# PIVOT
Transforms rows into columns, creating a cross-tabulation summary of data.
## Syntax
```basic
result = PIVOT data, row_column, column_column, value_column
result = PIVOT data, row_column, column_column, value_column, aggregate
```
## Parameters
| Parameter | Type | Description |
|-----------|------|-------------|
| `data` | Array | The data array to pivot |
| `row_column` | String | Column to use for row headers |
| `column_column` | String | Column to use for column headers |
| `value_column` | String | Column containing values to aggregate |
| `aggregate` | String | Aggregate function: SUM, AVG, COUNT, MIN, MAX (default: SUM) |
## Description
`PIVOT` reorganizes data from a normalized format into a cross-tabulation format, making it easier to compare values across two dimensions. This is useful for creating summary reports and dashboards.
## Examples
### Basic Pivot
```basic
sales = FIND "sales", "year=2025"
summary = PIVOT sales, "region", "quarter", "amount"
' Result: regions as rows, quarters as columns
' Shows total sales for each region/quarter combination
```
### With Aggregate Function
```basic
orders = FIND "orders", "status=completed"
avg_order = PIVOT orders, "product", "month", "total", "AVG"
FOR EACH row IN avg_order
TALK row.product + ": Q1=$" + row.Q1 + ", Q2=$" + row.Q2
NEXT
```
### Sales by Region and Product
```basic
data = FIND "sales", "year=2025"
pivot_table = PIVOT data, "region", "product", "revenue", "SUM"
TALK "Revenue by Region and Product:"
FOR EACH region IN pivot_table
TALK region.row_header + ":"
TALK " Widgets: $" + region.Widgets
TALK " Gadgets: $" + region.Gadgets
NEXT
```
## Return Value
Returns an array of objects where:
- Each object represents a row
- `row_header` contains the row label
- Dynamic properties contain pivoted column values
## Sample Conversation
Show quarterly sales by region
10:15
📊 Quarterly Sales Report 2025
Region | Q1 | Q2 | Q3 | Q4
━━━━━━━━━━━━━━━━━━━━━━━━
North | $125K | $142K | $138K | $167K
South | $98K | $105K | $112K | $128K
East | $87K | $92K | $95K | $103K
West | $156K | $168K | $175K | $189K
10:15
## Use Cases
| Scenario | Row | Column | Value |
|----------|-----|--------|-------|
| Sales dashboard | Region | Quarter | Revenue |
| Attendance report | Employee | Month | Days |
| Product comparison | Product | Store | Units sold |
| Time tracking | Project | Week | Hours |
## See Also
- [GROUP BY](./keyword-group-by.md) - Group data by columns
- [AGGREGATE](./keyword-aggregate.md) - Calculate summary values
- [TABLE](./keyword-table.md) - Display formatted tables
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