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Grouping Aggregation

When grouping is active , you can apply aggregation functions to individual columns to control how values are summarized inside each group.

For example, you can group first by Region, then by Sales Rep, then by Product. Aggregations are calculated independently at each grouping level.

In group rows, columns with an aggregation display their summarized value. Columns that are neither grouped nor aggregated are hidden in the group header row to avoid misleading output.

Quick guide

  1. Enable Grouping and choose one or more group-by columns.
  2. Open Column settings → Aggregation for each column you want to summarize.
  3. Select one of the available functions: Sum, Avg, Min, Max, Count, Count distinct, or Mode.
  4. Review the result in the group header row.
  5. Click Save to apply the configuration.

Tip: Different columns can use different aggregation functions within the same grouped table.

At a glance

Function Good for Example (input → output)
Sum Revenue, totals, hours [120, 80, 50]250
Avg Scores, ratings, numeric KPIs [4.6, 4.9, 4.0]4.5
Min Earliest date, smallest value [7, 3, 5]3
Max Largest value, latest date [5000, 12000, 8000]12000
Count Rows, tickets, items 42 rows42
Count distinct Unique contributors, SKUs, labels ["Ana","Ken","Ken","Maya"]3
Mode Predominant status, label, or owner ["In progress","Blocked","In progress","Done"]In progress

Examples are illustrative. Final formatting depends on the column type and display settings.

Sum

What it does: Adds all numeric values in the group. Blank values are ignored.

Use case: Sales totals by region
Sales Lead avatar

Sales Lead

KantoSoft — Sales Team

Setup: Group by Region; set Revenue aggregation to Sum.

Impact: Managers can see total revenue for each region directly in the grouped view.

Avg

What it does: Calculates the arithmetic mean of numeric values. Blank values are ignored.

Use case: Satisfaction score by department
HR Manager avatar

HR Manager

Midori Inc — People Team

Setup: Group by Department; set Score aggregation to Avg.

Impact: Teams can compare average satisfaction values across departments.

Min

What it does: Returns the smallest value in the group. Works well for numbers and dates.

Use case: Earliest due date by team
Operations avatar

Operations

Nippon Express — Operations

Setup: Group by Route; set Delivery Time or Due Date to Min.

Impact: Helps identify the earliest value in each group at a glance.

Max

What it does: Returns the largest value in the group. Works well for numbers and dates.

Use case: Largest contract per rep
Sales avatar

Deals Team

KyotoTech — Sales

Setup: Group by Rep; set Contract Value to Max.

Impact: Surfaces the largest value in each group without opening individual rows.

Count

What it does: Counts the number of rows in the group.

Use case: Tickets per category
Support Agent avatar

Support Agent

ZenDeskura — Support

Setup: Group by Issue Type; apply Count to a column in the grouped table.

Impact: Makes it easy to compare workload volume between categories.

Count distinct

What it does: Counts unique, non-blank values in the group.

Use case: Unique contributors per repository
Engineering Lead avatar

Engineering Lead

NaraCode — DevOps

Setup: Group by Repository; set Author to Count distinct.

Impact: Shows how many different contributors are active in each repository.

Mode

What it does: Returns the most frequent value in the group. This is especially useful for categorical fields such as Status, Priority, or Owner. Blank values are ignored.

If there is a tie, the result will be one of the most frequent values.

Use case: Predominant status by project
Project Ops avatar

Project Ops

Hikari Solutions — PMO

Setup: Group by Project; set Status aggregation to Mode.

Impact: Helps teams see the predominant state of work per project at a glance.

Notes
• Aggregations only affect the group header row. Underlying row values are unchanged.
• Different columns can use different aggregation functions in the same grouped table.
• Nested grouping calculates aggregations independently at each grouping level.

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