Great Expectations

https://docs.greatexpectations.io
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Great Expectations is a data validation platform that provides a fully-managed SaaS solution (GX...
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### Install GX Core

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Use this command to install the GX Core library via pip.

```bash
pip install great_expectations
```

--------------------------------

### Verify GX Core Installation

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Run this code in a Python environment to confirm that the library is installed and to check the current version.

```python
import great_expectations as gx

print(gx.__version__)
```

--------------------------------

### ExpectationSuiteValidationResult Meta Example

Source: https://docs.greatexpectations.io/docs/reference/api/core/ExpectationSuiteValidationResult_class

Example structure of the meta property containing resource and execution metadata.

```json
{
  "active_batch_definition": {
    "batch_identifiers": {},
    "data_asset_name": "taxi_data_1.csv",
    "data_connector_name": "default_inferred_data_connector_name",
    "datasource_name": "pandas"
  },
  "batch_markers": {
    "ge_load_time": "20220727T154327.630107Z",
    "pandas_data_fingerprint": "c4f929e6d4fab001fedc9e075bf4b612"
  },
  "batch_spec": {
    "path": "/Users/username/work/gx_example_projects/great_expectations/../data/taxi_data_1.csv"
  },
  "checkpoint_name": "single_validation_checkpoint",
  "expectation_suite_name": "taxi_suite_1",
  "great_expectations_version": "0.15.15",
  "run_id": {
    "run_name": "20220727-114327-my-run-name-template",
    "run_time": "2022-07-27T11:43:27.625252+00:00"
  },
  "validation_time": "20220727T154327.701100Z"
}
```

--------------------------------

### ExpectationSuiteValidationResult Statistics Example

Source: https://docs.greatexpectations.io/docs/reference/api/core/ExpectationSuiteValidationResult_class

Example structure of the statistics dictionary containing counts and success percentages.

```json
{
  "evaluated_expectations": 14,
  "success_percent": 71.42857142857143,
  "successful_expectations": 10,
  "unsuccessful_expectations": 4
}
```

--------------------------------

### Full example code

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

A complete script combining all steps to validate a Pandas DataFrame.

```python
# Import required modules from GX library.  
import great_expectations as gx  
  
import pandas as pd  
  
# Create Data Context.  
context = gx.get_context()  
  
# Import sample data into Pandas DataFrame.  
df = pd.read_csv(  
    "https://raw.githubusercontent.com/great-expectations/gx_tutorials/main/data/yellow_tripdata_sample_2019-01.csv"  
)  
  
# Connect to data.  
# Create Data Source, Data Asset, Batch Definition, and Batch.  
data_source = context.data_sources.add_pandas("pandas")  
data_asset = data_source.add_dataframe_asset(name="pd dataframe asset")  
  
batch_definition = data_asset.add_batch_definition_whole_dataframe("batch definition")  
batch = batch_definition.get_batch(batch_parameters={"dataframe": df})  
  
# Create Expectation.  
expectation = gx.expectations.ExpectColumnValuesToBeBetween(  
    column="passenger_count", min_value=1, max_value=6, severity="warning"  
)  
  
# Validate Batch using Expectation.  
validation_result = batch.validate(expectation)  
print(validation_result)  
```

--------------------------------

### Delete Expectation

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Method signature and usage example for removing an expectation from the suite.

```python
delete_expectation(  
 expectation: Expectation  
) → Expectation
```

```python
>>> suite.delete_expectation(suite.expectations[0])
```

--------------------------------

### Import GX and Pandas

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Initialize the environment by importing the necessary libraries.

```python
import great_expectations as gx  
  
import pandas as pd  
```

--------------------------------

### Initialize Data Context

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Create a Data Context object to serve as the entrypoint for GX components.

```python
context = gx.get_context()  
```

--------------------------------

### Connect to SQL Data and Create a Batch

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Define a Postgres data source and create a batch from a specific table.

```python
connection_string = "postgresql+psycopg2://try_gx:try_gx@postgres.workshops.greatexpectations.io/gx_example_db"  
  
data_source = context.data_sources.add_postgres(  
    "postgres db", connection_string=connection_string  
)  
data_asset = data_source.add_table_asset(name="taxi data", table_name="nyc_taxi_data")  
  
batch_definition = data_asset.add_batch_definition_whole_table("batch definition")  
batch = batch_definition.get_batch()  
```

--------------------------------

### Import Great Expectations

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Initialize the GX library in your Python environment.

```python
import great_expectations as gx  
```

--------------------------------

### Initialize ExpectationSuite

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Constructor signature for creating a new ExpectationSuite instance.

```python
class great_expectations.ExpectationSuite(  
 name: Optional[str] = None,  
 expectations: Optional[Sequence[Union[dict,  
 ExpectationConfiguration,  
 Expectation]]] = None,  
 suite_parameters: Optional[dict] = None,  
 meta: Optional[dict] = None,  
 notes: str | list[str] | None = None,  
 id: Optional[str] = None  
)
```

--------------------------------

### Connect to data and create a Batch

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Define a Data Source, Data Asset, and Batch Definition to prepare the DataFrame for validation.

```python
data_source = context.data_sources.add_pandas("pandas")  
data_asset = data_source.add_dataframe_asset(name="pd dataframe asset")  
  
batch_definition = data_asset.add_batch_definition_whole_dataframe("batch definition")  
batch = batch_definition.get_batch(batch_parameters={"dataframe": df})  
```

--------------------------------

### Load sample data

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Read a CSV file into a Pandas DataFrame for validation.

```python
df = pd.read_csv(  
    "https://raw.githubusercontent.com/great-expectations/gx_tutorials/main/data/yellow_tripdata_sample_2019-01.csv"  
)  
```

--------------------------------

### Run a Checkpoint

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Execute the validation process using a Checkpoint and display the results.

```python
checkpoint = context.checkpoints.add(  
    gx.checkpoint.checkpoint.Checkpoint(  
        name="checkpoint", validation_definitions=[validation_definition]  
    )  
)  
  
checkpoint_result = checkpoint.run()  
print(checkpoint_result.describe())  
```

--------------------------------

### ValidationDefinition Initialization

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Constructor signature for creating a new ValidationDefinition instance.

```python
class great_expectations.ValidationDefinition(  
 *,
 name: str,  
 data: great_expectations.core.batch_definition.BatchDefinition,  
 suite: great_expectations.core.expectation_suite.ExpectationSuite,  
 id: Optional[str] = None  
)
```

--------------------------------

### Create an Expectation Suite

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Define a suite of expectations to validate column values for passenger counts and fare amounts.

```python
suite = context.suites.add(  
    gx.core.expectation_suite.ExpectationSuite(name="expectations")  
)  
suite.add_expectation(  
    gx.expectations.ExpectColumnValuesToBeBetween(  
        column="passenger_count", min_value=1, max_value=6, severity="warning"  
    )  
)  
suite.add_expectation(  
    gx.expectations.ExpectColumnValuesToBeBetween(  
        column="fare_amount", min_value=0, severity="critical"  
    )  
)  
```

--------------------------------

### list_data_docs_sites

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Lists all Data Docs sites with their configurations.

```APIDOC
## list_data_docs_sites() -> dict[str, DataDocsSiteConfigTypedDict]

### Description
List all Data Docs Sites with configurations.
```

--------------------------------

### Create a Validation Definition

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Link the batch definition and expectation suite into a validation definition.

```python
validation_definition = context.validation_definitions.add(  
    gx.core.validation_definition.ValidationDefinition(  
        name="validation definition",  
        data=batch_definition,  
        suite=suite,  
    )  
)  
```

--------------------------------

### Checkpoint(name, validation_definitions, actions, result_format, id)

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Initializes a new Checkpoint instance, which serves as the primary mechanism for data validation in production deployments.

```APIDOC
## Constructor: Checkpoint

### Description
Initializes a Checkpoint to manage validation definitions and post-validation actions.

### Parameters
- **name** (str) - Required - The name of the checkpoint.
- **validation_definitions** (List[ValidationDefinition]) - Required - List of validation definitions to be run.
- **actions** (List[ValidationAction]) - Optional - List of actions to be taken after the validation definitions are run.
- **result_format** (Union[ResultFormat, dict, str]) - Optional - The format in which to return the results. Defaults to ResultFormat.SUMMARY.
- **id** (str) - Optional - An optional unique identifier for the checkpoint.
```

--------------------------------

### Full Great Expectations Workflow

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

This script covers the entire lifecycle of a GX validation, from context initialization to running a checkpoint against a PostgreSQL data source.

```python
# Import required modules from GX library.  
import great_expectations as gx  
  
# Create Data Context.  
context = gx.get_context()  
  
# Connect to data.  
# Create Data Source, Data Asset, Batch Definition, and Batch.  
connection_string = "postgresql+psycopg2://try_gx:try_gx@postgres.workshops.greatexpectations.io/gx_example_db"  
  
data_source = context.data_sources.add_postgres(  
    "postgres db", connection_string=connection_string  
)  
data_asset = data_source.add_table_asset(name="taxi data", table_name="nyc_taxi_data")  
  
batch_definition = data_asset.add_batch_definition_whole_table("batch definition")  
batch = batch_definition.get_batch()  
  
# Create Expectation Suite containing two Expectations.  
suite = context.suites.add(  
    gx.core.expectation_suite.ExpectationSuite(name="expectations")  
)  
suite.add_expectation(  
    gx.expectations.ExpectColumnValuesToBeBetween(  
        column="passenger_count", min_value=1, max_value=6, severity="warning"  
    )  
)  
suite.add_expectation(  
    gx.expectations.ExpectColumnValuesToBeBetween(  
        column="fare_amount", min_value=0, severity="critical"  
    )  
)  
  
# Create Validation Definition.  
validation_definition = context.validation_definitions.add(  
    gx.core.validation_definition.ValidationDefinition(  
        name="validation definition",  
        data=batch_definition,  
        suite=suite,  
    )  
)  
  
# Create Checkpoint, run Checkpoint, and capture result.  
checkpoint = context.checkpoints.add(  
    gx.checkpoint.checkpoint.Checkpoint(  
        name="checkpoint", validation_definitions=[validation_definition]  
    )  
)  
  
checkpoint_result = checkpoint.run()  
print(checkpoint_result.describe())
```

--------------------------------

### run(batch_parameters, expectation_parameters, run_id)

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Executes the Checkpoint's underlying Validation Definitions and Actions.

```APIDOC
## Method: run

### Description
Runs the Checkpoint's underlying Validation Definitions and Actions.

### Parameters
- **batch_parameters** (Dict[str, Any]) - Optional - Parameters to be used when loading the Batch.
- **expectation_parameters** (dict) - Optional - Parameters to be used when validating the Batch.
- **run_id** (RunIdentifier) - Optional - An optional unique identifier for the run.

### Returns
- **CheckpointResult** - A CheckpointResult object containing the results of the run.

### Raises
- **CheckpointRunWithoutValidationDefinitionError** - If the Checkpoint is run without any Validation Definitions.
- **CheckpointNotAddedError** - If the Checkpoint has not been added to the Store.
- **CheckpointNotFreshError** - If the Checkpoint has been modified since it was last added to the Store.
```

--------------------------------

### build_data_docs

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Builds Data Docs for the project.

```APIDOC
## build_data_docs(site_names: list[str] | None = None, resource_identifiers: list | None = None, dry_run: bool = False, build_index: bool = True) -> dict[str, str]

### Description
Builds Data Docs for your project.

### Parameters
- **site_names** (list[str] | None) - If specified, build data docs only for these sites, otherwise, build all the sites specified in the context's config.
- **resource_identifiers** (list) - A list of resource identifiers. If specified, rebuild HTML only for the resources in this list.
- **dry_run** (bool) - If True, returns a structure containing the URLs of the sites that would be built, but does not build them.
- **build_index** (bool) - If False, skips building the index page.

### Returns
- **dict[str, str]** - A dictionary with the names of the updated data documentation sites as keys and the location info of their index.html files as values.
```

--------------------------------

### Checkpoint Class Signature

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Constructor signature for initializing a new Checkpoint instance with validation definitions and actions.

```python
class great_expectations.Checkpoint(  
 *,
 name: str,
 validation_definitions: List[great_expectations.core.validation_definition.ValidationDefinition],
 actions: List[great_expectations.checkpoint.actions.ValidationAction] = None,
 result_format: Union[great_expectations.core.result_format.ResultFormat,
 dict,
 Literal['BOOLEAN_ONLY',
 'BASIC',
 'SUMMARY',
 'COMPLETE']] = ResultFormat.SUMMARY,
 id: Optional[str] = None
)
```

--------------------------------

### save()

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Persists the current state of the Checkpoint.

```APIDOC
## Method: save

### Description
Save the current state of this Checkpoint.

### Returns
- **None**
```

--------------------------------

### Checkpoint run Method

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Executes the underlying validation definitions and actions associated with the checkpoint.

```python
run(  
 batch_parameters: Optional[Dict[str,
 Any]] = None,
 expectation_parameters: dict | None = None,
 run_id: great_expectations.core.run_identifier.RunIdentifier | None = None
) → great_expectations.checkpoint.checkpoint.CheckpointResult
```

--------------------------------

### Serialize to JSON Dictionary

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Method signature for converting the suite into a JSON-serializable dictionary.

```python
to_json_dict() → Dict[str,  
 JSONValues]
```

--------------------------------

### print_diagnostic_checklist

Source: https://docs.greatexpectations.io/docs/reference/api/expectations/Expectation_class

Generates and prints a diagnostic checklist for the Expectation. This method is experimental and acts as a wrapper for ExpectationDiagnostics.generate_checklist().

```APIDOC
## print_diagnostic_checklist(diagnostics=None, show_failed_tests=False, backends=None, show_debug_messages=False)

### Description
Runs diagnostics and generates a diagnostic checklist for the current Expectation.

### Parameters
- **diagnostics** (Optional[ExpectationDiagnostics]) - Optional - If not provided, diagnostics will be run on self.
- **show_failed_tests** (bool) - Optional - If true, failing tests will be printed.
- **backends** (Optional[List[str]]) - Optional - List of backends to pass to run_diagnostics.
- **show_debug_messages** (bool) - Optional - If true, creates a logger and passes it to run_diagnostics.

### Returns
- **str** - A string representation of the diagnostic checklist.
```

--------------------------------

### RunIdentifier Constructor

Source: https://docs.greatexpectations.io/docs/reference/api/RunIdentifier_class

Initializes a new instance of the RunIdentifier class to identify a collection of validations.

```APIDOC
## Constructor: RunIdentifier(run_name, run_time)

### Description
Creates a new RunIdentifier object to track a specific run.

### Parameters
- **run_name** (Optional[str]) - A string identifying the run.
- **run_time** (Optional[Union[datetime.datetime, str]]) - A datetime instance or string representing the time of the run.
```

--------------------------------

### Checkpoint save Method

Source: https://docs.greatexpectations.io/docs/reference/api/Checkpoint_class

Persists the current state of the Checkpoint instance.

```python
save() → None
```

--------------------------------

### ExpectationSuiteValidationResult Class Signature

Source: https://docs.greatexpectations.io/docs/reference/api/core/ExpectationSuiteValidationResult_class

The constructor signature for initializing an ExpectationSuiteValidationResult object.

```python
class great_expectations.core.ExpectationSuiteValidationResult(  
 success: bool,  
 results: list[great_expectations.core.expectation_validation_result.ExpectationValidationResult],  
 suite_name: str,  
 suite_parameters: Optional[dict] = None,  
 statistics: Optional[dict] = None,  
 meta: Optional[Union[great_expectations.core.expectation_validation_result.ExpectationSuiteValidationResultMeta,  
 dict]] = None,  
 batch_id: Optional[str] = None,  
 result_url: Optional[str] = None,  
 id: Optional[str] = None  
)
```

--------------------------------

### update_project_config

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Updates the context's configuration.

```APIDOC
## update_project_config(project_config: Union[DataContextConfig, Mapping]) -> DataContextConfig

### Description
Update the context's config with the values from another config object.

### Parameters
- **project_config** (Union[DataContextConfig, Mapping]) - The config to use to update the context's internal state.

### Returns
- **DataContextConfig** - The updated project config.
```

--------------------------------

### ValidationDefinition.run

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Executes a validation run using the configured data and expectation suite.

```APIDOC
## run(checkpoint_id=None, batch_parameters=None, expectation_parameters=None, result_format=ResultFormat.SUMMARY, run_id=None)

### Description
Runs a validation using the configured data and suite.

### Parameters
- **checkpoint_id** (Optional[str]) - Optional. Used by checkpoints code.
- **batch_parameters** (Optional[BatchParameters]) - Optional. Parameters for selecting the correct batch.
- **expectation_parameters** (Optional[SuiteParameterDict]) - Optional. Values for expectations using $PARAMETER syntax.
- **result_format** (ResultFormatUnion) - Optional. Controls diagnostic information level.
- **run_id** (RunIdentifier | None) - Optional. Identifier for the run.

### Returns
- **ExpectationSuiteValidationResult** - The result of the validation run.
```

--------------------------------

### add_data_docs_site

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Adds a new Data Docs site to the DataContext.

```APIDOC
## add_data_docs_site(site_name: str, site_config: DataDocsSiteConfigTypedDict) -> None

### Description
Adds a new Data Docs Site to the DataContext.

### Parameters
- **site_name** (str) - New site name to add.
- **site_config** (DataDocsSiteConfigTypedDict) - Config dict for the new site.
```

--------------------------------

### Run Validation

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Signature for executing a validation run using the configured data and suite.

```python
run(  
 *,
 checkpoint_id: Optional[str] = None,  
 batch_parameters: Optional[BatchParameters] = None,  
 expectation_parameters: Optional[SuiteParameterDict] = None,  
 result_format: ResultFormatUnion = ResultFormat.SUMMARY,  
 run_id: RunIdentifier | None = None  
) → ExpectationSuiteValidationResult
```

--------------------------------

### ExpectationSuiteValidationResult Methods

Source: https://docs.greatexpectations.io/docs/reference/api/core/ExpectationSuiteValidationResult_class

Signatures for methods available on the ExpectationSuiteValidationResult class.

```python
describe() → str
```

```python
get_max_severity_failure() → FailureSeverity | None
```

```python
to_json_dict()
```

--------------------------------

### get_asset

Source: https://docs.greatexpectations.io/docs/reference/api/datasource/fluent/AlloyDatasource_class

Retrieves a data asset from the datasource by name.

```APIDOC
## get_asset(name: str) -> _DataAssetT

### Description
Returns the DataAsset referred to by name.

### Parameters
- **name** (str) - Required - Name of the DataAsset sought.

### Returns
- **_DataAssetT** - The requested DataAsset object; raises an exception if not found.
```

--------------------------------

### save

Source: https://docs.greatexpectations.io/docs/reference/api/expectations/Expectation_class

Persists the current state of the Expectation.

```APIDOC
## save()

### Description
Saves the current state of this Expectation.
```

--------------------------------

### ActionContext Class Signature

Source: https://docs.greatexpectations.io/docs/reference/api/checkpoint/ActionContext_class

The class definition for ActionContext within the checkpoint module.

```python
class great_expectations.checkpoint.ActionContext
```

--------------------------------

### Define an Expectation

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Create a warning-level Expectation to verify that column values fall within a specific range.

```python
expectation = gx.expectations.ExpectColumnValuesToBeBetween(  
    column="passenger_count", min_value=1, max_value=6, severity="warning"  
)  
```

--------------------------------

### Validate data

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Execute the validation against the batch and output the results.

```python
validation_result = batch.validate(expectation)  
print(validation_result)  
```

--------------------------------

### to_json_dict()

Source: https://docs.greatexpectations.io/docs/reference/api/RunIdentifier_class

Serializes the RunIdentifier instance into a dictionary format suitable for JSON conversion.

```APIDOC
## Method: to_json_dict()

### Description
Returns a JSON-serializable dictionary representation of the RunIdentifier instance.

### Returns
- **Dict** - A dictionary containing the serialized representation of the RunIdentifier.
```

--------------------------------

### save() -> None

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Persists the current state of the ExpectationSuite.

```APIDOC
## save

### Description
Save this ExpectationSuite.
```

--------------------------------

### enable_analytics

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Enables or disables analytics for the DataContext.

```APIDOC
## enable_analytics(enable: Optional[bool]) -> None

### Description
Enable or disable analytics for this DataContext. If set to None, the GX_ANALYTICS_ENABLED environment variable will be used.
```

--------------------------------

### run_diagnostics

Source: https://docs.greatexpectations.io/docs/reference/api/expectations/Expectation_class

Produces a diagnostic report about the Expectation, useful for populating the Public Expectation Gallery and development loops.

```APIDOC
## run_diagnostics(raise_exceptions_for_backends=False, ignore_suppress=False, ignore_only_for=False, for_gallery=False, debug_logger=None, only_consider_these_backends=None, context=None)

### Description
Produces a diagnostic report about this Expectation. The report includes metrics, renderers, and execution details based on test cases.

### Parameters
- **raise_exceptions_for_backends** (bool) - Optional - If True, raises an Exception if a backend fails to connect.
- **ignore_suppress** (bool) - Optional - If True, ignores the suppress_test_for list on sample tests.
- **ignore_only_for** (bool) - Optional - If True, ignores the only_for list on sample tests.
- **for_gallery** (bool) - Optional - If True, creates empty arrays to use as examples for the Expectation Diagnostics.
- **debug_logger** (Optional[logging.Logger]) - Optional - Logger object for debug messages.
- **only_consider_these_backends** (Optional[List[str]]) - Optional - List of backends to consider.
- **context** (Optional[AbstractDataContext]) - Optional - Instance of an AbstractDataContext.

### Returns
- **ExpectationDiagnostics** - An Expectation Diagnostics report object.
```

--------------------------------

### Expectation Class Signature

Source: https://docs.greatexpectations.io/docs/reference/api/expectations/Expectation_class

The constructor signature for the base Expectation class.

```python
class great_expectations.expectations.Expectation(  
 *,
 id: Optional[str] = None,  
 meta: Optional[dict] = None,  
 notes: Optional[Union[str,  
 List[str]]] = None,  
 result_format: Union[great_expectations.core.result_format.ResultFormat,  
 dict] = ResultFormat.BASIC,  
 description: Optional[str] = None,  
 catch_exceptions: bool = False,  
 rendered_content: Optional[List[great_expectations.render.components.RenderedAtomicContent]] = None,  
 severity: great_expectations.expectations.metadata_types.FailureSeverity = FailureSeverity.CRITICAL,  
 windows: Optional[List[great_expectations.expectations.window.Window]] = None  
)
```

--------------------------------

### Add Expectation

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Method signature for adding an expectation to the suite.

```python
add_expectation(  
 expectation: _TExpectation  
) → _TExpectation
```

--------------------------------

### Fetch Unexpected Rows

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Signature for retrieving failing rows for an UnexpectedRowsExpectation without the standard 200-row limit.

```python
get_unexpected_rows(  
 expectation: Expectation,  
 batch_parameters: Optional[BatchParameters] = None,  
 expectation_parameters: Optional[SuiteParameterDict] = None  
) → list[dict]
```

--------------------------------

### add_query_asset

Source: https://docs.greatexpectations.io/docs/reference/api/datasource/fluent/AlloyDatasource_class

Adds a query asset to the datasource using a SQL SELECT statement.

```APIDOC
## add_query_asset(name: str, query: str, batch_metadata: Optional[BatchMetadata] = None) -> QueryAsset

### Description
Adds a query asset to this datasource. The query must be a valid SELECT statement.

### Parameters
- **name** (str) - Required - The name of this query asset.
- **query** (str) - Required - The SELECT query to select the data to validate.
- **batch_metadata** (BatchMetadata) - Optional - BatchMetadata to associate with this DataAsset.

### Returns
- **QueryAsset** - The query asset that is added to the datasource.
```

--------------------------------

### Filter Action Results by Class

Source: https://docs.greatexpectations.io/docs/reference/api/checkpoint/ActionContext_class

Filters the results of actions stored in the context based on the provided action class.

```python
filter_results(  
 class_: Type[great_expectations.checkpoint.actions.ValidationAction]  
) → list[dict]
```

--------------------------------

### to_json_dict() -> Dict[str, JSONValues]

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Converts the ExpectationSuite into a JSON-serializable dictionary.

```APIDOC
## to_json_dict

### Description
Returns a JSON-serializable dict representation of this ExpectationSuite.

### Returns
- **Dict[str, JSONValues]** - A JSON-serializable dict representation of this ExpectationSuite.
```

--------------------------------

### add_expectation(expectation: _TExpectation) -> _TExpectation

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Adds an Expectation object to the current ExpectationSuite collection.

```APIDOC
## add_expectation

### Description
Adds an Expectation to the collection.

### Parameters
- **expectation** (_TExpectation) - Required - The Expectation object to add to the suite.

### Returns
- **_TExpectation** - The added Expectation object.
```

--------------------------------

### update_data_docs_site

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Updates an existing Data Docs site.

```APIDOC
## update_data_docs_site(site_name: str, site_config: DataDocsSiteConfigTypedDict) -> None

### Description
Update an existing Data Docs Site.

### Parameters
- **site_name** (str) - Site name to update.
- **site_config** (DataDocsSiteConfigTypedDict) - Config dict that replaces the existing.
```

--------------------------------

### ValidationDefinition.save

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Persists the current state of the ValidationDefinition.

```APIDOC
## save()

### Description
Save the current state of this ValidationDefinition.

### Returns
- **None**
```

--------------------------------

### view_validation_result

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Opens a validation result in a browser.

```APIDOC
## view_validation_result(result: CheckpointResult) -> None

### Description
Opens a validation result in a browser.

### Parameters
- **result** (CheckpointResult) - The result of a Checkpoint run.
```

--------------------------------

### Validation result output

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

The JSON output returned after a successful validation.

```json
{
  "success": true,
  "expectation_config": {
    "type": "expect_column_values_to_be_between",
    "severity": "warning",
    "kwargs": {
      "batch_id": "pandas-pd dataframe asset",
      "column": "passenger_count",
      "min_value": 1.0,
      "max_value": 6.0
    },
    "meta": {}
  },
  "result": {
    "element_count": 10000,
    "unexpected_count": 0,
    "unexpected_percent": 0.0,
    "partial_unexpected_list": [],
    "missing_count": 0,
    "missing_percent": 0.0,
    "unexpected_percent_total": 0.0,
    "unexpected_percent_nonmissing": 0.0,
    "partial_unexpected_counts": [],
    "partial_unexpected_index_list": []
  },
  "meta": {},
  "exception_info": {
    "raised_exception": false,
    "exception_traceback": null,
    "exception_message": null
  }
}
```

--------------------------------

### add_table_asset

Source: https://docs.greatexpectations.io/docs/reference/api/datasource/fluent/AlloyDatasource_class

Adds a table asset to the datasource to reference a specific database table.

```APIDOC
## add_table_asset(name: str, table_name: str = '', schema_name: str | Missing | None = <Missing>, batch_metadata: Optional[BatchMetadata] = None) -> TableAsset

### Description
Adds a table asset to this datasource.

### Parameters
- **name** (str) - Required - The name of this table asset.
- **table_name** (str) - Optional - The table where the data resides.
- **schema_name** (str) - Optional - The schema that holds the table.
- **batch_metadata** (BatchMetadata) - Optional - BatchMetadata to associate with this DataAsset.

### Returns
- **TableAsset** - The table asset that is added to the datasource.
```

--------------------------------

### delete_data_docs_site

Source: https://docs.greatexpectations.io/docs/reference/api/data_context/AbstractDataContext_class

Deletes an existing Data Docs site.

```APIDOC
## delete_data_docs_site(site_name: str)

### Description
Delete an existing Data Docs Site.

### Parameters
- **site_name** (str) - Site name to delete.
```

--------------------------------

### ValidationDefinition.get_unexpected_rows

Source: https://docs.greatexpectations.io/docs/reference/api/ValidationDefinition_class

Fetches all failing rows for an UnexpectedRowsExpectation without the standard 200-row limit.

```APIDOC
## get_unexpected_rows(expectation, batch_parameters=None, expectation_parameters=None)

### Description
Fetch all failing rows for an UnexpectedRowsExpectation without the 200-row limit.

### Parameters
- **expectation** (Expectation) - Required. The UnexpectedRowsExpectation to fetch rows for.
- **batch_parameters** (Optional[BatchParameters]) - Optional. Batch parameters for selecting the correct batch.
- **expectation_parameters** (Optional[SuiteParameterDict]) - Optional. Values for resolving parameterized queries.

### Returns
- **list[dict]** - A list of dicts, one per failing row.
```

--------------------------------

### filter_results

Source: https://docs.greatexpectations.io/docs/reference/api/checkpoint/ActionContext_class

Filters the results of actions stored in the context based on the specified action class.

```APIDOC
## filter_results(class_)

### Description
Filters the results of the actions in the context by the provided action class.

### Parameters
- **class_** (Type[great_expectations.checkpoint.actions.ValidationAction]) - Required - The class to filter by.

### Returns
- **list[dict]** - A list of action results matching the specified class.
```

--------------------------------

### Validation Result JSON Structure

Source: https://docs.greatexpectations.io/docs/core/introduction/try_gx

Represents a typical Validation Result output containing success status, statistics, and detailed expectation results.

```json
{
    "success": false,
    "statistics": {
        "evaluated_validations": 1,
        "success_percent": 0.0,
        "successful_validations": 0,
        "unsuccessful_validations": 1
    },
    "validation_results": [
        {
            "success": false,
            "statistics": {
                "evaluated_expectations": 2,
                "successful_expectations": 1,
                "unsuccessful_expectations": 1,
                "success_percent": 50.0
            },
            "expectations": [
                {
                    "type": "expect_column_values_to_be_between",
                    "success": true,
                    "severity": "warning",
                    "kwargs": {
                        "batch_id": "postgres db-taxi data",
                        "column": "passenger_count",
                        "min_value": 1.0,
                        "max_value": 6.0
                    },
                    "result": {
                        "element_count": 20000,
                        "unexpected_count": 0,
                        "unexpected_percent": 0.0,
                        "partial_unexpected_list": [],
                        "missing_count": 0,
                        "missing_percent": 0.0,
                        "unexpected_percent_total": 0.0,
                        "unexpected_percent_nonmissing": 0.0,
                        "partial_unexpected_counts": []
                    }
                },
                {
                    "type": "expect_column_values_to_be_between",
                    "success": false,
                    "severity": "critical",
                    "kwargs": {
                        "batch_id": "postgres db-taxi data",
                        "column": "fare_amount",
                        "min_value": 0.0
                    },
                    "result": {
                        "element_count": 20000,
                        "unexpected_count": 14,
                        "unexpected_percent": 0.06999999999999999,
                        "partial_unexpected_list": [
                            -0.01,
                            -52.0,
                            -0.1,
                            -5.5,
                            -3.0,
                            -52.0,
                            -4.0,
                            -0.01,
                            -52.0,
                            -0.1,
                            -5.5,
                            -3.0,
                            -52.0,
                            -4.0
                        ],
                        "missing_count": 0,
                        "missing_percent": 0.0,
                        "unexpected_percent_total": 0.06999999999999999,
                        "unexpected_percent_nonmissing": 0.06999999999999999,
                        "partial_unexpected_counts": [
                            {
                                "value": -52.0,
                                "count": 4
                            },
                            {
                                "value": -5.5,
                                "count": 2
                            },
                            {
                                "value": -4.0,
                                "count": 2
                            },
                            {
                                "value": -3.0,
                                "count": 2
                            },
                            {
                                "value": -0.1,
                                "count": 2
                            },
                            {
                                "value": -0.01,
                                "count": 2
                            }
                        ]
                    }
                }
            ],
            "result_url": null
        }
    ]
}
```

--------------------------------

### delete_asset

Source: https://docs.greatexpectations.io/docs/reference/api/datasource/fluent/AlloyDatasource_class

Removes a data asset from the datasource by name.

```APIDOC
## delete_asset(name: str) -> None

### Description
Removes the DataAsset referred to by name from the internal list of available DataAsset objects.

### Parameters
- **name** (str) - Required - Name of the DataAsset to be deleted.
```

--------------------------------

### delete_expectation(expectation: Expectation) -> Expectation

Source: https://docs.greatexpectations.io/docs/reference/api/ExpectationSuite_class

Removes an existing Expectation from the ExpectationSuite.

```APIDOC
## delete_expectation

### Description
Delete an Expectation from the collection.

### Parameters
- **expectation** (Expectation) - Required - The Expectation object to remove.

### Raises
- **KeyError** - Raised if the expectation is not found in the suite.

### Returns
- **Expectation** - The deleted Expectation object.
```
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