688 lines
23 KiB
Python
688 lines
23 KiB
Python
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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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NaT,
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Series,
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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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from pandas.tseries import offsets
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@pytest.fixture
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def regular():
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return DataFrame(
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{"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
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).set_index("A")
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@pytest.fixture
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def ragged():
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df = DataFrame({"B": range(5)})
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df.index = [
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Timestamp("20130101 09:00:00"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:05"),
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Timestamp("20130101 09:00:06"),
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]
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return df
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class TestRollingTS:
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# rolling time-series friendly
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# xref GH13327
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def test_doc_string(self):
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df = DataFrame(
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{"B": [0, 1, 2, np.nan, 4]},
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index=[
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Timestamp("20130101 09:00:00"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:05"),
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Timestamp("20130101 09:00:06"),
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],
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)
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df
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df.rolling("2s").sum()
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def test_invalid_window_non_int(self, regular):
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# not a valid freq
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msg = "passed window foobar is not compatible with a datetimelike index"
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with pytest.raises(ValueError, match=msg):
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regular.rolling(window="foobar")
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# not a datetimelike index
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msg = "window must be an integer"
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with pytest.raises(ValueError, match=msg):
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regular.reset_index().rolling(window="foobar")
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@pytest.mark.parametrize("freq", ["2MS", offsets.MonthBegin(2)])
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def test_invalid_window_nonfixed(self, freq, regular):
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# non-fixed freqs
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msg = "\\<2 \\* MonthBegins\\> is a non-fixed frequency"
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with pytest.raises(ValueError, match=msg):
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regular.rolling(window=freq)
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@pytest.mark.parametrize("freq", ["1D", offsets.Day(2), "2ms"])
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def test_valid_window(self, freq, regular):
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regular.rolling(window=freq)
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@pytest.mark.parametrize("minp", [1.0, "foo", np.array([1, 2, 3])])
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def test_invalid_minp(self, minp, regular):
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# non-integer min_periods
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msg = (
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r"local variable 'minp' referenced before assignment|"
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"min_periods must be an integer"
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)
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with pytest.raises(ValueError, match=msg):
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regular.rolling(window="1D", min_periods=minp)
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def test_on(self, regular):
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df = regular
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# not a valid column
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msg = (
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r"invalid on specified as foobar, must be a column "
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"\\(of DataFrame\\), an Index or None"
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)
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with pytest.raises(ValueError, match=msg):
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df.rolling(window="2s", on="foobar")
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# column is valid
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df = df.copy()
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df["C"] = date_range("20130101", periods=len(df))
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df.rolling(window="2d", on="C").sum()
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# invalid columns
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msg = "window must be an integer"
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with pytest.raises(ValueError, match=msg):
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df.rolling(window="2d", on="B")
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# ok even though on non-selected
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df.rolling(window="2d", on="C").B.sum()
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def test_monotonic_on(self):
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# on/index must be monotonic
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df = DataFrame(
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{"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
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)
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assert df.A.is_monotonic_increasing
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df.rolling("2s", on="A").sum()
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df = df.set_index("A")
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assert df.index.is_monotonic_increasing
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df.rolling("2s").sum()
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def test_non_monotonic_on(self):
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# GH 19248
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df = DataFrame(
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{"A": date_range("20130101", periods=5, freq="s"), "B": range(5)}
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)
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df = df.set_index("A")
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non_monotonic_index = df.index.to_list()
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non_monotonic_index[0] = non_monotonic_index[3]
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df.index = non_monotonic_index
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assert not df.index.is_monotonic_increasing
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msg = "index values must be monotonic"
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with pytest.raises(ValueError, match=msg):
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df.rolling("2s").sum()
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df = df.reset_index()
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msg = (
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r"invalid on specified as A, must be a column "
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"\\(of DataFrame\\), an Index or None"
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)
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with pytest.raises(ValueError, match=msg):
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df.rolling("2s", on="A").sum()
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def test_frame_on(self):
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df = DataFrame(
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{"B": range(5), "C": date_range("20130101 09:00:00", periods=5, freq="3s")}
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)
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df["A"] = [
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Timestamp("20130101 09:00:00"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:05"),
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Timestamp("20130101 09:00:06"),
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]
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# we are doing simulating using 'on'
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expected = df.set_index("A").rolling("2s").B.sum().reset_index(drop=True)
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result = df.rolling("2s", on="A").B.sum()
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tm.assert_series_equal(result, expected)
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# test as a frame
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# we should be ignoring the 'on' as an aggregation column
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# note that the expected is setting, computing, and resetting
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# so the columns need to be switched compared
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# to the actual result where they are ordered as in the
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# original
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expected = (
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df.set_index("A").rolling("2s")[["B"]].sum().reset_index()[["B", "A"]]
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)
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result = df.rolling("2s", on="A")[["B"]].sum()
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tm.assert_frame_equal(result, expected)
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def test_frame_on2(self):
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# using multiple aggregation columns
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df = DataFrame(
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{
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"A": [0, 1, 2, 3, 4],
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"B": [0, 1, 2, np.nan, 4],
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"C": Index(
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[
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Timestamp("20130101 09:00:00"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:05"),
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Timestamp("20130101 09:00:06"),
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]
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),
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},
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columns=["A", "C", "B"],
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)
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expected1 = DataFrame(
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{"A": [0.0, 1, 3, 3, 7], "B": [0, 1, 3, np.nan, 4], "C": df["C"]},
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columns=["A", "C", "B"],
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)
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result = df.rolling("2s", on="C").sum()
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expected = expected1
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tm.assert_frame_equal(result, expected)
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expected = Series([0, 1, 3, np.nan, 4], name="B")
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result = df.rolling("2s", on="C").B.sum()
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tm.assert_series_equal(result, expected)
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expected = expected1[["A", "B", "C"]]
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result = df.rolling("2s", on="C")[["A", "B", "C"]].sum()
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tm.assert_frame_equal(result, expected)
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def test_basic_regular(self, regular):
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df = regular.copy()
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df.index = date_range("20130101", periods=5, freq="D")
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expected = df.rolling(window=1, min_periods=1).sum()
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result = df.rolling(window="1D").sum()
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tm.assert_frame_equal(result, expected)
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df.index = date_range("20130101", periods=5, freq="2D")
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expected = df.rolling(window=1, min_periods=1).sum()
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result = df.rolling(window="2D", min_periods=1).sum()
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tm.assert_frame_equal(result, expected)
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expected = df.rolling(window=1, min_periods=1).sum()
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result = df.rolling(window="2D", min_periods=1).sum()
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tm.assert_frame_equal(result, expected)
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expected = df.rolling(window=1).sum()
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result = df.rolling(window="2D").sum()
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tm.assert_frame_equal(result, expected)
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def test_min_periods(self, regular):
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# compare for min_periods
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df = regular
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# these slightly different
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expected = df.rolling(2, min_periods=1).sum()
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result = df.rolling("2s").sum()
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tm.assert_frame_equal(result, expected)
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expected = df.rolling(2, min_periods=1).sum()
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result = df.rolling("2s", min_periods=1).sum()
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tm.assert_frame_equal(result, expected)
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def test_closed(self, regular):
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# xref GH13965
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df = DataFrame(
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{"A": [1] * 5},
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index=[
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Timestamp("20130101 09:00:01"),
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Timestamp("20130101 09:00:02"),
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Timestamp("20130101 09:00:03"),
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Timestamp("20130101 09:00:04"),
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Timestamp("20130101 09:00:06"),
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],
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)
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# closed must be 'right', 'left', 'both', 'neither'
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msg = "closed must be 'right', 'left', 'both' or 'neither'"
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with pytest.raises(ValueError, match=msg):
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regular.rolling(window="2s", closed="blabla")
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expected = df.copy()
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expected["A"] = [1.0, 2, 2, 2, 1]
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result = df.rolling("2s", closed="right").sum()
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tm.assert_frame_equal(result, expected)
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# default should be 'right'
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result = df.rolling("2s").sum()
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tm.assert_frame_equal(result, expected)
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expected = df.copy()
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expected["A"] = [1.0, 2, 3, 3, 2]
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result = df.rolling("2s", closed="both").sum()
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tm.assert_frame_equal(result, expected)
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expected = df.copy()
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expected["A"] = [np.nan, 1.0, 2, 2, 1]
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result = df.rolling("2s", closed="left").sum()
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tm.assert_frame_equal(result, expected)
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expected = df.copy()
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expected["A"] = [np.nan, 1.0, 1, 1, np.nan]
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result = df.rolling("2s", closed="neither").sum()
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tm.assert_frame_equal(result, expected)
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def test_ragged_sum(self, ragged):
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df = ragged
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result = df.rolling(window="1s", min_periods=1).sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 2, 3, 4]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="2s", min_periods=1).sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 3, 3, 7]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="2s", min_periods=2).sum()
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expected = df.copy()
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expected["B"] = [np.nan, np.nan, 3, np.nan, 7]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="3s", min_periods=1).sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 3, 5, 7]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="3s").sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 3, 5, 7]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="4s", min_periods=1).sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 3, 6, 9]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="4s", min_periods=3).sum()
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expected = df.copy()
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expected["B"] = [np.nan, np.nan, 3, 6, 9]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="5s", min_periods=1).sum()
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expected = df.copy()
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expected["B"] = [0.0, 1, 3, 6, 10]
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tm.assert_frame_equal(result, expected)
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def test_ragged_mean(self, ragged):
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df = ragged
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result = df.rolling(window="1s", min_periods=1).mean()
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expected = df.copy()
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expected["B"] = [0.0, 1, 2, 3, 4]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="2s", min_periods=1).mean()
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expected = df.copy()
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expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
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tm.assert_frame_equal(result, expected)
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def test_ragged_median(self, ragged):
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df = ragged
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result = df.rolling(window="1s", min_periods=1).median()
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expected = df.copy()
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expected["B"] = [0.0, 1, 2, 3, 4]
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tm.assert_frame_equal(result, expected)
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result = df.rolling(window="2s", min_periods=1).median()
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expected = df.copy()
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expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
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tm.assert_frame_equal(result, expected)
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def test_ragged_quantile(self, ragged):
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df = ragged
|
||
|
|
result = df.rolling(window="1s", min_periods=1).quantile(0.5)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="2s", min_periods=1).quantile(0.5)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 1.5, 3.0, 3.5]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_std(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="1s", min_periods=1).std(ddof=0)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="1s", min_periods=1).std(ddof=1)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="3s", min_periods=1).std(ddof=0)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0] + [0.5] * 4
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).std(ddof=1)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan, 0.707107, 1.0, 1.0, 1.290994]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_var(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="1s", min_periods=1).var(ddof=0)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="1s", min_periods=1).var(ddof=1)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="3s", min_periods=1).var(ddof=0)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0] + [0.25] * 4
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).var(ddof=1)
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.0]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_skew(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="3s", min_periods=1).skew()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).skew()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 2 + [0.0, 0.0, 0.0]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_kurt(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="3s", min_periods=1).kurt()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 5
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).kurt()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan] * 4 + [-1.2]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_count(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="1s", min_periods=1).count()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [1.0, 1, 1, 1, 1]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
df = ragged
|
||
|
|
result = df.rolling(window="1s").count()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="2s", min_periods=1).count()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [1.0, 1, 2, 1, 2]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="2s", min_periods=2).count()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [np.nan, np.nan, 2, np.nan, 2]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_regular_min(self):
|
||
|
|
df = DataFrame(
|
||
|
|
{"A": date_range("20130101", periods=5, freq="s"), "B": [0.0, 1, 2, 3, 4]}
|
||
|
|
).set_index("A")
|
||
|
|
result = df.rolling("1s").min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
df = DataFrame(
|
||
|
|
{"A": date_range("20130101", periods=5, freq="s"), "B": [5, 4, 3, 4, 5]}
|
||
|
|
).set_index("A")
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
result = df.rolling("2s").min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [5.0, 4, 3, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling("5s").min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [5.0, 4, 3, 3, 3]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_ragged_min(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
|
||
|
|
result = df.rolling(window="1s", min_periods=1).min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="2s", min_periods=1).min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 1, 3, 3]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).min()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 0, 0, 1, 1]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_perf_min(self):
|
||
|
|
N = 10000
|
||
|
|
|
||
|
|
dfp = DataFrame(
|
||
|
|
{"B": np.random.randn(N)}, index=date_range("20130101", periods=N, freq="s")
|
||
|
|
)
|
||
|
|
expected = dfp.rolling(2, min_periods=1).min()
|
||
|
|
result = dfp.rolling("2s").min()
|
||
|
|
assert ((result - expected) < 0.01).all().bool()
|
||
|
|
|
||
|
|
expected = dfp.rolling(200, min_periods=1).min()
|
||
|
|
result = dfp.rolling("200s").min()
|
||
|
|
assert ((result - expected) < 0.01).all().bool()
|
||
|
|
|
||
|
|
def test_ragged_max(self, ragged):
|
||
|
|
df = ragged
|
||
|
|
|
||
|
|
result = df.rolling(window="1s", min_periods=1).max()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="2s", min_periods=1).max()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.rolling(window="5s", min_periods=1).max()
|
||
|
|
expected = df.copy()
|
||
|
|
expected["B"] = [0.0, 1, 2, 3, 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"freq, op, result_data",
|
||
|
|
[
|
||
|
|
("ms", "min", [0.0] * 10),
|
||
|
|
("ms", "mean", [0.0] * 9 + [2.0 / 9]),
|
||
|
|
("ms", "max", [0.0] * 9 + [2.0]),
|
||
|
|
("s", "min", [0.0] * 10),
|
||
|
|
("s", "mean", [0.0] * 9 + [2.0 / 9]),
|
||
|
|
("s", "max", [0.0] * 9 + [2.0]),
|
||
|
|
("min", "min", [0.0] * 10),
|
||
|
|
("min", "mean", [0.0] * 9 + [2.0 / 9]),
|
||
|
|
("min", "max", [0.0] * 9 + [2.0]),
|
||
|
|
("h", "min", [0.0] * 10),
|
||
|
|
("h", "mean", [0.0] * 9 + [2.0 / 9]),
|
||
|
|
("h", "max", [0.0] * 9 + [2.0]),
|
||
|
|
("D", "min", [0.0] * 10),
|
||
|
|
("D", "mean", [0.0] * 9 + [2.0 / 9]),
|
||
|
|
("D", "max", [0.0] * 9 + [2.0]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_freqs_ops(self, freq, op, result_data):
|
||
|
|
# GH 21096
|
||
|
|
index = date_range(start="2018-1-1 01:00:00", freq=f"1{freq}", periods=10)
|
||
|
|
# Explicit cast to float to avoid implicit cast when setting nan
|
||
|
|
s = Series(data=0, index=index, dtype="float")
|
||
|
|
s.iloc[1] = np.nan
|
||
|
|
s.iloc[-1] = 2
|
||
|
|
result = getattr(s.rolling(window=f"10{freq}"), op)()
|
||
|
|
expected = Series(data=result_data, index=index)
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"f",
|
||
|
|
[
|
||
|
|
"sum",
|
||
|
|
"mean",
|
||
|
|
"count",
|
||
|
|
"median",
|
||
|
|
"std",
|
||
|
|
"var",
|
||
|
|
"kurt",
|
||
|
|
"skew",
|
||
|
|
"min",
|
||
|
|
"max",
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_all(self, f, regular):
|
||
|
|
# simple comparison of integer vs time-based windowing
|
||
|
|
df = regular * 2
|
||
|
|
er = df.rolling(window=1)
|
||
|
|
r = df.rolling(window="1s")
|
||
|
|
|
||
|
|
result = getattr(r, f)()
|
||
|
|
expected = getattr(er, f)()
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = r.quantile(0.5)
|
||
|
|
expected = er.quantile(0.5)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_all2(self, arithmetic_win_operators):
|
||
|
|
f = arithmetic_win_operators
|
||
|
|
# more sophisticated comparison of integer vs.
|
||
|
|
# time-based windowing
|
||
|
|
df = DataFrame(
|
||
|
|
{"B": np.arange(50)}, index=date_range("20130101", periods=50, freq="H")
|
||
|
|
)
|
||
|
|
# in-range data
|
||
|
|
dft = df.between_time("09:00", "16:00")
|
||
|
|
|
||
|
|
r = dft.rolling(window="5H")
|
||
|
|
|
||
|
|
result = getattr(r, f)()
|
||
|
|
|
||
|
|
# we need to roll the days separately
|
||
|
|
# to compare with a time-based roll
|
||
|
|
# finally groupby-apply will return a multi-index
|
||
|
|
# so we need to drop the day
|
||
|
|
def agg_by_day(x):
|
||
|
|
x = x.between_time("09:00", "16:00")
|
||
|
|
return getattr(x.rolling(5, min_periods=1), f)()
|
||
|
|
|
||
|
|
expected = (
|
||
|
|
df.groupby(df.index.day).apply(agg_by_day).reset_index(level=0, drop=True)
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rolling_cov_offset(self):
|
||
|
|
# GH16058
|
||
|
|
|
||
|
|
idx = date_range("2017-01-01", periods=24, freq="1h")
|
||
|
|
ss = Series(np.arange(len(idx)), index=idx)
|
||
|
|
|
||
|
|
result = ss.rolling("2h").cov()
|
||
|
|
expected = Series([np.nan] + [0.5] * (len(idx) - 1), index=idx)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected2 = ss.rolling(2, min_periods=1).cov()
|
||
|
|
tm.assert_series_equal(result, expected2)
|
||
|
|
|
||
|
|
result = ss.rolling("3h").cov()
|
||
|
|
expected = Series([np.nan, 0.5] + [1.0] * (len(idx) - 2), index=idx)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected2 = ss.rolling(3, min_periods=1).cov()
|
||
|
|
tm.assert_series_equal(result, expected2)
|
||
|
|
|
||
|
|
def test_rolling_on_decreasing_index(self):
|
||
|
|
# GH-19248, GH-32385
|
||
|
|
index = [
|
||
|
|
Timestamp("20190101 09:00:30"),
|
||
|
|
Timestamp("20190101 09:00:27"),
|
||
|
|
Timestamp("20190101 09:00:20"),
|
||
|
|
Timestamp("20190101 09:00:18"),
|
||
|
|
Timestamp("20190101 09:00:10"),
|
||
|
|
]
|
||
|
|
|
||
|
|
df = DataFrame({"column": [3, 4, 4, 5, 6]}, index=index)
|
||
|
|
result = df.rolling("5s").min()
|
||
|
|
expected = DataFrame({"column": [3.0, 3.0, 4.0, 4.0, 6.0]}, index=index)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rolling_on_empty(self):
|
||
|
|
# GH-32385
|
||
|
|
df = DataFrame({"column": []}, index=[])
|
||
|
|
result = df.rolling("5s").min()
|
||
|
|
expected = DataFrame({"column": []}, index=[])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rolling_on_multi_index_level(self):
|
||
|
|
# GH-15584
|
||
|
|
df = DataFrame(
|
||
|
|
{"column": range(6)},
|
||
|
|
index=MultiIndex.from_product(
|
||
|
|
[date_range("20190101", periods=3), range(2)], names=["date", "seq"]
|
||
|
|
),
|
||
|
|
)
|
||
|
|
result = df.rolling("10d", on=df.index.get_level_values("date")).sum()
|
||
|
|
expected = DataFrame(
|
||
|
|
{"column": [0.0, 1.0, 3.0, 6.0, 10.0, 15.0]}, index=df.index
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("msg, axis", [["column", 1], ["index", 0]])
|
||
|
|
def test_nat_axis_error(msg, axis):
|
||
|
|
idx = [Timestamp("2020"), NaT]
|
||
|
|
kwargs = {"columns" if axis == 1 else "index": idx}
|
||
|
|
df = DataFrame(np.eye(2), **kwargs)
|
||
|
|
with pytest.raises(ValueError, match=f"{msg} values must not have NaT"):
|
||
|
|
df.rolling("D", axis=axis).mean()
|