> ## Documentation Index > Fetch the complete documentation index at: https://elementary-devin-1782754750-bigquery-permissions-docs.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Test Decorators Reference Complete reference for all test decorators available in the Elementary Python SDK. ## Import Statement ```python theme={null} from elementary_python_sdk.core.tests import ( boolean_test, expected_range, expected_values, row_count, ) ``` ## @boolean\_test Tests that return a boolean (True/False) result. ### Signature ```python theme={null} @boolean_test( name: str, severity: str | TestSeverity = "ERROR", description: str | None = None, tags: list[str] | None = None, owners: list[str] | None = None, metadata: dict | None = None, column_name: str | None = None, quality_dimension: QualityDimension | None = None, skip: bool = False, ) def test_function(df: pd.DataFrame) -> bool: # Your test logic return True # or False ``` ### Parameters | Parameter | Type | Required | Default | Description | | ------------------- | ---------------- | -------- | --------- | ----------------------------------------------------------------------------------------------------------------------------------- | | `name` | str | Yes | - | Test name | | `severity` | str | No | `"ERROR"` | Test severity: `"ERROR"` or `"WARNING"` | | `description` | str | No | `None` | Test description | | `column_name` | str | No | `None` | Column being tested (for column-level tests) | | `tags` | list\[str] | No | `None` | List of tags | | `owners` | list\[str] | No | `None` | List of owners | | `metadata` | dict | No | `None` | Additional metadata | | `quality_dimension` | QualityDimension | No | `None` | Quality dimension (defaults to VALIDITY) | | `skip` | bool | No | `False` | Whether to skip this test. Useful if you want the test to appear in Elementary Cloud, but you don't want to execute it in this run. | ### Example ```python theme={null} @boolean_test( name="unique_ids", description="All user IDs must be unique", column_name="id", severity="ERROR", ) def test_unique_ids(df: pd.DataFrame) -> bool: ids = df["id"].dropna().tolist() return len(ids) == len(set(ids)) ``` ## @expected\_range Tests that return a numeric value that should fall within a range. They can also return a list of numeric values or a pandas Series. ### Signature ```python theme={null} @expected_range( name: str, min: float | None = None, max: float | None = None, severity: str | TestSeverity = "ERROR", description: str | None = None, tags: list[str] | None = None, owners: list[str] | None = None, metadata: dict | None = None, column_name: str | None = None, quality_dimension: QualityDimension | None = None, skip: bool = False, ) def test_function(df: pd.DataFrame) -> float | list[float] | pd.Series: # Your test logic return df["age"].mean() # Numeric value # return [1, 2, 3] # Numeric values # return df["age"] # pandas Series ``` ### Parameters | Parameter | Type | Required | Default | Description | | --------------------------------------------------------------------------------------------------- | ----- | -------- | ------- | ---------------------------------- | | `name` | str | Yes | - | Test name | | `min` | float | No | `None` | Minimum expected value (inclusive) | | `max` | float | No | `None` | Maximum expected value (inclusive) | | `severity`, `description`, `column_name`, `tags`, `owners`, `metadata`, `quality_dimension`, `skip` | - | No | - | Same as `@boolean_test` | ### Example ```python theme={null} @expected_range( name="average_age", min=18, max=50, description="Average age should be between 18 and 50", column_name="age", severity="ERROR", ) def test_average_age(df: pd.DataFrame) -> float: return df["age"].mean() ``` ## @expected\_values Tests that return a value (or values) that should match one of a list of expected values. ### Signature ```python theme={null} @expected_values( name: str, expected: Any | list[Any], allow_none: bool = False, severity: str | TestSeverity = "ERROR", description: str | None = None, tags: list[str] | None = None, owners: list[str] | None = None, metadata: dict | None = None, column_name: str | None = None, quality_dimension: QualityDimension | None = None, skip: bool = False, ) def test_function(df: pd.DataFrame) -> Any: # Your test logic return value # Should match one of expected values ``` ### Parameters | Parameter | Type | Required | Default | Description | | --------------------------------------------------------------------------------------------------- | ----------------- | -------- | ------- | ----------------------------------------------- | | `name` | str | Yes | - | Test name | | `expected` | Any \| list\[Any] | Yes | - | Expected value(s) - can be single value or list | | `allow_none` | bool | No | `False` | Whether to allow None values | | `severity`, `description`, `column_name`, `tags`, `owners`, `metadata`, `quality_dimension`, `skip` | - | No | - | Same as `@boolean_test` | ### Example ```python theme={null} @expected_values( name="country_count", expected=2, severity="ERROR", description="Should have exactly 2 countries", column_name="country", ) def count_unique_countries(df: pd.DataFrame) -> int: return df["country"].nunique() ``` ## @row\_count Tests that return a Sized object (DataFrame, list, etc.) to check row count. ### Signature ```python theme={null} @row_count( name: str, min: int | None = None, max: int | None = None, severity: str | TestSeverity = "ERROR", description: str | None = None, tags: list[str] | None = None, owners: list[str] | None = None, metadata: dict | None = None, skip: bool = False, ) def test_function(df: pd.DataFrame) -> Sized: # Your test logic - return DataFrame, list, etc. return df # or any object with __len__ ``` ### Parameters | Parameter | Type | Required | Default | Description | | --------------------------------------------------------------- | ---- | -------- | ------- | -------------------------------------- | | `name` | str | Yes | - | Test name | | `min` | int | No | `None` | Minimum expected row count (inclusive) | | `max` | int | No | `None` | Maximum expected row count (inclusive) | | `severity`, `description`, `tags`, `owners`, `metadata`, `skip` | - | No | - | Same as `@boolean_test` | ### Example ```python theme={null} @row_count( name="user_count_range", min=1, max=1000000, severity="WARNING", description="Validate user count is within expected range", ) def get_users_df(df: pd.DataFrame) -> pd.DataFrame: """Return the DataFrame; the decorator calls len() on it.""" return df ``` ## Common Parameters All decorators support these common parameters: * **`name`** (required): Unique test name * **`severity`**: `"ERROR"` or `"WARNING"` (default: `"ERROR"`) * **`description`**: Human-readable test description * **`tags`**: List of tags for categorization * **`owners`**: List of owner emails/usernames * **`metadata`**: Dictionary of additional metadata * **`skip`**: Boolean to skip the test ## Return Types * **`@boolean_test`**: Must return `bool` * **`@expected_range`**: Must return numeric value (int or float) * **`@expected_values`**: Can return any type that can be compared * **`@row_count`**: Must return a Sized object (has `__len__` method) ## Related Documentation * [Quickstart](/python-sdk/quickstart) - Get started with test decorators * [API Reference](/python-sdk/api-reference/overview) - Overview of the SDK API * [Table Assets](/python-sdk/api-reference/table-assets) - Register tables and views in your data warehouse