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726 lines
22 KiB
726 lines
22 KiB
1 year ago
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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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DatetimeIndex,
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Series,
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date_range,
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)
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import pandas._testing as tm
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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.ewm(com=0.5).mean()
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def test_constructor(frame_or_series):
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c = frame_or_series(range(5)).ewm
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# valid
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c(com=0.5)
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c(span=1.5)
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c(alpha=0.5)
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c(halflife=0.75)
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c(com=0.5, span=None)
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c(alpha=0.5, com=None)
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c(halflife=0.75, alpha=None)
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# not valid: mutually exclusive
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msg = "comass, span, halflife, and alpha are mutually exclusive"
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with pytest.raises(ValueError, match=msg):
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c(com=0.5, alpha=0.5)
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with pytest.raises(ValueError, match=msg):
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c(span=1.5, halflife=0.75)
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with pytest.raises(ValueError, match=msg):
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c(alpha=0.5, span=1.5)
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# not valid: com < 0
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msg = "comass must satisfy: comass >= 0"
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with pytest.raises(ValueError, match=msg):
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c(com=-0.5)
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# not valid: span < 1
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msg = "span must satisfy: span >= 1"
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with pytest.raises(ValueError, match=msg):
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c(span=0.5)
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# not valid: halflife <= 0
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msg = "halflife must satisfy: halflife > 0"
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with pytest.raises(ValueError, match=msg):
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c(halflife=0)
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# not valid: alpha <= 0 or alpha > 1
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msg = "alpha must satisfy: 0 < alpha <= 1"
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for alpha in (-0.5, 1.5):
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with pytest.raises(ValueError, match=msg):
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c(alpha=alpha)
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def test_ewma_times_not_datetime_type():
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msg = r"times must be datetime64\[ns\] dtype."
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with pytest.raises(ValueError, match=msg):
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Series(range(5)).ewm(times=np.arange(5))
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def test_ewma_times_not_same_length():
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msg = "times must be the same length as the object."
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with pytest.raises(ValueError, match=msg):
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Series(range(5)).ewm(times=np.arange(4).astype("datetime64[ns]"))
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def test_ewma_halflife_not_correct_type():
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msg = "halflife must be a timedelta convertible object"
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with pytest.raises(ValueError, match=msg):
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Series(range(5)).ewm(halflife=1, times=np.arange(5).astype("datetime64[ns]"))
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def test_ewma_halflife_without_times(halflife_with_times):
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msg = "halflife can only be a timedelta convertible argument if times is not None."
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with pytest.raises(ValueError, match=msg):
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Series(range(5)).ewm(halflife=halflife_with_times)
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@pytest.mark.parametrize(
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"times",
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[
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np.arange(10).astype("datetime64[D]").astype("datetime64[ns]"),
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date_range("2000", freq="D", periods=10),
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date_range("2000", freq="D", periods=10).tz_localize("UTC"),
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],
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)
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@pytest.mark.parametrize("min_periods", [0, 2])
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def test_ewma_with_times_equal_spacing(halflife_with_times, times, min_periods):
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halflife = halflife_with_times
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data = np.arange(10.0)
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data[::2] = np.nan
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df = DataFrame({"A": data})
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result = df.ewm(halflife=halflife, min_periods=min_periods, times=times).mean()
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expected = df.ewm(halflife=1.0, min_periods=min_periods).mean()
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tm.assert_frame_equal(result, expected)
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def test_ewma_with_times_variable_spacing(tz_aware_fixture):
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tz = tz_aware_fixture
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halflife = "23 days"
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times = DatetimeIndex(
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["2020-01-01", "2020-01-10T00:04:05", "2020-02-23T05:00:23"]
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).tz_localize(tz)
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data = np.arange(3)
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df = DataFrame(data)
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result = df.ewm(halflife=halflife, times=times).mean()
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expected = DataFrame([0.0, 0.5674161888241773, 1.545239952073459])
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tm.assert_frame_equal(result, expected)
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def test_ewm_with_nat_raises(halflife_with_times):
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# GH#38535
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ser = Series(range(1))
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times = DatetimeIndex(["NaT"])
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with pytest.raises(ValueError, match="Cannot convert NaT values to integer"):
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ser.ewm(com=0.1, halflife=halflife_with_times, times=times)
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def test_ewm_with_times_getitem(halflife_with_times):
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# GH 40164
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halflife = halflife_with_times
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data = np.arange(10.0)
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data[::2] = np.nan
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times = date_range("2000", freq="D", periods=10)
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df = DataFrame({"A": data, "B": data})
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result = df.ewm(halflife=halflife, times=times)["A"].mean()
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expected = df.ewm(halflife=1.0)["A"].mean()
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("arg", ["com", "halflife", "span", "alpha"])
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def test_ewm_getitem_attributes_retained(arg, adjust, ignore_na):
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# GH 40164
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kwargs = {arg: 1, "adjust": adjust, "ignore_na": ignore_na}
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ewm = DataFrame({"A": range(1), "B": range(1)}).ewm(**kwargs)
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expected = {attr: getattr(ewm, attr) for attr in ewm._attributes}
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ewm_slice = ewm["A"]
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result = {attr: getattr(ewm, attr) for attr in ewm_slice._attributes}
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assert result == expected
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def test_ewma_times_adjust_false_raises():
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# GH 40098
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with pytest.raises(
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NotImplementedError, match="times is not supported with adjust=False."
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):
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Series(range(1)).ewm(
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0.1, adjust=False, times=date_range("2000", freq="D", periods=1)
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)
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@pytest.mark.parametrize(
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"func, expected",
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[
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[
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"mean",
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DataFrame(
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{
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0: range(5),
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1: range(4, 9),
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2: [7.428571, 9, 10.571429, 12.142857, 13.714286],
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},
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dtype=float,
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),
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],
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[
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"std",
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DataFrame(
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{
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0: [np.nan] * 5,
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1: [4.242641] * 5,
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2: [4.6291, 5.196152, 5.781745, 6.380775, 6.989788],
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}
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),
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],
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[
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"var",
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DataFrame(
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{
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0: [np.nan] * 5,
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1: [18.0] * 5,
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2: [21.428571, 27, 33.428571, 40.714286, 48.857143],
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}
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),
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],
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],
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)
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def test_float_dtype_ewma(func, expected, float_numpy_dtype):
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# GH#42452
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df = DataFrame(
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{0: range(5), 1: range(6, 11), 2: range(10, 20, 2)}, dtype=float_numpy_dtype
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)
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msg = "Support for axis=1 in DataFrame.ewm is deprecated"
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with tm.assert_produces_warning(FutureWarning, match=msg):
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e = df.ewm(alpha=0.5, axis=1)
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result = getattr(e, func)()
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tm.assert_frame_equal(result, expected)
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def test_times_string_col_raises():
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# GH 43265
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df = DataFrame(
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{"A": np.arange(10.0), "time_col": date_range("2000", freq="D", periods=10)}
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)
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with pytest.raises(ValueError, match="times must be datetime64"):
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df.ewm(halflife="1 day", min_periods=0, times="time_col")
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def test_ewm_sum_adjust_false_notimplemented():
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data = Series(range(1)).ewm(com=1, adjust=False)
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with pytest.raises(NotImplementedError, match="sum is not"):
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data.sum()
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@pytest.mark.parametrize(
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"expected_data, ignore",
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[[[10.0, 5.0, 2.5, 11.25], False], [[10.0, 5.0, 5.0, 12.5], True]],
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)
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def test_ewm_sum(expected_data, ignore):
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# xref from Numbagg tests
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# https://github.com/numbagg/numbagg/blob/v0.2.1/numbagg/test/test_moving.py#L50
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data = Series([10, 0, np.nan, 10])
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result = data.ewm(alpha=0.5, ignore_na=ignore).sum()
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expected = Series(expected_data)
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tm.assert_series_equal(result, expected)
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def test_ewma_adjust():
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vals = Series(np.zeros(1000))
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vals[5] = 1
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result = vals.ewm(span=100, adjust=False).mean().sum()
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assert np.abs(result - 1) < 1e-2
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def test_ewma_cases(adjust, ignore_na):
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# try adjust/ignore_na args matrix
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s = Series([1.0, 2.0, 4.0, 8.0])
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if adjust:
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expected = Series([1.0, 1.6, 2.736842, 4.923077])
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else:
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expected = Series([1.0, 1.333333, 2.222222, 4.148148])
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result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()
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tm.assert_series_equal(result, expected)
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def test_ewma_nan_handling():
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s = Series([1.0] + [np.nan] * 5 + [1.0])
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result = s.ewm(com=5).mean()
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tm.assert_series_equal(result, Series([1.0] * len(s)))
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s = Series([np.nan] * 2 + [1.0] + [np.nan] * 2 + [1.0])
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result = s.ewm(com=5).mean()
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tm.assert_series_equal(result, Series([np.nan] * 2 + [1.0] * 4))
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@pytest.mark.parametrize(
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"s, adjust, ignore_na, w",
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[
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(
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Series([np.nan, 1.0, 101.0]),
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True,
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False,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
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),
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(
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Series([np.nan, 1.0, 101.0]),
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True,
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True,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0],
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),
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(
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Series([np.nan, 1.0, 101.0]),
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False,
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False,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
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),
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(
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Series([np.nan, 1.0, 101.0]),
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False,
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True,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))],
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),
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(
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Series([1.0, np.nan, 101.0]),
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True,
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False,
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[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, 1.0],
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),
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(
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Series([1.0, np.nan, 101.0]),
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True,
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True,
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[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, 1.0],
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),
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(
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Series([1.0, np.nan, 101.0]),
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False,
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False,
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[(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, (1.0 / (1.0 + 2.0))],
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),
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(
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Series([1.0, np.nan, 101.0]),
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False,
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True,
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[(1.0 - (1.0 / (1.0 + 2.0))), np.nan, (1.0 / (1.0 + 2.0))],
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),
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(
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Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
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True,
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False,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))) ** 3, np.nan, np.nan, 1.0, np.nan],
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),
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(
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Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
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True,
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True,
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[np.nan, (1.0 - (1.0 / (1.0 + 2.0))), np.nan, np.nan, 1.0, np.nan],
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),
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(
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Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
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False,
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False,
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[
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np.nan,
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(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
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np.nan,
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np.nan,
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(1.0 / (1.0 + 2.0)),
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np.nan,
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],
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),
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(
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Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]),
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False,
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True,
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[
|
||
|
|
np.nan,
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))),
|
||
|
|
np.nan,
|
||
|
|
np.nan,
|
||
|
|
(1.0 / (1.0 + 2.0)),
|
||
|
|
np.nan,
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
Series([1.0, np.nan, 101.0, 50.0]),
|
||
|
|
True,
|
||
|
|
False,
|
||
|
|
[
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
|
||
|
|
np.nan,
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))),
|
||
|
|
1.0,
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
Series([1.0, np.nan, 101.0, 50.0]),
|
||
|
|
True,
|
||
|
|
True,
|
||
|
|
[
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
|
||
|
|
np.nan,
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))),
|
||
|
|
1.0,
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
Series([1.0, np.nan, 101.0, 50.0]),
|
||
|
|
False,
|
||
|
|
False,
|
||
|
|
[
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) ** 3,
|
||
|
|
np.nan,
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
|
||
|
|
(1.0 / (1.0 + 2.0))
|
||
|
|
* ((1.0 - (1.0 / (1.0 + 2.0))) ** 2 + (1.0 / (1.0 + 2.0))),
|
||
|
|
],
|
||
|
|
),
|
||
|
|
(
|
||
|
|
Series([1.0, np.nan, 101.0, 50.0]),
|
||
|
|
False,
|
||
|
|
True,
|
||
|
|
[
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) ** 2,
|
||
|
|
np.nan,
|
||
|
|
(1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)),
|
||
|
|
(1.0 / (1.0 + 2.0)),
|
||
|
|
],
|
||
|
|
),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_ewma_nan_handling_cases(s, adjust, ignore_na, w):
|
||
|
|
# GH 7603
|
||
|
|
expected = (s.multiply(w).cumsum() / Series(w).cumsum()).ffill()
|
||
|
|
result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean()
|
||
|
|
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
if ignore_na is False:
|
||
|
|
# check that ignore_na defaults to False
|
||
|
|
result = s.ewm(com=2.0, adjust=adjust).mean()
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ewm_alpha():
|
||
|
|
# GH 10789
|
||
|
|
arr = np.random.default_rng(2).standard_normal(100)
|
||
|
|
locs = np.arange(20, 40)
|
||
|
|
arr[locs] = np.nan
|
||
|
|
|
||
|
|
s = Series(arr)
|
||
|
|
a = s.ewm(alpha=0.61722699889169674).mean()
|
||
|
|
b = s.ewm(com=0.62014947789973052).mean()
|
||
|
|
c = s.ewm(span=2.240298955799461).mean()
|
||
|
|
d = s.ewm(halflife=0.721792864318).mean()
|
||
|
|
tm.assert_series_equal(a, b)
|
||
|
|
tm.assert_series_equal(a, c)
|
||
|
|
tm.assert_series_equal(a, d)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ewm_domain_checks():
|
||
|
|
# GH 12492
|
||
|
|
arr = np.random.default_rng(2).standard_normal(100)
|
||
|
|
locs = np.arange(20, 40)
|
||
|
|
arr[locs] = np.nan
|
||
|
|
|
||
|
|
s = Series(arr)
|
||
|
|
msg = "comass must satisfy: comass >= 0"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(com=-0.1)
|
||
|
|
s.ewm(com=0.0)
|
||
|
|
s.ewm(com=0.1)
|
||
|
|
|
||
|
|
msg = "span must satisfy: span >= 1"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(span=-0.1)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(span=0.0)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(span=0.9)
|
||
|
|
s.ewm(span=1.0)
|
||
|
|
s.ewm(span=1.1)
|
||
|
|
|
||
|
|
msg = "halflife must satisfy: halflife > 0"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(halflife=-0.1)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(halflife=0.0)
|
||
|
|
s.ewm(halflife=0.1)
|
||
|
|
|
||
|
|
msg = "alpha must satisfy: 0 < alpha <= 1"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(alpha=-0.1)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(alpha=0.0)
|
||
|
|
s.ewm(alpha=0.1)
|
||
|
|
s.ewm(alpha=1.0)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(alpha=1.1)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("method", ["mean", "std", "var"])
|
||
|
|
def test_ew_empty_series(method):
|
||
|
|
vals = Series([], dtype=np.float64)
|
||
|
|
|
||
|
|
ewm = vals.ewm(3)
|
||
|
|
result = getattr(ewm, method)()
|
||
|
|
tm.assert_almost_equal(result, vals)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("min_periods", [0, 1])
|
||
|
|
@pytest.mark.parametrize("name", ["mean", "var", "std"])
|
||
|
|
def test_ew_min_periods(min_periods, name):
|
||
|
|
# excluding NaNs correctly
|
||
|
|
arr = np.random.default_rng(2).standard_normal(50)
|
||
|
|
arr[:10] = np.nan
|
||
|
|
arr[-10:] = np.nan
|
||
|
|
s = Series(arr)
|
||
|
|
|
||
|
|
# check min_periods
|
||
|
|
# GH 7898
|
||
|
|
result = getattr(s.ewm(com=50, min_periods=2), name)()
|
||
|
|
assert result[:11].isna().all()
|
||
|
|
assert not result[11:].isna().any()
|
||
|
|
|
||
|
|
result = getattr(s.ewm(com=50, min_periods=min_periods), name)()
|
||
|
|
if name == "mean":
|
||
|
|
assert result[:10].isna().all()
|
||
|
|
assert not result[10:].isna().any()
|
||
|
|
else:
|
||
|
|
# ewm.std, ewm.var (with bias=False) require at least
|
||
|
|
# two values
|
||
|
|
assert result[:11].isna().all()
|
||
|
|
assert not result[11:].isna().any()
|
||
|
|
|
||
|
|
# check series of length 0
|
||
|
|
result = getattr(Series(dtype=object).ewm(com=50, min_periods=min_periods), name)()
|
||
|
|
tm.assert_series_equal(result, Series(dtype="float64"))
|
||
|
|
|
||
|
|
# check series of length 1
|
||
|
|
result = getattr(Series([1.0]).ewm(50, min_periods=min_periods), name)()
|
||
|
|
if name == "mean":
|
||
|
|
tm.assert_series_equal(result, Series([1.0]))
|
||
|
|
else:
|
||
|
|
# ewm.std, ewm.var with bias=False require at least
|
||
|
|
# two values
|
||
|
|
tm.assert_series_equal(result, Series([np.nan]))
|
||
|
|
|
||
|
|
# pass in ints
|
||
|
|
result2 = getattr(Series(np.arange(50)).ewm(span=10), name)()
|
||
|
|
assert result2.dtype == np.float64
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("name", ["cov", "corr"])
|
||
|
|
def test_ewm_corr_cov(name):
|
||
|
|
A = Series(np.random.default_rng(2).standard_normal(50), index=range(50))
|
||
|
|
B = A[2:] + np.random.default_rng(2).standard_normal(48)
|
||
|
|
|
||
|
|
A[:10] = np.nan
|
||
|
|
B.iloc[-10:] = np.nan
|
||
|
|
|
||
|
|
result = getattr(A.ewm(com=20, min_periods=5), name)(B)
|
||
|
|
assert np.isnan(result.values[:14]).all()
|
||
|
|
assert not np.isnan(result.values[14:]).any()
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("min_periods", [0, 1, 2])
|
||
|
|
@pytest.mark.parametrize("name", ["cov", "corr"])
|
||
|
|
def test_ewm_corr_cov_min_periods(name, min_periods):
|
||
|
|
# GH 7898
|
||
|
|
A = Series(np.random.default_rng(2).standard_normal(50), index=range(50))
|
||
|
|
B = A[2:] + np.random.default_rng(2).standard_normal(48)
|
||
|
|
|
||
|
|
A[:10] = np.nan
|
||
|
|
B.iloc[-10:] = np.nan
|
||
|
|
|
||
|
|
result = getattr(A.ewm(com=20, min_periods=min_periods), name)(B)
|
||
|
|
# binary functions (ewmcov, ewmcorr) with bias=False require at
|
||
|
|
# least two values
|
||
|
|
assert np.isnan(result.values[:11]).all()
|
||
|
|
assert not np.isnan(result.values[11:]).any()
|
||
|
|
|
||
|
|
# check series of length 0
|
||
|
|
empty = Series([], dtype=np.float64)
|
||
|
|
result = getattr(empty.ewm(com=50, min_periods=min_periods), name)(empty)
|
||
|
|
tm.assert_series_equal(result, empty)
|
||
|
|
|
||
|
|
# check series of length 1
|
||
|
|
result = getattr(Series([1.0]).ewm(com=50, min_periods=min_periods), name)(
|
||
|
|
Series([1.0])
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, Series([np.nan]))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("name", ["cov", "corr"])
|
||
|
|
def test_different_input_array_raise_exception(name):
|
||
|
|
A = Series(np.random.default_rng(2).standard_normal(50), index=range(50))
|
||
|
|
A[:10] = np.nan
|
||
|
|
|
||
|
|
msg = "other must be a DataFrame or Series"
|
||
|
|
# exception raised is Exception
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
getattr(A.ewm(com=20, min_periods=5), name)(
|
||
|
|
np.random.default_rng(2).standard_normal(50)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("name", ["var", "std", "mean"])
|
||
|
|
def test_ewma_series(series, name):
|
||
|
|
series_result = getattr(series.ewm(com=10), name)()
|
||
|
|
assert isinstance(series_result, Series)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("name", ["var", "std", "mean"])
|
||
|
|
def test_ewma_frame(frame, name):
|
||
|
|
frame_result = getattr(frame.ewm(com=10), name)()
|
||
|
|
assert isinstance(frame_result, DataFrame)
|
||
|
|
|
||
|
|
|
||
|
|
def test_ewma_span_com_args(series):
|
||
|
|
A = series.ewm(com=9.5).mean()
|
||
|
|
B = series.ewm(span=20).mean()
|
||
|
|
tm.assert_almost_equal(A, B)
|
||
|
|
msg = "comass, span, halflife, and alpha are mutually exclusive"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm(com=9.5, span=20)
|
||
|
|
|
||
|
|
msg = "Must pass one of comass, span, halflife, or alpha"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm().mean()
|
||
|
|
|
||
|
|
|
||
|
|
def test_ewma_halflife_arg(series):
|
||
|
|
A = series.ewm(com=13.932726172912965).mean()
|
||
|
|
B = series.ewm(halflife=10.0).mean()
|
||
|
|
tm.assert_almost_equal(A, B)
|
||
|
|
msg = "comass, span, halflife, and alpha are mutually exclusive"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm(span=20, halflife=50)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm(com=9.5, halflife=50)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm(com=9.5, span=20, halflife=50)
|
||
|
|
msg = "Must pass one of comass, span, halflife, or alpha"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
series.ewm()
|
||
|
|
|
||
|
|
|
||
|
|
def test_ewm_alpha_arg(series):
|
||
|
|
# GH 10789
|
||
|
|
s = series
|
||
|
|
msg = "Must pass one of comass, span, halflife, or alpha"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm()
|
||
|
|
|
||
|
|
msg = "comass, span, halflife, and alpha are mutually exclusive"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(com=10.0, alpha=0.5)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(span=10.0, alpha=0.5)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.ewm(halflife=10.0, alpha=0.5)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("func", ["cov", "corr"])
|
||
|
|
def test_ewm_pairwise_cov_corr(func, frame):
|
||
|
|
result = getattr(frame.ewm(span=10, min_periods=5), func)()
|
||
|
|
result = result.loc[(slice(None), 1), 5]
|
||
|
|
result.index = result.index.droplevel(1)
|
||
|
|
expected = getattr(frame[1].ewm(span=10, min_periods=5), func)(frame[5])
|
||
|
|
tm.assert_series_equal(result, expected, check_names=False)
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
ewm = df.ewm(span=2, min_periods=1)
|
||
|
|
op = getattr(ewm, kernel, None)
|
||
|
|
if op is not None:
|
||
|
|
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 ()
|
||
|
|
ewm = df.ewm(span=2, min_periods=1)
|
||
|
|
op = getattr(ewm, 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 ()
|
||
|
|
ewm2 = df2.ewm(span=2, min_periods=1)
|
||
|
|
op2 = getattr(ewm2, kernel)
|
||
|
|
expected = op2(*arg2, numeric_only=numeric_only)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
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@pytest.mark.parametrize("dtype", [int, object])
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def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype):
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# GH#46560
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kernel = arithmetic_win_operators
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ser = Series([1], dtype=dtype)
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ewm = ser.ewm(span=2, min_periods=1)
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op = getattr(ewm, kernel, None)
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if op is None:
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# Nothing to test
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pytest.skip("No op to test")
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if numeric_only and dtype is object:
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msg = f"ExponentialMovingWindow.{kernel} does not implement numeric_only"
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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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|
|
ewm = ser.ewm(span=2, min_periods=1)
|
||
|
|
op = getattr(ewm, kernel)
|
||
|
|
if numeric_only and dtype is object:
|
||
|
|
msg = f"ExponentialMovingWindow.{kernel} does not implement numeric_only"
|
||
|
|
with pytest.raises(NotImplementedError, match=msg):
|
||
|
|
op(*arg, numeric_only=numeric_only)
|
||
|
|
else:
|
||
|
|
result = op(*arg, numeric_only=numeric_only)
|
||
|
|
|
||
|
|
ser2 = ser.astype(float)
|
||
|
|
arg2 = (ser2,) if use_arg else ()
|
||
|
|
ewm2 = ser2.ewm(span=2, min_periods=1)
|
||
|
|
op2 = getattr(ewm2, kernel)
|
||
|
|
expected = op2(*arg2, numeric_only=numeric_only)
|
||
|
|
tm.assert_series_equal(result, expected)
|