Predefined Constraint Classes
Last updated
Last updated
Copyright (c) 2023, DataCebo, Inc.
Predefined constraint classes are available for frequently occurring logic. The logic can be isolated to a single column, incorporate multiple columns in a table or be applied to multiple rows.
This logic can be applied to a single column of your data.
Constraint Class | Description | Example |
---|---|---|
This logic requires multiple columns.
Constraint Class | Description | Example |
---|---|---|
All the values in the column must be >0
The room_rate
must be positive
All the values in the column must be <0
The credit_balance
must be negative
All the values in the column have a fixed lower or upper bound
All checkin_date
values must occur on or after Jan 1, 2020
All the values in the column have fixed lower and upper bounds
All values in amenities_fee
must be between 0 and 500.00
All the numerical values are increments of a whole number
All values in salary
must be divisible by 1000
No shuffling is allowed other than what's already observed in the data
The city
and country
values cannot be shuffled to create new permutations.
No shuffling is around for the missing values, other than what's already observed in the data
The city
and country
columns must both either be null together or not at all.
The value of one categorical column determines the scale of another numerical column.
If the value of test_type
is 'blood_pressure'
then the value of test_result
must be within a reasonable for this test only.
The original data columns represent a one hot encoding scheme
Exactly 1 of the following columns has a 1
in each row: not_subscribed
, basic_subscriber
, premium
The value in one column must always be greater than the other
The checkout_date
must always be after the checkin_date
The value in one column is bounded by the values in other columns
The parent_age
must be in between child_age
and grandparent_age
A chain of 2 or more columns in an inequality
purchase_date
< start_date
< end_date
< expiration_date
< termination_date