Column Aggregations | Apply Math at Scale, No Code Required
Column-level math is fundamental to analytics—whether you’re calculating revenue, adjusting values, or deriving ratios. But in most tools, even basic arithmetic across columns or constants requires writing formulas or scripts.
Edilitics eliminates this barrier with a point-and-click column aggregation interface, allowing users to apply math operations—like addition, multiplication, and logarithms—at scale, without coding.
Why Column Aggregations Matter
Column aggregations enable you to:
-
✅ Compute derived metrics like profit margin or cost per unit
-
✅ Perform batch arithmetic across columns (e.g., scale, offset, normalize)
-
✅ Chain multiple operations to build layered logic
-
✅ Avoid reliance on scripts or formula builders
Each operation in Edilitics supports:
-
Column-to-column and column-to-constant operations
-
Safe execution with preview and undo
-
Support for numeric, decimal, and datetime-compatible inputs
Common Use Cases for Aggregations
| Industry | New Metric | Inputs | Operation |
|---|---|---|---|
| Retail | total_revenue | unit_price × quantity_sold | Multiply |
| Finance | growth_rate | end_value ÷ start_value | Divide |
| Healthcare | BMI | weight ÷ (height^2) | Exponent + Divide |
| Manufacturing | cost_per_unit | total_cost ÷ units_produced | Divide |
| Education | weighted_score | score × weight | Multiply |
How to Apply Column Aggregations in Edilitics
-
Choose the input column(s)
Select one or more columns to apply the operation on (or add a constant).
-
Select the operator
Pick from supported math operators:
+,-,*,/,%,^,log,percent. -
Set the operand
Use another column or type in a numeric constant (e.g.,
0.1for 10%). -
Name the output column
Give a clear name to the resulting derived field (e.g.,
adjusted_price). -
Preview & Run
Preview the output in real time and click Submit to apply.
Edilitics also supports chained operations—you can use a derived column (like total_cost) as input to another operation (like cost_per_unit).
Manual Equivalent – SQL & Pandas Examples
To help technical users understand the logic, here’s how common aggregations are implemented manually.
SQL Example – Redshift
SELECT unit_price * quantity_sold AS total_revenue, (weight_kg / POWER(height_m, 2)) AS bmiFROM patient_data;
Pandas Example
df['total_revenue'] = df['unit_price'] * df['quantity_sold']df['bmi'] = df['weight_kg'] / (df['height_m'] ** 2)
In Edilitics, both operations are performed through a simple dropdown and input—no code needed.
Reliable and Error-Tolerant
Column aggregations in Edilitics are:
-
✅ Type-safe – Supports numeric types only, with warnings for mismatches
-
✅ Chainable – Use output columns in subsequent steps
-
✅ Previewable – Confirm results before applying
-
✅ Flexible – Use constants or fields interchangeably
If incompatible data types are encountered, Edilitics guides users to correct them via the Cast Data Types operation.
Column Aggregations make it simple to create new metrics, perform calculations, and build derived columns—without touching SQL. From basic math to chained expressions, Edilitics provides a guided, schema-safe interface that makes numerical transformation intuitive and reusable. Say goodbye to manual calculations and hello to governed logic built for real-world scale.
Next: Derive Smarter Metrics
Column Aggregations are foundational to metric creation. Once in place, they unlock powerful transformations like:
Enterprise Support & Technical Assistance