Code Studio

Code Studio is a Zoho Analytics feature that enables users to write custom Python code for advanced data analysis. It provides a Python environment with pre-loaded libraries to build and deploy scripts for data modeling, preparation, and analysis.

Code Studio empowers you to go beyond default capabilities by authoring custom Python scripts for their unique data preparation, transformation, enrichment, and analytical modeling needs.

Pre-requisites

The Code Studio requires a minimum of 1GB of RAM to execute basic data operations like transformations, enhancements, and enrichments. It can process up to 1 million rows of data.

The higher RAM options are also available, including 512MB, 1GB, and 2GB, which support larger datasets and more complex scripts.

If you need to analyze larger datasets or require more advanced customization beyond these limits, please contact the Zoho Analytics support team at support@zohoanalytics.com to explore extending capabilities further aligned to your use case requirements.

Using Code Studio

The Code Studio supports Python 3.9 version of Python Editor. Using this editor, we can create and deploy code to transform, enrich, filter, reshape, and manage data, etc.,

The Code Studio in Zoho Analytics comes with two default Python libraries - Numpy and Pandas, to enable data analysis capabilities.

  • Numpy - It provides support for large multi-dimensional arrays and matrices along with mathematical and statistical functions to transform and compute the dataset values.
  • Pandas - It offers easy-to-use data structures and data manipulation tools to clean, analyze, and process data. It can be used for data preparation functions like joining, reshaping, cleaning, normalization, and more to convert raw data into meaningful analytics-ready datasets.

To use Code Studio,

  • Open any workspace, and click + Create from the left navigation bar.
  • Click Code/ML Model from the Create New Code / ML Model section.
  • In the Code Studio page that opens, you can enter the Python script in the code Editor section.
  • You can script for your specific data needs, leveraging Python's flexibility for custom modeling, visualization, and advanced analytics.
  • Click Save to save the Python script.
  • Execute your script in Code Studio by clicking the Test Run button at the top to visualize the output.
  • Below the Editor section, there are two tabs - Console and Output.
    • Console - A console is a text-based interface that is used for debugging and troubleshooting.
    • Output - A preview table is created in the form of a sample response for the executed script. 
  • Once you have finalized the script, click Deploy at the top right corner to create and save the new data table based on the script.
  • You choose the Deploy & Schedule option from the Deploy dropdown to create a new data table and schedule data sync to it at scheduled intervals.
  • You can schedule data sync at the following intervals:
    • Daily
    • Hourly - 3, 6, 12 hour intervals
  • You can create reports over the data tables created by deploying the Python script.