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def test_rolling_corr_cov_pairwise(self, f, roll_frame):
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g = roll_frame.groupby("A")
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r = g.rolling(window=4)
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result = getattr(r.B, f)(pairwise=True)
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def func(x):
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return getattr(x.B.rolling(4), f)(pairwise=True)
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expected = g.apply(func)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"func, expected_values",
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[("cov", [[1.0, 1.0], [1.0, 4.0]]), ("corr", [[1.0, 0.5], [0.5, 1.0]])],
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)
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def test_rolling_corr_cov_unordered(self, func, expected_values):
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# GH 43386
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df = DataFrame(
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{
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"a": ["g1", "g2", "g1", "g1"],
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"b": [0, 0, 1, 2],
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"c": [2, 0, 6, 4],
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}
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)
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rol = df.groupby("a").rolling(3)
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result = getattr(rol, func)()
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expected = DataFrame(
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{
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"b": 4 * [np.nan] + expected_values[0] + 2 * [np.nan],
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"c": 4 * [np.nan] + expected_values[1] + 2 * [np.nan],
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},
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index=MultiIndex.from_tuples(
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[
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("g1", 0, "b"),
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("g1", 0, "c"),
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("g1", 2, "b"),
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("g1", 2, "c"),
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("g1", 3, "b"),
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("g1", 3, "c"),
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("g2", 1, "b"),
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("g2", 1, "c"),
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],
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names=["a", None, 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_rolling_apply(self, raw, roll_frame):
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g = roll_frame.groupby("A", group_keys=False)
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r = g.rolling(window=4)
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# reduction
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result = r.apply(lambda x: x.sum(), raw=raw)
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expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw))
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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def test_rolling_apply_mutability(self):
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# GH 14013
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df = DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6})
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g = df.groupby("A")
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mi = MultiIndex.from_tuples(
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[("bar", 3), ("bar", 4), ("bar", 5), ("foo", 0), ("foo", 1), ("foo", 2)]
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)
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mi.names = ["A", None]
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# Grouped column should not be a part of the output
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expected = DataFrame([np.nan, 2.0, 2.0] * 2, columns=["B"], index=mi)
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result = g.rolling(window=2).sum()
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tm.assert_frame_equal(result, expected)
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# Call an arbitrary function on the groupby
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g.sum()
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# Make sure nothing has been mutated
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result = g.rolling(window=2).sum()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]])
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def test_groupby_rolling(self, expected_value, raw_value):
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# GH 31754
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def foo(x):
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return int(isinstance(x, np.ndarray))
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df = DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]})
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result = df.groupby("id").value.rolling(1).apply(foo, raw=raw_value)
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expected = Series(
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[expected_value] * 3,
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index=MultiIndex.from_tuples(((1, 0), (1, 1), (1, 2)), names=["id", None]),
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name="value",
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)
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tm.assert_series_equal(result, expected)
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def test_groupby_rolling_center_center(self):
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# GH 35552
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series = Series(range(1, 6))
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result = series.groupby(series).rolling(center=True, window=3).mean()
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expected = Series(
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[np.nan] * 5,
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index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3), (5, 4))),
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)
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tm.assert_series_equal(result, expected)
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series = Series(range(1, 5))
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result = series.groupby(series).rolling(center=True, window=3).mean()
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expected = Series(
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[np.nan] * 4,
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index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3))),
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)
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tm.assert_series_equal(result, expected)
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df = DataFrame({"a": ["a"] * 5 + ["b"] * 6, "b": range(11)})
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result = df.groupby("a").rolling(center=True, window=3).mean()
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expected = DataFrame(
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[np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, 9, np.nan],
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index=MultiIndex.from_tuples(
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(
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("a", 0),
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("a", 1),
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("a", 2),
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("a", 3),
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("a", 4),
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("b", 5),
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("b", 6),
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("b", 7),
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("b", 8),
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("b", 9),
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("b", 10),
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),
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names=["a", None],
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),
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columns=["b"],
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)
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tm.assert_frame_equal(result, expected)
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df = DataFrame({"a": ["a"] * 5 + ["b"] * 5, "b": range(10)})
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result = df.groupby("a").rolling(center=True, window=3).mean()
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expected = DataFrame(
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[np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, np.nan],
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index=MultiIndex.from_tuples(
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(
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("a", 0),
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("a", 1),
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("a", 2),
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("a", 3),
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("a", 4),
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("b", 5),
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("b", 6),
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("b", 7),
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("b", 8),
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("b", 9),
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),
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names=["a", None],
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),
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columns=["b"],
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)
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tm.assert_frame_equal(result, expected)
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def test_groupby_rolling_center_on(self):
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# GH 37141
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df = DataFrame(
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data={
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"Date": date_range("2020-01-01", "2020-01-10"),
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"gb": ["group_1"] * 6 + ["group_2"] * 4,
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"value": range(10),
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}
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)
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result = (
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df.groupby("gb")
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.rolling(6, on="Date", center=True, min_periods=1)
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.value.mean()
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)
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expected = Series(
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[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 7.0, 7.5, 7.5, 7.5],
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name="value",
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index=MultiIndex.from_tuples(
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(
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("group_1", Timestamp("2020-01-01")),
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("group_1", Timestamp("2020-01-02")),
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("group_1", Timestamp("2020-01-03")),
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("group_1", Timestamp("2020-01-04")),
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("group_1", Timestamp("2020-01-05")),
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("group_1", Timestamp("2020-01-06")),
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("group_2", Timestamp("2020-01-07")),
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("group_2", Timestamp("2020-01-08")),
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("group_2", Timestamp("2020-01-09")),
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("group_2", Timestamp("2020-01-10")),
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),
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names=["gb", "Date"],
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),
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)
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tm.assert_series_equal(result, expected)
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|
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@pytest.mark.parametrize("min_periods", [5, 4, 3])
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def test_groupby_rolling_center_min_periods(self, min_periods):
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# GH 36040
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df = DataFrame({"group": ["A"] * 10 + ["B"] * 10, "data": range(20)})
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|
window_size = 5
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result = (
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df.groupby("group")
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.rolling(window_size, center=True, min_periods=min_periods)
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.mean()
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)
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result = result.reset_index()[["group", "data"]]
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|
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grp_A_mean = [1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.5, 8.0]
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grp_B_mean = [x + 10.0 for x in grp_A_mean]
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|
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num_nans = max(0, min_periods - 3) # For window_size of 5
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nans = [np.nan] * num_nans
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grp_A_expected = nans + grp_A_mean[num_nans : 10 - num_nans] + nans
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grp_B_expected = nans + grp_B_mean[num_nans : 10 - num_nans] + nans
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|
|
expected = DataFrame(
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{"group": ["A"] * 10 + ["B"] * 10, "data": grp_A_expected + grp_B_expected}
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)
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tm.assert_frame_equal(result, expected)
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|
|
|
def test_groupby_subselect_rolling(self):
|
|
# GH 35486
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|
df = DataFrame(
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{"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0], "c": [10, 20, 30, 20]}
|
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)
|
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result = df.groupby("a")[["b"]].rolling(2).max()
|
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expected = DataFrame(
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[np.nan, np.nan, 2.0, np.nan],
|
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columns=["b"],
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index=MultiIndex.from_tuples(
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((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
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),
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)
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tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.groupby("a")["b"].rolling(2).max()
|
|
expected = Series(
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[np.nan, np.nan, 2.0, np.nan],
|
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index=MultiIndex.from_tuples(
|
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((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
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),
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name="b",
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)
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tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_rolling_custom_indexer(self):
|
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# GH 35557
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|
class SimpleIndexer(BaseIndexer):
|
|
def get_window_bounds(
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self,
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num_values=0,
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min_periods=None,
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center=None,
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|
closed=None,
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step=None,
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):
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min_periods = self.window_size if min_periods is None else 0
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end = np.arange(num_values, dtype=np.int64) + 1
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start = end.copy() - self.window_size
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start[start < 0] = min_periods
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return start, end
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|
|
df = DataFrame(
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{"a": [1.0, 2.0, 3.0, 4.0, 5.0] * 3}, index=[0] * 5 + [1] * 5 + [2] * 5
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)
|
|
result = (
|
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df.groupby(df.index)
|
|
.rolling(SimpleIndexer(window_size=3), min_periods=1)
|
|
.sum()
|
|
)
|
|
expected = df.groupby(df.index).rolling(window=3, min_periods=1).sum()
|
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tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_rolling_subset_with_closed(self):
|
|
# GH 35549
|
|
df = DataFrame(
|
|
{
|
|
"column1": range(6),
|
|
"column2": range(6),
|
|
"group": 3 * ["A", "B"],
|
|
"date": [Timestamp("2019-01-01")] * 6,
|
|
}
|
|
)
|
|
result = (
|
|
df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum()
|
|
)
|
|
expected = Series(
|
|
[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
|
|
index=MultiIndex.from_tuples(
|
|
[("A", Timestamp("2019-01-01"))] * 3
|
|
+ [("B", Timestamp("2019-01-01"))] * 3,
|
|
names=["group", "date"],
|
|
),
|
|
name="column1",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_subset_rolling_subset_with_closed(self):
|
|
# GH 35549
|
|
df = DataFrame(
|
|
{
|
|
"column1": range(6),
|
|
"column2": range(6),
|
|
"group": 3 * ["A", "B"],
|
|
"date": [Timestamp("2019-01-01")] * 6,
|
|
}
|
|
)
|
|
|
|
result = (
|
|
df.groupby("group")[["column1", "date"]]
|
|
.rolling("1D", on="date", closed="left")["column1"]
|
|
.sum()
|
|
)
|
|
expected = Series(
|
|
[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
|
|
index=MultiIndex.from_tuples(
|
|
[("A", Timestamp("2019-01-01"))] * 3
|
|
+ [("B", Timestamp("2019-01-01"))] * 3,
|
|
names=["group", "date"],
|
|
),
|
|
name="column1",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("func", ["max", "min"])
|
|
def test_groupby_rolling_index_changed(self, func):
|
|
# GH: #36018 nlevels of MultiIndex changed
|
|
ds = Series(
|
|
[1, 2, 2],
|
|
index=MultiIndex.from_tuples(
|
|
[("a", "x"), ("a", "y"), ("c", "z")], names=["1", "2"]
|
|
),
|
|
name="a",
|
|
)
|
|
|
|
result = getattr(ds.groupby(ds).rolling(2), func)()
|
|
expected = Series(
|
|
[np.nan, np.nan, 2.0],
|
|
index=MultiIndex.from_tuples(
|
|
[(1, "a", "x"), (2, "a", "y"), (2, "c", "z")], names=["a", "1", "2"]
|
|
),
|
|
name="a",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_rolling_empty_frame(self):
|
|
# GH 36197
|
|
expected = DataFrame({"s1": []})
|
|
result = expected.groupby("s1").rolling(window=1).sum()
|
|
# GH 32262
|
|
expected = expected.drop(columns="s1")
|
|
# GH-38057 from_tuples gives empty object dtype, we now get float/int levels
|
|
# expected.index = MultiIndex.from_tuples([], names=["s1", None])
|
|
expected.index = MultiIndex.from_product(
|
|
[Index([], dtype="float64"), Index([], dtype="int64")], names=["s1", None]
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame({"s1": [], "s2": []})
|
|
result = expected.groupby(["s1", "s2"]).rolling(window=1).sum()
|
|
# GH 32262
|
|
expected = expected.drop(columns=["s1", "s2"])
|
|
expected.index = MultiIndex.from_product(
|
|
[
|
|
Index([], dtype="float64"),
|
|
Index([], dtype="float64"),
|
|
Index([], dtype="int64"),
|
|
],
|
|
names=["s1", "s2", None],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_rolling_string_index(self):
|
|
# GH: 36727
|
|
df = DataFrame(
|
|
[
|
|
["A", "group_1", Timestamp(2019, 1, 1, 9)],
|
|
["B", "group_1", Timestamp(2019, 1, 2, 9)],
|
|
["Z", "group_2", Timestamp(2019, 1, 3, 9)],
|
|
["H", "group_1", Timestamp(2019, 1, 6, 9)],
|
|
["E", "group_2", Timestamp(2019, 1, 20, 9)],
|
|
],
|
|
columns=["index", "group", "eventTime"],
|
|
).set_index("index")
|
|
|
|
groups = df.groupby("group")
|
|
df["count_to_date"] = groups.cumcount()
|
|
rolling_groups = groups.rolling("10d", on="eventTime")
|
|
result = rolling_groups.apply(lambda df: df.shape[0])
|
|
expected = DataFrame(
|
|
[
|
|
["A", "group_1", Timestamp(2019, 1, 1, 9), 1.0],
|
|
["B", "group_1", Timestamp(2019, 1, 2, 9), 2.0],
|
|
["H", "group_1", Timestamp(2019, 1, 6, 9), 3.0],
|
|
["Z", "group_2", Timestamp(2019, 1, 3, 9), 1.0],
|
|
["E", "group_2", Timestamp(2019, 1, 20, 9), 1.0],
|
|
],
|
|
columns=["index", "group", "eventTime", "count_to_date"],
|
|
).set_index(["group", "index"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_rolling_no_sort(self):
|
|
# GH 36889
|
|
result = (
|
|
DataFrame({"foo": [2, 1], "bar": [2, 1]})
|
|
.groupby("foo", sort=False)
|
|
.rolling(1)
|
|
.min()
|
|
)
|
|
expected = DataFrame(
|
|
np.array([[2.0, 2.0], [1.0, 1.0]]),
|
|
columns=["foo", "bar"],
|
|
index=MultiIndex.from_tuples([(2, 0), (1, 1)], names=["foo", None]),
|
|
)
|
|
# GH 32262
|
|
expected = expected.drop(columns="foo")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_rolling_count_closed_on(self):
|
|
# GH 35869
|
|
df = DataFrame(
|
|
{
|
|
"column1": range(6),
|
|
"column2": range(6),
|
|
"group": 3 * ["A", "B"],
|
|
"date": date_range(end="20190101", periods=6),
|
|
}
|
|
)
|
|
result = (
|
|
df.groupby("group")
|
|
.rolling("3d", on="date", closed="left")["column1"]
|
|
.count()
|
|
)
|
|
expected = Series(
|
|
[np.nan, 1.0, 1.0, np.nan, 1.0, 1.0],
|
|
name="column1",
|
|
index=MultiIndex.from_tuples(
|
|
[
|
|
("A", Timestamp("2018-12-27")),
|
|
("A", Timestamp("2018-12-29")),
|
|
("A", Timestamp("2018-12-31")),
|
|
("B", Timestamp("2018-12-28")),
|
|
("B", Timestamp("2018-12-30")),
|
|
("B", Timestamp("2019-01-01")),
|
|
],
|
|
names=["group", "date"],
|
|
),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
("func", "kwargs"),
|
|
[("rolling", {"window": 2, "min_periods": 1}), ("expanding", {})],
|
|
)
|
|
def test_groupby_rolling_sem(self, func, kwargs):
|
|
# GH: 26476
|
|
df = DataFrame(
|
|
[["a", 1], ["a", 2], ["b", 1], ["b", 2], ["b", 3]], columns=["a", "b"]
|
|
)
|
|
result = getattr(df.groupby("a"), func)(**kwargs).sem()
|
|
expected = DataFrame(
|
|
{"a": [np.nan] * 5, "b": [np.nan, 0.70711, np.nan, 0.70711, 0.70711]},
|
|
index=MultiIndex.from_tuples(
|
|
[("a", 0), ("a", 1), ("b", 2), ("b", 3), ("b", 4)], names=["a", None]
|
|
),
|
|
)
|
|
# GH 32262
|
|
expected = expected.drop(columns="a")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
("rollings", "key"), [({"on": "a"}, "a"), ({"on": None}, "index")]
|
|
)
|
|
def test_groupby_rolling_nans_in_index(self, rollings, key):
|
|
# GH: 34617
|
|
df = DataFrame(
|
|
{
|
|
"a": to_datetime(["2020-06-01 12:00", "2020-06-01 14:00", np.nan]),
|
|
"b": [1, 2, 3],
|
|
"c": [1, 1, 1],
|
|
}
|
|
)
|
|
if key == "index":
|
|
df = df.set_index("a")
|
|
with pytest.raises(ValueError, match=f"{key} values must not have NaT"):
|
|
df.groupby("c").rolling("60min", **rollings)
|
|
|
|
@pytest.mark.parametrize("group_keys", [True, False])
|
|
def test_groupby_rolling_group_keys(self, group_keys):
|
|
# GH 37641
|
|
# GH 38523: GH 37641 actually was not a bug.
|
|
# group_keys only applies to groupby.apply directly
|
|
arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
|
|
index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
|
|
|
|
s = Series([1, 2, 3], index=index)
|
|
result = s.groupby(["idx1", "idx2"], group_keys=group_keys).rolling(1).mean()
|
|
expected = Series(
|
|
[1.0, 2.0, 3.0],
|
|
index=MultiIndex.from_tuples(
|
|
[
|
|
("val1", "val1", "val1", "val1"),
|
|
("val1", "val1", "val1", "val1"),
|
|
("val2", "val2", "val2", "val2"),
|
|
],
|
|
names=["idx1", "idx2", "idx1", "idx2"],
|
|
),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_rolling_index_level_and_column_label(self):
|
|
# The groupby keys should not appear as a resulting column
|
|
arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
|
|
index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
|
|
|
|
df = DataFrame({"A": [1, 1, 2], "B": range(3)}, index=index)
|
|
result = df.groupby(["idx1", "A"]).rolling(1).mean()
|
|
expected = DataFrame(
|
|
{"B": [0.0, 1.0, 2.0]},
|
|
index=MultiIndex.from_tuples(
|
|
[
|
|
("val1", 1, "val1", "val1"),
|
|
("val1", 1, "val1", "val1"),
|
|
("val2", 2, "val2", "val2"),
|
|
],
|
|
names=["idx1", "A", "idx1", "idx2"],
|
|
),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_groupby_rolling_resulting_multiindex(self):
|
|
# a few different cases checking the created MultiIndex of the result
|
|
# https://github.com/pandas-dev/pandas/pull/38057
|
|
|
|
# grouping by 1 columns -> 2-level MI as result
|
|
df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4})
|
|
result = df.groupby("b").rolling(3).mean()
|
|
expected_index = MultiIndex.from_tuples(
|
|
[(1, 0), (1, 2), (1, 4), (1, 6), (2, 1), (2, 3), (2, 5), (2, 7)],
|
|
names=["b", None],
|
|
)
|
|
tm.assert_index_equal(result.index, expected_index)
|
|
|
|
def test_groupby_rolling_resulting_multiindex2(self):
|
|
# grouping by 2 columns -> 3-level MI as result
|
|
df = DataFrame({"a": np.arange(12.0), "b": [1, 2] * 6, "c": [1, 2, 3, 4] * 3})
|
|
result = df.groupby(["b", "c"]).rolling(2).sum()
|
|
expected_index = MultiIndex.from_tuples(
|
|
[
|
|
(1, 1, 0),
|
|
(1, 1, 4),
|
|
(1, 1, 8),
|
|
(1, 3, 2),
|
|
(1, 3, 6),
|
|
(1, 3, 10),
|
|
(2, 2, 1),
|
|
(2, 2, 5),
|
|
(2, 2, 9),
|
|
(2, 4, 3),
|
|
(2, 4, 7),
|
|
(2, 4, 11),
|
|
],
|
|
names=["b", "c", None],
|
|
)
|
|
tm.assert_index_equal(result.index, expected_index)
|
|
|
|
def test_groupby_rolling_resulting_multiindex3(self):
|
|
# grouping with 1 level on dataframe with 2-level MI -> 3-level MI as result
|
|
df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4, "c": [1, 2, 3, 4] * 2})
|
|
df = df.set_index("c", append=True)
|
|
result = df.groupby("b").rolling(3).mean()
|
|
expected_index = MultiIndex.from_tuples(
|
|
[
|
|
(1, 0, 1),
|
|
(1, 2, 3),
|
|
(1, 4, 1),
|
|
(1, 6, 3),
|
|
(2, 1, 2),
|
|
(2, 3, 4),
|
|
(2, 5, 2),
|
|
(2, 7, 4),
|
|
],
|
|
names=["b", None, "c"],
|
|
)
|
|
tm.assert_index_equal(result.index, expected_index, exact="equiv")
|
|
|
|
def test_groupby_rolling_object_doesnt_affect_groupby_apply(self, roll_frame):
|
|
# GH 39732
|
|
g = roll_frame.groupby("A", group_keys=False)
|
|
expected = g.apply(lambda x: x.rolling(4).sum()).index
|
|
_ = g.rolling(window=4)
|
|
result = g.apply(lambda x: x.rolling(4).sum()).index
|
|
tm.assert_index_equal(result, expected)
|
|
assert not g.mutated
|
|
assert not g.grouper.mutated
|
|
|
|
@pytest.mark.parametrize(
|
|
("window", "min_periods", "closed", "expected"),
|
|
[
|
|
(2, 0, "left", [None, 0.0, 1.0, 1.0, None, 0.0, 1.0, 1.0]),
|
|
(2, 2, "left", [None, None, 1.0, 1.0, None, None, 1.0, 1.0]),
|
|
(4, 4, "left", [None, None, None, None, None, None, None, None]),
|
|
(4, 4, "right", [None, None, None, 5.0, None, None, None, 5.0]),
|
|
],
|
|
)
|
|
def test_groupby_rolling_var(self, window, min_periods, closed, expected):
|
|
df = DataFrame([1, 2, 3, 4, 5, 6, 7, 8])
|
|
result = (
|
|
df.groupby([1, 2, 1, 2, 1, 2, 1, 2])
|
|
.rolling(window=window, min_periods=min_periods, closed=closed)
|
|
.var(0)
|
|
)
|
|
expected_result = DataFrame(
|
|
np.array(expected, dtype="float64"),
|
|
index=MultiIndex(
|
|
levels=[[1, 2], [0, 1, 2, 3, 4, 5, 6, 7]],
|
|
codes=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 2, 4, 6, 1, 3, 5, 7]],
|
|
),
|
|
)
|
|
tm.assert_frame_equal(result, expected_result)
|
|
|
|
@pytest.mark.parametrize(
|
|
"columns", [MultiIndex.from_tuples([("A", ""), ("B", "C")]), ["A", "B"]]
|
|
)
|
|
def test_by_column_not_in_values(self, columns):
|
|
# GH 32262
|
|
df = DataFrame([[1, 0]] * 20 + [[2, 0]] * 12 + [[3, 0]] * 8, columns=columns)
|
|
g = df.groupby("A")
|
|
original_obj = g.obj.copy(deep=True)
|
|
r = g.rolling(4)
|
|
result = r.sum()
|
|
assert "A" not in result.columns
|
|
tm.assert_frame_equal(g.obj, original_obj)
|
|
|
|
def test_groupby_level(self):
|
|
# GH 38523, 38787
|
|
arrays = [
|
|
["Falcon", "Falcon", "Parrot", "Parrot"],
|
|
["Captive", "Wild", "Captive", "Wild"],
|
|
]
|
|
index = MultiIndex.from_arrays(arrays, names=("Animal", "Type"))
|
|
df = DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index)
|
|
result = df.groupby(level=0)["Max Speed"].rolling(2).sum()
|
|
expected = Series(
|
|
[np.nan, 740.0, np.nan, 50.0],
|
|
index=MultiIndex.from_tuples(
|
|
[
|
|
("Falcon", "Falcon", "Captive"),
|
|
("Falcon", "Falcon", "Wild"),
|
|
("Parrot", "Parrot", "Captive"),
|
|
("Parrot", "Parrot", "Wild"),
|
|
],
|
|
names=["Animal", "Animal", "Type"],
|
|
),
|
|
name="Max Speed",
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"by, expected_data",
|
|
[
|
|
[["id"], {"num": [100.0, 150.0, 150.0, 200.0]}],
|
|
[
|
|
["id", "index"],
|
|
{
|
|
"date": [
|
|
Timestamp("2018-01-01"),
|
|
Timestamp("2018-01-02"),
|
|
Timestamp("2018-01-01"),
|
|
Timestamp("2018-01-02"),
|
|
],
|
|
"num": [100.0, 200.0, 150.0, 250.0],
|
|
},
|
|
],
|
|
],
|
|
)
|
|
def test_as_index_false(self, by, expected_data):
|
|
# GH 39433
|
|
data = [
|
|
["A", "2018-01-01", 100.0],
|
|
["A", "2018-01-02", 200.0],
|
|
["B", "2018-01-01", 150.0],
|
|
["B", "2018-01-02", 250.0],
|
|
]
|
|
df = DataFrame(data, columns=["id", "date", "num"])
|
|
df["date"] = to_datetime(df["date"])
|
|
df = df.set_index(["date"])
|
|
|
|
gp_by = [getattr(df, attr) for attr in by]
|
|
result = (
|
|
df.groupby(gp_by, as_index=False).rolling(window=2, min_periods=1).mean()
|
|
)
|
|
|
|
expected = {"id": ["A", "A", "B", "B"]}
|
|
expected.update(expected_data)
|
|
expected = DataFrame(
|
|
expected,
|
|
index=df.index,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_nan_and_zero_endpoints(self):
|
|
# https://github.com/twosigma/pandas/issues/53
|
|
size = 1000
|
|
idx = np.repeat(0, size)
|
|
idx[-1] = 1
|
|
|
|
val = 5e25
|
|
arr = np.repeat(val, size)
|
|
arr[0] = np.nan
|
|
arr[-1] = 0
|
|
|
|
df = DataFrame(
|
|
{
|
|
"index": idx,
|
|
"adl2": arr,
|
|
}
|
|
).set_index("index")
|
|
result = df.groupby("index")["adl2"].rolling(window=10, min_periods=1).mean()
|
|
expected = Series(
|
|
arr,
|
|
name="adl2",
|
|
index=MultiIndex.from_arrays(
|
|
[[0] * 999 + [1], [0] * 999 + [1]], names=["index", "index"]
|
|
),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_rolling_non_monotonic(self):
|
|
# GH 43909
|
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shuffled = [3, 0, 1, 2]
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sec = 1_000
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df = DataFrame(
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[{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled]
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)
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with pytest.raises(ValueError, match=r".* must be monotonic"):
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df.groupby("c").rolling(on="t", window="3s")
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def test_groupby_monotonic(self):
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# GH 15130
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# we don't need to validate monotonicity when grouping
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# GH 43909 we should raise an error here to match
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# behaviour of non-groupby rolling.
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data = [
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["David", "1/1/2015", 100],
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["David", "1/5/2015", 500],
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["David", "5/30/2015", 50],
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["David", "7/25/2015", 50],
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["Ryan", "1/4/2014", 100],
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["Ryan", "1/19/2015", 500],
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["Ryan", "3/31/2016", 50],
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["Joe", "7/1/2015", 100],
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["Joe", "9/9/2015", 500],
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["Joe", "10/15/2015", 50],
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]
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df = DataFrame(data=data, columns=["name", "date", "amount"])
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df["date"] = to_datetime(df["date"])
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df = df.sort_values("date")
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expected = (
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df.set_index("date")
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.groupby("name")
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.apply(lambda x: x.rolling("180D")["amount"].sum())
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)
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result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
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tm.assert_series_equal(result, expected)
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def test_datelike_on_monotonic_within_each_group(self):
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# GH 13966 (similar to #15130, closed by #15175)
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# superseded by 43909
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# GH 46061: OK if the on is monotonic relative to each each group
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dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
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df = DataFrame(
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{
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"A": [1] * 20 + [2] * 12 + [3] * 8,
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"B": np.concatenate((dates, dates)),
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"C": np.arange(40),
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}
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)
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expected = (
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df.set_index("B").groupby("A").apply(lambda x: x.rolling("4s")["C"].mean())
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)
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result = df.groupby("A").rolling("4s", on="B").C.mean()
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tm.assert_series_equal(result, expected)
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def test_datelike_on_not_monotonic_within_each_group(self):
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# GH 46061
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df = DataFrame(
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{
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"A": [1] * 3 + [2] * 3,
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"B": [Timestamp(year, 1, 1) for year in [2020, 2021, 2019]] * 2,
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"C": range(6),
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}
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)
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with pytest.raises(ValueError, match="Each group within B must be monotonic."):
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df.groupby("A").rolling("365D", on="B")
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class TestExpanding:
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def setup_method(self):
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self.frame = DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
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@pytest.mark.parametrize(
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"f", ["sum", "mean", "min", "max", "count", "kurt", "skew"]
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)
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def test_expanding(self, f):
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g = self.frame.groupby("A", group_keys=False)
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r = g.expanding()
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result = getattr(r, f)()
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expected = g.apply(lambda x: getattr(x.expanding(), f)())
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([self.frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f", ["std", "var"])
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def test_expanding_ddof(self, f):
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g = self.frame.groupby("A", group_keys=False)
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r = g.expanding()
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result = getattr(r, f)(ddof=0)
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expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([self.frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
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)
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def test_expanding_quantile(self, interpolation):
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g = self.frame.groupby("A", group_keys=False)
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r = g.expanding()
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result = r.quantile(0.4, interpolation=interpolation)
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expected = g.apply(
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lambda x: x.expanding().quantile(0.4, interpolation=interpolation)
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)
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([self.frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("f", ["corr", "cov"])
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def test_expanding_corr_cov(self, f):
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g = self.frame.groupby("A")
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r = g.expanding()
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result = getattr(r, f)(self.frame)
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def func(x):
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return getattr(x.expanding(), f)(self.frame)
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expected = g.apply(func)
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# GH 39591: groupby.apply returns 1 instead of nan for windows
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# with all nan values
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null_idx = list(range(20, 61)) + list(range(72, 113))
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expected.iloc[null_idx, 1] = np.nan
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# GH 39591: The grouped column should be all np.nan
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# (groupby.apply inserts 0s for cov)
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expected["A"] = np.nan
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tm.assert_frame_equal(result, expected)
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result = getattr(r.B, f)(pairwise=True)
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def func(x):
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return getattr(x.B.expanding(), f)(pairwise=True)
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expected = g.apply(func)
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tm.assert_series_equal(result, expected)
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def test_expanding_apply(self, raw):
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g = self.frame.groupby("A", group_keys=False)
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r = g.expanding()
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# reduction
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result = r.apply(lambda x: x.sum(), raw=raw)
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expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw))
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# groupby.apply doesn't drop the grouped-by column
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expected = expected.drop("A", axis=1)
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# GH 39732
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expected_index = MultiIndex.from_arrays([self.frame["A"], range(40)])
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expected.index = expected_index
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tm.assert_frame_equal(result, expected)
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class TestEWM:
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@pytest.mark.parametrize(
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"method, expected_data",
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[
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["mean", [0.0, 0.6666666666666666, 1.4285714285714286, 2.2666666666666666]],
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["std", [np.nan, 0.707107, 0.963624, 1.177164]],
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["var", [np.nan, 0.5, 0.9285714285714286, 1.3857142857142857]],
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],
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)
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def test_methods(self, method, expected_data):
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# GH 16037
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df = DataFrame({"A": ["a"] * 4, "B": range(4)})
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result = getattr(df.groupby("A").ewm(com=1.0), method)()
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expected = DataFrame(
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{"B": expected_data},
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index=MultiIndex.from_tuples(
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[
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("a", 0),
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("a", 1),
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("a", 2),
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("a", 3),
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],
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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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with tm.assert_produces_warning(FutureWarning, match="nuisance"):
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# GH#42738
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expected = df.groupby("A", group_keys=True).apply(
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lambda x: getattr(x.ewm(com=1.0), method)()
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"method, expected_data",
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[["corr", [np.nan, 1.0, 1.0, 1]], ["cov", [np.nan, 0.5, 0.928571, 1.385714]]],
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)
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def test_pairwise_methods(self, method, expected_data):
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# GH 16037
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df = DataFrame({"A": ["a"] * 4, "B": range(4)})
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result = getattr(df.groupby("A").ewm(com=1.0), method)()
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expected = DataFrame(
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{"B": expected_data},
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index=MultiIndex.from_tuples(
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[
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("a", 0, "B"),
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("a", 1, "B"),
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("a", 2, "B"),
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("a", 3, "B"),
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],
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names=["A", None, None],
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),
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)
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tm.assert_frame_equal(result, expected)
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expected = df.groupby("A").apply(lambda x: getattr(x.ewm(com=1.0), method)())
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tm.assert_frame_equal(result, expected)
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def test_times(self, times_frame):
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# GH 40951
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halflife = "23 days"
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with tm.assert_produces_warning(FutureWarning, match="nuisance"):
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# GH#42738
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result = times_frame.groupby("A").ewm(halflife=halflife, times="C").mean()
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expected = DataFrame(
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{
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"B": [
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0.0,
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0.507534,
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1.020088,
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1.537661,
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0.0,
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0.567395,
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1.221209,
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0.0,
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0.653141,
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1.195003,
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]
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},
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index=MultiIndex.from_tuples(
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[
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("a", 0),
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("a", 3),
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("a", 6),
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("a", 9),
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("b", 1),
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("b", 4),
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("b", 7),
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("c", 2),
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("c", 5),
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("c", 8),
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],
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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_times_vs_apply(self, times_frame):
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# GH 40951
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halflife = "23 days"
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with tm.assert_produces_warning(FutureWarning, match="nuisance"):
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# GH#42738
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result = times_frame.groupby("A").ewm(halflife=halflife, times="C").mean()
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expected = times_frame.groupby("A", group_keys=True).apply(
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lambda x: x.ewm(halflife=halflife, times="C").mean()
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)
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tm.assert_frame_equal(result, expected)
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def test_times_array(self, times_frame):
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# GH 40951
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halflife = "23 days"
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gb = times_frame.groupby("A")
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with tm.assert_produces_warning(FutureWarning, match="nuisance"):
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# GH#42738
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result = gb.ewm(halflife=halflife, times="C").mean()
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expected = gb.ewm(halflife=halflife, times=times_frame["C"].values).mean()
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tm.assert_frame_equal(result, expected)
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def test_dont_mutate_obj_after_slicing(self):
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# GH 43355
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df = DataFrame(
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{
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"id": ["a", "a", "b", "b", "b"],
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"timestamp": date_range("2021-9-1", periods=5, freq="H"),
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"y": range(5),
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}
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)
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grp = df.groupby("id").rolling("1H", on="timestamp")
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result = grp.count()
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expected_df = DataFrame(
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{
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"timestamp": date_range("2021-9-1", periods=5, freq="H"),
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"y": [1.0] * 5,
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},
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index=MultiIndex.from_arrays(
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[["a", "a", "b", "b", "b"], list(range(5))], names=["id", None]
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),
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)
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tm.assert_frame_equal(result, expected_df)
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result = grp["y"].count()
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expected_series = Series(
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[1.0] * 5,
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index=MultiIndex.from_arrays(
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[
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["a", "a", "b", "b", "b"],
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date_range("2021-9-1", periods=5, freq="H"),
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],
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names=["id", "timestamp"],
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),
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name="y",
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)
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tm.assert_series_equal(result, expected_series)
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# This is the key test
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result = grp.count()
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tm.assert_frame_equal(result, expected_df)
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