> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Column-Level Test Definitions Reference

> Complete reference for all column-level data quality test types available in the OpenMetadata Python SDK

# Column-Level Test Definitions

Column-level tests validate properties of specific columns. For table-level tests and importing instructions, see the [Test Definitions Reference](/v1.13.x/how-to-guides/data-quality-observability/quality/data-quality-as-code/test-definitions).

## Column-Level Tests

### ColumnValuesToBeNotNull

Validates that a column contains no null or missing values.

**Parameters:**

* `column` (str, required): Name of the column to validate

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeNotNull

# Email column must not contain nulls
test = ColumnValuesToBeNotNull(column="email")
```

**Use Cases:**

* Ensure required fields are populated
* Validate data completeness
* Enforce NOT NULL constraints

***

### ColumnValuesToBeUnique

Validates that all values in a column are unique with no duplicates.

**Parameters:**

* `column` (str, required): Name of the column to validate

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeUnique

# Customer ID must be unique
test = ColumnValuesToBeUnique(column="customer_id")
```

**Use Cases:**

* Validate primary keys
* Ensure unique identifiers
* Detect duplicate records

***

### ColumnValuesToBeInSet

Validates that all values in a column belong to a specified set.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `allowed_values` (list\[str], required): List of acceptable values

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeInSet

# Status must be one of these values
test = ColumnValuesToBeInSet(
    column="status",
    allowed_values=["active", "inactive", "pending"]
)

# Country codes must be valid
test = ColumnValuesToBeInSet(
    column="country_code",
    allowed_values=["US", "UK", "CA", "AU", "DE"]
)
```

**Use Cases:**

* Validate enum values
* Enforce categorical constraints
* Validate lookup values

***

### ColumnValuesToBeNotInSet

Validates that column values do not contain any forbidden values.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `forbidden_values` (list\[str], required): List of values that must not appear

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeNotInSet

# Prevent test email addresses
test = ColumnValuesToBeNotInSet(
    column="email",
    forbidden_values=["test@test.com", "admin@admin.com", "noreply@example.com"]
)
```

**Use Cases:**

* Detect test data in production
* Blacklist invalid values
* Filter out placeholder values

***

### ColumnValuesToMatchRegex

Validates that column values match a specified regular expression pattern.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `regex` (str, required): Regular expression pattern

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToMatchRegex

# Validate email format
test = ColumnValuesToMatchRegex(
    column="email",
    regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$"
)

# Validate phone number format (US)
test = ColumnValuesToMatchRegex(
    column="phone",
    regex=r"^\+?1?\d{10}$"
)

# Validate date format (YYYY-MM-DD)
test = ColumnValuesToMatchRegex(
    column="date_string",
    regex=r"^\d{4}-\d{2}-\d{2}$"
)
```

**Use Cases:**

* Validate data format consistency
* Ensure pattern compliance
* Detect malformed data

***

### ColumnValuesToNotMatchRegex

Validates that column values do not match a forbidden regular expression pattern.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `regex` (str, required): Regular expression pattern that values must NOT match

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToNotMatchRegex

# Prevent test emails
test = ColumnValuesToNotMatchRegex(
    column="email",
    regex=r".*@test\.com$"
)

# Detect test data markers
test = ColumnValuesToNotMatchRegex(
    column="name",
    regex=r"^test.*"
)
```

**Use Cases:**

* Detect test data patterns
* Prevent specific formats
* Identify security risks

***

### ColumnValuesToBeBetween

Validates that all values in a column fall within a specified numeric range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable value
* `max_value` (float, optional): Maximum acceptable value

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeBetween

# Age should be reasonable
test = ColumnValuesToBeBetween(
    column="age",
    min_value=0,
    max_value=120
)

# Price must be positive
test = ColumnValuesToBeBetween(
    column="price",
    min_value=0.01,
    max_value=999999.99
)

# Percentage values
test = ColumnValuesToBeBetween(
    column="completion_rate",
    min_value=0,
    max_value=100
)
```

**Use Cases:**

* Validate numeric constraints
* Detect outliers
* Ensure value ranges

***

### ColumnValueMaxToBeBetween

Validates that the maximum value in a column falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable maximum value
* `max_value` (float, optional): Maximum acceptable maximum value

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueMaxToBeBetween

# Max temperature should be reasonable
test = ColumnValueMaxToBeBetween(
    column="temperature",
    min_value=-50,
    max_value=50
)

# Highest score should not exceed 100
test = ColumnValueMaxToBeBetween(
    column="score",
    min_value=90,
    max_value=100
)
```

**Use Cases:**

* Monitor data ranges
* Detect upper outliers
* Validate maximum constraints

***

### ColumnValueMinToBeBetween

Validates that the minimum value in a column falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable minimum value
* `max_value` (float, optional): Maximum acceptable minimum value

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueMinToBeBetween

# Min temperature validation
test = ColumnValueMinToBeBetween(
    column="temperature",
    min_value=-50,
    max_value=0
)

# Minimum age should be at least 18
test = ColumnValueMinToBeBetween(
    column="age",
    min_value=18,
    max_value=25
)
```

**Use Cases:**

* Monitor lower bounds
* Detect lower outliers
* Validate minimum constraints

***

### ColumnValueMeanToBeBetween

Validates that the mean (average) value falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable mean value
* `max_value` (float, optional): Maximum acceptable mean value

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueMeanToBeBetween

# Average rating should be between 3 and 4.5
test = ColumnValueMeanToBeBetween(
    column="rating",
    min_value=3.0,
    max_value=4.5
)

# Average response time monitoring
test = ColumnValueMeanToBeBetween(
    column="response_time_ms",
    min_value=100,
    max_value=500
)
```

**Use Cases:**

* Statistical validation
* Detect data drift
* Monitor averages

***

### ColumnValueMedianToBeBetween

Validates that the median value falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable median value
* `max_value` (float, optional): Maximum acceptable median value

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueMedianToBeBetween

# Median salary range
test = ColumnValueMedianToBeBetween(
    column="salary",
    min_value=50000,
    max_value=75000
)
```

**Use Cases:**

* Robust central tendency checks
* Detect skewed distributions
* Monitor typical values

***

### ColumnValueStdDevToBeBetween

Validates that the standard deviation falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable standard deviation
* `max_value` (float, optional): Maximum acceptable standard deviation

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueStdDevToBeBetween

# Response time variability
test = ColumnValueStdDevToBeBetween(
    column="response_time",
    min_value=0,
    max_value=100
)
```

**Use Cases:**

* Detect unexpected variability
* Monitor data consistency
* Validate distribution stability

***

### ColumnValuesSumToBeBetween

Validates that the sum of all values falls within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_value` (float, optional): Minimum acceptable sum
* `max_value` (float, optional): Maximum acceptable sum

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesSumToBeBetween

# Total revenue validation
test = ColumnValuesSumToBeBetween(
    column="revenue",
    min_value=1000000,
    max_value=5000000
)
```

**Use Cases:**

* Validate totals
* Monitor aggregates
* Detect unexpected volumes

***

### ColumnValuesMissingCount

Validates the count of missing or null values.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `missing_count_value` (int, optional): Expected number of missing values
* `missing_value_match` (list\[str], optional): Additional strings to treat as missing

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesMissingCount

# Expect exactly 100 missing values
test = ColumnValuesMissingCount(
    column="optional_field",
    missing_count_value=100
)

# Treat custom strings as missing
test = ColumnValuesMissingCount(
    column="status",
    missing_value_match=["N/A", "Unknown", ""]
)
```

**Use Cases:**

* Monitor data completeness
* Track missing data patterns
* Validate optional fields

***

### ColumnValueLengthsToBeBetween

Validates that string lengths fall within a specified range.

**Parameters:**

* `column` (str, required): Name of the column to validate
* `min_length` (int, optional): Minimum acceptable string length
* `max_length` (int, optional): Maximum acceptable string length

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValueLengthsToBeBetween

# Username length validation
test = ColumnValueLengthsToBeBetween(
    column="username",
    min_length=3,
    max_length=20
)

# Description length constraints
test = ColumnValueLengthsToBeBetween(
    column="description",
    min_length=10,
    max_length=500
)
```

**Use Cases:**

* Validate string constraints
* Prevent truncation
* Ensure format compliance

***

### ColumnValuesToBeAtExpectedLocation

Validates that latitude and longitude values are within a configured radius of an expected location from a reference column, such as a city or postal code.

**Parameters:**

* `column` (str, required): Reference location column, such as a city or postal code column
* `location_reference_type` (str, required): Type of location reference. Supported values are `CITY` and `POSTAL_CODE`
* `longitude_column_name` (str, required): Name of the longitude column in the table
* `latitude_column_name` (str, required): Name of the latitude column in the table
* `radius` (float, required): Allowed distance from the expected location in meters

**Example:**

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeAtExpectedLocation

test = ColumnValuesToBeAtExpectedLocation(
    column="city",
    location_reference_type="CITY",
    longitude_column_name="longitude",
    latitude_column_name="latitude",
    radius=5000.0,
)
```

**Use Cases:**

* Validate latitude and longitude coordinates against a city or postal code column
* Detect records with coordinates outside an expected delivery or service area
* Check geospatial data quality before downstream mapping or distance calculations

***

## Customizing Tests

All tests support customization through fluent methods:

```python theme={null}
from metadata.sdk.data_quality import ColumnValuesToBeNotNull

test = ColumnValuesToBeNotNull(column="email") \
    .with_name("email_completeness_check") \
    .with_display_name("Email Completeness Validation") \
    .with_description("Ensures all customer records have email addresses") \
    .with_compute_row_count(True)
```

Or pass values directly to the constructor:

```python theme={null}
test = ColumnValuesToBeNotNull(
    column="email",
    name="email_completeness_check",
    display_name="Email Completeness Validation",
    description="Ensures all customer records have email addresses"
)
```

## Next Steps

* Learn how to use these tests with [TestRunner](/v1.13.x/how-to-guides/data-quality-observability/quality/data-quality-as-code/test-runner)
* Apply tests to [DataFrame Validation](/v1.13.x/how-to-guides/data-quality-observability/quality/data-quality-as-code/dataframe-validation)
* Explore [Advanced Usage](/v1.13.x/how-to-guides/data-quality-observability/quality/data-quality-as-code/advanced-usage) patterns