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How to use int_column_lt_100 method in pandera

Best Python code snippet using pandera_python
test_model.py
Source:test_model.py
...176    """Test validate method on valid data."""177    class Schema(pa.SchemaModel):178        a: Series[int]179        @pa.check("a")180        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:181            # pylint:disable=no-self-argument182            assert cls is Schema183            return series < 100184    df = pd.DataFrame({"a": [99]})185    assert isinstance(Schema.validate(df, lazy=True), pd.DataFrame)186def test_check_validate_method_field() -> None:187    """Test validate method on valid data."""188    class Schema(pa.SchemaModel):189        a: Series[int] = pa.Field()190        b: Series[int]191        @pa.check(a)192        def int_column_lt_200(cls, series: pd.Series) -> Iterable[bool]:193            # pylint:disable=no-self-argument194            assert cls is Schema195            return series < 200196        @pa.check(a, "b")197        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:198            # pylint:disable=no-self-argument199            assert cls is Schema200            return series < 100201    df = pd.DataFrame({"a": [99], "b": [99]})202    assert isinstance(Schema.validate(df, lazy=True), pd.DataFrame)203def test_check_validate_method_aliased_field() -> None:204    """Test validate method on valid data."""205    class Schema(pa.SchemaModel):206        a: Series[int] = pa.Field(alias=2020, gt=50)207        @pa.check(a)208        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:209            # pylint:disable=no-self-argument210            assert cls is Schema211            return series < 100212    df = pd.DataFrame({2020: [99]})213    assert len(Schema.to_schema().columns[2020].checks) == 2214    assert isinstance(Schema.validate(df, lazy=True), pd.DataFrame)215def test_check_single_column() -> None:216    """Test the behaviour of a check on a single column."""217    class Schema(pa.SchemaModel):218        a: Series[int]219        @pa.check("a")220        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:221            # pylint:disable=no-self-argument222            assert cls is Schema223            return series < 100224    df = pd.DataFrame({"a": [101]})225    schema = Schema.to_schema()226    err_msg = r"Column\s*a\s*int_column_lt_100\s*\[101\]\s*1"227    with pytest.raises(pa.errors.SchemaErrors, match=err_msg):228        schema.validate(df, lazy=True)229def test_check_single_index() -> None:230    """Test the behaviour of a check on a single index."""231    class Schema(pa.SchemaModel):232        a: Index[str]233        @pa.check("a")234        def not_dog(cls, idx: pd.Index) -> Iterable[bool]:235            # pylint:disable=no-self-argument236            assert cls is Schema237            return ~idx.str.contains("dog")238    df = pd.DataFrame(index=["cat", "dog"])239    err_msg = r"Index\s*<NA>\s*not_dog\s*\[dog\]\s*"240    with pytest.raises(pa.errors.SchemaErrors, match=err_msg):241        Schema.validate(df, lazy=True)242def test_field_and_check() -> None:243    """Test the combination of a field and a check on the same column."""244    class Schema(pa.SchemaModel):245        a: Series[int] = pa.Field(eq=1)246        @pa.check("a")247        @classmethod248        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:249            return series < 100250    schema = Schema.to_schema()251    assert len(schema.columns["a"].checks) == 2252def test_check_non_existing() -> None:253    """Test a check on a non-existing column."""254    class Schema(pa.SchemaModel):255        a: Series[int]256        @pa.check("nope")257        @classmethod258        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:259            return series < 100260    err_msg = (261        "Check int_column_lt_100 is assigned to a non-existing field 'nope'"262    )263    with pytest.raises(pa.errors.SchemaInitError, match=err_msg):264        Schema.to_schema()265def test_multiple_checks() -> None:266    """Test multiple checks on the same column."""267    class Schema(pa.SchemaModel):268        a: Series[int]269        @pa.check("a")270        @classmethod271        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:272            return series < 100273        @pa.check("a")274        @classmethod275        def int_column_gt_0(cls, series: pd.Series) -> Iterable[bool]:276            return series > 0277    schema = Schema.to_schema()278    assert len(schema.columns["a"].checks) == 2279    df = pd.DataFrame({"a": [0]})280    err_msg = r"Column\s*a\s*int_column_gt_0\s*\[0\]\s*1"281    with pytest.raises(pa.errors.SchemaErrors, match=err_msg):282        schema.validate(df, lazy=True)283    df = pd.DataFrame({"a": [101]})284    err_msg = r"Column\s*a\s*int_column_lt_100\s*\[101\]\s*1"285    with pytest.raises(pa.errors.SchemaErrors, match=err_msg):286        schema.validate(df, lazy=True)287def test_check_multiple_columns() -> None:288    """Test a single check decorator targeting multiple columns."""289    class Schema(pa.SchemaModel):290        a: Series[int]291        b: Series[int]292        @pa.check("a", "b")293        @classmethod294        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:295            return series < 100296    df = pd.DataFrame({"a": [101], "b": [200]})297    with pytest.raises(298        pa.errors.SchemaErrors, match="2 schema errors were found"299    ):300        Schema.validate(df, lazy=True)301def test_check_regex() -> None:302    """Test the regex argument of the check decorator."""303    class Schema(pa.SchemaModel):304        a: Series[int]305        abc: Series[int]306        cba: Series[int]307        @pa.check("^a", regex=True)308        @classmethod309        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:310            return series < 100311    df = pd.DataFrame({"a": [101], "abc": [1], "cba": [200]})312    with pytest.raises(313        pa.errors.SchemaErrors, match="1 schema errors were found"314    ):315        Schema.validate(df, lazy=True)316def test_inherit_schemamodel_fields() -> None:317    """Test that columns and indices are inherited."""318    class Base(pa.SchemaModel):319        a: Series[int]320        idx: Index[str]321    class Mid(Base):322        b: Series[str]323        idx: Index[str]324    class Child(Mid):325        b: Series[int]326    expected = pa.DataFrameSchema(327        columns={"a": pa.Column(int), "b": pa.Column(int)},328        index=pa.Index(str),329    )330    assert expected == Child.to_schema()331def test_inherit_schemamodel_fields_alias() -> None:332    """Test that columns and index aliases are inherited."""333    class Base(pa.SchemaModel):334        a: Series[int]335        idx: Index[str]336    class Mid(Base):337        b: Series[str] = pa.Field(alias="_b")338        idx: Index[str]339    class ChildOverrideAttr(Mid):340        b: Series[int]341    class ChildOverrideAlias(Mid):342        b: Series[str] = pa.Field(alias="new_b")343    class ChildNewAttr(Mid):344        c: Series[int]345    class ChildEmpty(Mid):346        pass347    expected_mid = pa.DataFrameSchema(348        columns={"a": pa.Column(int), "_b": pa.Column(str)},349        index=pa.Index(str),350    )351    expected_child_override_attr = expected_mid.rename_columns(352        {"_b": "b"}353    ).update_column("b", dtype=int)354    expected_child_override_alias = expected_mid.rename_columns(355        {"_b": "new_b"}356    )357    expected_child_new_attr = expected_mid.add_columns(358        {359            "c": pa.Column(int),360        }361    )362    assert expected_mid == Mid.to_schema()363    assert expected_child_override_attr == ChildOverrideAttr.to_schema()364    assert expected_child_override_alias == ChildOverrideAlias.to_schema()365    assert expected_child_new_attr == ChildNewAttr.to_schema()366    assert expected_mid == ChildEmpty.to_schema()367def test_inherit_field_checks() -> None:368    """Test that checks are inherited and overridden."""369    class Base(pa.SchemaModel):370        a: Series[int]371        abc: Series[int]372        @pa.check("^a", regex=True)373        @classmethod374        def a_max(cls, series: pd.Series) -> Iterable[bool]:375            return series < 100376        @pa.check("a")377        @classmethod378        def a_min(cls, series: pd.Series) -> Iterable[bool]:379            return series > 1380    class Child(Base):381        @pa.check("a")382        @classmethod383        def a_max(cls, series: pd.Series) -> Iterable[bool]:384            return series < 10385    schema = Child.to_schema()386    assert len(schema.columns["a"].checks) == 2387    assert len(schema.columns["abc"].checks) == 0388    df = pd.DataFrame({"a": [15], "abc": [100]})389    err_msg = r"Column\s*a\s*a_max\s*\[15\]\s*1"390    with pytest.raises(pa.errors.SchemaErrors, match=err_msg):391        schema.validate(df, lazy=True)392def test_dataframe_check() -> None:393    """Test dataframe checks."""394    class Base(pa.SchemaModel):395        a: Series[int]396        b: Series[int]397        @pa.dataframe_check398        @classmethod399        def value_max(cls, df: pd.DataFrame) -> Iterable[bool]:400            return df < 200401    class Child(Base):402        @pa.dataframe_check()403        @classmethod404        def value_min(cls, df: pd.DataFrame) -> Iterable[bool]:405            return df > 0406        @pa.dataframe_check407        @classmethod408        def value_max(cls, df: pd.DataFrame) -> Iterable[bool]:409            return df < 100410    schema = Child.to_schema()411    assert len(schema.checks) == 2412    df = pd.DataFrame({"a": [101, 1], "b": [1, 0]})413    with pytest.raises(414        pa.errors.SchemaErrors, match="2 schema errors were found"415    ):416        schema.validate(df, lazy=True)417def test_registered_dataframe_checks(418    extra_registered_checks: None,  # pylint: disable=unused-argument419) -> None:420    """Check that custom check inheritance works"""421    # pylint: disable=unused-variable422    @pax.register_check_method(statistics=["one_arg"])423    def base_check(df, *, one_arg):424        # pylint: disable=unused-argument425        return True426    @pax.register_check_method(statistics=["one_arg", "two_arg"])427    def child_check(df, *, one_arg, two_arg):428        # pylint: disable=unused-argument429        return True430    # pylint: enable=unused-variable431    check_vals = {432        "one_arg": 150,433        "two_arg": "hello",434        "one_arg_prime": "not_150",435    }436    class Base(pa.SchemaModel):437        a: Series[int]438        b: Series[int]439        class Config:440            no_param_check = ()441            base_check = check_vals["one_arg"]442    class Child(Base):443        class Config:444            base_check = check_vals["one_arg_prime"]445            child_check = {446                "one_arg": check_vals["one_arg"],447                "two_arg": check_vals["two_arg"],448            }449    base = Base.to_schema()450    child = Child.to_schema()451    expected_stats_base = {452        "no_param_check": {},453        "base_check": {"one_arg": check_vals["one_arg"]},454    }455    expected_stats_child = {456        "no_param_check": {},457        "base_check": {"one_arg": check_vals["one_arg_prime"]},458        "child_check": {459            "one_arg": check_vals["one_arg"],460            "two_arg": check_vals["two_arg"],461        },462    }463    assert {b.name: b.statistics for b in base.checks} == expected_stats_base464    assert {c.name: c.statistics for c in child.checks} == expected_stats_child465    # check that unregistered checks raise466    with pytest.raises(AttributeError, match=".*custom checks.*"):467        class ErrorSchema(pa.SchemaModel):468            class Config:469                unknown_check = {}  # type: ignore[var-annotated]470        # Check lookup happens at validation/to_schema conversion time471        # This means that you can register checks after defining a Config,472        # but also because of caching you can refer to a check that no longer473        # exists for some order of operations.474        ErrorSchema.to_schema()475def test_config() -> None:476    """Test that Config can be inherited and translate into DataFrameSchema options."""477    class Base(pa.SchemaModel):478        a: Series[int]479        idx_1: Index[str]480        idx_2: Index[str]481        class Config:482            name = "Base schema"483            coerce = True484            ordered = True485            multiindex_coerce = True486            multiindex_strict = True487            multiindex_name: Optional[str] = "mi"488    class Child(Base):489        b: Series[int]490        class Config:491            name = "Child schema"492            strict = True493            multiindex_strict = False494            description = "foo"495            title = "bar"496    expected = pa.DataFrameSchema(497        columns={"a": pa.Column(int), "b": pa.Column(int)},498        index=pa.MultiIndex(499            [pa.Index(str, name="idx_1"), pa.Index(str, name="idx_2")],500            coerce=True,501            strict=False,502            name="mi",503        ),504        name="Child schema",505        coerce=True,506        strict=True,507        ordered=True,508        description="foo",509        title="bar",510    )511    assert expected == Child.to_schema()512def test_config_docstrings() -> None:513    class Model(pa.SchemaModel):514        """foo"""515        a: Series[int]516    assert Model.__doc__ == Model.to_schema().description517class Input(pa.SchemaModel):518    a: Series[int]519    b: Series[int]520    idx: Index[str]521class Output(Input):522    c: Series[int]523def test_check_types() -> None:524    @pa.check_types525    def transform(df: DataFrame[Input]) -> DataFrame[Output]:526        return df.assign(c=lambda x: x.a + x.b)527    data = pd.DataFrame(528        {"a": [1, 2, 3], "b": [4, 5, 6]}, index=pd.Index(["a", "b", "c"])529    )530    assert isinstance(transform(data), pd.DataFrame)531    for invalid_data in [532        data.drop("a", axis="columns"),533        data.drop("b", axis="columns"),534        data.assign(a=["a", "b", "c"]),535        data.assign(b=["a", "b", "c"]),536        data.reset_index(drop=True),537    ]:538        with pytest.raises(pa.errors.SchemaError):539            transform(invalid_data)540def test_alias() -> None:541    """Test that columns and indices can be aliased."""542    class Schema(pa.SchemaModel):543        col_2020: Series[int] = pa.Field(alias=2020)544        idx: Index[int] = pa.Field(alias="_idx", check_name=True)545        @pa.check(2020)546        @classmethod547        def int_column_lt_100(cls, series: pd.Series) -> Iterable[bool]:548            return series < 100549    schema = Schema.to_schema()550    assert len(schema.columns) == 1551    assert schema.columns.get(2020, None) is not None552    assert schema.index.name == "_idx"553    df = pd.DataFrame({2020: [99]}, index=[0])554    df.index.name = "_idx"555    assert len(Schema.to_schema().columns[2020].checks) == 1556    assert isinstance(Schema.validate(df), pd.DataFrame)557    # test multiindex558    class MISchema(pa.SchemaModel):559        idx1: Index[int] = pa.Field(alias="index0")560        idx2: Index[int] = pa.Field(alias="index1")561    actual = [index.name for index in MISchema.to_schema().index.indexes]...
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