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How to use custom_check_teardown method in pandera

Best Python code snippet using pandera_python

test_extensions.py

Source:test_extensions.py

1# pylint: disable=no-member,redefined-outer-name,unused-argument2# pylint: disable=unused-variable3"""Unit tests for pandera API extensions."""4from typing import Any, Optional, Union5import pandas as pd6import pytest7import pandera as pa8import pandera.strategies as st9from pandera import DataType, extensions10from pandera.checks import Check11def test_custom_checks_in_dir(extra_registered_checks):12    """Ensures that autocomplete works with registered custom checks."""13    assert "no_param_check" in dir(pa.Check)14@pytest.mark.parametrize(15    "data",16    [17        pd.Series([10, 10, 10]),18        pd.DataFrame([[10, 10, 10], [10, 10, 10]]),19    ],20)21def test_register_vectorized_custom_check(22    custom_check_teardown: None, data: Union[pd.Series, pd.DataFrame]23) -> None:24    """Test registering a vectorized custom check."""25    @extensions.register_check_method(26        statistics=["val"],27        supported_types=(pd.Series, pd.DataFrame),28        check_type="vectorized",29    )30    def custom_check(pandas_obj, *, val):31        return pandas_obj == val32    check = Check.custom_check(val=10)33    check_result = check(data)34    assert check_result.check_passed35    for kwargs in [36        {"element_wise": True},37        {"element_wise": False},38        {"groupby": "column"},39        {"groups": ["group1", "group2"]},40    ]:41        with pytest.warns(UserWarning):42            Check.custom_check(val=10, **kwargs)43    with pytest.raises(44        ValueError,45        match="method with name 'custom_check' already defined",46    ):47        # pylint: disable=function-redefined48        @extensions.register_check_method(statistics=["val"])49        def custom_check(pandas_obj, val):  # noqa50            return pandas_obj != val51@pytest.mark.parametrize(52    "data",53    [54        pd.Series([10, 10, 10]),55        pd.DataFrame([[10, 10, 10], [10, 10, 10]]),56    ],57)58def test_register_element_wise_custom_check(59    custom_check_teardown: None, data: Union[pd.Series, pd.DataFrame]60) -> None:61    """Test registering an element-wise custom check."""62    @extensions.register_check_method(63        statistics=["val"],64        supported_types=(pd.Series, pd.DataFrame),65        check_type="element_wise",66    )67    def custom_check(element, *, val):68        return element == val69    check = Check.custom_check(val=10)70    check_result = check(data)71    assert check_result.check_passed72    for kwargs in [73        {"element_wise": True},74        {"element_wise": False},75        {"groupby": "column"},76        {"groups": ["group1", "group2"]},77    ]:78        with pytest.warns(UserWarning):79            Check.custom_check(val=10, **kwargs)80    with pytest.raises(81        ValueError,82        match="Element-wise checks should support DataFrame and Series "83        "validation",84    ):85        @extensions.register_check_method(86            supported_types=pd.Series,87            check_type="element_wise",88        )89        def invalid_custom_check(*args):90            pass91def test_register_custom_groupby_check(custom_check_teardown: None) -> None:92    """Test registering a custom groupby check."""93    @extensions.register_check_method(94        statistics=["group_a", "group_b"],95        supported_types=(pd.Series, pd.DataFrame),96        check_type="groupby",97    )98    def custom_check(dict_groups, *, group_a, group_b):99        """100        Test that the mean values in group A is larger than that of group B.101        Note that this function can handle groups of both dataframes and102        series.103        """104        return (105            dict_groups[group_a].values.mean()106            > dict_groups[group_b].values.mean()107        )108    # column groupby check109    data_column_check = pd.DataFrame(110        {111            "col1": [20, 20, 10, 10],112            "col2": list("aabb"),113        }114    )115    schema_column_check = pa.DataFrameSchema(116        {117            "col1": pa.Column(118                int,119                Check.custom_check(group_a="a", group_b="b", groupby="col2"),120            ),121            "col2": pa.Column(str),122        }123    )124    assert isinstance(schema_column_check(data_column_check), pd.DataFrame)125    # dataframe groupby check126    data_df_check = pd.DataFrame(127        {128            "col1": [20, 20, 10, 10],129            "col2": [30, 30, 5, 5],130            "col3": [10, 10, 1, 1],131        },132        index=pd.Index(list("aabb"), name="my_index"),133    )134    schema_df_check = pa.DataFrameSchema(135        columns={136            "col1": pa.Column(int),137            "col2": pa.Column(int),138            "col3": pa.Column(int),139        },140        index=pa.Index(str, name="my_index"),141        checks=Check.custom_check(142            group_a="a", group_b="b", groupby="my_index"143        ),144    )145    assert isinstance(schema_df_check(data_df_check), pd.DataFrame)146    for kwargs in [{"element_wise": True}, {"element_wise": False}]:147        with pytest.warns(UserWarning):148            Check.custom_check(val=10, **kwargs)149@pytest.mark.parametrize(150    "supported_types",151    [152        1,153        10.0,154        "foo",155        {"foo": "bar"},156        {1: 10},157        ["foo", "bar"],158        [1, 10],159        ("foo", "bar"),160        (1, 10),161    ],162)163def test_register_check_invalid_supported_types(supported_types: Any) -> None:164    """Test that TypeError is raised on invalid supported_types arg."""165    with pytest.raises(TypeError):166        @extensions.register_check_method(supported_types=supported_types)167        def custom_check(*args, **kwargs):168            pass169@pytest.mark.skipif(170    not st.HAS_HYPOTHESIS, reason='needs "strategies" module dependencies'171)172def test_register_check_with_strategy(custom_check_teardown: None) -> None:173    """Test registering a custom check with a data generation strategy."""174    import hypothesis  # pylint: disable=import-outside-toplevel,import-error175    def custom_ge_strategy(176        pandas_dtype: DataType,177        strategy: Optional[st.SearchStrategy] = None,178        *,179        min_value: Any,180    ) -> st.SearchStrategy:181        if strategy is None:182            return st.pandas_dtype_strategy(183                pandas_dtype,184                min_value=min_value,185                exclude_min=False,186            )187        return strategy.filter(lambda x: x > min_value)188    @extensions.register_check_method(189        statistics=["min_value"], strategy=custom_ge_strategy190    )191    def custom_ge_check(pandas_obj, *, min_value):192        return pandas_obj >= min_value193    check = Check.custom_ge_check(min_value=0)194    strat = check.strategy(pa.Int)195    with pytest.warns(hypothesis.errors.NonInteractiveExampleWarning):196        assert strat.example() >= 0197def test_schema_model_field_kwarg(custom_check_teardown: None) -> None:198    """Test that registered checks can be specified in a Field."""199    # pylint: disable=missing-class-docstring,too-few-public-methods200    @extensions.register_check_method(201        statistics=["val"],202        supported_types=(pd.Series, pd.DataFrame),203        check_type="vectorized",204    )205    def custom_gt(pandas_obj, val):206        return pandas_obj > val207    @extensions.register_check_method(208        statistics=["min_value", "max_value"],209        supported_types=(pd.Series, pd.DataFrame),210        check_type="vectorized",211    )212    def custom_in_range(pandas_obj, min_value, max_value):213        return (min_value <= pandas_obj) & (pandas_obj <= max_value)214    class Schema(pa.SchemaModel):215        """Schema that uses registered checks in Field."""216        col1: pa.typing.Series[int] = pa.Field(custom_gt=100)217        col2: pa.typing.Series[float] = pa.Field(218            custom_in_range={"min_value": -10, "max_value": 10}219        )220        class Config:221            coerce = True222    data = pd.DataFrame(223        {224            "col1": [101, 1000, 2000],225            "col2": [-5.0, 0.0, 6.0],226        }227    )228    Schema.validate(data)229    for invalid_data in [230        pd.DataFrame({"col1": [0], "col2": [-10.0]}),231        pd.DataFrame({"col1": [1000], "col2": [-100.0]}),232    ]:233        with pytest.raises(pa.errors.SchemaError):234            Schema.validate(invalid_data)235def test_register_before_schema_definitions() -> None:236    """Test that custom checks need to be registered before use."""237    # pylint: disable=missing-class-docstring,too-few-public-methods238    # pylint: disable=function-redefined239    with pytest.raises(240        pa.errors.SchemaInitError,241        match="custom check 'custom_eq' is not available",242    ):243        class Schema1(pa.SchemaModel):244            col: pa.typing.Series[int] = pa.Field(custom_eq=1)245    with pytest.raises(AttributeError):246        pa.Check.custom_eq(1)247    @extensions.register_check_method(statistics=["val"])248    def custom_eq(pandas_obj, val):249        return pandas_obj == val250    class Schema2(pa.SchemaModel):  # noqa F811251        col: pa.typing.Series[int] = pa.Field(custom_eq=1)...

checks_fixtures.py

Source:checks_fixtures.py

...6import pandera as pa7import pandera.extensions as pa_ext8__all__ = "custom_check_teardown", "extra_registered_checks"9@pytest.fixture(scope="function")10def custom_check_teardown() -> Generator[None, None, None]:11    """Remove all custom checks after execution of each pytest function."""12    yield13    for check_name in list(pa.Check.REGISTERED_CUSTOM_CHECKS):14        del pa.Check.REGISTERED_CUSTOM_CHECKS[check_name]15@pytest.fixture(scope="function")16def extra_registered_checks() -> Generator[None, None, None]:17    """temporarily registers custom checks onto the Check class"""18    # pylint: disable=unused-variable19    with mock.patch(20        "pandera.Check.REGISTERED_CUSTOM_CHECKS", new_callable=dict21    ):22        # register custom checks here23        @pa_ext.register_check_method()24        def no_param_check(_: pd.DataFrame) -> bool:...

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