Skip to main content
Version: 1.0.2

PandasGoogleCloudStorageDatasource

class great_expectations.datasource.fluent.PandasGoogleCloudStorageDatasource(*, type: Literal['pandas_gcs'] = 'pandas_gcs', name: str, id: Optional[uuid.UUID] = None, assets: List[great_expectations.datasource.fluent.data_asset.path.file_asset.FileDataAsset] = [], bucket_or_name: str, gcs_options: Dict[str, Union[great_expectations.datasource.fluent.config_str.ConfigStr, Any]] = )#

add_csv_asset(name: str, *, id: <pydantic.v1.fields.DeferredType object at 0x7fd9fa58fd90> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa58fe50> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa58ffa0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa59e100> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa59e250> = 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 0x7fd9fa5b76a0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa5b7760> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa5b7880> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa5b7a00> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa5b7b50> = 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 0x7fd9fa4c7880> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4c79a0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4c7a00> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4c7b80> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4c7cd0> = 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 0x7fd9fa4d8400> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4d84c0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4d85e0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4d8760> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4d88b0> = 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 0x7fd9fa4e4040> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e4100> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e4220> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e43a0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e44f0> = 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 0x7fd9fa4e4c40> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e4d00> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e4e20> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4e4f70> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4ee100> = 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 0x7fd9fa4eebb0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4eec70> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4eed90> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4eeee0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa47e070> = 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 0x7fd9fa47eb20> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa47ebe0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa47ed00> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa47ee80> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa47efd0> = 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 0x7fd9fa48c6a0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa48c760> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa48c880> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa48ca00> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa48cb20> = 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 0x7fd9fa49a2e0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa49a3a0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa49a4c0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa49a640> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa49a790> = 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 0x7fd9fa49ae20> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa49aee0> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1040> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a11c0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1310> = 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 0x7fd9fa4a1a90> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1b50> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1c70> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1df0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4a1f40> = 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 0x7fd9fa4aa670> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4aa730> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4aa850> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4aa9d0> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4aab20> = 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 0x7fd9fa4ba3d0> = None, order_by: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4ba490> = None, batch_metadata: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4ba5b0> = None, batch_definitions: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4ba730> = None, connect_options: <pydantic.v1.fields.DeferredType object at 0x7fd9fa4ba880> = 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#