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How to use _get_fn_argnames method in pandera

Best Python code snippet using pandera_python

decorators.py

Source:decorators.py

...30Schemas = Union[schemas.DataFrameSchema, schemas.SeriesSchema]31InputGetter = Union[str, int]32OutputGetter = Union[str, int, Callable]33F = TypeVar("F", bound=Callable)34def _get_fn_argnames(fn: Callable) -> List[str]:35    """Get argument names of a function.36    :param fn: get argument names for this function.37    :returns: list of argument names to be matched with the positional38    args passed in the decorator.39    .. note::40       Excludes first positional "self" or "cls" arguments if needed:41       - exclude self:42           - if fn is a method (self being an implicit argument)43       - exclude cls:44           - if fn is a decorated classmethod in Python 3.9+45           - if fn is declared as a regular method on a metaclass46    For functions decorated with ``@classmethod``, cls is excluded only in Python 3.9+47    because that is when Python's handling of classmethods changed and wrapt mirrors it.48    See: https://github.com/GrahamDumpleton/wrapt/issues/18249    """50    arg_spec_args = inspect.getfullargspec(fn).args51    first_arg_is_self = arg_spec_args[0] == "self"52    is_py_newer_than_39 = sys.version_info[:2] >= (3, 9)53    # Exclusion criteria54    is_regular_method = inspect.ismethod(fn) and first_arg_is_self55    is_decorated_cls_method = (56        is_decorated_classmethod(fn) and is_py_newer_than_3957    )58    is_cls_method_from_meta_method = is_classmethod_from_meta(fn)59    if (60        is_regular_method61        or is_decorated_cls_method62        or is_cls_method_from_meta_method63    ):64        # Don't include "self" / "cls" argument65        arg_spec_args = arg_spec_args[1:]66    return arg_spec_args67def _handle_schema_error(68    decorator_name,69    fn: Callable,70    schema: Union[schemas.DataFrameSchema, schemas.SeriesSchema],71    arg_df: pd.DataFrame,72    schema_error: errors.SchemaError,73) -> NoReturn:74    """Reraise schema validation error with decorator context.75    :param fn: check the DataFrame or Series input of this function.76    :param schema: dataframe/series schema object77    :param arg_df: dataframe/series we are validating.78    :param schema_error: original exception.79    :raises SchemaError: when ``DataFrame`` violates built-in or custom80        checks.81    """82    msg = f"error in {decorator_name} decorator of function '{fn.__name__}': {schema_error}"83    raise errors.SchemaError(84        schema,85        arg_df,86        msg,87        failure_cases=schema_error.failure_cases,88        check=schema_error.check,89        check_index=schema_error.check_index,90    ) from schema_error91def check_input(92    schema: Schemas,93    obj_getter: Optional[InputGetter] = None,94    head: Optional[int] = None,95    tail: Optional[int] = None,96    sample: Optional[int] = None,97    random_state: Optional[int] = None,98    lazy: bool = False,99    inplace: bool = False,100) -> Callable[[F], F]:101    # pylint: disable=duplicate-code102    """Validate function argument when function is called.103    This is a decorator function that validates the schema of a dataframe104    argument in a function.105    :param schema: dataframe/series schema object106    :param obj_getter:  (Default value = None) if int, obj_getter refers to the107        the index of the pandas dataframe/series to be validated in the args108        part of the function signature. If str, obj_getter refers to the109        argument name of the pandas dataframe/series in the function signature.110        This works even if the series/dataframe is passed in as a positional111        argument when the function is called. If None, assumes that the112        dataframe/series is the first argument of the decorated function113    :param head: validate the first n rows. Rows overlapping with `tail` or114        `sample` are de-duplicated.115    :param tail: validate the last n rows. Rows overlapping with `head` or116        `sample` are de-duplicated.117    :param sample: validate a random sample of n rows. Rows overlapping118        with `head` or `tail` are de-duplicated.119    :param random_state: random seed for the ``sample`` argument.120    :param lazy: if True, lazily evaluates dataframe against all validation121        checks and raises a ``SchemaErrors``. Otherwise, raise122        ``SchemaError`` as soon as one occurs.123    :param inplace: if True, applies coercion to the object of validation,124        otherwise creates a copy of the data.125    :returns: wrapped function126    :example:127    Check the input of a decorated function.128    >>> import pandas as pd129    >>> import pandera as pa130    >>>131    >>>132    >>> schema = pa.DataFrameSchema({"column": pa.Column(int)})133    >>>134    >>> @pa.check_input(schema)135    ... def transform_data(df: pd.DataFrame) -> pd.DataFrame:136    ...     df["doubled_column"] = df["column"] * 2137    ...     return df138    >>>139    >>> df = pd.DataFrame({140    ...     "column": range(5),141    ... })142    >>>143    >>> transform_data(df)144       column  doubled_column145    0       0               0146    1       1               2147    2       2               4148    3       3               6149    4       4               8150    See :ref:`here<decorators>` for more usage details.151    """152    @wrapt.decorator153    def _wrapper(154        fn: Callable,155        instance: Union[None, Any],156        args: Tuple[Any, ...],157        kwargs: Dict[str, Any],158    ):159        # pylint: disable=unused-argument160        """Check pandas DataFrame or Series before calling the function.161        :param fn: check the DataFrame or Series input of this function162        :param instance: the object to which the wrapped function was bound163            when it was called. Only applies to methods.164        :param args: the list of positional arguments supplied when the165            decorated function was called.166        :param kwargs: the dictionary of keyword arguments supplied when the167            decorated function was called.168        """169        args = list(args)170        validate_args = (head, tail, sample, random_state, lazy, inplace)171        if isinstance(obj_getter, int):172            try:173                args[obj_getter] = schema.validate(args[obj_getter])174            except IndexError as exc:175                raise IndexError(176                    f"error in check_input decorator of function '{fn.__name__}': the "177                    f"index '{obj_getter}' was supplied to the check but this "178                    f"function accepts '{len(_get_fn_argnames(fn))}' arguments, so the maximum "179                    f"index is 'max(0, len(_get_fn_argnames(fn)) - 1)'. The full error is: '{exc}'"180                ) from exc181        elif isinstance(obj_getter, str):182            if obj_getter in kwargs:183                kwargs[obj_getter] = schema.validate(184                    kwargs[obj_getter], *validate_args185                )186            else:187                arg_spec_args = _get_fn_argnames(fn)188                args_dict = OrderedDict(zip(arg_spec_args, args))189                args_dict[obj_getter] = schema.validate(190                    args_dict[obj_getter], *validate_args191                )192                args = list(args_dict.values())193        elif obj_getter is None and args:194            try:195                args[0] = schema.validate(args[0], *validate_args)196            except errors.SchemaError as e:197                _handle_schema_error("check_input", fn, schema, args[0], e)198        elif obj_getter is None and kwargs:199            # get the first key in the same order specified in the200            # function argument.201            args_names = _get_fn_argnames(fn)202            try:203                kwargs[args_names[0]] = schema.validate(204                    kwargs[args_names[0]], *validate_args205                )206            except errors.SchemaError as e:207                _handle_schema_error(208                    "check_input", fn, schema, kwargs[args_names[0]], e209                )210        else:211            raise TypeError(212                f"obj_getter is unrecognized type: {type(obj_getter)}"213            )214        return fn(*args, **kwargs)215    return _wrapper...

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