Overview

For some Data Tables destined to display a number of general properties, it is sometimes hard to drill down and focus on wanted information. That is where the Pivot Table Processor comes in handy. It transforms input into a pivot table grouped by one or many columns, pivoting another column.

Input

The Processor requires an input table with at least two columns. The first column should be displayed as rows in the pivot output table, the second column should be displayed as columns in the pivot output table. 

An accurate functionality of the rocessor would need a third numerical column for aggregation. This should fill in the table cells.

Configuration

Output

The Processor provides two different outputs:

• SQL Compatible Pivot Table: The desired output pivot table, displayed as configured in the processor.
• Mapping: Distinct value -> Column name: A table mapping the columns in the output to the columns in the pivot table.

Besides that, the result tab in the Processor configuration delivers an output preview similar the the SQL Compatible Pivot Table output.

Examples

In the following example, we use a dataset of music CDs inventory. The dataset includes both nominal column for grouping and numerical columns for aggregation.

Example Input

Workflow

Example Configuration

Result

SQL Compatible Pivot Table

The selected column for rows (Band) is displayed and grouped in the first column (one row for each entry).
The selected column for columns (Quarter) is displayed as multiple columns for the pivot table (one column for each entry).

The sales are therefore aggregated by SUM and affected to the corresponding Quarter.

Mapping

This table maps the column name displayed in the first output with the original entry name.