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151 lines
5.1 KiB
151 lines
5.1 KiB
from datetime import datetime
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Series,
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Timestamp,
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isna,
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notna,
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)
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import pandas._testing as tm
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class TestSeriesClip:
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def test_clip(self, datetime_series):
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val = datetime_series.median()
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assert datetime_series.clip(lower=val).min() == val
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assert datetime_series.clip(upper=val).max() == val
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result = datetime_series.clip(-0.5, 0.5)
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expected = np.clip(datetime_series, -0.5, 0.5)
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tm.assert_series_equal(result, expected)
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assert isinstance(expected, Series)
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def test_clip_types_and_nulls(self):
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sers = [
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Series([np.nan, 1.0, 2.0, 3.0]),
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Series([None, "a", "b", "c"]),
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Series(pd.to_datetime([np.nan, 1, 2, 3], unit="D")),
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]
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for s in sers:
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thresh = s[2]
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lower = s.clip(lower=thresh)
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upper = s.clip(upper=thresh)
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assert lower[notna(lower)].min() == thresh
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assert upper[notna(upper)].max() == thresh
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assert list(isna(s)) == list(isna(lower))
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assert list(isna(s)) == list(isna(upper))
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def test_series_clipping_with_na_values(self, any_numeric_ea_dtype, nulls_fixture):
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# Ensure that clipping method can handle NA values with out failing
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# GH#40581
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if nulls_fixture is pd.NaT:
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# constructor will raise, see
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# test_constructor_mismatched_null_nullable_dtype
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return
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ser = Series([nulls_fixture, 1.0, 3.0], dtype=any_numeric_ea_dtype)
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s_clipped_upper = ser.clip(upper=2.0)
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s_clipped_lower = ser.clip(lower=2.0)
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expected_upper = Series([nulls_fixture, 1.0, 2.0], dtype=any_numeric_ea_dtype)
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expected_lower = Series([nulls_fixture, 2.0, 3.0], dtype=any_numeric_ea_dtype)
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tm.assert_series_equal(s_clipped_upper, expected_upper)
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tm.assert_series_equal(s_clipped_lower, expected_lower)
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def test_clip_with_na_args(self):
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"""Should process np.nan argument as None"""
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# GH#17276
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s = Series([1, 2, 3])
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tm.assert_series_equal(s.clip(np.nan), Series([1, 2, 3]))
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tm.assert_series_equal(s.clip(upper=np.nan, lower=np.nan), Series([1, 2, 3]))
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# GH#19992
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tm.assert_series_equal(s.clip(lower=[0, 4, np.nan]), Series([1, 4, 3]))
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tm.assert_series_equal(s.clip(upper=[1, np.nan, 1]), Series([1, 2, 1]))
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# GH#40420
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s = Series([1, 2, 3])
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result = s.clip(0, [np.nan, np.nan, np.nan])
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tm.assert_series_equal(s, result)
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def test_clip_against_series(self):
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# GH#6966
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s = Series([1.0, 1.0, 4.0])
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lower = Series([1.0, 2.0, 3.0])
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upper = Series([1.5, 2.5, 3.5])
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tm.assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5]))
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tm.assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5]))
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@pytest.mark.parametrize("inplace", [True, False])
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@pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])])
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def test_clip_against_list_like(self, inplace, upper):
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# GH#15390
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original = Series([5, 6, 7])
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result = original.clip(upper=upper, inplace=inplace)
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expected = Series([1, 2, 3])
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if inplace:
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result = original
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tm.assert_series_equal(result, expected, check_exact=True)
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def test_clip_with_datetimes(self):
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# GH#11838
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# naive and tz-aware datetimes
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t = Timestamp("2015-12-01 09:30:30")
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s = Series([Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:31:00")])
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result = s.clip(upper=t)
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expected = Series(
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[Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:30:30")]
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)
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tm.assert_series_equal(result, expected)
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t = Timestamp("2015-12-01 09:30:30", tz="US/Eastern")
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s = Series(
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[
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Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
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Timestamp("2015-12-01 09:31:00", tz="US/Eastern"),
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]
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)
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result = s.clip(upper=t)
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expected = Series(
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[
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Timestamp("2015-12-01 09:30:00", tz="US/Eastern"),
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Timestamp("2015-12-01 09:30:30", tz="US/Eastern"),
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]
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)
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tm.assert_series_equal(result, expected)
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def test_clip_with_timestamps_and_oob_datetimes(self):
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# GH-42794
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ser = Series([datetime(1, 1, 1), datetime(9999, 9, 9)])
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result = ser.clip(lower=Timestamp.min, upper=Timestamp.max)
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expected = Series([Timestamp.min, Timestamp.max], dtype="object")
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tm.assert_series_equal(result, expected)
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def test_clip_pos_args_deprecation(self):
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# https://github.com/pandas-dev/pandas/issues/41485
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ser = Series([1, 2, 3])
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msg = (
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r"In a future version of pandas all arguments of Series.clip except "
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r"for the arguments 'lower' and 'upper' will be keyword-only"
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)
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with tm.assert_produces_warning(FutureWarning, match=msg):
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result = ser.clip(0, 1, 0)
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expected = Series([1, 1, 1])
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tm.assert_series_equal(result, expected)
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