PandasDatasource
Adds a single-batch pandas datasource to the data context.
Parameters
Name Description name
The name of this datasource.
assets
An optional dictionary whose keys are Pandas DataAsset names and whose values are Pandas DataAsset objects.
Add a clipboard data asset to the datasource.
Parameters
Name Description name
The name of the clipboard asset. This can be any arbitrary string.
**kwargs
Additional keyword arguments to pass to pandas.read_clipboard().
Returns
Type Description pydantic.v1.main.ClipboardAsset
The ClipboardAsset that has been added to this datasource.
Add a CSV data asset to the datasource.
Parameters
Name Description name
The name of the CSV asset. This can be any arbitrary string.
filepath_or_buffer
The path to the CSV file or a URL pointing to the CSV file.
**kwargs
Additional keyword arguments to pass to pandas.read_csv().
Returns
Type Description pydantic.v1.main.CSVAsset
The CSVAsset that has been added to this datasource.
Adds a Dataframe DataAsset to this PandasDatasource object.
Parameters
Name Description name
The name of the Dataframe asset. This can be any arbitrary string.
batch_metadata
An arbitrary user defined dictionary with string keys which will get inherited by any batches created from the asset.
Returns
Type Description DataFrameAsset
The DataFrameAsset that has been added to this datasource.
Add an Excel data asset to the datasource.
Parameters
Name Description name
The name of the Excel asset. This can be any arbitrary string.
io
The path to the Excel file or a URL pointing to the Excel file.
**kwargs
Additional keyword arguments to pass to pandas.read_excel().
Returns
Type Description ExcelAsset
The ExcelAsset that has been added to this datasource.
Add a Feather data asset to the datasource.
Parameters
Name Description name
The name of the Feather asset. This can be any arbitrary string.
path
The path to the Feather file or a URL pointing to the Feather file.
**kwargs
Additional keyword arguments to pass to pandas.read_feather().
Returns
Type Description pydantic.v1.main.FeatherAsset
The FeatherAsset that has been added to this datasource.
Adds a Fixed Width File DataAsset to the datasource.
Parameters
Name Description filepath_or_buffer
The path to the file or a URL pointing to the Feather file.
asset_name
The name of the asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_fwf().
Returns
Type Description pydantic.v1.main.FeatherAsset
The FWFAsset that has been added to this datasource.
Add a GBQ data asset to the datasource.
Parameters
Name Description name
The name of the GBQ asset. This can be any arbitrary string.
query
The SQL query to send to Google BigQuery.
**kwargs
Additional keyword arguments to pass to pandas.read_gbq().
Returns
Type Description pydantic.v1.main.GBQAsset
The GBQAsset that has been added to this datasource.
Add an HDF data asset to the datasource.
Parameters
Name Description name
The name of the HDF asset. This can be any arbitrary string.
path_or_buf
The path to the HDF file or a URL pointing to the HDF file.
**kwargs
Additional keyword arguments to pass to pandas.read_hdf().
Returns
Type Description HDFAsset
The HDFAsset that has been added to this datasource.
Add an HTML data asset to the datasource.
Parameters
Name Description name
The name of the HTML asset. This can be any arbitrary string.
io
The path to the HTML file or a URL pointing to the HTML file.
**kwargs
Additional keyword arguments to pass to pandas.read_html().
Returns
Type Description HTMLAsset
The HTMLAsset that has been added to this datasource.
Add a JSON data asset to the datasource.
Parameters
Name Description name
The name of the JSON asset. This can be any arbitrary string.
path_or_buf
The path to the JSON file or a URL pointing to the JSON file.
**kwargs
Additional keyword arguments to pass to pandas.read_json().
Returns
Type Description pydantic.v1.main.JSONAsset
The JSONAsset that has been added to this datasource.
Add an ORC file as a DataAsset to this PandasDatasource object.
Parameters
Name Description name
The name to use for the ORC asset. This can be any arbitrary string.
path
The path to the ORC file.
**kwargs
Additional kwargs to pass to the ORC reader.
Returns
Type Description pydantic.v1.main.ORCAsset
The ORCAsset that has been added to this datasource.
Add a parquet file as a DataAsset to this PandasDatasource object.
Parameters
Name Description name
The name to use for the parquet asset. This can be any arbitrary string.
path
The path to the parquet file.
**kwargs
Additional kwargs to pass to the parquet reader.
Returns
Type Description pydantic.v1.main.ParquetAsset
The ParquetAsset that has been added to this datasource.
Add a pickle file as a DataAsset to this PandasDatasource object.
Parameters
Name Description name
The name to use for the pickle asset. This can be any arbitrary string.
filepath_or_buffer
The path to the pickle file.
**kwargs
Additional kwargs to pass to the pickle reader.
Returns
Type Description pydantic.v1.main.PickleAsset
The PickleAsset that has been added to this datasource.
Add a SAS data asset to the datasource.
Parameters
Name Description name
The name of the SAS asset. This can be any arbitrary string.
filepath_or_buffer
The path to the SAS file or a URL pointing to the SAS file.
**kwargs
Additional keyword arguments to pass to pandas.read_sas().
Returns
Type Description pydantic.v1.main.SASAsset
The SASAsset that has been added to this datasource.
Add an SPSS data asset to the datasource.
Parameters
Name Description name
The name of the SPSS asset. This can be any arbitrary string.
path
The path to the SPSS file.
**kwargs
Additional keyword arguments to pass to pandas.read_spss().
Returns
Type Description pydantic.v1.main.SPSSAsset
The SPSSAsset that has been added to this datasource.
Add a SQL data asset to the datasource.
Parameters
Name Description name
The name of the SQL asset. This can be any arbitrary string.
sql
The SQL query to send to the database.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
**kwargs
Additional keyword arguments to pass to pandas.read_sql().
Returns
Type Description SQLAsset
The SQLAsset that has been added to this datasource.
Add a SQL query data asset to the datasource.
Parameters
Name Description name
The name of the SQL query asset. This can be any arbitrary string.
sql
The SQL query to send to the database.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
**kwargs
Additional keyword arguments to pass to pandas.read_sql_query().
Returns
Type Description SQLQueryAsset
The SQLQueryAsset that has been added to this datasource.
Add a SQL table data asset to the datasource.
Parameters
Name Description name
The name of the SQL table asset. This can be any arbitrary string.
table_name
The name of the SQL table to read.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
**kwargs
Additional keyword arguments to pass to pandas.read_sql_table().
Returns
Type Description pydantic.v1.main.SQLTableAsset
The SQLTableAsset that has been added to this datasource.
Add a Stata data asset to the datasource.
Parameters
Name Description name
The name of the Stata asset. This can be any arbitrary string.
filepath_or_buffer
The path to the Stata file or a URL pointing to the Stata file.
**kwargs
Additional keyword arguments to pass to pandas.read_stata().
Returns
Type Description pydantic.v1.main.StataAsset
The StataAsset that has been added to this datasource.
Add a Table data asset to the datasource.
Parameters
Name Description name
The name of the Table asset. This can be any arbitrary string.
filepath_or_buffer
The path to the Table file or a URL pointing to the Table file.
**kwargs
Additional keyword arguments to pass to pandas.read_table().
Returns
Type Description pydantic.v1.main.TableAsset
The TableAsset that has been added to this datasource.
Add an XML data asset to the datasource.
Parameters
Name Description name
The name of the XML asset. This can be any arbitrary string.
path_or_buffer
The path to the XML file or a URL pointing to the XML file.
**kwargs
Additional keyword arguments to pass to pandas.read_xml().
Returns
Type Description pydantic.v1.main.XMLAsset
The XMLAsset that has been added to this datasource.
Removes the DataAsset referred to by asset_name from internal list of available DataAsset objects.
Parameters
Name Description name
name of DataAsset to be deleted.
Returns the DataAsset referred to by asset_name
Parameters
Name Description name
name of DataAsset sought.
Returns
Type Description great_expectations.datasource.fluent.interfaces._DataAssetT
if named "DataAsset" object exists; otherwise, exception is raised.
Read a clipboard and return a Batch containing the data.
Parameters
Name Description asset_name
The name of the clipboard asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_clipboard().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral ClipboardAsset.
Read a CSV file and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the CSV file or a URL pointing to the CSV file.
asset_name
The name of the CSV asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_csv().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral CSVAsset.
Reads a Dataframe and returns a Batch containing the data.
Parameters
Name Description dataframe
The Dataframe containing the data for this data asset.
asset_name
The name of the Dataframe asset, should you wish to use it again.
batch_metadata
An arbitrary user defined dictionary with string keys which will get inherited by any batches created from the asset.
Returns
Type Description Batch
A Batch using an ephemeral DataFrameAsset.
Read an Excel file and return a Batch containing the data.
Parameters
Name Description io
The path to the Excel file or a URL pointing to the Excel file.
asset_name
The name of the Excel asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_excel().
Returns
Type Description Batch
A Batch using an ephemeral ExcelAsset.
Read a Feather file and return a Batch containing the data.
Parameters
Name Description path
The path to the Feather file or a URL pointing to the Feather file.
asset_name
The name of the Feather asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_feather().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral FeatherAsset.
Read a Fixed Width File and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the file or a URL pointing to the Feather file.
asset_name
The name of the asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_fwf().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral FWFAsset.
Read a Google BigQuery query and return a Batch containing the data.
Parameters
Name Description query
The SQL query to send to Google BigQuery.
asset_name
The name of the GBQ asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_gbq().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral GBQAsset.
Read an HDF file and return a Batch containing the data.
Parameters
Name Description path_or_buf
The path to the HDF file or a URL pointing to the HDF file.
asset_name
The name of the HDF asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_hdf().
Returns
Type Description Batch
A Batch using an ephemeral HDFAsset.
Read an HTML file and return a Batch containing the data.
Parameters
Name Description io
The path to the HTML file or a URL pointing to the HTML file.
asset_name
The name of the HTML asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_html().
Returns
Type Description Batch
A Batch using an ephemeral HTMLAsset.
Read a JSON file and return a Batch containing the data.
Parameters
Name Description path_or_buf
The path to the JSON file or a URL pointing to the JSON file.
asset_name
The name of the JSON asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_json().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral JSONAsset.
Read an ORC file and return a Batch containing the data.
Parameters
Name Description path
The path to the ORC file.
asset_name (optional)
The asset name to use for the ORC file, should you wish to use or refer to it again.
**kwargs
Additional kwargs to pass to the ORC reader.
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral ORCAsset.
Read a parquet file and return a Batch containing the data.
Parameters
Name Description path
The path to the parquet file.
asset_name (optional)
The asset name to use for the parquet file, should you wish to use or refer to it again.
**kwargs
Additional kwargs to pass to the parquet reader.
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral ParquetAsset.
Read a pickle file and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the pickle file.
asset_name (optional)
The asset name to use for the pickle file, should you wish to use or refer to it again.
**kwargs
Additional kwargs to pass to the pickle reader.
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral PickleAsset.
Read a SAS file and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the SAS file or a URL pointing to the SAS file.
asset_name
The name of the SAS asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_sas().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral SASAsset.
Read an SPSS file and return a Batch containing the data.
Parameters
Name Description path
The path to the SPSS file.
asset_name
The name of the SPSS asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_spss().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral SPSSAsset.
Read a SQL query and return a Batch containing the data.
Parameters
Name Description sql
The SQL query to send to the database.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
asset_name
The name of the SQL asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_sql().
Returns
Type Description Batch
A Batch using an ephemeral SQLAsset.
Read a SQL query and return a Batch containing the data.
Parameters
Name Description sql
The SQL query to send to the database.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
asset_name
The name of the SQL query asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_sql_query().
Returns
Type Description Batch
A Batch using an ephemeral SQLQueryAsset.
Read a SQL table and return a Batch containing the data.
Parameters
Name Description table_name
The name of the SQL table to read.
con
The SQLAlchemy connection engine or a string URL to connect to the database.
asset_name
The name of the SQL table asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_sql_table().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral SQLTableAsset.
Read a Stata file and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the Stata file or a URL pointing to the Stata file.
asset_name
The name of the Stata asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_stata().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral StataAsset.
Read a Table file and return a Batch containing the data.
Parameters
Name Description filepath_or_buffer
The path to the Table file or a URL pointing to the Table file.
asset_name
The name of the Table asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_table().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral TableAsset.
Read an XML file and return a Batch containing the data.
Parameters
Name Description path_or_buffer
The path to the XML file or a URL pointing to the XML file.
asset_name
The name of the XML asset, should you wish to use it again.
**kwargs
Additional keyword arguments to pass to pandas.read_xml().
Returns
Type Description great_expectations.datasource.fluent.interfaces.Batch
A Batch using an ephemeral XMLAsset.
class great_expectations.datasource.fluent.PandasDatasource(
*,
type: Literal['pandas'] = 'pandas',
name: str,
id: Optional[uuid.UUID] = None,
assets: List[great_expectations.datasource.fluent.pandas_datasource._PandasDataAsset] = []
)
Methods
add_clipboard_asset
add_clipboard_asset(
name: str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: str = '\s+',
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → pydantic.v1.main.ClipboardAsset
add_csv_asset
add_csv_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: Optional[str] = None,
delimiter: 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: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: Optional[dict] = None,
engine: Union[CSVEngine,
None] = None,
true_values: Optional[List] = None,
false_values: Optional[List] = None,
skipinitialspace: bool = False,
skiprows: Optional[Union[Sequence[int],
int]] = None,
skipfooter: int = 0,
nrows: Optional[int] = None,
na_values: Union[Sequence[str],
None] = 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_format: Optional[str] = None,
dayfirst: bool = False,
cache_dates: bool = True,
iterator: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
thousands: Optional[str] = None,
decimal: str = '.',
lineterminator: Optional[str] = None,
quotechar: str = '"',
quoting: int = 0,
doublequote: bool = True,
escapechar: Optional[str] = None,
comment: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
dialect: Optional[str] = None,
on_bad_lines: str = 'error',
delim_whitespace: bool = False,
low_memory: bool = True,
memory_map: bool = False,
float_precision: Union[Literal['high',
'legacy'],
None] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.CSVAsset
add_dataframe_asset
add_dataframe_asset(
name: str,
batch_metadata: Optional[BatchMetadata] = None
) → DataFrameAsset
add_excel_asset
add_excel_asset(
name: str,
io: os.PathLike | str | bytes,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sheet_name: Optional[Union[str,
int,
List[Union[int,
str]]]] = 0,
header: Union[int,
Sequence[int],
None] = 0,
names: Optional[List[str]] = None,
index_col: Union[int,
Sequence[int],
None] = None,
usecols: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: 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: Optional[Union[Sequence[int],
int]] = None,
nrows: Optional[int] = None,
na_values: Any = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool = False,
parse_dates: Union[List,
Dict,
bool] = False,
date_format: Optional[str] = None,
thousands: Optional[str] = None,
decimal: str = '.',
comment: Optional[str] = None,
skipfooter: int = 0,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
engine_kwargs: Optional[Dict] = None,
**extra_data: Any
) → ExcelAsset
add_feather_asset
add_feather_asset(
name: str,
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
columns: Union[Sequence[str],
None] = None,
use_threads: bool = True,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.FeatherAsset
add_fwf_asset
add_fwf_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = 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: Optional[dict] = None,
**extra_data: Any
) → pydantic.v1.main.FeatherAsset
add_gbq_asset
add_gbq_asset(
name: str,
query: str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
project_id: Optional[str] = None,
index_col: Optional[str] = None,
col_order: Optional[List[str]] = None,
reauth: bool = False,
auth_local_webserver: bool = True,
dialect: Optional[str] = None,
location: Optional[str] = None,
configuration: Optional[Dict[str,
Any]] = None,
use_bqstorage_api: Optional[bool] = None,
max_results: Optional[int] = None,
progress_bar_type: Optional[str] = None,
**extra_data: Any
) → pydantic.v1.main.GBQAsset
add_hdf_asset
add_hdf_asset(
name: str,
path_or_buf: pd.HDFStore | os.PathLike | str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
key: Any = None,
mode: str = 'r',
errors: str = 'strict',
where: Optional[Union[str,
List]] = None,
start: Optional[int] = None,
stop: Optional[int] = None,
columns: Optional[List[str]] = None,
iterator: bool = False,
chunksize: Optional[int] = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → HDFAsset
add_html_asset
add_html_asset(
name: str,
io: os.PathLike | str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
match: Union[str,
Pattern] = '.+',
flavor: Optional[str] = None,
header: Union[int,
Sequence[int],
None] = None,
index_col: Union[int,
Sequence[int],
None] = None,
skiprows: Optional[Union[Sequence[int],
int]] = None,
attrs: Optional[Dict[str,
str]] = None,
parse_dates: bool = False,
thousands: Optional[str] = ',
',
encoding: Optional[str] = None,
decimal: str = '.',
converters: Optional[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,
storage_options: StorageOptions = None,
**extra_data: Any
) → HTMLAsset
add_json_asset
add_json_asset(
name: str,
path_or_buf: pydantic.v1.types.Json | pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
orient: Optional[str] = None,
typ: Literal['frame',
'series'] = 'frame',
dtype: Optional[dict] = None,
convert_axes: Optional[bool] = None,
convert_dates: Union[bool,
List[str]] = True,
keep_default_dates: bool = True,
precise_float: bool = False,
date_unit: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
lines: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
nrows: Optional[int] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.JSONAsset
add_orc_asset
add_orc_asset(
name: str,
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
columns: Optional[List[str]] = None,
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → pydantic.v1.main.ORCAsset
add_parquet_asset
add_parquet_asset(
name: str,
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
engine: str = 'auto',
columns: Optional[List[str]] = None,
storage_options: Union[StorageOptions,
None] = None,
use_nullable_dtypes: bool = None,
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → pydantic.v1.main.ParquetAsset
add_pickle_asset
add_pickle_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
**extra_data: Any
) → pydantic.v1.main.PickleAsset
add_sas_asset
add_sas_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
format: Optional[str] = None,
index: Optional[str] = None,
encoding: Optional[str] = None,
chunksize: Optional[int] = None,
iterator: bool = False,
compression: CompressionOptions = 'infer',
**extra_data: Any
) → pydantic.v1.main.SASAsset
add_spss_asset
add_spss_asset(
name: str,
path: pydantic.v1.types.FilePath,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
usecols: Optional[Union[int,
str,
Sequence[int]]] = None,
convert_categoricals: bool = True,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.SPSSAsset
add_sql_asset
add_sql_asset(
name: str,
sql: sa.select | sa.text | str,
con: sqlalchemy.Engine | sqlite3.Connection | str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
params: Any = None,
parse_dates: Any = None,
columns: Optional[List[str]] = None,
chunksize: Optional[int] = None,
dtype_backend: DtypeBackend = None,
dtype: Optional[dict] = None,
**extra_data: Any
) → SQLAsset
add_sql_query_asset
add_sql_query_asset(
name: str,
sql: sa.select | sa.text | str,
con: sqlalchemy.Engine | sqlite3.Connection | str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
parse_dates: Optional[Union[List[str],
Dict[str,
str]]] = None,
chunksize: Optional[int] = None,
dtype: Optional[dict] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → SQLQueryAsset
add_sql_table_asset
add_sql_table_asset(
name: str,
table_name: str,
con: sqlalchemy.engine.base.Engine | str,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
schema: Optional[str] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
parse_dates: Optional[Union[List[str],
Dict[str,
str]]] = None,
columns: Optional[List[str]] = None,
chunksize: Optional[int] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.SQLTableAsset
add_stata_asset
add_stata_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
convert_dates: bool = True,
convert_categoricals: bool = True,
index_col: Optional[str] = None,
convert_missing: bool = False,
preserve_dtypes: bool = True,
columns: Union[Sequence[str],
None] = None,
order_categoricals: bool = True,
chunksize: Optional[int] = None,
iterator: bool = False,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
**extra_data: Any
) → pydantic.v1.main.StataAsset
add_table_asset
add_table_asset(
name: str,
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: Optional[str] = None,
delimiter: 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: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: Optional[dict] = None,
engine: Union[CSVEngine,
None] = None,
true_values: Optional[List] = None,
false_values: Optional[List] = None,
skipinitialspace: bool = False,
skiprows: Optional[Union[Sequence[int],
int]] = None,
skipfooter: int = 0,
nrows: Optional[int] = None,
na_values: Union[Sequence[str],
None] = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool = False,
skip_blank_lines: bool = True,
parse_dates: Union[bool,
Sequence[str]] = False,
infer_datetime_format: bool = None,
keep_date_col: bool = False,
date_format: Optional[str] = None,
dayfirst: bool = False,
cache_dates: bool = True,
iterator: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
thousands: Optional[str] = None,
decimal: str = '.',
lineterminator: Optional[str] = None,
quotechar: str = '"',
quoting: int = 0,
doublequote: bool = True,
escapechar: Optional[str] = None,
comment: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
dialect: Optional[str] = None,
on_bad_lines: str = 'error',
delim_whitespace: bool = False,
low_memory: bool = True,
memory_map: bool = False,
float_precision: Optional[str] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.TableAsset
add_xml_asset
add_xml_asset(
name: str,
path_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
*,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
xpath: str = './*',
namespaces: Optional[Dict[str,
str]] = None,
elems_only: bool = False,
attrs_only: bool = False,
names: Union[Sequence[str],
None] = None,
dtype: Optional[dict] = None,
encoding: Optional[str] = 'utf-8',
stylesheet: Union[FilePath,
None] = None,
iterparse: Optional[Dict[str,
List[str]]] = None,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → pydantic.v1.main.XMLAsset
delete_asset
delete_asset(
name: str
) → None
get_asset
get_asset(
name: str
) → great_expectations.datasource.fluent.interfaces._DataAssetT
read_clipboard
read_clipboard(
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: str = '\s+',
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_csv
read_csv(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: Optional[str] = None,
delimiter: 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: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: Optional[dict] = None,
engine: Union[CSVEngine,
None] = None,
true_values: Optional[List] = None,
false_values: Optional[List] = None,
skipinitialspace: bool = False,
skiprows: Optional[Union[Sequence[int],
int]] = None,
skipfooter: int = 0,
nrows: Optional[int] = None,
na_values: Union[Sequence[str],
None] = 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_format: Optional[str] = None,
dayfirst: bool = False,
cache_dates: bool = True,
iterator: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
thousands: Optional[str] = None,
decimal: str = '.',
lineterminator: Optional[str] = None,
quotechar: str = '"',
quoting: int = 0,
doublequote: bool = True,
escapechar: Optional[str] = None,
comment: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
dialect: Optional[str] = None,
on_bad_lines: str = 'error',
delim_whitespace: bool = False,
low_memory: bool = True,
memory_map: bool = False,
float_precision: Union[Literal['high',
'legacy'],
None] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_dataframe
read_dataframe(
dataframe: pd.DataFrame,
asset_name: Optional[str] = None,
batch_metadata: Optional[BatchMetadata] = None
) → Batch
read_excel
read_excel(
io: os.PathLike | str | bytes,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sheet_name: Optional[Union[str,
int,
List[Union[int,
str]]]] = 0,
header: Union[int,
Sequence[int],
None] = 0,
names: Optional[List[str]] = None,
index_col: Union[int,
Sequence[int],
None] = None,
usecols: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: 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: Optional[Union[Sequence[int],
int]] = None,
nrows: Optional[int] = None,
na_values: Any = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool = False,
parse_dates: Union[List,
Dict,
bool] = False,
date_format: Optional[str] = None,
thousands: Optional[str] = None,
decimal: str = '.',
comment: Optional[str] = None,
skipfooter: int = 0,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
engine_kwargs: Optional[Dict] = None,
**extra_data: Any
) → Batch
read_feather
read_feather(
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
columns: Union[Sequence[str],
None] = None,
use_threads: bool = True,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_fwf
read_fwf(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = 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: Optional[dict] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_gbq
read_gbq(
query: str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
project_id: Optional[str] = None,
index_col: Optional[str] = None,
col_order: Optional[List[str]] = None,
reauth: bool = False,
auth_local_webserver: bool = True,
dialect: Optional[str] = None,
location: Optional[str] = None,
configuration: Optional[Dict[str,
Any]] = None,
use_bqstorage_api: Optional[bool] = None,
max_results: Optional[int] = None,
progress_bar_type: Optional[str] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_hdf
read_hdf(
path_or_buf: pd.HDFStore | os.PathLike | str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
key: Any = None,
mode: str = 'r',
errors: str = 'strict',
where: Optional[Union[str,
List]] = None,
start: Optional[int] = None,
stop: Optional[int] = None,
columns: Optional[List[str]] = None,
iterator: bool = False,
chunksize: Optional[int] = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → Batch
read_html
read_html(
io: os.PathLike | str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
match: Union[str,
Pattern] = '.+',
flavor: Optional[str] = None,
header: Union[int,
Sequence[int],
None] = None,
index_col: Union[int,
Sequence[int],
None] = None,
skiprows: Optional[Union[Sequence[int],
int]] = None,
attrs: Optional[Dict[str,
str]] = None,
parse_dates: bool = False,
thousands: Optional[str] = ',
',
encoding: Optional[str] = None,
decimal: str = '.',
converters: Optional[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,
storage_options: StorageOptions = None,
**extra_data: Any
) → Batch
read_json
read_json(
path_or_buf: pydantic.v1.types.Json | pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
orient: Optional[str] = None,
typ: Literal['frame',
'series'] = 'frame',
dtype: Optional[dict] = None,
convert_axes: Optional[bool] = None,
convert_dates: Union[bool,
List[str]] = True,
keep_default_dates: bool = True,
precise_float: bool = False,
date_unit: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
lines: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
nrows: Optional[int] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_orc
read_orc(
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
columns: Optional[List[str]] = None,
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_parquet
read_parquet(
path: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
engine: str = 'auto',
columns: Optional[List[str]] = None,
storage_options: Union[StorageOptions,
None] = None,
use_nullable_dtypes: bool = None,
dtype_backend: DtypeBackend = None,
kwargs: Optional[dict] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_pickle
read_pickle(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_sas
read_sas(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
format: Optional[str] = None,
index: Optional[str] = None,
encoding: Optional[str] = None,
chunksize: Optional[int] = None,
iterator: bool = False,
compression: CompressionOptions = 'infer',
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_spss
read_spss(
path: pydantic.v1.types.FilePath,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
usecols: Optional[Union[int,
str,
Sequence[int]]] = None,
convert_categoricals: bool = True,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_sql
read_sql(
sql: sa.select | sa.text | str,
con: sqlalchemy.Engine | sqlite3.Connection | str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
params: Any = None,
parse_dates: Any = None,
columns: Optional[List[str]] = None,
chunksize: Optional[int] = None,
dtype_backend: DtypeBackend = None,
dtype: Optional[dict] = None,
**extra_data: Any
) → Batch
read_sql_query
read_sql_query(
sql: sa.select | sa.text | str,
con: sqlalchemy.Engine | sqlite3.Connection | str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
parse_dates: Optional[Union[List[str],
Dict[str,
str]]] = None,
chunksize: Optional[int] = None,
dtype: Optional[dict] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → Batch
read_sql_table
read_sql_table(
table_name: str,
con: sqlalchemy.engine.base.Engine | str,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
schema: Optional[str] = None,
index_col: Optional[Union[str,
List[str]]] = None,
coerce_float: bool = True,
parse_dates: Optional[Union[List[str],
Dict[str,
str]]] = None,
columns: Optional[List[str]] = None,
chunksize: Optional[int] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_stata
read_stata(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
convert_dates: bool = True,
convert_categoricals: bool = True,
index_col: Optional[str] = None,
convert_missing: bool = False,
preserve_dtypes: bool = True,
columns: Union[Sequence[str],
None] = None,
order_categoricals: bool = True,
chunksize: Optional[int] = None,
iterator: bool = False,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_table
read_table(
filepath_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
sep: Optional[str] = None,
delimiter: 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: Optional[Union[int,
str,
Sequence[int]]] = None,
dtype: Optional[dict] = None,
engine: Union[CSVEngine,
None] = None,
true_values: Optional[List] = None,
false_values: Optional[List] = None,
skipinitialspace: bool = False,
skiprows: Optional[Union[Sequence[int],
int]] = None,
skipfooter: int = 0,
nrows: Optional[int] = None,
na_values: Union[Sequence[str],
None] = None,
keep_default_na: bool = True,
na_filter: bool = True,
verbose: bool = False,
skip_blank_lines: bool = True,
parse_dates: Union[bool,
Sequence[str]] = False,
infer_datetime_format: bool = None,
keep_date_col: bool = False,
date_format: Optional[str] = None,
dayfirst: bool = False,
cache_dates: bool = True,
iterator: bool = False,
chunksize: Optional[int] = None,
compression: CompressionOptions = 'infer',
thousands: Optional[str] = None,
decimal: str = '.',
lineterminator: Optional[str] = None,
quotechar: str = '"',
quoting: int = 0,
doublequote: bool = True,
escapechar: Optional[str] = None,
comment: Optional[str] = None,
encoding: Optional[str] = None,
encoding_errors: Optional[str] = 'strict',
dialect: Optional[str] = None,
on_bad_lines: str = 'error',
delim_whitespace: bool = False,
low_memory: bool = True,
memory_map: bool = False,
float_precision: Optional[str] = None,
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch
read_xml
read_xml(
path_or_buffer: pydantic.v1.types.FilePath | pydantic.v1.networks.AnyUrl,
asset_name: Optional[str] = None,
*,
name: str,
id: Optional[uuid.UUID] = None,
order_by: List[great_expectations.datasource.fluent.interfaces.Sorter] = None,
batch_metadata: Dict[str,
Any] = None,
batch_definitions: List[great_expectations.core.batch_definition.BatchDefinition] = None,
xpath: str = './*',
namespaces: Optional[Dict[str,
str]] = None,
elems_only: bool = False,
attrs_only: bool = False,
names: Union[Sequence[str],
None] = None,
dtype: Optional[dict] = None,
encoding: Optional[str] = 'utf-8',
stylesheet: Union[FilePath,
None] = None,
iterparse: Optional[Dict[str,
List[str]]] = None,
compression: CompressionOptions = 'infer',
storage_options: Union[StorageOptions,
None] = None,
dtype_backend: DtypeBackend = None,
**extra_data: Any
) → great_expectations.datasource.fluent.interfaces.Batch