Grouping Aggregation

When grouping is active, you can apply column-level aggregation functions to control how each column summarizes its data inside each group.

You can group by one column, then nest by a second, third, and so on—producing multi-level groupings. For example: first by Region, then by Sales Rep, then by Product.

When a column is grouped, any other columns with an aggregation (e.g., Avg, Sum) will display their computed value inside the same row as the group label. If other columns (not used for grouping or aggregation) contain values, they are hidden in the group row to avoid confusion, as only the aggregation result is meaningful at that level.

Supported functions:

  • Sum: total of all values
  • Avg: average of values
  • Min: lowest value
  • Max: highest value
  • Count: total row count
  • Count distinct: number of unique values
Use case: Sum — Sales totals by region
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Sales Lead

KantoSoft — Sales Team

Goal: Understand total revenue per sales region.

Solution: Group by “Region” and apply Sum to the “Revenue” column.

Impact: Managers instantly see how each region performs.

Use case: Avg — Satisfaction score by department
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HR Manager

Midori Inc — People Team

Goal: Compare average satisfaction across teams.

Solution: Group by “Department” and apply Avg to the “Score” column.

Impact: Helps identify where to improve team morale.

Use case: Min — Fastest delivery time
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Logistics Ops

Nippon Express — Operations

Goal: Identify top-performing delivery routes.

Solution: Group by “Route” and apply Min to the “Delivery Time” column.

Impact: Reveals fastest delivery benchmarks.

Use case: Max — Largest contract per rep
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Deals Boss

KyotoTech — Sales

Goal: Showcase biggest wins per sales rep.

Solution: Group by “Rep” and apply Max to the “Contract Value” column.

Impact: Highlights top deal makers.

Use case: Count — Tickets per category
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Support Agent

ZenDeskura — Support

Goal: Count how many tickets exist per issue type.

Solution: Group by “Issue Type” and apply Count.

Impact: Informs resource allocation for support teams.

Use case: Count distinct — Unique contributors per repo
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Engineering Lead

NaraCode — DevOps

Goal: See how many developers contributed to each repo.

Solution: Group by “Repository” and apply Count distinct to the “Author” column.

Impact: Promotes knowledge sharing and team visibility.