1936 lines
58 KiB
Python
1936 lines
58 KiB
Python
from datetime import (
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datetime,
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timedelta,
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)
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import numpy as np
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import pytest
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from pandas.compat import (
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is_platform_arm,
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is_platform_mac,
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is_platform_power,
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)
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from pandas import (
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DataFrame,
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DatetimeIndex,
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MultiIndex,
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Series,
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Timedelta,
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Timestamp,
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date_range,
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period_range,
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to_datetime,
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to_timedelta,
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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.indexers.objects import VariableOffsetWindowIndexer
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from pandas.tseries.offsets import BusinessDay
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def test_doc_string():
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df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
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df
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df.rolling(2).sum()
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df.rolling(2, min_periods=1).sum()
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def test_constructor(frame_or_series):
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# GH 12669
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c = frame_or_series(range(5)).rolling
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# valid
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c(0)
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c(window=2)
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c(window=2, min_periods=1)
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c(window=2, min_periods=1, center=True)
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c(window=2, min_periods=1, center=False)
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# GH 13383
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msg = "window must be an integer 0 or greater"
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with pytest.raises(ValueError, match=msg):
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c(-1)
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@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
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def test_invalid_constructor(frame_or_series, w):
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# not valid
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c = frame_or_series(range(5)).rolling
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msg = "|".join(
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[
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"window must be an integer",
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"passed window foo is not compatible with a datetimelike index",
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]
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)
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with pytest.raises(ValueError, match=msg):
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c(window=w)
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msg = "min_periods must be an integer"
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with pytest.raises(ValueError, match=msg):
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c(window=2, min_periods=w)
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msg = "center must be a boolean"
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with pytest.raises(ValueError, match=msg):
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c(window=2, min_periods=1, center=w)
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@pytest.mark.parametrize(
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"window",
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[
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timedelta(days=3),
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Timedelta(days=3),
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"3D",
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VariableOffsetWindowIndexer(
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index=date_range("2015-12-25", periods=5), offset=BusinessDay(1)
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),
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],
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)
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def test_freq_window_not_implemented(window):
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# GH 15354
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df = DataFrame(
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np.arange(10),
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index=date_range("2015-12-24", periods=10, freq="D"),
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)
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with pytest.raises(
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NotImplementedError, match="step is not supported with frequency windows"
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):
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df.rolling("3D", step=3)
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@pytest.mark.parametrize("agg", ["cov", "corr"])
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def test_step_not_implemented_for_cov_corr(agg):
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# GH 15354
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roll = DataFrame(range(2)).rolling(1, step=2)
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with pytest.raises(NotImplementedError, match="step not implemented"):
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getattr(roll, agg)()
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@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3)])
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def test_constructor_with_timedelta_window(window):
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# GH 15440
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n = 10
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df = DataFrame(
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{"value": np.arange(n)},
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index=date_range("2015-12-24", periods=n, freq="D"),
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)
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expected_data = np.append([0.0, 1.0], np.arange(3.0, 27.0, 3))
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result = df.rolling(window=window).sum()
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expected = DataFrame(
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{"value": expected_data},
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index=date_range("2015-12-24", periods=n, freq="D"),
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)
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tm.assert_frame_equal(result, expected)
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expected = df.rolling("3D").sum()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3), "3D"])
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def test_constructor_timedelta_window_and_minperiods(window, raw):
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# GH 15305
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n = 10
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df = DataFrame(
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{"value": np.arange(n)},
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index=date_range("2017-08-08", periods=n, freq="D"),
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)
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expected = DataFrame(
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{"value": np.append([np.NaN, 1.0], np.arange(3.0, 27.0, 3))},
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index=date_range("2017-08-08", periods=n, freq="D"),
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)
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result_roll_sum = df.rolling(window=window, min_periods=2).sum()
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result_roll_generic = df.rolling(window=window, min_periods=2).apply(sum, raw=raw)
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tm.assert_frame_equal(result_roll_sum, expected)
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tm.assert_frame_equal(result_roll_generic, expected)
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def test_closed_fixed(closed, arithmetic_win_operators):
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# GH 34315
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func_name = arithmetic_win_operators
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df_fixed = DataFrame({"A": [0, 1, 2, 3, 4]})
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df_time = DataFrame({"A": [0, 1, 2, 3, 4]}, index=date_range("2020", periods=5))
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result = getattr(
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df_fixed.rolling(2, closed=closed, min_periods=1),
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func_name,
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)()
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expected = getattr(
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df_time.rolling("2D", closed=closed, min_periods=1),
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func_name,
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)().reset_index(drop=True)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"closed, window_selections",
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[
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(
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"both",
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[
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[True, True, False, False, False],
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[True, True, True, False, False],
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[False, True, True, True, False],
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[False, False, True, True, True],
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[False, False, False, True, True],
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],
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),
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(
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"left",
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[
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[True, False, False, False, False],
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[True, True, False, False, False],
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[False, True, True, False, False],
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[False, False, True, True, False],
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[False, False, False, True, True],
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],
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),
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(
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"right",
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[
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[True, True, False, False, False],
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[False, True, True, False, False],
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[False, False, True, True, False],
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[False, False, False, True, True],
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[False, False, False, False, True],
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],
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),
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(
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"neither",
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[
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[True, False, False, False, False],
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[False, True, False, False, False],
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[False, False, True, False, False],
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[False, False, False, True, False],
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[False, False, False, False, True],
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],
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),
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],
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)
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def test_datetimelike_centered_selections(
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closed, window_selections, arithmetic_win_operators
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):
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# GH 34315
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func_name = arithmetic_win_operators
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df_time = DataFrame(
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{"A": [0.0, 1.0, 2.0, 3.0, 4.0]}, index=date_range("2020", periods=5)
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)
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expected = DataFrame(
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{"A": [getattr(df_time["A"].iloc[s], func_name)() for s in window_selections]},
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index=date_range("2020", periods=5),
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)
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if func_name == "sem":
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kwargs = {"ddof": 0}
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else:
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kwargs = {}
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result = getattr(
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df_time.rolling("2D", closed=closed, min_periods=1, center=True),
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func_name,
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)(**kwargs)
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tm.assert_frame_equal(result, expected, check_dtype=False)
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@pytest.mark.parametrize(
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"window,closed,expected",
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[
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("3s", "right", [3.0, 3.0, 3.0]),
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("3s", "both", [3.0, 3.0, 3.0]),
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("3s", "left", [3.0, 3.0, 3.0]),
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("3s", "neither", [3.0, 3.0, 3.0]),
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("2s", "right", [3.0, 2.0, 2.0]),
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("2s", "both", [3.0, 3.0, 3.0]),
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("2s", "left", [1.0, 3.0, 3.0]),
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("2s", "neither", [1.0, 2.0, 2.0]),
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],
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)
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def test_datetimelike_centered_offset_covers_all(
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window, closed, expected, frame_or_series
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):
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# GH 42753
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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 09:00:02"),
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]
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df = frame_or_series([1, 1, 1], index=index)
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result = df.rolling(window, closed=closed, center=True).sum()
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expected = frame_or_series(expected, index=index)
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize(
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"window,closed,expected",
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[
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("2D", "right", [4, 4, 4, 4, 4, 4, 2, 2]),
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("2D", "left", [2, 2, 4, 4, 4, 4, 4, 4]),
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("2D", "both", [4, 4, 6, 6, 6, 6, 4, 4]),
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("2D", "neither", [2, 2, 2, 2, 2, 2, 2, 2]),
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],
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)
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def test_datetimelike_nonunique_index_centering(
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window, closed, expected, frame_or_series
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):
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index = DatetimeIndex(
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[
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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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"2020-01-02",
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"2020-01-03",
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"2020-01-03",
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"2020-01-04",
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"2020-01-04",
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]
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)
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df = frame_or_series([1] * 8, index=index, dtype=float)
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expected = frame_or_series(expected, index=index, dtype=float)
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result = df.rolling(window, center=True, closed=closed).sum()
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tm.assert_equal(result, expected)
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def test_even_number_window_alignment():
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# see discussion in GH 38780
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s = Series(range(3), index=date_range(start="2020-01-01", freq="D", periods=3))
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# behavior of index- and datetime-based windows differs here!
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# s.rolling(window=2, min_periods=1, center=True).mean()
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result = s.rolling(window="2D", min_periods=1, center=True).mean()
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expected = Series([0.5, 1.5, 2], index=s.index)
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tm.assert_series_equal(result, expected)
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def test_closed_fixed_binary_col(center, step):
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# GH 34315
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data = [0, 1, 1, 0, 0, 1, 0, 1]
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df = DataFrame(
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{"binary_col": data},
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index=date_range(start="2020-01-01", freq="min", periods=len(data)),
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)
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if center:
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expected_data = [2 / 3, 0.5, 0.4, 0.5, 0.428571, 0.5, 0.571429, 0.5]
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else:
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expected_data = [np.nan, 0, 0.5, 2 / 3, 0.5, 0.4, 0.5, 0.428571]
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expected = DataFrame(
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expected_data,
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columns=["binary_col"],
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index=date_range(start="2020-01-01", freq="min", periods=len(expected_data)),
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)[::step]
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rolling = df.rolling(
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window=len(df), closed="left", min_periods=1, center=center, step=step
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)
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result = rolling.mean()
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("closed", ["neither", "left"])
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def test_closed_empty(closed, arithmetic_win_operators):
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# GH 26005
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func_name = arithmetic_win_operators
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ser = Series(data=np.arange(5), index=date_range("2000", periods=5, freq="2D"))
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roll = ser.rolling("1D", closed=closed)
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result = getattr(roll, func_name)()
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expected = Series([np.nan] * 5, index=ser.index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("func", ["min", "max"])
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def test_closed_one_entry(func):
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# GH24718
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ser = Series(data=[2], index=date_range("2000", periods=1))
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result = getattr(ser.rolling("10D", closed="left"), func)()
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tm.assert_series_equal(result, Series([np.nan], index=ser.index))
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@pytest.mark.parametrize("func", ["min", "max"])
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def test_closed_one_entry_groupby(func):
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# GH24718
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ser = DataFrame(
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data={"A": [1, 1, 2], "B": [3, 2, 1]},
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index=date_range("2000", periods=3),
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)
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result = getattr(
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ser.groupby("A", sort=False)["B"].rolling("10D", closed="left"), func
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)()
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exp_idx = MultiIndex.from_arrays(arrays=[[1, 1, 2], ser.index], names=("A", None))
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expected = Series(data=[np.nan, 3, np.nan], index=exp_idx, name="B")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("input_dtype", ["int", "float"])
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@pytest.mark.parametrize(
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"func,closed,expected",
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[
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("min", "right", [0.0, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
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("min", "both", [0.0, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
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("min", "neither", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
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("min", "left", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
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("max", "right", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
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("max", "both", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
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("max", "neither", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
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("max", "left", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
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],
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)
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def test_closed_min_max_datetime(input_dtype, func, closed, expected):
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# see gh-21704
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ser = Series(
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data=np.arange(10).astype(input_dtype),
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index=date_range("2000", periods=10),
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)
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result = getattr(ser.rolling("3D", closed=closed), func)()
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expected = Series(expected, index=ser.index)
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tm.assert_series_equal(result, expected)
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def test_closed_uneven():
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# see gh-21704
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ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
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# uneven
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ser = ser.drop(index=ser.index[[1, 5]])
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result = ser.rolling("3D", closed="left").min()
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expected = Series([np.nan, 0, 0, 2, 3, 4, 6, 6], index=ser.index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"func,closed,expected",
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[
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("min", "right", [np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
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("min", "both", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, np.nan]),
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("min", "neither", [np.nan, np.nan, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
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("min", "left", [np.nan, np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan]),
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("max", "right", [np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan, np.nan]),
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("max", "both", [np.nan, 1, 2, 3, 4, 5, 6, 6, 6, np.nan]),
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("max", "neither", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, np.nan, np.nan]),
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("max", "left", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan]),
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],
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)
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def test_closed_min_max_minp(func, closed, expected):
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# see gh-21704
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ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
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# Explicit cast to float to avoid implicit cast when setting nan
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ser = ser.astype("float")
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ser[ser.index[-3:]] = np.nan
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result = getattr(ser.rolling("3D", min_periods=2, closed=closed), func)()
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expected = Series(expected, index=ser.index)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"closed,expected",
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[
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("right", [0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8]),
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("both", [0, 0.5, 1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
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("neither", [np.nan, 0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
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("left", [np.nan, 0, 0.5, 1, 2, 3, 4, 5, 6, 7]),
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],
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)
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def test_closed_median_quantile(closed, expected):
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# GH 26005
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ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
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roll = ser.rolling("3D", closed=closed)
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expected = Series(expected, index=ser.index)
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result = roll.median()
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tm.assert_series_equal(result, expected)
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result = roll.quantile(0.5)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("roller", ["1s", 1])
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def tests_empty_df_rolling(roller):
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# GH 15819 Verifies that datetime and integer rolling windows can be
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# applied to empty DataFrames
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expected = DataFrame()
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result = DataFrame().rolling(roller).sum()
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tm.assert_frame_equal(result, expected)
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# Verifies that datetime and integer rolling windows can be applied to
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# empty DataFrames with datetime index
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expected = DataFrame(index=DatetimeIndex([]))
|
|
result = DataFrame(index=DatetimeIndex([])).rolling(roller).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_empty_window_median_quantile():
|
|
# GH 26005
|
|
expected = Series([np.nan, np.nan, np.nan])
|
|
roll = Series(np.arange(3)).rolling(0)
|
|
|
|
result = roll.median()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = roll.quantile(0.1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_missing_minp_zero():
|
|
# https://github.com/pandas-dev/pandas/pull/18921
|
|
# minp=0
|
|
x = Series([np.nan])
|
|
result = x.rolling(1, min_periods=0).sum()
|
|
expected = Series([0.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# minp=1
|
|
result = x.rolling(1, min_periods=1).sum()
|
|
expected = Series([np.nan])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_missing_minp_zero_variable():
|
|
# https://github.com/pandas-dev/pandas/pull/18921
|
|
x = Series(
|
|
[np.nan] * 4,
|
|
index=DatetimeIndex(["2017-01-01", "2017-01-04", "2017-01-06", "2017-01-07"]),
|
|
)
|
|
result = x.rolling(Timedelta("2d"), min_periods=0).sum()
|
|
expected = Series(0.0, index=x.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_multi_index_names():
|
|
# GH 16789, 16825
|
|
cols = MultiIndex.from_product([["A", "B"], ["C", "D", "E"]], names=["1", "2"])
|
|
df = DataFrame(np.ones((10, 6)), columns=cols)
|
|
result = df.rolling(3).cov()
|
|
|
|
tm.assert_index_equal(result.columns, df.columns)
|
|
assert result.index.names == [None, "1", "2"]
|
|
|
|
|
|
def test_rolling_axis_sum(axis_frame):
|
|
# see gh-23372.
|
|
df = DataFrame(np.ones((10, 20)))
|
|
axis = df._get_axis_number(axis_frame)
|
|
|
|
if axis == 0:
|
|
expected = DataFrame({i: [np.nan] * 2 + [3.0] * 8 for i in range(20)})
|
|
else:
|
|
# axis == 1
|
|
expected = DataFrame([[np.nan] * 2 + [3.0] * 18] * 10)
|
|
|
|
result = df.rolling(3, axis=axis_frame).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_axis_count(axis_frame):
|
|
# see gh-26055
|
|
df = DataFrame({"x": range(3), "y": range(3)})
|
|
|
|
axis = df._get_axis_number(axis_frame)
|
|
|
|
if axis in [0, "index"]:
|
|
expected = DataFrame({"x": [1.0, 2.0, 2.0], "y": [1.0, 2.0, 2.0]})
|
|
else:
|
|
expected = DataFrame({"x": [1.0, 1.0, 1.0], "y": [2.0, 2.0, 2.0]})
|
|
|
|
result = df.rolling(2, axis=axis_frame, min_periods=0).count()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_readonly_array():
|
|
# GH-27766
|
|
arr = np.array([1, 3, np.nan, 3, 5])
|
|
arr.setflags(write=False)
|
|
result = Series(arr).rolling(2).mean()
|
|
expected = Series([np.nan, 2, np.nan, np.nan, 4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_datetime(axis_frame, tz_naive_fixture):
|
|
# GH-28192
|
|
tz = tz_naive_fixture
|
|
df = DataFrame(
|
|
{i: [1] * 2 for i in date_range("2019-8-01", "2019-08-03", freq="D", tz=tz)}
|
|
)
|
|
if axis_frame in [0, "index"]:
|
|
result = df.T.rolling("2D", axis=axis_frame).sum().T
|
|
else:
|
|
result = df.rolling("2D", axis=axis_frame).sum()
|
|
expected = DataFrame(
|
|
{
|
|
**{
|
|
i: [1.0] * 2
|
|
for i in date_range("2019-8-01", periods=1, freq="D", tz=tz)
|
|
},
|
|
**{
|
|
i: [2.0] * 2
|
|
for i in date_range("2019-8-02", "2019-8-03", freq="D", tz=tz)
|
|
},
|
|
}
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"center, expected_data",
|
|
[
|
|
(
|
|
True,
|
|
(
|
|
[88.0] * 7
|
|
+ [97.0] * 9
|
|
+ [98.0]
|
|
+ [99.0] * 21
|
|
+ [95.0] * 16
|
|
+ [93.0] * 5
|
|
+ [89.0] * 5
|
|
+ [96.0] * 21
|
|
+ [94.0] * 14
|
|
+ [90.0] * 13
|
|
+ [88.0] * 2
|
|
+ [90.0] * 9
|
|
+ [96.0] * 21
|
|
+ [95.0] * 6
|
|
+ [91.0]
|
|
+ [87.0] * 6
|
|
+ [92.0] * 21
|
|
+ [83.0] * 2
|
|
+ [86.0] * 10
|
|
+ [87.0] * 5
|
|
+ [98.0] * 21
|
|
+ [97.0] * 14
|
|
+ [93.0] * 7
|
|
+ [87.0] * 4
|
|
+ [86.0] * 4
|
|
+ [95.0] * 21
|
|
+ [85.0] * 14
|
|
+ [83.0] * 2
|
|
+ [76.0] * 5
|
|
+ [81.0] * 2
|
|
+ [98.0] * 21
|
|
+ [95.0] * 14
|
|
+ [91.0] * 7
|
|
+ [86.0]
|
|
+ [93.0] * 3
|
|
+ [95.0] * 29
|
|
+ [77.0] * 2
|
|
),
|
|
),
|
|
(
|
|
False,
|
|
(
|
|
[np.nan] * 2
|
|
+ [88.0] * 16
|
|
+ [97.0] * 9
|
|
+ [98.0]
|
|
+ [99.0] * 21
|
|
+ [95.0] * 16
|
|
+ [93.0] * 5
|
|
+ [89.0] * 5
|
|
+ [96.0] * 21
|
|
+ [94.0] * 14
|
|
+ [90.0] * 13
|
|
+ [88.0] * 2
|
|
+ [90.0] * 9
|
|
+ [96.0] * 21
|
|
+ [95.0] * 6
|
|
+ [91.0]
|
|
+ [87.0] * 6
|
|
+ [92.0] * 21
|
|
+ [83.0] * 2
|
|
+ [86.0] * 10
|
|
+ [87.0] * 5
|
|
+ [98.0] * 21
|
|
+ [97.0] * 14
|
|
+ [93.0] * 7
|
|
+ [87.0] * 4
|
|
+ [86.0] * 4
|
|
+ [95.0] * 21
|
|
+ [85.0] * 14
|
|
+ [83.0] * 2
|
|
+ [76.0] * 5
|
|
+ [81.0] * 2
|
|
+ [98.0] * 21
|
|
+ [95.0] * 14
|
|
+ [91.0] * 7
|
|
+ [86.0]
|
|
+ [93.0] * 3
|
|
+ [95.0] * 20
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_rolling_window_as_string(center, expected_data):
|
|
# see gh-22590
|
|
date_today = datetime.now()
|
|
days = date_range(date_today, date_today + timedelta(365), freq="D")
|
|
|
|
npr = np.random.RandomState(seed=421)
|
|
|
|
data = npr.randint(1, high=100, size=len(days))
|
|
df = DataFrame({"DateCol": days, "metric": data})
|
|
|
|
df.set_index("DateCol", inplace=True)
|
|
result = df.rolling(window="21D", min_periods=2, closed="left", center=center)[
|
|
"metric"
|
|
].agg("max")
|
|
|
|
index = days.rename("DateCol")
|
|
index = index._with_freq(None)
|
|
expected = Series(expected_data, index=index, name="metric")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_min_periods1():
|
|
# GH#6795
|
|
df = DataFrame([0, 1, 2, 1, 0], columns=["a"])
|
|
result = df["a"].rolling(3, center=True, min_periods=1).max()
|
|
expected = Series([1.0, 2.0, 2.0, 2.0, 1.0], name="a")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_count_with_min_periods(frame_or_series):
|
|
# GH 26996
|
|
result = frame_or_series(range(5)).rolling(3, min_periods=3).count()
|
|
expected = frame_or_series([np.nan, np.nan, 3.0, 3.0, 3.0])
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
def test_rolling_count_default_min_periods_with_null_values(frame_or_series):
|
|
# GH 26996
|
|
values = [1, 2, 3, np.nan, 4, 5, 6]
|
|
expected_counts = [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 3.0]
|
|
|
|
# GH 31302
|
|
result = frame_or_series(values).rolling(3, min_periods=0).count()
|
|
expected = frame_or_series(expected_counts)
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"df,expected,window,min_periods",
|
|
[
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
3,
|
|
None,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
2,
|
|
1,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
2,
|
|
2,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
1,
|
|
1,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
1,
|
|
0,
|
|
),
|
|
(DataFrame({"A": [1], "B": [4]}), [], 2, None),
|
|
(DataFrame({"A": [1], "B": [4]}), [], 2, 1),
|
|
(DataFrame(), [({}, [])], 2, None),
|
|
(
|
|
DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
|
|
[
|
|
({"A": [1.0], "B": [np.nan]}, [0]),
|
|
({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
|
|
({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
3,
|
|
2,
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_dataframe(df, expected, window, min_periods):
|
|
# GH 11704
|
|
expected = [DataFrame(values, index=index) for (values, index) in expected]
|
|
|
|
for expected, actual in zip(expected, df.rolling(window, min_periods=min_periods)):
|
|
tm.assert_frame_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"expected,window",
|
|
[
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
"2D",
|
|
),
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
"3D",
|
|
),
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
"1D",
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_on_dataframe(expected, window):
|
|
# GH 11704, 40373
|
|
df = DataFrame(
|
|
{
|
|
"A": [1, 2, 3, 4, 5],
|
|
"B": [4, 5, 6, 7, 8],
|
|
"C": date_range(start="2016-01-01", periods=5, freq="D"),
|
|
}
|
|
)
|
|
|
|
expected = [
|
|
DataFrame(values, index=df.loc[index, "C"]) for (values, index) in expected
|
|
]
|
|
for expected, actual in zip(expected, df.rolling(window, on="C")):
|
|
tm.assert_frame_equal(actual, expected)
|
|
|
|
|
|
def test_iter_rolling_on_dataframe_unordered():
|
|
# GH 43386
|
|
df = DataFrame({"a": ["x", "y", "x"], "b": [0, 1, 2]})
|
|
results = list(df.groupby("a").rolling(2))
|
|
expecteds = [df.iloc[idx, [1]] for idx in [[0], [0, 2], [1]]]
|
|
for result, expected in zip(results, expecteds):
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ser,expected,window, min_periods",
|
|
[
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
|
|
3,
|
|
None,
|
|
),
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
|
|
3,
|
|
1,
|
|
),
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])],
|
|
2,
|
|
1,
|
|
),
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])],
|
|
2,
|
|
2,
|
|
),
|
|
(Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 0),
|
|
(Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 1),
|
|
(Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2, 0),
|
|
(Series([], dtype="int64"), [], 2, 1),
|
|
],
|
|
)
|
|
def test_iter_rolling_series(ser, expected, window, min_periods):
|
|
# GH 11704
|
|
expected = [Series(values, index=index) for (values, index) in expected]
|
|
|
|
for expected, actual in zip(expected, ser.rolling(window, min_periods=min_periods)):
|
|
tm.assert_series_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"expected,expected_index,window",
|
|
[
|
|
(
|
|
[[0], [1], [2], [3], [4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-02", periods=1, freq="D"),
|
|
date_range("2020-01-03", periods=1, freq="D"),
|
|
date_range("2020-01-04", periods=1, freq="D"),
|
|
date_range("2020-01-05", periods=1, freq="D"),
|
|
],
|
|
"1D",
|
|
),
|
|
(
|
|
[[0], [0, 1], [1, 2], [2, 3], [3, 4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-01", periods=2, freq="D"),
|
|
date_range("2020-01-02", periods=2, freq="D"),
|
|
date_range("2020-01-03", periods=2, freq="D"),
|
|
date_range("2020-01-04", periods=2, freq="D"),
|
|
],
|
|
"2D",
|
|
),
|
|
(
|
|
[[0], [0, 1], [0, 1, 2], [1, 2, 3], [2, 3, 4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-01", periods=2, freq="D"),
|
|
date_range("2020-01-01", periods=3, freq="D"),
|
|
date_range("2020-01-02", periods=3, freq="D"),
|
|
date_range("2020-01-03", periods=3, freq="D"),
|
|
],
|
|
"3D",
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_datetime(expected, expected_index, window):
|
|
# GH 11704
|
|
ser = Series(range(5), index=date_range(start="2020-01-01", periods=5, freq="D"))
|
|
|
|
expected = [
|
|
Series(values, index=idx) for (values, idx) in zip(expected, expected_index)
|
|
]
|
|
|
|
for expected, actual in zip(expected, ser.rolling(window)):
|
|
tm.assert_series_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"grouping,_index",
|
|
[
|
|
(
|
|
{"level": 0},
|
|
MultiIndex.from_tuples(
|
|
[(0, 0), (0, 0), (1, 1), (1, 1), (1, 1)], names=[None, None]
|
|
),
|
|
),
|
|
(
|
|
{"by": "X"},
|
|
MultiIndex.from_tuples(
|
|
[(0, 0), (1, 0), (2, 1), (3, 1), (4, 1)], names=["X", None]
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_rolling_positional_argument(grouping, _index, raw):
|
|
# GH 34605
|
|
|
|
def scaled_sum(*args):
|
|
if len(args) < 2:
|
|
raise ValueError("The function needs two arguments")
|
|
array, scale = args
|
|
return array.sum() / scale
|
|
|
|
df = DataFrame(data={"X": range(5)}, index=[0, 0, 1, 1, 1])
|
|
|
|
expected = DataFrame(data={"X": [0.0, 0.5, 1.0, 1.5, 2.0]}, index=_index)
|
|
# GH 40341
|
|
if "by" in grouping:
|
|
expected = expected.drop(columns="X", errors="ignore")
|
|
result = df.groupby(**grouping).rolling(1).apply(scaled_sum, raw=raw, args=(2,))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("add", [0.0, 2.0])
|
|
def test_rolling_numerical_accuracy_kahan_mean(add):
|
|
# GH: 36031 implementing kahan summation
|
|
df = DataFrame(
|
|
{"A": [3002399751580331.0 + add, -0.0, -0.0]},
|
|
index=[
|
|
Timestamp("19700101 09:00:00"),
|
|
Timestamp("19700101 09:00:03"),
|
|
Timestamp("19700101 09:00:06"),
|
|
],
|
|
)
|
|
result = (
|
|
df.resample("1s").ffill().rolling("3s", closed="left", min_periods=3).mean()
|
|
)
|
|
dates = date_range("19700101 09:00:00", periods=7, freq="S")
|
|
expected = DataFrame(
|
|
{
|
|
"A": [
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
3002399751580330.5,
|
|
2001599834386887.25,
|
|
1000799917193443.625,
|
|
0.0,
|
|
]
|
|
},
|
|
index=dates,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_numerical_accuracy_kahan_sum():
|
|
# GH: 13254
|
|
df = DataFrame([2.186, -1.647, 0.0, 0.0, 0.0, 0.0], columns=["x"])
|
|
result = df["x"].rolling(3).sum()
|
|
expected = Series([np.nan, np.nan, 0.539, -1.647, 0.0, 0.0], name="x")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_numerical_accuracy_jump():
|
|
# GH: 32761
|
|
index = date_range(start="2020-01-01", end="2020-01-02", freq="60s").append(
|
|
DatetimeIndex(["2020-01-03"])
|
|
)
|
|
data = np.random.rand(len(index))
|
|
|
|
df = DataFrame({"data": data}, index=index)
|
|
result = df.rolling("60s").mean()
|
|
tm.assert_frame_equal(result, df[["data"]])
|
|
|
|
|
|
def test_rolling_numerical_accuracy_small_values():
|
|
# GH: 10319
|
|
s = Series(
|
|
data=[0.00012456, 0.0003, -0.0, -0.0],
|
|
index=date_range("1999-02-03", "1999-02-06"),
|
|
)
|
|
result = s.rolling(1).mean()
|
|
tm.assert_series_equal(result, s)
|
|
|
|
|
|
def test_rolling_numerical_too_large_numbers():
|
|
# GH: 11645
|
|
dates = date_range("2015-01-01", periods=10, freq="D")
|
|
ds = Series(data=range(10), index=dates, dtype=np.float64)
|
|
ds[2] = -9e33
|
|
result = ds.rolling(5).mean()
|
|
expected = Series(
|
|
[
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
np.nan,
|
|
-1.8e33,
|
|
-1.8e33,
|
|
-1.8e33,
|
|
5.0,
|
|
6.0,
|
|
7.0,
|
|
],
|
|
index=dates,
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("func", "value"),
|
|
[("sum", 2.0), ("max", 1.0), ("min", 1.0), ("mean", 1.0), ("median", 1.0)],
|
|
)
|
|
def test_rolling_mixed_dtypes_axis_1(func, value):
|
|
# GH: 20649
|
|
df = DataFrame(1, index=[1, 2], columns=["a", "b", "c"])
|
|
df["c"] = 1.0
|
|
result = getattr(df.rolling(window=2, min_periods=1, axis=1), func)()
|
|
expected = DataFrame(
|
|
{"a": [1.0, 1.0], "b": [value, value], "c": [value, value]},
|
|
index=[1, 2],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_axis_one_with_nan():
|
|
# GH: 35596
|
|
df = DataFrame(
|
|
[
|
|
[0, 1, 2, 4, np.nan, np.nan, np.nan],
|
|
[0, 1, 2, np.nan, np.nan, np.nan, np.nan],
|
|
[0, 2, 2, np.nan, 2, np.nan, 1],
|
|
]
|
|
)
|
|
result = df.rolling(window=7, min_periods=1, axis="columns").sum()
|
|
expected = DataFrame(
|
|
[
|
|
[0.0, 1.0, 3.0, 7.0, 7.0, 7.0, 7.0],
|
|
[0.0, 1.0, 3.0, 3.0, 3.0, 3.0, 3.0],
|
|
[0.0, 2.0, 4.0, 4.0, 6.0, 6.0, 7.0],
|
|
]
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"value",
|
|
["test", to_datetime("2019-12-31"), to_timedelta("1 days 06:05:01.00003")],
|
|
)
|
|
def test_rolling_axis_1_non_numeric_dtypes(value):
|
|
# GH: 20649
|
|
df = DataFrame({"a": [1, 2]})
|
|
df["b"] = value
|
|
result = df.rolling(window=2, min_periods=1, axis=1).sum()
|
|
expected = DataFrame({"a": [1.0, 2.0]})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_on_df_transposed():
|
|
# GH: 32724
|
|
df = DataFrame({"A": [1, None], "B": [4, 5], "C": [7, 8]})
|
|
expected = DataFrame({"A": [1.0, np.nan], "B": [5.0, 5.0], "C": [11.0, 13.0]})
|
|
result = df.rolling(min_periods=1, window=2, axis=1).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.T.rolling(min_periods=1, window=2).sum().T
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("index", "window"),
|
|
[
|
|
(
|
|
period_range(start="2020-01-01 08:00", end="2020-01-01 08:08", freq="T"),
|
|
"2T",
|
|
),
|
|
(
|
|
period_range(start="2020-01-01 08:00", end="2020-01-01 12:00", freq="30T"),
|
|
"1h",
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
("func", "values"),
|
|
[
|
|
("min", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6]),
|
|
("max", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7]),
|
|
("sum", [np.nan, 0, 1, 3, 5, 7, 9, 11, 13]),
|
|
],
|
|
)
|
|
def test_rolling_period_index(index, window, func, values):
|
|
# GH: 34225
|
|
ds = Series([0, 1, 2, 3, 4, 5, 6, 7, 8], index=index)
|
|
result = getattr(ds.rolling(window, closed="left"), func)()
|
|
expected = Series(values, index=index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_sem(frame_or_series):
|
|
# GH: 26476
|
|
obj = frame_or_series([0, 1, 2])
|
|
result = obj.rolling(2, min_periods=1).sem()
|
|
if isinstance(result, DataFrame):
|
|
result = Series(result[0].values)
|
|
expected = Series([np.nan] + [0.7071067811865476] * 2)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.xfail(
|
|
(is_platform_arm() and not is_platform_mac()) or is_platform_power(),
|
|
reason="GH 38921",
|
|
)
|
|
@pytest.mark.parametrize(
|
|
("func", "third_value", "values"),
|
|
[
|
|
("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
|
|
("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
|
|
("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
|
|
("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
|
|
],
|
|
)
|
|
def test_rolling_var_numerical_issues(func, third_value, values):
|
|
# GH: 37051
|
|
ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
|
|
result = getattr(ds.rolling(2), func)()
|
|
expected = Series([np.nan] + values)
|
|
tm.assert_series_equal(result, expected)
|
|
# GH 42064
|
|
# new `roll_var` will output 0.0 correctly
|
|
tm.assert_series_equal(result == 0, expected == 0)
|
|
|
|
|
|
def test_timeoffset_as_window_parameter_for_corr():
|
|
# GH: 28266
|
|
exp = DataFrame(
|
|
{
|
|
"B": [
|
|
np.nan,
|
|
np.nan,
|
|
0.9999999999999998,
|
|
-1.0,
|
|
1.0,
|
|
-0.3273268353539892,
|
|
0.9999999999999998,
|
|
1.0,
|
|
0.9999999999999998,
|
|
1.0,
|
|
],
|
|
"A": [
|
|
np.nan,
|
|
np.nan,
|
|
-1.0,
|
|
1.0000000000000002,
|
|
-0.3273268353539892,
|
|
0.9999999999999966,
|
|
1.0,
|
|
1.0000000000000002,
|
|
1.0,
|
|
1.0000000000000002,
|
|
],
|
|
},
|
|
index=MultiIndex.from_tuples(
|
|
[
|
|
(Timestamp("20130101 09:00:00"), "B"),
|
|
(Timestamp("20130101 09:00:00"), "A"),
|
|
(Timestamp("20130102 09:00:02"), "B"),
|
|
(Timestamp("20130102 09:00:02"), "A"),
|
|
(Timestamp("20130103 09:00:03"), "B"),
|
|
(Timestamp("20130103 09:00:03"), "A"),
|
|
(Timestamp("20130105 09:00:05"), "B"),
|
|
(Timestamp("20130105 09:00:05"), "A"),
|
|
(Timestamp("20130106 09:00:06"), "B"),
|
|
(Timestamp("20130106 09:00:06"), "A"),
|
|
]
|
|
),
|
|
)
|
|
|
|
df = DataFrame(
|
|
{"B": [0, 1, 2, 4, 3], "A": [7, 4, 6, 9, 3]},
|
|
index=[
|
|
Timestamp("20130101 09:00:00"),
|
|
Timestamp("20130102 09:00:02"),
|
|
Timestamp("20130103 09:00:03"),
|
|
Timestamp("20130105 09:00:05"),
|
|
Timestamp("20130106 09:00:06"),
|
|
],
|
|
)
|
|
|
|
res = df.rolling(window="3d").corr()
|
|
|
|
tm.assert_frame_equal(exp, res)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["var", "sum", "mean", "skew", "kurt", "min", "max"])
|
|
def test_rolling_decreasing_indices(method):
|
|
"""
|
|
Make sure that decreasing indices give the same results as increasing indices.
|
|
|
|
GH 36933
|
|
"""
|
|
df = DataFrame({"values": np.arange(-15, 10) ** 2})
|
|
df_reverse = DataFrame({"values": df["values"][::-1]}, index=df.index[::-1])
|
|
|
|
increasing = getattr(df.rolling(window=5), method)()
|
|
decreasing = getattr(df_reverse.rolling(window=5), method)()
|
|
|
|
assert np.abs(decreasing.values[::-1][:-4] - increasing.values[4:]).max() < 1e-12
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"window,closed,expected",
|
|
[
|
|
("2s", "right", [1.0, 3.0, 5.0, 3.0]),
|
|
("2s", "left", [0.0, 1.0, 3.0, 5.0]),
|
|
("2s", "both", [1.0, 3.0, 6.0, 5.0]),
|
|
("2s", "neither", [0.0, 1.0, 2.0, 3.0]),
|
|
("3s", "right", [1.0, 3.0, 6.0, 5.0]),
|
|
("3s", "left", [1.0, 3.0, 6.0, 5.0]),
|
|
("3s", "both", [1.0, 3.0, 6.0, 5.0]),
|
|
("3s", "neither", [1.0, 3.0, 6.0, 5.0]),
|
|
],
|
|
)
|
|
def test_rolling_decreasing_indices_centered(window, closed, expected, frame_or_series):
|
|
"""
|
|
Ensure that a symmetrical inverted index return same result as non-inverted.
|
|
"""
|
|
# GH 43927
|
|
|
|
index = date_range("2020", periods=4, freq="1s")
|
|
df_inc = frame_or_series(range(4), index=index)
|
|
df_dec = frame_or_series(range(4), index=index[::-1])
|
|
|
|
expected_inc = frame_or_series(expected, index=index)
|
|
expected_dec = frame_or_series(expected, index=index[::-1])
|
|
|
|
result_inc = df_inc.rolling(window, closed=closed, center=True).sum()
|
|
result_dec = df_dec.rolling(window, closed=closed, center=True).sum()
|
|
|
|
tm.assert_equal(result_inc, expected_inc)
|
|
tm.assert_equal(result_dec, expected_dec)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"window,expected",
|
|
[
|
|
("1ns", [1.0, 1.0, 1.0, 1.0]),
|
|
("3ns", [2.0, 3.0, 3.0, 2.0]),
|
|
],
|
|
)
|
|
def test_rolling_center_nanosecond_resolution(
|
|
window, closed, expected, frame_or_series
|
|
):
|
|
index = date_range("2020", periods=4, freq="1ns")
|
|
df = frame_or_series([1, 1, 1, 1], index=index, dtype=float)
|
|
expected = frame_or_series(expected, index=index, dtype=float)
|
|
result = df.rolling(window, closed=closed, center=True).sum()
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"method,expected",
|
|
[
|
|
(
|
|
"var",
|
|
[
|
|
float("nan"),
|
|
43.0,
|
|
float("nan"),
|
|
136.333333,
|
|
43.5,
|
|
94.966667,
|
|
182.0,
|
|
318.0,
|
|
],
|
|
),
|
|
(
|
|
"mean",
|
|
[float("nan"), 7.5, float("nan"), 21.5, 6.0, 9.166667, 13.0, 17.5],
|
|
),
|
|
(
|
|
"sum",
|
|
[float("nan"), 30.0, float("nan"), 86.0, 30.0, 55.0, 91.0, 140.0],
|
|
),
|
|
(
|
|
"skew",
|
|
[
|
|
float("nan"),
|
|
0.709296,
|
|
float("nan"),
|
|
0.407073,
|
|
0.984656,
|
|
0.919184,
|
|
0.874674,
|
|
0.842418,
|
|
],
|
|
),
|
|
(
|
|
"kurt",
|
|
[
|
|
float("nan"),
|
|
-0.5916711736073559,
|
|
float("nan"),
|
|
-1.0028993131317954,
|
|
-0.06103844629409494,
|
|
-0.254143227116194,
|
|
-0.37362637362637585,
|
|
-0.45439658241367054,
|
|
],
|
|
),
|
|
],
|
|
)
|
|
def test_rolling_non_monotonic(method, expected):
|
|
"""
|
|
Make sure the (rare) branch of non-monotonic indices is covered by a test.
|
|
|
|
output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
|
|
manually been verified.
|
|
|
|
GH 36933.
|
|
"""
|
|
# Based on an example found in computation.rst
|
|
use_expanding = [True, False, True, False, True, True, True, True]
|
|
df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})
|
|
|
|
class CustomIndexer(BaseIndexer):
|
|
def get_window_bounds(self, num_values, min_periods, center, closed, step):
|
|
start = np.empty(num_values, dtype=np.int64)
|
|
end = np.empty(num_values, dtype=np.int64)
|
|
for i in range(num_values):
|
|
if self.use_expanding[i]:
|
|
start[i] = 0
|
|
end[i] = i + 1
|
|
else:
|
|
start[i] = i
|
|
end[i] = i + self.window_size
|
|
return start, end
|
|
|
|
indexer = CustomIndexer(window_size=4, use_expanding=use_expanding)
|
|
|
|
result = getattr(df.rolling(indexer), method)()
|
|
expected = DataFrame({"values": expected})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("index", "window"),
|
|
[
|
|
([0, 1, 2, 3, 4], 2),
|
|
(date_range("2001-01-01", freq="D", periods=5), "2D"),
|
|
],
|
|
)
|
|
def test_rolling_corr_timedelta_index(index, window):
|
|
# GH: 31286
|
|
x = Series([1, 2, 3, 4, 5], index=index)
|
|
y = x.copy()
|
|
x.iloc[0:2] = 0.0
|
|
result = x.rolling(window).corr(y)
|
|
expected = Series([np.nan, np.nan, 1, 1, 1], index=index)
|
|
tm.assert_almost_equal(result, expected)
|
|
|
|
|
|
def test_groupby_rolling_nan_included():
|
|
# GH 35542
|
|
data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}
|
|
df = DataFrame(data)
|
|
result = df.groupby("group", dropna=False).rolling(1, min_periods=1).mean()
|
|
expected = DataFrame(
|
|
{"B": [0.0, 2.0, 3.0, 1.0, 4.0]},
|
|
# GH-38057 from_tuples puts the NaNs in the codes, result expects them
|
|
# to be in the levels, at the moment
|
|
# index=MultiIndex.from_tuples(
|
|
# [("g1", 0), ("g1", 2), ("g2", 3), (np.nan, 1), (np.nan, 4)],
|
|
# names=["group", None],
|
|
# ),
|
|
index=MultiIndex(
|
|
[["g1", "g2", np.nan], [0, 1, 2, 3, 4]],
|
|
[[0, 0, 1, 2, 2], [0, 2, 3, 1, 4]],
|
|
names=["group", None],
|
|
),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["skew", "kurt"])
|
|
def test_rolling_skew_kurt_numerical_stability(method):
|
|
# GH#6929
|
|
ser = Series(np.random.rand(10))
|
|
ser_copy = ser.copy()
|
|
expected = getattr(ser.rolling(3), method)()
|
|
tm.assert_series_equal(ser, ser_copy)
|
|
ser = ser + 50000
|
|
result = getattr(ser.rolling(3), method)()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
("method", "values"),
|
|
[
|
|
("skew", [2.0, 0.854563, 0.0, 1.999984]),
|
|
("kurt", [4.0, -1.289256, -1.2, 3.999946]),
|
|
],
|
|
)
|
|
def test_rolling_skew_kurt_large_value_range(method, values):
|
|
# GH: 37557
|
|
s = Series([3000000, 1, 1, 2, 3, 4, 999])
|
|
result = getattr(s.rolling(4), method)()
|
|
expected = Series([np.nan] * 3 + values)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_invalid_method():
|
|
with pytest.raises(ValueError, match="method must be 'table' or 'single"):
|
|
Series(range(1)).rolling(1, method="foo")
|
|
|
|
|
|
@pytest.mark.parametrize("window", [1, "1d"])
|
|
def test_rolling_descending_date_order_with_offset(window, frame_or_series):
|
|
# GH#40002
|
|
idx = date_range(start="2020-01-01", end="2020-01-03", freq="1d")
|
|
obj = frame_or_series(range(1, 4), index=idx)
|
|
result = obj.rolling("1d", closed="left").sum()
|
|
expected = frame_or_series([np.nan, 1, 2], index=idx)
|
|
tm.assert_equal(result, expected)
|
|
|
|
result = obj.iloc[::-1].rolling("1d", closed="left").sum()
|
|
idx = date_range(start="2020-01-03", end="2020-01-01", freq="-1d")
|
|
expected = frame_or_series([np.nan, 3, 2], index=idx)
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
def test_rolling_var_floating_artifact_precision():
|
|
# GH 37051
|
|
s = Series([7, 5, 5, 5])
|
|
result = s.rolling(3).var()
|
|
expected = Series([np.nan, np.nan, 4 / 3, 0])
|
|
tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15)
|
|
# GH 42064
|
|
# new `roll_var` will output 0.0 correctly
|
|
tm.assert_series_equal(result == 0, expected == 0)
|
|
|
|
|
|
def test_rolling_std_small_values():
|
|
# GH 37051
|
|
s = Series(
|
|
[
|
|
0.00000054,
|
|
0.00000053,
|
|
0.00000054,
|
|
]
|
|
)
|
|
result = s.rolling(2).std()
|
|
expected = Series([np.nan, 7.071068e-9, 7.071068e-9])
|
|
tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"start, exp_values",
|
|
[
|
|
(1, [0.03, 0.0155, 0.0155, 0.011, 0.01025]),
|
|
(2, [0.001, 0.001, 0.0015, 0.00366666]),
|
|
],
|
|
)
|
|
def test_rolling_mean_all_nan_window_floating_artifacts(start, exp_values):
|
|
# GH#41053
|
|
df = DataFrame(
|
|
[
|
|
0.03,
|
|
0.03,
|
|
0.001,
|
|
np.NaN,
|
|
0.002,
|
|
0.008,
|
|
np.NaN,
|
|
np.NaN,
|
|
np.NaN,
|
|
np.NaN,
|
|
np.NaN,
|
|
np.NaN,
|
|
0.005,
|
|
0.2,
|
|
]
|
|
)
|
|
|
|
values = exp_values + [
|
|
0.00366666,
|
|
0.005,
|
|
0.005,
|
|
0.008,
|
|
np.NaN,
|
|
np.NaN,
|
|
0.005,
|
|
0.102500,
|
|
]
|
|
expected = DataFrame(
|
|
values,
|
|
index=list(range(start, len(values) + start)),
|
|
)
|
|
result = df.iloc[start:].rolling(5, min_periods=0).mean()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_sum_all_nan_window_floating_artifacts():
|
|
# GH#41053
|
|
df = DataFrame([0.002, 0.008, 0.005, np.NaN, np.NaN, np.NaN])
|
|
result = df.rolling(3, min_periods=0).sum()
|
|
expected = DataFrame([0.002, 0.010, 0.015, 0.013, 0.005, 0.0])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_zero_window():
|
|
# GH 22719
|
|
s = Series(range(1))
|
|
result = s.rolling(0).min()
|
|
expected = Series([np.nan])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_float_dtype(float_numpy_dtype):
|
|
# GH#42452
|
|
df = DataFrame({"A": range(5), "B": range(10, 15)}, dtype=float_numpy_dtype)
|
|
expected = DataFrame(
|
|
{"A": [np.nan] * 5, "B": range(10, 20, 2)},
|
|
dtype=float_numpy_dtype,
|
|
)
|
|
result = df.rolling(2, axis=1).sum()
|
|
tm.assert_frame_equal(result, expected, check_dtype=False)
|
|
|
|
|
|
def test_rolling_numeric_dtypes():
|
|
# GH#41779
|
|
df = DataFrame(np.arange(40).reshape(4, 10), columns=list("abcdefghij")).astype(
|
|
{
|
|
"a": "float16",
|
|
"b": "float32",
|
|
"c": "float64",
|
|
"d": "int8",
|
|
"e": "int16",
|
|
"f": "int32",
|
|
"g": "uint8",
|
|
"h": "uint16",
|
|
"i": "uint32",
|
|
"j": "uint64",
|
|
}
|
|
)
|
|
result = df.rolling(window=2, min_periods=1, axis=1).min()
|
|
expected = DataFrame(
|
|
{
|
|
"a": range(0, 40, 10),
|
|
"b": range(0, 40, 10),
|
|
"c": range(1, 40, 10),
|
|
"d": range(2, 40, 10),
|
|
"e": range(3, 40, 10),
|
|
"f": range(4, 40, 10),
|
|
"g": range(5, 40, 10),
|
|
"h": range(6, 40, 10),
|
|
"i": range(7, 40, 10),
|
|
"j": range(8, 40, 10),
|
|
},
|
|
dtype="float64",
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("window", [1, 3, 10, 20])
|
|
@pytest.mark.parametrize("method", ["min", "max", "average"])
|
|
@pytest.mark.parametrize("pct", [True, False])
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
|
@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"])
|
|
def test_rank(window, method, pct, ascending, test_data):
|
|
length = 20
|
|
if test_data == "default":
|
|
ser = Series(data=np.random.rand(length))
|
|
elif test_data == "duplicates":
|
|
ser = Series(data=np.random.choice(3, length))
|
|
elif test_data == "nans":
|
|
ser = Series(
|
|
data=np.random.choice([1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length)
|
|
)
|
|
|
|
expected = ser.rolling(window).apply(
|
|
lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1]
|
|
)
|
|
result = ser.rolling(window).rank(method=method, pct=pct, ascending=ascending)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_quantile_np_percentile():
|
|
# #9413: Tests that rolling window's quantile default behavior
|
|
# is analogous to Numpy's percentile
|
|
row = 10
|
|
col = 5
|
|
idx = date_range("20100101", periods=row, freq="B")
|
|
df = DataFrame(np.random.rand(row * col).reshape((row, -1)), index=idx)
|
|
|
|
df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)
|
|
np_percentile = np.percentile(df, [25, 50, 75], axis=0)
|
|
|
|
tm.assert_almost_equal(df_quantile.values, np.array(np_percentile))
|
|
|
|
|
|
@pytest.mark.parametrize("quantile", [0.0, 0.1, 0.45, 0.5, 1])
|
|
@pytest.mark.parametrize(
|
|
"interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"data",
|
|
[
|
|
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
|
|
[8.0, 1.0, 3.0, 4.0, 5.0, 2.0, 6.0, 7.0],
|
|
[0.0, np.nan, 0.2, np.nan, 0.4],
|
|
[np.nan, np.nan, np.nan, np.nan],
|
|
[np.nan, 0.1, np.nan, 0.3, 0.4, 0.5],
|
|
[0.5],
|
|
[np.nan, 0.7, 0.6],
|
|
],
|
|
)
|
|
def test_rolling_quantile_interpolation_options(quantile, interpolation, data):
|
|
# Tests that rolling window's quantile behavior is analogous to
|
|
# Series' quantile for each interpolation option
|
|
s = Series(data)
|
|
|
|
q1 = s.quantile(quantile, interpolation)
|
|
q2 = s.expanding(min_periods=1).quantile(quantile, interpolation).iloc[-1]
|
|
|
|
if np.isnan(q1):
|
|
assert np.isnan(q2)
|
|
else:
|
|
assert q1 == q2
|
|
|
|
|
|
def test_invalid_quantile_value():
|
|
data = np.arange(5)
|
|
s = Series(data)
|
|
|
|
msg = "Interpolation 'invalid' is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
s.rolling(len(data), min_periods=1).quantile(0.5, interpolation="invalid")
|
|
|
|
|
|
def test_rolling_quantile_param():
|
|
ser = Series([0.0, 0.1, 0.5, 0.9, 1.0])
|
|
msg = "quantile value -0.1 not in \\[0, 1\\]"
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser.rolling(3).quantile(-0.1)
|
|
|
|
msg = "quantile value 10.0 not in \\[0, 1\\]"
|
|
with pytest.raises(ValueError, match=msg):
|
|
ser.rolling(3).quantile(10.0)
|
|
|
|
msg = "must be real number, not str"
|
|
with pytest.raises(TypeError, match=msg):
|
|
ser.rolling(3).quantile("foo")
|
|
|
|
|
|
def test_rolling_std_1obs():
|
|
vals = Series([1.0, 2.0, 3.0, 4.0, 5.0])
|
|
|
|
result = vals.rolling(1, min_periods=1).std()
|
|
expected = Series([np.nan] * 5)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = vals.rolling(1, min_periods=1).std(ddof=0)
|
|
expected = Series([0.0] * 5)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = Series([np.nan, np.nan, 3, 4, 5]).rolling(3, min_periods=2).std()
|
|
assert np.isnan(result[2])
|
|
|
|
|
|
def test_rolling_std_neg_sqrt():
|
|
# unit test from Bottleneck
|
|
|
|
# Test move_nanstd for neg sqrt.
|
|
|
|
a = Series(
|
|
[
|
|
0.0011448196318903589,
|
|
0.00028718669878572767,
|
|
0.00028718669878572767,
|
|
0.00028718669878572767,
|
|
0.00028718669878572767,
|
|
]
|
|
)
|
|
b = a.rolling(window=3).std()
|
|
assert np.isfinite(b[2:]).all()
|
|
|
|
b = a.ewm(span=3).std()
|
|
assert np.isfinite(b[2:]).all()
|
|
|
|
|
|
def test_step_not_integer_raises():
|
|
with pytest.raises(ValueError, match="step must be an integer"):
|
|
DataFrame(range(2)).rolling(1, step="foo")
|
|
|
|
|
|
def test_step_not_positive_raises():
|
|
with pytest.raises(ValueError, match="step must be >= 0"):
|
|
DataFrame(range(2)).rolling(1, step=-1)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
["values", "window", "min_periods", "expected"],
|
|
[
|
|
[
|
|
[20, 10, 10, np.inf, 1, 1, 2, 3],
|
|
3,
|
|
1,
|
|
[np.nan, 50, 100 / 3, 0, 40.5, 0, 1 / 3, 1],
|
|
],
|
|
[
|
|
[20, 10, 10, np.nan, 10, 1, 2, 3],
|
|
3,
|
|
1,
|
|
[np.nan, 50, 100 / 3, 0, 0, 40.5, 73 / 3, 1],
|
|
],
|
|
[
|
|
[np.nan, 5, 6, 7, 5, 5, 5],
|
|
3,
|
|
3,
|
|
[np.nan] * 3 + [1, 1, 4 / 3, 0],
|
|
],
|
|
[
|
|
[5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3],
|
|
3,
|
|
3,
|
|
[np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [1 / 3, 0],
|
|
],
|
|
[
|
|
[5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3],
|
|
3,
|
|
3,
|
|
[np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [16 / 3, 0],
|
|
],
|
|
[
|
|
[5, 7] * 4,
|
|
3,
|
|
3,
|
|
[np.nan] * 2 + [4 / 3] * 6,
|
|
],
|
|
[
|
|
[5, 7, 5, np.nan, 7, 5, 7],
|
|
3,
|
|
2,
|
|
[np.nan, 2, 4 / 3] + [2] * 3 + [4 / 3],
|
|
],
|
|
],
|
|
)
|
|
def test_rolling_var_same_value_count_logic(values, window, min_periods, expected):
|
|
# GH 42064.
|
|
|
|
expected = Series(expected)
|
|
sr = Series(values)
|
|
|
|
# With new algo implemented, result will be set to .0 in rolling var
|
|
# if sufficient amount of consecutively same values are found.
|
|
result_var = sr.rolling(window, min_periods=min_periods).var()
|
|
|
|
# use `assert_series_equal` twice to check for equality,
|
|
# because `check_exact=True` will fail in 32-bit tests due to
|
|
# precision loss.
|
|
|
|
# 1. result should be close to correct value
|
|
# non-zero values can still differ slightly from "truth"
|
|
# as the result of online algorithm
|
|
tm.assert_series_equal(result_var, expected)
|
|
# 2. zeros should be exactly the same since the new algo takes effect here
|
|
tm.assert_series_equal(expected == 0, result_var == 0)
|
|
|
|
# std should also pass as it's just a sqrt of var
|
|
result_std = sr.rolling(window, min_periods=min_periods).std()
|
|
tm.assert_series_equal(result_std, np.sqrt(expected))
|
|
tm.assert_series_equal(expected == 0, result_std == 0)
|
|
|
|
|
|
def test_rolling_mean_sum_floating_artifacts():
|
|
# GH 42064.
|
|
|
|
sr = Series([1 / 3, 4, 0, 0, 0, 0, 0])
|
|
r = sr.rolling(3)
|
|
result = r.mean()
|
|
assert (result[-3:] == 0).all()
|
|
result = r.sum()
|
|
assert (result[-3:] == 0).all()
|
|
|
|
|
|
def test_rolling_skew_kurt_floating_artifacts():
|
|
# GH 42064 46431
|
|
|
|
sr = Series([1 / 3, 4, 0, 0, 0, 0, 0])
|
|
r = sr.rolling(4)
|
|
result = r.skew()
|
|
assert (result[-2:] == 0).all()
|
|
result = r.kurt()
|
|
assert (result[-2:] == -3).all()
|
|
|
|
|
|
def test_numeric_only_frame(arithmetic_win_operators, numeric_only):
|
|
# GH#46560
|
|
kernel = arithmetic_win_operators
|
|
df = DataFrame({"a": [1], "b": 2, "c": 3})
|
|
df["c"] = df["c"].astype(object)
|
|
rolling = df.rolling(2, min_periods=1)
|
|
op = getattr(rolling, kernel)
|
|
result = op(numeric_only=numeric_only)
|
|
|
|
columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
|
|
expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float)
|
|
assert list(expected.columns) == columns
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("kernel", ["corr", "cov"])
|
|
@pytest.mark.parametrize("use_arg", [True, False])
|
|
def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg):
|
|
# GH#46560
|
|
df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3})
|
|
df["c"] = df["c"].astype(object)
|
|
arg = (df,) if use_arg else ()
|
|
rolling = df.rolling(2, min_periods=1)
|
|
op = getattr(rolling, kernel)
|
|
result = op(*arg, numeric_only=numeric_only)
|
|
|
|
# Compare result to op using float dtypes, dropping c when numeric_only is True
|
|
columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
|
|
df2 = df[columns].astype(float)
|
|
arg2 = (df2,) if use_arg else ()
|
|
rolling2 = df2.rolling(2, min_periods=1)
|
|
op2 = getattr(rolling2, kernel)
|
|
expected = op2(*arg2, numeric_only=numeric_only)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("dtype", [int, object])
|
|
def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype):
|
|
# GH#46560
|
|
kernel = arithmetic_win_operators
|
|
ser = Series([1], dtype=dtype)
|
|
rolling = ser.rolling(2, min_periods=1)
|
|
op = getattr(rolling, kernel)
|
|
if numeric_only and dtype is object:
|
|
msg = f"Rolling.{kernel} does not implement numeric_only"
|
|
with pytest.raises(NotImplementedError, match=msg):
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op(numeric_only=numeric_only)
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else:
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result = op(numeric_only=numeric_only)
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expected = ser.agg([kernel]).reset_index(drop=True).astype(float)
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tm.assert_series_equal(result, expected)
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|
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@pytest.mark.parametrize("kernel", ["corr", "cov"])
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@pytest.mark.parametrize("use_arg", [True, False])
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@pytest.mark.parametrize("dtype", [int, object])
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def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype):
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# GH#46560
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ser = Series([1, 2, 3], dtype=dtype)
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arg = (ser,) if use_arg else ()
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rolling = ser.rolling(2, min_periods=1)
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op = getattr(rolling, kernel)
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if numeric_only and dtype is object:
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msg = f"Rolling.{kernel} does not implement numeric_only"
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with pytest.raises(NotImplementedError, match=msg):
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|
op(*arg, numeric_only=numeric_only)
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|
else:
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result = op(*arg, numeric_only=numeric_only)
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|
|
|
ser2 = ser.astype(float)
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arg2 = (ser2,) if use_arg else ()
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rolling2 = ser2.rolling(2, min_periods=1)
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op2 = getattr(rolling2, kernel)
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expected = op2(*arg2, numeric_only=numeric_only)
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|
tm.assert_series_equal(result, expected)
|