Batch
- class great_expectations.core.batch.Batch(data: BatchDataType | None = None, batch_request: BatchRequestBase | dict | None = None, batch_definition: BatchDefinition | None = None, batch_spec: BatchSpec | None = None, batch_markers: BatchMarkers | None = None, data_context=None, datasource_name=None, batch_parameters=None, batch_kwargs=None)#
A Batch is a selection of records from a Data Asset.
A Datasource produces Batch objects to interact directly with data. Creating a Batch does NOT require moving data; the Batch facilitates access to the data and maintains metadata.
- -Relevant Documentation Links -
- Parameters:
data – A BatchDataType object which interacts directly with the ExecutionEngine.
batch_request – BatchRequest that was used to obtain the data.
batch_definition – Complete BatchDefinition that describes the data.
batch_spec – Complete BatchSpec that describes the data.
batch_markers – Additional metadata that may be useful to understand batch.
data_context –
DataContext connected to the
Deprecated since version 0.14.0.
datasource_name –
name of datasource used to obtain the batch
Deprecated since version 0.14.0.
batch_parameters –
keyword arguments describing the batch data
Deprecated since version 0.14.0.
batch_kwargs –
keyword arguments used to request a batch from a Datasource
Deprecated since version 0.14.0.
- Returns:
Batch instance created.
- head(n_rows: int = 5, fetch_all: bool = False) pandas.core.frame.DataFrame#
Return the first n rows from the Batch.
This function returns the first n_rows rows. It is useful for quickly testing if your object has the data you expected.
It will always obtain data from the Datasource and return a Pandas DataFrame available locally.
- Parameters:
n_rows – the number of rows to return
fetch_all – whether to fetch all rows; overrides n_rows if set to True
- Returns:
A Pandas DataFrame
- to_json_dict() dict[str, JSONValues]#
Returns a JSON-serializable dict representation of this Batch.
- Returns:
A JSON-serializable dict representation of this Batch.