great_expectations.render.renderer.datasource_new_notebook_renderer

Module Contents

Classes

DatasourceNewNotebookRenderer(context: DataContext, datasource_type: DatasourceTypes, datasource_yaml: str, datasource_name: Optional[str] = ‘my_datasource’, sql_credentials_snippet: Optional[str] = None)

Abstract base class for methods that help with rendering a jupyter notebook.

class great_expectations.render.renderer.datasource_new_notebook_renderer.DatasourceNewNotebookRenderer(context: DataContext, datasource_type: DatasourceTypes, datasource_yaml: str, datasource_name: Optional[str] = 'my_datasource', sql_credentials_snippet: Optional[str] = None)

Bases: great_expectations.render.renderer.notebook_renderer.BaseNotebookRenderer

Abstract base class for methods that help with rendering a jupyter notebook.

SQL_DOCS = ### For SQL based Datasources:

Here we are creating an example configuration using SimpleSqlalchemyDatasource based on the database backend you specified in the CLI.

Credentials will not be saved until you run the last cell. The credentials will be saved in uncommitted/config_variables.yml which should not be added to source control.

FILES_DOCS = ### For files based Datasources:

Here we are creating an example configuration using an InferredAssetDataConnector which will add a Data Asset for each file in the base directory you provided. This is just a sample, you may customize this as you wish!

See our docs for other methods to organize assets, handle multi-file assets, name assets based on parts of a filename, etc.

DOCS_INTRO
_add_header(self)
_add_docs_cell(self)
_add_sql_credentials_cell(self)
_add_template_cell(self)
_add_test_yaml_cells(self)
_add_save_datasource_cell(self)
render(self)

Render a notebook from parameters.

render_to_disk(self, notebook_file_path: str)

Render a notebook to disk from arguments