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How to use docstring_substitution method in pandera

Best Python code snippet using pandera_python

model.py

Source:model.py

...49_CONFIG_KEY = "Config"50MODEL_CACHE: Dict[Type["SchemaModel"], DataFrameSchema] = {}51F = TypeVar("F", bound=Callable)52TSchemaModel = TypeVar("TSchemaModel", bound="SchemaModel")53def docstring_substitution(*args: Any, **kwargs: Any) -> Callable[[F], F]:54    """Typed wrapper around pd.util.Substitution."""55    def decorator(func: F) -> F:56        return cast(F, pd.util.Substitution(*args, **kwargs)(func))57    return decorator58def _is_field(name: str) -> bool:59    """Ignore private and reserved keywords."""60    return not name.startswith("_") and name != _CONFIG_KEY61_config_options = [attr for attr in vars(BaseConfig) if _is_field(attr)]62def _extract_config_options_and_extras(63    config: Any,64) -> Tuple[Dict[str, Any], Dict[str, Any]]:65    config_options, extras = {}, {}66    for name, value in vars(config).items():67        if name in _config_options:68            config_options[name] = value69        elif _is_field(name):70            extras[name] = value71        # drop private/reserved keywords72    return config_options, extras73def _convert_extras_to_checks(extras: Dict[str, Any]) -> List[Check]:74    """75    New in GH#383.76    Any key not in BaseConfig keys is interpreted as defining a dataframe check. This function77    defines this conversion as follows:78        - Look up the key name in Check79        - If value is80            - tuple: interpret as args81            - dict: interpret as kwargs82            - anything else: interpret as the only argument to pass to Check83    """84    checks = []85    for name, value in extras.items():86        if isinstance(value, tuple):87            args, kwargs = value, {}88        elif isinstance(value, dict):89            args, kwargs = (), value90        else:91            args, kwargs = (value,), {}92        # dispatch directly to getattr to raise the correct exception93        checks.append(Check.__getattr__(name)(*args, **kwargs))94    return checks95class _MetaSchema(type):96    """Add string representations, mainly for pydantic."""97    def __repr__(cls):98        return str(cls)99    def __str__(cls):100        return cls.__name__101class SchemaModel(metaclass=_MetaSchema):102    """Definition of a :class:`~pandera.DataFrameSchema`.103    *new in 0.5.0*104    See the :ref:`User Guide <schema_models>` for more.105    """106    Config: Type[BaseConfig] = BaseConfig107    __extras__: Optional[Dict[str, Any]] = None108    __schema__: Optional[DataFrameSchema] = None109    __config__: Optional[Type[BaseConfig]] = None110    #: Key according to `FieldInfo.name`111    __fields__: Dict[str, Tuple[AnnotationInfo, FieldInfo]] = {}112    __checks__: Dict[str, List[Check]] = {}113    __dataframe_checks__: List[Check] = []114    # This is syntantic sugar that delegates to the validate method115    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)116    def __new__(cls, *args, **kwargs) -> DataFrameBase[TSchemaModel]:  # type: ignore [misc]117        """%(validate_doc)s"""118        return cast(DataFrameBase[TSchemaModel], cls.validate(*args, **kwargs))119    def __init_subclass__(cls, **kwargs):120        """Ensure :class:`~pandera.model_components.FieldInfo` instances."""121        super().__init_subclass__(**kwargs)122        # pylint:disable=no-member123        subclass_annotations = cls.__dict__.get("__annotations__", {})124        for field_name in subclass_annotations.keys():125            if _is_field(field_name) and field_name not in cls.__dict__:126                # Field omitted127                field = Field()128                field.__set_name__(cls, field_name)129                setattr(cls, field_name, field)130        cls.__config__, cls.__extras__ = cls._collect_config_and_extras()131    @classmethod132    def to_schema(cls) -> DataFrameSchema:133        """Create :class:`~pandera.DataFrameSchema` from the :class:`.SchemaModel`."""134        if cls in MODEL_CACHE:135            return MODEL_CACHE[cls]136        mi_kwargs = {137            name[len("multiindex_") :]: value138            for name, value in vars(cls.__config__).items()139            if name.startswith("multiindex_")140        }141        cls.__fields__ = cls._collect_fields()142        check_infos = typing.cast(143            List[FieldCheckInfo], cls._collect_check_infos(CHECK_KEY)144        )145        cls.__checks__ = cls._extract_checks(146            check_infos, field_names=list(cls.__fields__.keys())147        )148        df_check_infos = cls._collect_check_infos(DATAFRAME_CHECK_KEY)149        df_custom_checks = cls._extract_df_checks(df_check_infos)150        df_registered_checks = _convert_extras_to_checks(151            {} if cls.__extras__ is None else cls.__extras__152        )153        cls.__dataframe_checks__ = df_custom_checks + df_registered_checks154        columns, index = cls._build_columns_index(155            cls.__fields__, cls.__checks__, **mi_kwargs156        )157        kwargs = {}158        if cls.__config__ is not None:159            kwargs = {160                "coerce": cls.__config__.coerce,161                "strict": cls.__config__.strict,162                "name": cls.__config__.name,163                "ordered": cls.__config__.ordered,164                "unique": cls.__config__.unique,165                "title": cls.__config__.title,166                "description": cls.__config__.description or cls.__doc__,167            }168        cls.__schema__ = DataFrameSchema(169            columns,170            index=index,171            checks=cls.__dataframe_checks__,  # type: ignore172            **kwargs,173        )174        if cls not in MODEL_CACHE:175            MODEL_CACHE[cls] = cls.__schema__  # type: ignore176        return cls.__schema__  # type: ignore177    @classmethod178    def to_yaml(cls, stream: Optional[os.PathLike] = None):179        """180        Convert `Schema` to yaml using `io.to_yaml`.181        """182        return cls.to_schema().to_yaml(stream)183    @classmethod184    @docstring_substitution(validate_doc=DataFrameSchema.validate.__doc__)185    def validate(186        cls: Type[TSchemaModel],187        check_obj: pd.DataFrame,188        head: Optional[int] = None,189        tail: Optional[int] = None,190        sample: Optional[int] = None,191        random_state: Optional[int] = None,192        lazy: bool = False,193        inplace: bool = False,194    ) -> DataFrameBase[TSchemaModel]:195        """%(validate_doc)s"""196        return cast(197            DataFrameBase[TSchemaModel],198            cls.to_schema().validate(199                check_obj, head, tail, sample, random_state, lazy, inplace200            ),201        )202    @classmethod203    @docstring_substitution(strategy_doc=DataFrameSchema.strategy.__doc__)204    @st.strategy_import_error205    def strategy(cls: Type[TSchemaModel], *, size: Optional[int] = None):206        """%(strategy_doc)s"""207        return cls.to_schema().strategy(size=size)208    @classmethod209    @docstring_substitution(example_doc=DataFrameSchema.strategy.__doc__)210    @st.strategy_import_error211    def example(212        cls: Type[TSchemaModel], *, size: Optional[int] = None213    ) -> DataFrameBase[TSchemaModel]:214        """%(example_doc)s"""215        return cast(216            DataFrameBase[TSchemaModel], cls.to_schema().example(size=size)217        )218    @classmethod219    def _build_columns_index(  # pylint:disable=too-many-locals220        cls,221        fields: Dict[str, Tuple[AnnotationInfo, FieldInfo]],222        checks: Dict[str, List[Check]],223        **multiindex_kwargs: Any,...

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