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How to use _is_valid_multiindex_tuple_str method in pandera

Best Python code snippet using pandera_python

schema_components.py

Source:schema_components.py

...13    DataFrameSchema,14    PandasDtypeInputTypes,15    SeriesSchemaBase,16)17def _is_valid_multiindex_tuple_str(x: Tuple[Any, ...]) -> bool:18    """Check that a multi-index tuple key has all string elements"""19    return isinstance(x, tuple) and all(isinstance(i, str) for i in x)20class Column(SeriesSchemaBase):21    """Validate types and properties of DataFrame columns."""22    @deprecate_pandas_dtype23    def __init__(24        self,25        dtype: PandasDtypeInputTypes = None,26        checks: CheckList = None,27        nullable: bool = False,28        unique: bool = False,29        allow_duplicates: Optional[bool] = None,30        coerce: bool = False,31        required: bool = True,32        name: Union[str, Tuple[str, ...], None] = None,33        regex: bool = False,34        pandas_dtype: PandasDtypeInputTypes = None,35        title: Optional[str] = None,36        description: Optional[str] = None,37    ) -> None:38        """Create column validator object.39        :param dtype: datatype of the column. A ``PandasDtype`` for40            type-checking dataframe. If a string is specified, then assumes41            one of the valid pandas string values:42            http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes43        :param checks: checks to verify validity of the column44        :param nullable: Whether or not column can contain null values.45        :param unique: whether column values should be unique46        :param allow_duplicates: Whether or not column can contain duplicate47            values.48            .. warning::49                This option will be deprecated in 0.8.0. Use the ``unique``50                argument instead.51        :param coerce: If True, when schema.validate is called the column will52            be coerced into the specified dtype. This has no effect on columns53            where ``pandas_dtype=None``.54        :param required: Whether or not column is allowed to be missing55        :param name: column name in dataframe to validate.56        :param regex: whether the ``name`` attribute should be treated as a57            regex pattern to apply to multiple columns in a dataframe.58        :param pandas_dtype: alias of ``dtype`` for backwards compatibility.59            .. warning:: This option will be deprecated in 0.8.060        :param title: A human-readable label for the column.61        :param description: An arbitrary textual description of the column.62        :raises SchemaInitError: if impossible to build schema from parameters63        :example:64        >>> import pandas as pd65        >>> import pandera as pa66        >>>67        >>>68        >>> schema = pa.DataFrameSchema({69        ...     "column": pa.Column(str)70        ... })71        >>>72        >>> schema.validate(pd.DataFrame({"column": ["foo", "bar"]}))73          column74        0    foo75        1    bar76        See :ref:`here<column>` for more usage details.77        """78        super().__init__(79            dtype,80            checks,81            nullable,82            unique,83            allow_duplicates,84            coerce,85            name,86            pandas_dtype,87            title,88            description,89        )90        if (91            name is not None92            and not isinstance(name, str)93            and not _is_valid_multiindex_tuple_str(name)94            and regex95        ):96            raise ValueError(97                "You cannot specify a non-string name when setting regex=True"98            )99        self.required = required100        self._name = name101        self._regex = regex102    @property103    def regex(self) -> bool:104        """True if ``name`` attribute should be treated as a regex pattern."""105        return self._regex106    @property107    def _allow_groupby(self) -> bool:...

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