Validate Expectation Suites

Introduction to Data Quality with Great Expectations

Davina Moossazadeh

Data Scientist

Validation Definitions

Validation Definition - A reference that links an Expectation Suite to data that it describes

A schematic showing a Validation Definition with Data and Expectation Suite as inputs and Validation Results as the output.

¹ https://docs.greatexpectations.io/docs/core/run_validations/create_a_validation_definition/

Creating a Validation Definition

Create a Validation Definition with the ValidationDefinition class:

validation_definition = gx.ValidationDefinition(

    name="my_validation_definition",

    data=batch_definition,

    suite=suite, 
)

print(validation_definition)

Viewing a Validation Definition

name='my_validation_definition' 
data=BatchDefinition(
    id='1fcb36d6-fac6-4b9a-8ba6-a659978fd59e', 
    name='my_batch_definition', 
    partitioner=None
) 
suite={
  "name": "my_suite",
  "id": "0a123b9c-e370-4b18-b703-785dde88732d",
  "expectations": [],
  "meta": {"great_expectations_version": "1.2.4"},
  "notes": null
} 
id=None

Viewing a Validation Definition

print(validation_definition.name)

'my_validation_definition'

print(validation_definition.data)

id='1fcb36d6-fac6-4b9a-8ba6-a659978fd59e' 
name='my_batch_definition' 
partitioner=None

print(validation_definition.suite)

{
  "name": "my_suite",
  "id": "0a123b9c-e370-4b18-b703-785dde88732d",
  "expectations": [],
  "meta": {"great_expectations_version": "1.2.4"},
  "notes": null
}

print(validation_definition.id)

None

Viewing a Validation Definition

print(validation_definition.data_source)

assets:
  - batch_definitions:
      - name: my_batch_definition
        partitioner: null
    batch_metadata: {}
    id: 83682084-3bc4-4898-a807-fadc0f911415
    name: 'my_dataframe_asset'
    type: dataframe
id: f71d275e-a5b2-402e-a53c-8dad6975cce5
name: 'my_pandas_data_source'
type: pandas

Running a Validation Definition

Run a Validation using the Validation Definition's .run() method, passing the DataFrame via batch_parameters:

validation_results = validation_definition.run(

    batch_parameters={"dataframe": dataframe}
)

Validation Definition errors

Note the error:

ValidationDefinitionRelatedResourcesFreshnessError:
ExpectationSuite 'my_suite' must be added to the DataContext before it can be 
updated. Please call `context.suites.add(<SUITE_OBJECT>)`, then try your action 
again.

Running a Validation Definition before adding the Expectation Suite to the Data Context raises an error

Adding an Expectation Suite

Add an Expectation Suite to the Data Context using .suites.add():

suite = context.suites.add(

    suite=suite
)

Assessing a Validation Definition

validation_results = validation_definition.run(
    batch_parameters={"dataframe": dataframe}
)

Calculating Metrics: 0/0 [00:00<?, ?it/s]

print(validation_results.success)

False

print(validation_results.describe())

Assessing a Validation Definition

{ "success": false,
  "statistics": {
    "evaluated_expectations": 1, "successful_expectations": 0,
    "unsuccessful_expectations": 1, "success_percent": 0.0
  },
  "expectations": [{
    "expectation_type": "expect_table_row_count_to_equal",
    "success": false,
    "kwargs": {"batch_id": ""my_datasource-my_dataframe_asset", "value": 118000}, 
    "result": {"observed_value": 11866}}
  ],
  "result_url": "https://app.greatexpectations.io/organizations/my_org/data-assets/*/validations/expectation-suites/0a123b9c-e370-4b18-b703-785dde88732d/results/cb093105-6ede-47d4-a141-dee10c632e18"
}

A note about GX

Great Expectations offers multiple workflows for similar tasks
- e.g., Batch Definitions vs. Validation Definitions
This course provides a broad understanding, but alternative GX implementations may use different approaches

Cheat sheet

Add Expectation Suite to Data Context:

context.suites.add(suite)

Create Validation Definition:

validation_definition = \
gx.ValidationDefinition(
  name: str, 
  data=batch_definition, 
  suite=suite
)

Run Validation:

validation_results = \
validation_definition.run(
  batch_parameters={"dataframe": dataframe}
)

Check Validation Results:

validation_results.success
validation_results.describe()

Let's practice!

Introduction to Data Quality with Great Expectations