mirror of
https://github.com/PiBrewing/craftbeerpi4.git
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546 lines
19 KiB
Python
546 lines
19 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, MultiIndex, Series
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import pandas._testing as tm
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from pandas.core.groupby.groupby import get_groupby
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class TestGrouperGrouping:
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def setup_method(self, method):
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self.series = Series(np.arange(10))
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self.frame = DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
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def test_mutated(self):
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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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self.frame.groupby("A", foo=1)
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g = self.frame.groupby("A")
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assert not g.mutated
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g = get_groupby(self.frame, by="A", mutated=True)
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assert g.mutated
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def test_getitem(self):
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g = self.frame.groupby("A")
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g_mutated = get_groupby(self.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 = self.frame.B.groupby(self.frame.A).rolling(2).mean()
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tm.assert_series_equal(result, expected)
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def test_getitem_multiple(self):
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# GH 13174
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g = self.frame.groupby("A")
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r = g.rolling(2)
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g_mutated = get_groupby(self.frame, by="A", mutated=True)
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expected = g_mutated.B.apply(lambda x: x.rolling(2).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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def test_rolling(self):
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g = self.frame.groupby("A")
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r = g.rolling(window=4)
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for f in ["sum", "mean", "min", "max", "count", "kurt", "skew"]:
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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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tm.assert_frame_equal(result, expected)
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for f in ["std", "var"]:
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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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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):
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g = self.frame.groupby("A")
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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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tm.assert_frame_equal(result, expected)
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def test_rolling_corr_cov(self):
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g = self.frame.groupby("A")
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r = g.rolling(window=4)
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for f in ["corr", "cov"]:
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result = getattr(r, f)(self.frame)
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def func(x):
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return getattr(x.rolling(4), f)(self.frame)
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expected = g.apply(func)
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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.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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def test_rolling_apply(self, raw):
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g = self.frame.groupby("A")
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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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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 = pd.DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6})
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g = df.groupby("A")
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mi = pd.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 = pd.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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def test_expanding(self):
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g = self.frame.groupby("A")
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r = g.expanding()
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for f in ["sum", "mean", "min", "max", "count", "kurt", "skew"]:
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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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tm.assert_frame_equal(result, expected)
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for f in ["std", "var"]:
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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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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")
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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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tm.assert_frame_equal(result, expected)
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def test_expanding_corr_cov(self):
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g = self.frame.groupby("A")
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r = g.expanding()
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for f in ["corr", "cov"]:
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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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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")
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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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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 = pd.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=pd.MultiIndex.from_tuples(
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((1, 0), (1, 1), (1, 2)), names=["id", None]
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),
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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=pd.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=pd.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 = pd.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 = pd.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=pd.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 = pd.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 = pd.DataFrame(
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[np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, np.nan],
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index=pd.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_subselect_rolling(self):
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# 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=pd.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)
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result = df.groupby("a")["b"].rolling(2).max()
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expected = Series(
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[np.nan, np.nan, 2.0, np.nan],
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index=pd.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)
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def test_groupby_rolling_custom_indexer(self):
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# GH 35557
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class SimpleIndexer(pd.api.indexers.BaseIndexer):
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def get_window_bounds(
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self, num_values=0, min_periods=None, center=None, closed=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 = pd.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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)
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result = (
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df.groupby(df.index)
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.rolling(SimpleIndexer(window_size=3), min_periods=1)
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.sum()
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)
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expected = df.groupby(df.index).rolling(window=3, min_periods=1).sum()
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tm.assert_frame_equal(result, expected)
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def test_groupby_rolling_subset_with_closed(self):
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# GH 35549
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df = pd.DataFrame(
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{
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"column1": range(6),
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"column2": range(6),
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"group": 3 * ["A", "B"],
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"date": [pd.Timestamp("2019-01-01")] * 6,
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}
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)
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result = (
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df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum()
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)
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expected = Series(
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[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
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index=pd.MultiIndex.from_tuples(
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[("A", pd.Timestamp("2019-01-01"))] * 3
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+ [("B", pd.Timestamp("2019-01-01"))] * 3,
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names=["group", "date"],
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),
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name="column1",
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)
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tm.assert_series_equal(result, expected)
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def test_groupby_subset_rolling_subset_with_closed(self):
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# GH 35549
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df = pd.DataFrame(
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{
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"column1": range(6),
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"column2": range(6),
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"group": 3 * ["A", "B"],
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"date": [pd.Timestamp("2019-01-01")] * 6,
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}
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)
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result = (
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df.groupby("group")[["column1", "date"]]
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.rolling("1D", on="date", closed="left")["column1"]
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.sum()
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)
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expected = Series(
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[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
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index=pd.MultiIndex.from_tuples(
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[("A", pd.Timestamp("2019-01-01"))] * 3
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+ [("B", pd.Timestamp("2019-01-01"))] * 3,
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names=["group", "date"],
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),
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name="column1",
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("func", ["max", "min"])
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def test_groupby_rolling_index_changed(self, func):
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# GH: #36018 nlevels of MultiIndex changed
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ds = Series(
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[1, 2, 2],
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index=pd.MultiIndex.from_tuples(
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[("a", "x"), ("a", "y"), ("c", "z")], names=["1", "2"]
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),
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name="a",
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)
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result = getattr(ds.groupby(ds).rolling(2), func)()
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expected = Series(
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[np.nan, np.nan, 2.0],
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index=pd.MultiIndex.from_tuples(
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[(1, "a", "x"), (2, "a", "y"), (2, "c", "z")], names=["a", "1", "2"]
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),
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name="a",
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)
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tm.assert_series_equal(result, expected)
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def test_groupby_rolling_empty_frame(self):
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# GH 36197
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expected = DataFrame({"s1": []})
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result = expected.groupby("s1").rolling(window=1).sum()
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# GH-38057 from_tuples gives empty object dtype, we now get float/int levels
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# expected.index = MultiIndex.from_tuples([], names=["s1", None])
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expected.index = MultiIndex.from_product(
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[Index([], dtype="float64"), Index([], dtype="int64")], names=["s1", None]
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)
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tm.assert_frame_equal(result, expected)
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expected = DataFrame({"s1": [], "s2": []})
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result = expected.groupby(["s1", "s2"]).rolling(window=1).sum()
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expected.index = MultiIndex.from_product(
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[
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Index([], dtype="float64"),
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Index([], dtype="float64"),
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Index([], dtype="int64"),
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],
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names=["s1", "s2", None],
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)
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tm.assert_frame_equal(result, expected)
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def test_groupby_rolling_string_index(self):
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# GH: 36727
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df = pd.DataFrame(
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[
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["A", "group_1", pd.Timestamp(2019, 1, 1, 9)],
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["B", "group_1", pd.Timestamp(2019, 1, 2, 9)],
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["Z", "group_2", pd.Timestamp(2019, 1, 3, 9)],
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["H", "group_1", pd.Timestamp(2019, 1, 6, 9)],
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["E", "group_2", pd.Timestamp(2019, 1, 20, 9)],
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],
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columns=["index", "group", "eventTime"],
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).set_index("index")
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groups = df.groupby("group")
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df["count_to_date"] = groups.cumcount()
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rolling_groups = groups.rolling("10d", on="eventTime")
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result = rolling_groups.apply(lambda df: df.shape[0])
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expected = pd.DataFrame(
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[
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["A", "group_1", pd.Timestamp(2019, 1, 1, 9), 1.0],
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["B", "group_1", pd.Timestamp(2019, 1, 2, 9), 2.0],
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["H", "group_1", pd.Timestamp(2019, 1, 6, 9), 3.0],
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["Z", "group_2", pd.Timestamp(2019, 1, 3, 9), 1.0],
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["E", "group_2", pd.Timestamp(2019, 1, 20, 9), 1.0],
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],
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columns=["index", "group", "eventTime", "count_to_date"],
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).set_index(["group", "index"])
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tm.assert_frame_equal(result, expected)
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def test_groupby_rolling_no_sort(self):
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# GH 36889
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result = (
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pd.DataFrame({"foo": [2, 1], "bar": [2, 1]})
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.groupby("foo", sort=False)
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.rolling(1)
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.min()
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)
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expected = pd.DataFrame(
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np.array([[2.0, 2.0], [1.0, 1.0]]),
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columns=["foo", "bar"],
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index=pd.MultiIndex.from_tuples([(2, 0), (1, 1)], names=["foo", None]),
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)
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tm.assert_frame_equal(result, expected)
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def test_groupby_rolling_group_keys(self):
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# GH 37641
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arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
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index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
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s = Series([1, 2, 3], index=index)
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result = s.groupby(["idx1", "idx2"], group_keys=False).rolling(1).mean()
|
|
expected = Series(
|
|
[1.0, 2.0, 3.0],
|
|
index=MultiIndex.from_tuples(
|
|
[("val1", "val1"), ("val1", "val1"), ("val2", "val2")],
|
|
names=["idx1", "idx2"],
|
|
),
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_groupby_rolling_index_level_and_column_label(self):
|
|
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", 1), ("val2", 2)], names=["idx1", "A"]
|
|
),
|
|
)
|
|
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)
|
|
|
|
# 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)
|
|
|
|
# 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)
|