flytekit.types.structured.StructuredDataset#

class flytekit.types.structured.StructuredDataset(dataframe=None, uri=None, metadata=None, **kwargs)[source]#

This is the user facing StructuredDataset class. Please don’t confuse it with the literals.StructuredDataset class (that is just a model, a Python class representation of the protobuf).

Methods

Parameters
all()[source]#
Return type

flytekit.types.structured.structured_dataset.DF

classmethod column_names()[source]#
Return type

List[str]

classmethod columns()[source]#
Return type

Dict[str, Type]

classmethod from_dict(kvs, *, infer_missing=False)#
Parameters

kvs (Optional[Union[dict, list, str, int, float, bool]]) –

Return type

dataclasses_json.api.A

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)#
Parameters

s (Union[str, bytes, bytearray]) –

Return type

dataclasses_json.api.A

iter()[source]#
Return type

Generator[flytekit.types.structured.structured_dataset.DF, None, None]

open(dataframe_type)[source]#
Parameters

dataframe_type (Type[flytekit.types.structured.structured_dataset.DF]) –

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)#
Parameters
  • infer_missing (bool) –

  • many (bool) –

  • partial (bool) –

Return type

dataclasses_json.mm.SchemaF[dataclasses_json.mm.A]

to_dict(encode_json=False)#
Return type

Dict[str, Optional[Union[dict, list, str, int, float, bool]]]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)#
Parameters
Return type

str

Attributes

DEFAULT_FILE_FORMAT = 'parquet'#
dataframe#
file_format: Optional[str] = 'parquet'#
literal#
metadata#
uri: Optional[os.PathLike] = None#