263 lines
7.9 KiB
Python
263 lines
7.9 KiB
Python
import numpy as np
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import pytest
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from pandas.errors import UnsupportedFunctionCall
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from pandas import DataFrame, DatetimeIndex, Series
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import pandas._testing as tm
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from pandas.core.window import Expanding
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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.expanding(2).sum()
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@pytest.mark.filterwarnings(
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"ignore:The `center` argument on `expanding` will be removed in the future"
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)
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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)).expanding
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# valid
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c(min_periods=1)
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c(min_periods=1, center=True)
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c(min_periods=1, center=False)
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@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
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@pytest.mark.filterwarnings(
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"ignore:The `center` argument on `expanding` will be removed in the future"
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)
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def test_constructor_invalid(frame_or_series, w):
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# not valid
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c = frame_or_series(range(5)).expanding
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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(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(min_periods=1, center=w)
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@pytest.mark.parametrize("method", ["std", "mean", "sum", "max", "min", "var"])
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def test_numpy_compat(method):
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# see gh-12811
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e = Expanding(Series([2, 4, 6]), window=2)
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msg = "numpy operations are not valid with window objects"
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with pytest.raises(UnsupportedFunctionCall, match=msg):
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getattr(e, method)(1, 2, 3)
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with pytest.raises(UnsupportedFunctionCall, match=msg):
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getattr(e, method)(dtype=np.float64)
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@pytest.mark.parametrize(
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"expander",
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[
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1,
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pytest.param(
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"ls",
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marks=pytest.mark.xfail(
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reason="GH#16425 expanding with offset not supported"
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),
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),
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],
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)
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def test_empty_df_expanding(expander):
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# GH 15819 Verifies that datetime and integer expanding windows can be
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# applied to empty DataFrames
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expected = DataFrame()
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result = DataFrame().expanding(expander).sum()
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tm.assert_frame_equal(result, expected)
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# Verifies that datetime and integer expanding windows can be applied
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# to empty DataFrames with datetime index
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expected = DataFrame(index=DatetimeIndex([]))
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result = DataFrame(index=DatetimeIndex([])).expanding(expander).sum()
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tm.assert_frame_equal(result, expected)
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def test_missing_minp_zero():
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# https://github.com/pandas-dev/pandas/pull/18921
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# minp=0
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x = Series([np.nan])
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result = x.expanding(min_periods=0).sum()
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expected = Series([0.0])
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tm.assert_series_equal(result, expected)
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# minp=1
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result = x.expanding(min_periods=1).sum()
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expected = Series([np.nan])
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tm.assert_series_equal(result, expected)
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def test_expanding_axis(axis_frame):
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# see gh-23372.
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df = DataFrame(np.ones((10, 20)))
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axis = df._get_axis_number(axis_frame)
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if axis == 0:
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expected = DataFrame(
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{i: [np.nan] * 2 + [float(j) for j in range(3, 11)] for i in range(20)}
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)
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else:
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# axis == 1
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expected = DataFrame([[np.nan] * 2 + [float(i) for i in range(3, 21)]] * 10)
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result = df.expanding(3, axis=axis_frame).sum()
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tm.assert_frame_equal(result, expected)
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def test_expanding_count_with_min_periods(frame_or_series):
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# GH 26996
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result = frame_or_series(range(5)).expanding(min_periods=3).count()
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expected = frame_or_series([np.nan, np.nan, 3.0, 4.0, 5.0])
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tm.assert_equal(result, expected)
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def test_expanding_count_default_min_periods_with_null_values(frame_or_series):
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# GH 26996
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values = [1, 2, 3, np.nan, 4, 5, 6]
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expected_counts = [1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0]
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result = frame_or_series(values).expanding().count()
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expected = frame_or_series(expected_counts)
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tm.assert_equal(result, expected)
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def test_expanding_count_with_min_periods_exceeding_series_length(frame_or_series):
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# GH 25857
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result = frame_or_series(range(5)).expanding(min_periods=6).count()
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expected = frame_or_series([np.nan, np.nan, np.nan, np.nan, np.nan])
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tm.assert_equal(result, expected)
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@pytest.mark.parametrize(
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"df,expected,min_periods",
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[
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(
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DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
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[
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({"A": [1], "B": [4]}, [0]),
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({"A": [1, 2], "B": [4, 5]}, [0, 1]),
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({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
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],
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3,
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),
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(
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DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
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[
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({"A": [1], "B": [4]}, [0]),
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({"A": [1, 2], "B": [4, 5]}, [0, 1]),
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({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
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],
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2,
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),
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(
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DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
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[
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({"A": [1], "B": [4]}, [0]),
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({"A": [1, 2], "B": [4, 5]}, [0, 1]),
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({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
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],
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1,
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),
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(DataFrame({"A": [1], "B": [4]}), [], 2),
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(DataFrame(), [({}, [])], 1),
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(
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DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
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[
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({"A": [1.0], "B": [np.nan]}, [0]),
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({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
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({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
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],
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3,
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),
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(
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DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
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[
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({"A": [1.0], "B": [np.nan]}, [0]),
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({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
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({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
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],
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2,
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),
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(
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DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
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[
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({"A": [1.0], "B": [np.nan]}, [0]),
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({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
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({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
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],
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1,
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),
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],
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)
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def test_iter_expanding_dataframe(df, expected, min_periods):
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# GH 11704
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expected = [DataFrame(values, index=index) for (values, index) in expected]
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for (expected, actual) in zip(expected, df.expanding(min_periods)):
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tm.assert_frame_equal(actual, expected)
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@pytest.mark.parametrize(
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"ser,expected,min_periods",
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[
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(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 3),
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(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 2),
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(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 1),
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(Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2),
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(Series([np.nan, 2]), [([np.nan], [0]), ([np.nan, 2], [0, 1])], 2),
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(Series([], dtype="int64"), [], 2),
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],
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)
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def test_iter_expanding_series(ser, expected, min_periods):
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# GH 11704
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expected = [Series(values, index=index) for (values, index) in expected]
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for (expected, actual) in zip(expected, ser.expanding(min_periods)):
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tm.assert_series_equal(actual, expected)
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def test_center_deprecate_warning():
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# GH 20647
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df = DataFrame()
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with tm.assert_produces_warning(FutureWarning):
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df.expanding(center=True)
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with tm.assert_produces_warning(FutureWarning):
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df.expanding(center=False)
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with tm.assert_produces_warning(None):
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df.expanding()
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def test_expanding_sem(frame_or_series):
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# GH: 26476
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obj = frame_or_series([0, 1, 2])
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result = obj.expanding().sem()
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if isinstance(result, DataFrame):
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result = Series(result[0].values)
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expected = Series([np.nan] + [0.707107] * 2)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("method", ["skew", "kurt"])
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def test_expanding_skew_kurt_numerical_stability(method):
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# GH: 6929
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s = Series(np.random.rand(10))
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expected = getattr(s.expanding(3), method)()
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s = s + 5000
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result = getattr(s.expanding(3), method)()
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tm.assert_series_equal(result, expected)
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