---
title: Split currencies
description: Separate amount and currency into individual columns from a combined value.
category: Data Transformation
tags: [split, currency, transform, column, amount]
---

# Split currencies

## Description

The **Split Currencies** activity processes columns that contain combined monetary values (e.g., `"75000 USD"`, `"85000 ₹"`) and separates them into two distinct columns: one for the **numeric amount** and one for the **currency symbol or code**.

This transformation is essential when analyzing or converting financial values, enabling downstream processes like currency conversion, aggregation, or filtering by currency type.

> **Use case**:  
> HR datasets containing `salary` fields such as `"75000 USD"` or `"72000 Rs"` can be normalized into separate `Amount` and `Currency` columns for reporting or standardization.

## Input

| Type     | Description                                                                       |
| -------- | --------------------------------------------------------------------------------- |
| **Data** | A dataset with at least one column containing combined numeric and currency text. |

## Output

| Type     | Description                                                                 |
| -------- | --------------------------------------------------------------------------- |
| **Data** | Transformed dataset with extracted amount and currency in separate columns. |

## Configuration Fields

| Field Name           | Description                                                                                                                                                                                           |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Column Name**      | Select the column that contains combined amount and currency values (e.g., `"85000 USD"`).                                                                                                            |
| **Include Original** | Determines whether the original column is retained in the output. <ul><li>**Enabled** – Keeps the original column.</li><li>**Disabled** – Only returns the new amount and currency columns.</li></ul> |

## Sample Input

| employee_id | name           | salary    |
| ----------- | -------------- | --------- |
| E001        | John Doe       | 75000 USD |
| E002        | Jane Smith     | 85000 $   |
| E003        | Ali Khan       | 90000 AED |
| E004        | Maria Gonzalez | 68000 USD |
| E005        | Rahul Sharma   | 72000 Rs  |

## Sample Configuration

| Field            | Value   |
| ---------------- | ------- |
| Column Name      | salary  |
| Include Original | Enabled |

## Sample Output

| employee_id | name           | salary    | salary_Amount | salary_Currency |
| ----------- | -------------- | --------- | ------------- | --------------- |
| E001        | John Doe       | 75000 USD | 75000         | USD             |
| E002        | Jane Smith     | 85000 $   | 85000         | $               |
| E003        | Ali Khan       | 90000 AED | 90000         | AED             |
| E004        | Maria Gonzalez | 68000 USD | 68000         | USD             |
| E005        | Rahul Sharma   | 72000 Rs  | 72000         | Rs              |

> The transformed data clearly separates monetary values, enabling easier processing, filtering, and reporting based on currency or amount.