Create a Minimal Great Expectations Suite Mock in Python

Build a small Python class that mimics a Great Expectations suite, storing and serializing column expectations as JSON.

Easy Python 3.9+ Aug 9, 2026 ML engineering pipelines 11 views 0 copies

great-expectations mock testing data-validation json

Python code

30 lines

Python 3.9+

import json


class GreatExpectationsSuite:
    """A minimal mock of a Great Expectations suite."""

    def __init__(self, suite_name, expectations=None):
        self.suite_name = suite_name
        self.expectations = expectations or []

    def add_expectation(self, expectation_type, column=None, kwargs=None):
        expectation = {"expectation_type": expectation_type, "column": column, "kwargs": kwargs or {}}
        self.expectations.append(expectation)
        return expectation

    def to_dict(self):
        return {
            "suite_name": self.suite_name,
            "expectations": self.expectations
        }


if __name__ == "__main__":
    suite = GreatExpectationsSuite("my_mock_suite")
    suite.add_expectation("expect_column_values_to_not_be_null", column="user_id")
    suite.add_expectation("expect_column_values_to_be_between", column="age",
                          kwargs={"min_value": 18, "max_value": 99})
    suite.add_expectation("expect_column_values_to_match_regex", column="email",
                          kwargs={"regex": r"^[\w\.-]+@[\w\.-]+\.\w+$"})
    print(json.dumps(suite.to_dict(), indent=2))

Output

stdout

{
  "suite_name": "my_mock_suite",
  "expectations": [
    {
      "expectation_type": "expect_column_values_to_not_be_null",
      "column": "user_id",
      "kwargs": {}
    },
    {
      "expectation_type": "expect_column_values_to_be_between",
      "column": "age",
      "kwargs": {
        "min_value": 18,
        "max_value": 99
      }
    },
    {
      "expectation_type": "expect_column_values_to_match_regex",
      "column": "email",
      "kwargs": {
        "regex": "^[\w\.-]+@[\w\.-]+\.\w+$"
      }
    }
  ]
}

How it works

This code defines a GreatExpectationsSuite class that keeps a list of expectations, each with an expectation type, column, and optional keyword arguments. The add_expectation method appends a dict representation to the list and returns it, allowing chained or inspected creation. to_dict serializes the suite state, which is then printed with json.dumps(indent=2) for readable output. This approach mirrors the data shape of real Great Expectations suites so downstream tooling can consume the structure without the full library dependency.

Common mistakes

Mutating the default `expectations or []` incorrectly when reusing the same list object across instances
Forgetting that `kwargs` is stored as a separate dict and unintentionally sharing references across expectations
Not accounting for optional columns in expectations that apply to the whole table, not a single column

Variations

Use a dataclass with `field(default_factory=list)` to avoid mutable default issues
Store expectations in a `collections.OrderedDict` keyed by tuple (type, column) to prevent duplicates

Real-world use cases

Mocking a Great Expectations suite in unit tests without installing the heavy library.
Generating portable validation specs that can be translated to SQL or pandas checks in a pipeline.
Creating lightweight documentation of data contracts to share with upstream data producers.

Create a Minimal Great Expectations Suite Mock in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Common mistakes

Variations

Real-world use cases

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