Version: 1.2.1

Test an Expectation

Data can be validated against individual Expectations. This workflow is generally used when engaging in exploration of new data, or when building out a set of Expectations to comprehensively describe the state that your data should conform to.

Prerequisites

Python version 3.9 to 3.12.
An installation of GX Core.
A preconfigured Data Context.
A Batch of sample data. This guide assumes the variable batch contains your sample data.
An Expectation. This guide assumes the variable expectation contains the Expectation to be tested.

Procedure

Instructions
Sample code

Run the Expectation on the Batch of data.

In this example, the Expectation to test was defined with preset parameters and is already stored in the variable expectation. The variable batch contains a Batch that was retrieved from a .csv file using the pandas defualt Data Source:
Python
```
validation_results = batch.validate(expectation)
```
In this example, the Expectation to test was defined to take Expectation Parameters at runtime:
Python
```
runtime_expectation_parameters = {
    "expect_passenger_max_to_be_above": 4,
    "expect_passenger_max_to_be_below": 6,
}
validation_results = batch.validate(
    expectation, expectation_parameters=runtime_expectation_parameters
)
```
Evaluate the returned Validation Results.
Python
```
print(validation_results)
```
When you print your Validation Results they will be presented in a dictionary format. There are a few key/value pairs in particular that are important for evaluating your Validation Results. These are:
- expectation_config: Provides a dictionary that describes the Expectation that was run and what its parameters are.
- success: The value of this key indicates if the data that was validated met the criteria described in the Expectation.
- result: Contains a dictionary with additional information that shows why the Expectation succeded or failed.
In the following example you can see the Validation Results for an Expectation that failed because the observed_value reported in the result dictionary is outside of the min_value and max_value range described in the expectation_config:
Python output
```
{
  "success": false,
  "expectation_config": {
    "expectation_type": "expect_column_max_to_be_between",
    "kwargs": {
      "batch_id": "2018-06_taxi",
      "column": "passenger_count",
      "min_value": 4.0,
      "max_value": 5.0
    },
    "meta": {},
    "id": "38368501-4599-433a-8c6a-28f5088a4d4a"
  },
  "result": {
    "observed_value": 6
  },
  "meta": {},
  "exception_info": {
    "raised_exception": false,
    "exception_traceback": null,
    "exception_message": null
  }
}
```
Optional. Adjust the Expectation's parameters and retest.

If the Expectation did not return the results you anticipated you can update it to reflect the actual state of your data, rather than recreating it from scratch. An Expectation object with preset parameters stores the parameters that were provided to initialize it as attributes. To modify the Expectation you overwrite those attributes.

For example, if your Expectation has took the parameters min_value and max_value, you could update them with:
Python input
```
expectation.min_value = 1
expectation.max_value = 6
```
Once you have set the new values for the Expectation's parameters you can reuse the Batch Definition from earlier and repeat this procedure to test your changes.
Python input
```
new_validation_results = batch.validate(expectation)
print(new_validation_results)
```
This time, the updated Expectation accurately describes the data and the validation succeds:
Python output
```
{
  "success": true,
  "expectation_config": {
    "expectation_type": "expect_column_max_to_be_between",
    "kwargs": {
      "batch_id": "2018-06_taxi",
      "column": "passenger_count",
      "min_value": 1.0,
      "max_value": 6.0
    },
    "meta": {},
    "id": "38368501-4599-433a-8c6a-28f5088a4d4a"
  },
  "result": {
    "observed_value": 6
  },
  "meta": {},
  "exception_info": {
    "raised_exception": false,
    "exception_traceback": null,
    "exception_message": null
  }
}
```
When an Expectation uses an Expectation Parameter dictionary you don't have to modify anything on the Expectation object. Instead, update the dictionary with new values and then test it with the updated dictionary:
Python input
```
runtime_expectation_parameters = {
    "expect_passenger_max_to_be_above": 1,
    "expect_passenger_max_to_be_below": 6,
}
validation_results = batch.validate(
    expectation, expectation_parameters=runtime_expectation_parameters
)
print(validation_results)
```
For more information about Validation Results, what they contain, and how to adjust their verbosity see Choose result format.

Python
import great_expectations as gx

context = gx.get_context()
set_up_context_for_example(context)


# Use the `pandas_default` Data Source to retrieve a Batch of sample Data from a data file:
file_path = "./data/folder_with_data/yellow_tripdata_sample_2019-01.csv"
batch = context.data_sources.pandas_default.read_csv(file_path)

# Define the Expectation to test:
expectation = gx.expectations.ExpectColumnMaxToBeBetween(
    column="passenger_count", min_value=1, max_value=6
)

# Test the Expectation:
validation_results = batch.validate(expectation)

# Evaluate the Validation Results:
print(validation_results)

# If needed, adjust the Expectation's preset parameters and test again:
expectation.min_value = 1
expectation.max_value = 6

# Test the modified expectation and review the new Validation Results:
new_validation_results = batch.validate(expectation)
print(new_validation_results)

Prerequisites

Procedure​

Procedure