PandasDBFSDatasource

class great_expectations.datasource.fluent.PandasDBFSDatasource(*, type: Literal['pandas_dbfs'] = 'pandas_dbfs', name: str, id: Optional[uuid.UUID] = None, assets: List[great_expectations.datasource.fluent.data_asset.path.file_asset.FileDataAsset] = [], base_directory: pathlib.Path, data_context_root_directory: Optional[pathlib.Path] = None)#

Pandas based Datasource for DataBricks File System (DBFS) based data assets.

add_csv_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd65e80> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd65f40> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd70070> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd701f0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd70340> = None, sep: typing.Optional[str] = None, delimiter: typing.Optional[str] = None, header: Union[int, Sequence[int], None, Literal['infer']] = 'infer', names: Union[Sequence[str], None] = None, index_col: Union[IndexLabel, Literal[False], None] = None, usecols: typing.Optional[typing.Union[int, str, typing.Sequence[int]]] = None, dtype: typing.Optional[dict] = None, engine: Union[CSVEngine, None] = None, converters: typing.Any = None, true_values: typing.Any = None, false_values: typing.Any = None, skipinitialspace: bool = False, skiprows: typing.Optional[typing.Union[typing.Sequence[int], int]] = None, skipfooter: int = 0, nrows: typing.Optional[int] = None, na_values: typing.Any = None, keep_default_na: bool = True, na_filter: bool = True, verbose: bool = False, skip_blank_lines: bool = True, parse_dates: Union[bool, Sequence[str], None] = None, infer_datetime_format: bool = None, keep_date_col: bool = False, date_parser: typing.Any = None, date_format: typing.Optional[str] = None, dayfirst: bool = False, cache_dates: bool = True, iterator: bool = False, chunksize: typing.Optional[int] = None, compression: CompressionOptions = 'infer', thousands: typing.Optional[str] = None, decimal: str = '.', lineterminator: typing.Optional[str] = None, quotechar: str = '"', quoting: int = 0, doublequote: bool = True, escapechar: typing.Optional[str] = None, comment: typing.Optional[str] = None, encoding: typing.Optional[str] = None, encoding_errors: typing.Optional[str] = 'strict', dialect: typing.Optional[str] = None, on_bad_lines: str = 'error', delim_whitespace: bool = False, low_memory: typing.Any = True, memory_map: bool = False, float_precision: Union[Literal[('high', 'legacy')], None] = None, storage_options: StorageOptions = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_excel_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd09730> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd097f0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd09910> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd09a60> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd09bb0> = None, sheet_name: typing.Optional[typing.Union[str, int, typing.List[typing.Union[int, str]]]] = 0, header: Union[int, Sequence[int], None] = 0, names: typing.Optional[typing.List[str]] = None, index_col: Union[int, Sequence[int], None] = None, usecols: typing.Optional[typing.Union[int, str, typing.Sequence[int]]] = None, dtype: typing.Optional[dict] = None, engine: Union[Literal[('xlrd', 'openpyxl', 'odf', 'pyxlsb')], None] = None, true_values: Union[Iterable[str], None] = None, false_values: Union[Iterable[str], None] = None, skiprows: typing.Optional[typing.Union[typing.Sequence[int], int]] = None, nrows: typing.Optional[int] = None, na_values: typing.Any = None, keep_default_na: bool = True, na_filter: bool = True, verbose: bool = False, parse_dates: typing.Union[typing.List, typing.Dict, bool] = False, date_format: typing.Optional[str] = None, thousands: typing.Optional[str] = None, decimal: str = '.', comment: typing.Optional[str] = None, skipfooter: int = 0, storage_options: StorageOptions = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_feather_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd178b0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd179d0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd17a30> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd17b80> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd17cd0> = None, columns: Union[Sequence[str], None] = None, use_threads: bool = True, storage_options: StorageOptions = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_fwf_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd243d0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd24490> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd245b0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd24700> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd24850> = None, colspecs: Union[Sequence[Tuple[int, int]], str, None] = 'infer', widths: Union[Sequence[int], None] = None, infer_nrows: int = 100, dtype_backend: DtypeBackend = None, kwargs: typing.Optional[dict] = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_hdf_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd24c10> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36070> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36190> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd362e0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36430> = None, key: typing.Any = None, mode: str = 'r', errors: str = 'strict', where: typing.Optional[typing.Union[str, typing.List]] = None, start: typing.Optional[int] = None, stop: typing.Optional[int] = None, columns: typing.Optional[typing.List[str]] = None, iterator: bool = False, chunksize: typing.Optional[int] = None, kwargs: typing.Optional[dict] = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_html_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36b50> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36c10> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36d30> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36e50> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dd36fa0> = None, match: Union[str, Pattern] = '.+', flavor: typing.Optional[str] = None, header: Union[int, Sequence[int], None] = None, index_col: Union[int, Sequence[int], None] = None, skiprows: typing.Optional[typing.Union[typing.Sequence[int], int]] = None, attrs: typing.Optional[typing.Dict[str, str]] = None, parse_dates: bool = False, thousands: typing.Optional[str] = ',', encoding: typing.Optional[str] = None, decimal: str = '.', converters: typing.Optional[typing.Dict] = None, na_values: Union[Iterable[object], None] = None, keep_default_na: bool = True, displayed_only: bool = True, extract_links: Literal[(None, 'header', 'footer', 'body', 'all')] = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_json_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcbea60> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcbeb20> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcbec40> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcbed60> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcbeeb0> = None, orient: typing.Optional[str] = None, typ: Literal[('frame', 'series')] = 'frame', dtype: typing.Optional[dict] = None, convert_axes: typing.Any = None, convert_dates: typing.Union[bool, typing.List[str]] = True, keep_default_dates: bool = True, precise_float: bool = False, date_unit: typing.Optional[str] = None, encoding: typing.Optional[str] = None, encoding_errors: typing.Optional[str] = 'strict', lines: bool = False, chunksize: typing.Optional[int] = None, compression: CompressionOptions = 'infer', nrows: typing.Optional[int] = None, storage_options: StorageOptions = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_orc_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcce970> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dccea30> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcceb50> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcceca0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dccedf0> = None, columns: typing.Optional[typing.List[str]] = None, dtype_backend: DtypeBackend = None, kwargs: typing.Optional[dict] = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_parquet_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcdf460> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcdf520> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcdf640> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcdf790> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcdf8e0> = None, engine: str = 'auto', columns: typing.Optional[typing.List[str]] = None, storage_options: StorageOptions = None, use_nullable_dtypes: bool = None, dtype_backend: DtypeBackend = None, kwargs: typing.Optional[dict] = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_pickle_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9070> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9130> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9250> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce93a0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce94f0> = None, compression: CompressionOptions = 'infer', storage_options: StorageOptions = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_sas_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9b50> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9c10> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9d30> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9e80> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dce9fd0> = None, format: typing.Optional[str] = None, index: typing.Optional[str] = None, encoding: typing.Optional[str] = None, chunksize: typing.Optional[int] = None, iterator: bool = False, compression: CompressionOptions = 'infer', **extra_data: typing.Any) → pydantic.BaseModel#

add_spss_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcf3760> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcf3820> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcf3940> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcf3a90> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dcf3be0> = None, usecols: typing.Optional[typing.Union[int, str, typing.Sequence[int]]] = None, convert_categoricals: bool = True, dtype_backend: DtypeBackend = None, **extra_data: typing.Any) → pydantic.BaseModel#

add_stata_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc012e0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc013a0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc014c0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc01610> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc01760> = None, convert_dates: bool = True, convert_categoricals: bool = True, index_col: typing.Optional[str] = None, convert_missing: bool = False, preserve_dtypes: bool = True, columns: Union[Sequence[str], None] = None, order_categoricals: bool = True, chunksize: typing.Optional[int] = None, iterator: bool = False, compression: CompressionOptions = 'infer', storage_options: StorageOptions = None, **extra_data: typing.Any)→ pydantic.BaseModel#

add_xml_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc01fd0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc0d0a0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc0d1c0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc0d310> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fcf8dc0d460> = None, xpath: str = './*', namespaces: typing.Optional[typing.Dict[str, str]] = None, elems_only: bool = False, attrs_only: bool = False, names: Union[Sequence[str], None] = None, dtype: typing.Optional[dict] = None, encoding: typing.Optional[str] = 'utf-8', stylesheet: Union[FilePath, None] = None, iterparse: typing.Optional[typing.Dict[str, typing.List[str]]] = None, compression: CompressionOptions = 'infer', storage_options: StorageOptions = None, dtype_backend: DtypeBackend = None, **extra_data: typing.Any)→ pydantic.BaseModel#