Median In Range

statisticalmedium

Asserts that the median of a numeric column falls within an expected range. The median is more robust to outliers than the mean, making this check ideal for skewed distributions or data with extreme values.

v1.0.0by dqhub190 downloads4.7 (14)

statisticsmedianpercentilerangerobustnumeric

Try This Rule

Parameters

column_namestringrequired

The column containing email addresses

thresholdfloatdefault: 0.99

Minimum fraction of valid emails (0.0 to 1.0)

Install

soda

checks for {{table_name}}:
  - invalid_percent({{column_name}}) < {{(1 - threshold) * 100}}:
      valid regex: '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'

dbt

{% test valid_email(model, column_name) %}
select {{ column_name }}
from {{ model }}
where {{ column_name }} not regexp '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
{% endtest %}

sql

SELECT COUNT(*) as total,
  SUM(CASE WHEN {{column_name}} REGEXP
    '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$'
    THEN 1 ELSE 0 END) as valid
FROM {{table_name}}

Great Expectations

{
  "expectation_type": "expect_column_values_to_match_regex",
  "kwargs": {
    "column": "{{column_name}}",
    "regex": "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$",
    "mostly": {{threshold}}
  }
}

spark

from pyspark.sql.functions import col
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
invalid = df.filter(~col("{{column_name}}").rlike(pattern)).count()

Test Data

Passing Examples

id	value
1	alice@example.com
2	bob.smith@company.co.uk
3	charlie+tag@domain.org

Failing Examples

id	value
1	not-an-email
2	@missing-local.com
3	spaces in@email.com

CLI

Terminal

npx dqhub install median-in-range --format soda --table YOUR_TABLE
npx dqhub install median-in-range --format dbt --model YOUR_MODEL
npx dqhub install median-in-range --format sql --dialect snowflake