Data transformation using Code Studio in Zoho DataPrep involves manipulating and preparing raw data for analysis in a structured data pipeline. By writing custom code, you can clean, transform, prepare, enrich and restructure data to suit your needs. This process allows you to integrate data from multiple sources, handle missing values, create new calculated fields, and apply complex logic to enhance data quality. For example, consider CRM data flowing in from various sources—we can consolidate and organize this data efficiently to generate more accurate insights.

1. Open an existing pipeline or create a new one and import data into it. This creates a stage for the data source. Right-click the stage and choose the Add Code Stage option.

2. This creates a code stage and a default output stage. Right-click the code stage to view the following options:

Edit: Opens the Code Studio Editor to modify the code.

Delete – Removes the code stage from the pipeline. You can click the  undo icon to revert the changes if needed or if it was deleted by mistake.

Add Code Input stage – This option highlights the code stage and you can connect any stage to the code stage title by clicking and dragging the arrow to add as input data to your code.

Add Code Output stage – Adds an additional output stage connected to the code stage.

3. The Code Studio Editor page will open with a sample code in it.

Code Editor: The central section of the Code Studio Editor is a dedicated Python environment for writing, editing, and executing code to clean and transform data. 

Library: Located on the left side of the Code Editor, this section includes default libraries. You may also upload files and libraries into Code Studio apart from the default libraries.Click the + Add icon at the top to create a new file, upload a file, or add a library. Library acts as a file directory for all the folders and its files. 

4. Enter the Python script into the Code Studio Editor to transform and prepare your data. Make sure you give a name and save the script.

5. Once completed, click the Test Run at the top to validate the script. The transforms will be applied on the first 100 rows of data.

6. You can check the results of the Test Run in the Output and logs in the Console sections at the bottom. 

Console: A text-based interface used for debugging and troubleshooting. Any errors in the script will be displayed here. You can fix them and execute a test run again.

Output: Displays a preview table for the executed script.