Numeric Between

Numeric Between#

Check: numeric-between-check

Purpose: Checks whether values in the specified numeric columns are within a defined range between min_value and max_value. A row fails the check if any of the selected columns contains a value below min_value or above max_value, based on the configured inclusivity.

You can control inclusivity for each boundary using the inclusive parameter:

  • inclusive: [False, False] (default): strictly between min < value < max

  • inclusive: [True, False]: include only the lower bound min <= value < max

  • inclusive: [False, True]: include only the upper bound min < value <= max

  • inclusive: [True, True]: include both bounds min <= value <= max

Python Configuration#

from sparkdq.checks import NumericBetweenCheckConfig
from sparkdq.core import Severity

NumericBetweenCheckConfig(
    check_id="allowed_discount_range",
    columns=["discount"],
    min_value=0.0,
    max_value=100.0,
    inclusive=(True, True),
    severity=Severity.CRITICAL
)

Declarative Configuration#

- check: numeric-between-check
  check-id: allowed_discount_range
  columns:
    - discount
  min-value: 0.0
  max-value: 100.0
  inclusive: [true, true]
  severity: critical

Typical Use Cases#

  • ✅ Ensure that numeric values fall within expected physical or business-defined ranges.

  • ✅ Validate that percentages, ratios, or scores stay between 0 and 100.

  • ✅ Detect outliers or incorrect data entries outside of acceptable thresholds.

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