import re
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from warnings import catch_warnings
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import numpy as np
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import pytest
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from pandas.core.dtypes import generic as gt
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import pandas as pd
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import pandas._testing as tm
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class TestABCClasses:
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tuples = [[1, 2, 2], ["red", "blue", "red"]]
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multi_index = pd.MultiIndex.from_arrays(tuples, names=("number", "color"))
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datetime_index = pd.to_datetime(["2000/1/1", "2010/1/1"])
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timedelta_index = pd.to_timedelta(np.arange(5), unit="s")
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period_index = pd.period_range("2000/1/1", "2010/1/1/", freq="M")
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categorical = pd.Categorical([1, 2, 3], categories=[2, 3, 1])
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categorical_df = pd.DataFrame({"values": [1, 2, 3]}, index=categorical)
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df = pd.DataFrame({"names": ["a", "b", "c"]}, index=multi_index)
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sparse_array = pd.arrays.SparseArray(np.random.randn(10))
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datetime_array = pd.core.arrays.DatetimeArray(datetime_index)
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timedelta_array = pd.core.arrays.TimedeltaArray(timedelta_index)
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abc_pairs = [
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("ABCMultiIndex", multi_index),
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("ABCDatetimeIndex", datetime_index),
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("ABCRangeIndex", pd.RangeIndex(3)),
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("ABCTimedeltaIndex", timedelta_index),
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("ABCIntervalIndex", pd.interval_range(start=0, end=3)),
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("ABCPeriodArray", pd.arrays.PeriodArray([2000, 2001, 2002], freq="D")),
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("ABCPandasArray", pd.arrays.PandasArray(np.array([0, 1, 2]))),
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("ABCPeriodIndex", period_index),
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("ABCCategoricalIndex", categorical_df.index),
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("ABCSeries", pd.Series([1, 2, 3])),
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("ABCDataFrame", df),
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("ABCCategorical", categorical),
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("ABCDatetimeArray", datetime_array),
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("ABCTimedeltaArray", timedelta_array),
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]
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@pytest.mark.parametrize("abctype1, inst", abc_pairs)
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@pytest.mark.parametrize("abctype2, _", abc_pairs)
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def test_abc_pairs_instance_check(self, abctype1, abctype2, inst, _):
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# GH 38588, 46719
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if abctype1 == abctype2:
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assert isinstance(inst, getattr(gt, abctype2))
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assert not isinstance(type(inst), getattr(gt, abctype2))
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else:
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assert not isinstance(inst, getattr(gt, abctype2))
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@pytest.mark.parametrize("abctype1, inst", abc_pairs)
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@pytest.mark.parametrize("abctype2, _", abc_pairs)
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def test_abc_pairs_subclass_check(self, abctype1, abctype2, inst, _):
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# GH 38588, 46719
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if abctype1 == abctype2:
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assert issubclass(type(inst), getattr(gt, abctype2))
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with pytest.raises(
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TypeError, match=re.escape("issubclass() arg 1 must be a class")
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):
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issubclass(inst, getattr(gt, abctype2))
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else:
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assert not issubclass(type(inst), getattr(gt, abctype2))
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abc_subclasses = {
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"ABCIndex": [
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abctype
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for abctype, _ in abc_pairs
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if "Index" in abctype and abctype != "ABCIndex"
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],
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"ABCNDFrame": ["ABCSeries", "ABCDataFrame"],
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"ABCExtensionArray": [
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"ABCCategorical",
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"ABCDatetimeArray",
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"ABCPeriodArray",
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"ABCTimedeltaArray",
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],
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}
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@pytest.mark.parametrize("parent, subs", abc_subclasses.items())
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@pytest.mark.parametrize("abctype, inst", abc_pairs)
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def test_abc_hierarchy(self, parent, subs, abctype, inst):
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# GH 38588
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if abctype in subs:
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assert isinstance(inst, getattr(gt, parent))
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else:
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assert not isinstance(inst, getattr(gt, parent))
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@pytest.mark.parametrize("abctype", [e for e in gt.__dict__ if e.startswith("ABC")])
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def test_abc_coverage(self, abctype):
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# GH 38588
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assert (
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abctype in (e for e, _ in self.abc_pairs) or abctype in self.abc_subclasses
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)
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def test_setattr_warnings():
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# GH7175 - GOTCHA: You can't use dot notation to add a column...
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d = {
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"one": pd.Series([1.0, 2.0, 3.0], index=["a", "b", "c"]),
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"two": pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "b", "c", "d"]),
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}
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df = pd.DataFrame(d)
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with catch_warnings(record=True) as w:
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# successfully add new column
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# this should not raise a warning
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df["three"] = df.two + 1
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assert len(w) == 0
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assert df.three.sum() > df.two.sum()
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with catch_warnings(record=True) as w:
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# successfully modify column in place
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# this should not raise a warning
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df.one += 1
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assert len(w) == 0
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assert df.one.iloc[0] == 2
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with catch_warnings(record=True) as w:
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# successfully add an attribute to a series
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# this should not raise a warning
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df.two.not_an_index = [1, 2]
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assert len(w) == 0
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with tm.assert_produces_warning(UserWarning):
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# warn when setting column to nonexistent name
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df.four = df.two + 2
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assert df.four.sum() > df.two.sum()
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