Conditional Column

Conditional Column adds a new column to your dataset whose value is determined by a set of IF/THEN rules you define. Rules are evaluated top-to-bottom - the first rule that matches a row sets that row's value. Rows that match no rule receive the Else value (or null if left empty). The source columns are unchanged.

Each rule can have one or more conditions combined with AND or OR logic. You can stack as many rules as needed in one operation.

When to Use Conditional Column

• Customer segmentation. Label rows as "Enterprise", "Growth", or "Starter" based on revenue thresholds.
• Discount flag. Mark rows where discount_pct is null as "No Discount" and non-null rows as "Discounted".
• Regional grouping. Map region values to broader buckets - "EMEA", "Americas", "APAC".
• Risk scoring. Assign "High", "Medium", or "Low" risk based on multiple conditions across revenue, tier, and nulls.
• Return classification. Flag rows where return_flag = true and revenue > 10000 as "High-value return".

Sample Dataset

The examples in this doc use the Edilitics sample orders dataset. Download it to follow along in your own workspace.

Relevant columns for Conditional Column examples:

How Rules Work

Each rule has three parts:

1. IF - one or more conditions. Each condition tests a column against an operator and a value.
2. AND / OR - when a rule has more than one condition, toggle AND (all conditions must match) or OR (any condition must match). AND/OR applies within one rule only - different rules always operate independently.
3. THEN - the value to assign when the rule matches.

After all rules, an optional Else value handles any row that matched no rule. Leave it empty and unmatched rows receive null.

Operators by Column Type

Operators available depend on the column type selected in the condition.

Numerical and Datetime columns:

String (categorical) columns:

Is null and Is not null hide the value input field - no comparison value is needed.

How to Apply Conditional Column

Before and After

Three rules applied to create a revenue_tier column:

1. IF revenue ≥ 20000 → "High"
2. IF revenue ≥ 5000 → "Mid"
3. IF revenue < 5000 → "Low"

Else: null

Input:

Output:

Source columns unchanged. One new column added.

Code Equivalent

-- Compatible with PostgreSQL, BigQuery, Snowflake, Redshift, DuckDB
SELECT
  *,
  CASE
    WHEN revenue >= 20000 THEN 'High'
    WHEN revenue >= 5000  THEN 'Mid'
    WHEN revenue < 5000   THEN 'Low'
    ELSE NULL
  END AS revenue_tier
FROM orders;

import polars as pl

df = df.with_columns(
    pl.when(pl.col("revenue") >= 20000)
    .then(pl.lit("High"))
    .when(pl.col("revenue") >= 5000)
    .then(pl.lit("Mid"))
    .when(pl.col("revenue") < 5000)
    .then(pl.lit("Low"))
    .otherwise(pl.lit(None))
    .alias("revenue_tier")
)

Operation Reference

Frequently Asked Questions

Next Steps

Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.