Seeq Data Lab

Python 3.8 has been removed on October 7, 2025

The Python 3.8 kernel has been removed from Data Lab on October 7, 2025. Any notebooks previously saved with the Python 3.8 kernel will now automatically use the Python 3.11 kernel by default.
For any compatibility issues, please see the Data Lab Python 3.11 Compatibility Guide for more information.

What is Seeq Data Lab?

Seeq Data Lab is a service that brings together Jupyter notebooks, which is a web application that combines a Python REPL and Markdown, with the Seeq API. This enables users to create Python-based Notebooks and use the custom SPy library (see documentation) that we distribute, to push, pull and manipulate data using Seeq and then develop reports and visualizations using that data.

Using Data Lab

Navigating Data Lab

Integrating Git

Enabling R Language Support

Python Versions and Packages

Troubleshooting Data Lab

Best Practices for Data Lab Performance

Data Lab Management

FAQ: When to Use a Seeq Data Lab Project vs. External Platforms

Frequently Asked Questions

Why use Seeq Data Lab vs. Seeq Workbench/Organizer?

Seeq Data Lab offers the ability to automate work ordinarily done in Seeq Workbench/Organizer. This could include scaling an analysis across a fleet of similar assets or creating dashboards for an entire unit, as examples. It also enables unique ways of visualizing your data – such as Radar Charts or Box-and-Whisker plots – that are not otherwise available in Seeq Workbench.

When should I use a dedicated ML-optimized platform (such as Azure ML or SageMaker) instead of Seeq Data Lab?

Seeq Data Lab is best suited for general purpose computing, including:

data visualization
workflow automation
modest data science applications
Seeq-embeddable interactive UI applications

Seeq Data Lab does not compete with ML-optimized platforms, which are the best fit for data science applications involving:

high performance and distributed compute management
management and versioning of datasets, models, and source code
modeling of hundreds of calculated features and years of data

In these cases, SPy is still recommended for usage in external platforms for easy data access and/or operationalization back to Seeq.

Can I use the Seeq Python (SPy) library outside of Seeq Data Lab?

You can use SPy without using it directly in Data Lab, but you will still need a Data Lab license. The SPy library may be imported and used in any project that supports Python. You could use SPy inside AWS Sagemaker, Azure Machine Learning, any other Jupyter Notebook-based development environment that supports Python, or any other Python environment.

If you want to use Add-on Tools in Seeq, you will also need a Data Lab license.

What other Python libraries can I use in Seeq Data Lab?

Any and all libraries accessible through the Seeq Data Lab Server (web or company repository) and approved in your organization’s environment.