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How to use to_pandera_column method in pandera

Best Python code snippet using pandera_python

io.py

Source:io.py

...487        This currently returns ``False`` for all fields within a frictionless488        schema.489        """490        return False491    def to_pandera_column(self) -> Dict:492        """Export this field to a column spec dictionary."""493        return {494            "checks": self.checks,495            "coerce": self.coerce,496            "nullable": self.nullable,497            "unique": self.unique,498            "dtype": self.dtype,499            "required": self.required,500            "name": self.name,501            "regex": self.regex,502        }503def from_frictionless_schema(504    schema: Union[str, Path, Dict, FrictionlessSchema]505) -> DataFrameSchema:506    # pylint: disable=line-too-long507    """Create a :class:`~pandera.schemas.DataFrameSchema` from either a508    frictionless json/yaml schema file saved on disk, or from a frictionless509    schema already loaded into memory.510    Each field from the frictionless schema will be converted to a pandera511    column specification using :class:`~pandera.io.FrictionlessFieldParser`512    to map field characteristics to pandera column specifications.513    :param schema: the frictionless schema object (or a514        string/Path to the location on disk of a schema specification) to515        parse.516    :returns: dataframe schema with frictionless field specs converted to517        pandera column checks and constraints for use as normal.518    :example:519    Here, we're defining a very basic frictionless schema in memory before520    parsing it and then querying the resulting521    :class:`~pandera.schemas.DataFrameSchema` object as per any other Pandera522    schema:523    >>> from pandera.io import from_frictionless_schema524    >>>525    >>> FRICTIONLESS_SCHEMA = {526    ...     "fields": [527    ...         {528    ...             "name": "column_1",529    ...             "type": "integer",530    ...             "constraints": {"minimum": 10, "maximum": 99}531    ...         },532    ...         {533    ...             "name": "column_2",534    ...             "type": "string",535    ...             "constraints": {"maxLength": 10, "pattern": "\\S+"}536    ...         },537    ...     ],538    ...     "primaryKey": "column_1"539    ... }540    >>> schema = from_frictionless_schema(FRICTIONLESS_SCHEMA)541    >>> schema.columns["column_1"].checks542    [<Check in_range: in_range(10, 99)>]543    >>> schema.columns["column_1"].required544    True545    >>> schema.columns["column_1"].unique546    True547    >>> schema.columns["column_2"].checks548    [<Check str_length: str_length(None, 10)>, <Check str_matches: str_matches(re.compile('^\\\\S+$'))>]549    """550    if not isinstance(schema, FrictionlessSchema):551        schema = FrictionlessSchema(schema)552    assembled_schema = {553        "columns": {554            field.name: FrictionlessFieldParser(555                field, schema.primary_key556            ).to_pandera_column()557            for field in schema.fields558        },559        "index": None,560        "checks": None,561        "coerce": True,562        "strict": True,563        # only set dataframe-level uniqueness if the frictionless primary564        # key property specifies more than one field565        "unique": (566            None if len(schema.primary_key) == 1 else list(schema.primary_key)567        ),568    }...

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