---
title: Pivot columns
description: Transform rows into columns by pivoting label values and summarizing data using a selected value column.
category: Data Transformation
tags: [pivot, transpose, reshape, table, columns]
---

# Pivot columns

## Description

The **Pivot Columns** activity restructures tabular data by converting unique values in a column into new columns, effectively transposing selected rows into columns. This transformation is useful when converting long-format data into wide-format, making it easier to visualize and analyze grouped or categorized datasets.

It creates a matrix where:

- Each unique value in the **Index Column** becomes a row
- Each unique value in the **Labels Column** becomes a new column
- The cell values come from the **Values Column**

Use this activity to:

- Convert transactional or long-form data into cross-tab reports
- Generate summaries where categories are converted into columns
- Restructure datasets for dashboarding or pivot-based analytics

> **Use case**:
> A retail company wants to analyze product prices across departments. Using Pivot Columns, they can convert individual product rows into columns under each department, making comparisons easier.

## Input

| Type     | Status   | Description                                                                                                              |
| -------- | -------- | ------------------------------------------------------------------------------------------------------------------------ |
| **Data** | Required | A table with at least three columns: one for row identifiers (index), one for labels (column names), and one for values. |

## Output

| Output Type | Format | Description                                                        |
| ----------- | ------ | ------------------------------------------------------------------ |
| **Data**    | Table  | Transformed table with rows as index values and columns as labels. |

## Configuration Fields

| Field Name        | Description                                                                                                                                      |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Index Column**  | The column whose unique values will be used to create the output rows. A new row is generated for each unique index value.                       |
| **Labels Column** | The column whose unique values will become column headers in the output. Each distinct value in this column becomes a column in the result.      |
| **Values Column** | The column that provides the values to fill in the pivoted table. If multiple entries exist for the same index-label pair, the last one is used. |

> If multiple rows have the same combination of index and label, only the last one is retained in the output.

## Sample Input

| id  | category    | product | price | in_stock |
| --- | ----------- | ------- | ----- | -------- |
| 1   | Electronics | Laptop  | 500   | true     |
| 2   | Electronics | Camera  | 350   | false    |
| 3   | Electronics | Tablet  | 250   | true     |
| 4   | Furniture   | Sofa    | 600   | true     |
| 5   | Furniture   | Bed     | 450   | true     |
| 6   | Furniture   | Table   | 150   | false    |

> In this example, we want to create one row per `category`, one column per `product`, and fill the table with `price` values.

## Sample Configuration

| Field         | Value    |
| ------------- | -------- |
| Index Column  | category |
| Labels Column | product  |
| Values Column | price    |

## Sample Output

| category    | Laptop | Camera | Tablet | Sofa | Bed | Table |
| ----------- | ------ | ------ | ------ | ---- | --- | ----- |
| Electronics | 500    | 350    | 250    |      |     |       |
| Furniture   |        |        |        | 600  | 450 | 150   |