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How to use transform_derived method in pandera

Best Python code snippet using pandera_python

test_decorators.py

...443def test_check_types_error_output() -> None:444    """Test that check_types raises an error when the output is not correct."""445    df = pd.DataFrame({"a": [1]}, index=["1"])446    @check_types447    def transform_derived(448        df: DataFrame[InSchema],449    ) -> DataFrame[DerivedOutSchema]:450        return df451    with pytest.raises(452        errors.SchemaError, match="column 'b' not in dataframe"453    ):454        transform_derived(df)455    try:456        transform_derived(df)457    except errors.SchemaError as exc:458        assert exc.schema == DerivedOutSchema.to_schema()459        assert exc.data.equals(df)460    df = pd.DataFrame({"a": [1]}, index=["1"])461    @check_types462    def transform(df: DataFrame[InSchema]) -> DataFrame[OutSchema]:463        return df464    with pytest.raises(465        errors.SchemaError, match="column 'b' not in dataframe"466    ):467        transform(df)468    try:469        transform(df)470    except errors.SchemaError as exc:471        assert exc.schema == OutSchema.to_schema()472        assert exc.data.equals(df)473def test_check_types_optional_out() -> None:474    """Test the check_types behaviour when the output schema is Optional."""475    @check_types476    def optional_derived_out(477        df: DataFrame[InSchema],  # pylint: disable=unused-argument478    ) -> typing.Optional[DataFrame[DerivedOutSchema]]:479        return None480    df = pd.DataFrame({"a": [1]}, index=["1"])481    assert optional_derived_out(df) is None482    @check_types483    def optional_out(484        df: DataFrame[InSchema],  # pylint: disable=unused-argument485    ) -> typing.Optional[DataFrame[OutSchema]]:486        return None487    df = pd.DataFrame({"a": [1]}, index=["1"])488    assert optional_out(df) is None489def test_check_types_optional_in() -> None:490    """Test the check_types behaviour when the input schema is Optional."""491    @check_types492    def optional_in(493        # pylint: disable=unused-argument494        df: typing.Optional[DataFrame[InSchema]],495    ) -> None:496        return None497    assert optional_in(None) is None498def test_check_types_optional_in_out() -> None:499    """500    Test the check_types behaviour when both input and outputs schemas are501    Optional.502    """503    @check_types504    def transform_derived(505        # pylint: disable=unused-argument506        df: typing.Optional[DataFrame[InSchema]],507    ) -> typing.Optional[DataFrame[DerivedOutSchema]]:508        return None509    assert transform_derived(None) is None510    @check_types511    def transform(512        # pylint: disable=unused-argument513        df: typing.Optional[DataFrame[InSchema]],514    ) -> typing.Optional[DataFrame[OutSchema]]:515        return None516    assert transform(None) is None517def test_check_types_coerce() -> None:518    """Test that check_types return the result of validate."""519    @check_types()520    def transform_in(df: DataFrame[InSchema]):521        return df522    df = transform_in(pd.DataFrame({"a": ["1"]}, index=["1"]))523    expected = InSchema.to_schema().columns["a"].dtype...

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