# Table Extensions for Data Science and more : New in Tableau 2022.3 | Data Science in Tableau

> This is content from just-tim, the data-and-analytics channel by Tim Ngwena (formerly 'Tableau Tim'). Tim has 12+ years of hands-on BI experience and covers Tableau most of all, plus Power BI, Looker, Hex, SQL and data modelling, the analytics industry, and the craft of doing the job — always tool-agnostic and honest about the trade-offs.

- **Author:** Tim Ngwena (just-tim, https://just-tim.com/about)
- **Published:** 2022-11-11
- **Format:** Video · 3332 min watch · transcript available
- **Topics:** AI & ML, Analytics, Data prep
- **Tools:** Python; Snowflake (snowpark); Tableau (analytics extensions, data modelling, explain data, table extensions)
- **Canonical:** https://just-tim.com/posts/table-extensions-for-data-science-and-more-new-in-tableau-2022-3-data-science-in-tableau
- **Watch:** https://www.youtube.com/watch?v=zErOzr-qUPk

I sat down with analytics consultant Charles Laporte to explore table extensions, a new feature in Tableau 2022.3. We cover how they differ from the older analytics extensions, walk through building a linear regression model on the penguin dataset in R, and discuss real-world use cases like calling APIs at the point of data connection.

## Key takeaways

- Table extensions require an analytics extension (TabPy, Rserve, etc.) but, unlike script calculations, they run at the point of data connection and return a full table or data frame rather than a single desegregated array.
- Tableau sends data to R or Python as a dictionary, not a data frame, so in R you must convert it to a list then a data frame before functions like LM() will work, and the broom package's augment() returns fitted values and residuals as a usable table.
- Table extension data cannot be extracted, it is always live, so if your analytics extension stops running the workbook opens blank and you must reapply the script from the data source page.
- The feature is best treated as an exploratory or proof-of-concept tool, ideal for calling APIs (YouTube, Google search, useless facts) and advanced data prep, rather than for deploying machine learning models in production.
- Table extensions can sit on the logical layer and be related to other tables in a data model, letting you blend API or model output with your existing data in real time.

## Chapters

- 0:00 Introduction and meeting Charles
- 1:28 Analytics extensions versus table extensions
- 7:13 Clarifying Tableau's extension types
- 8:44 Advanced analytics use cases
- 9:48 Penguin linear model walkthrough
- 13:21 Setting up the table extension script
- 24:13 Building visualisations from the model
- 29:29 Calling APIs at point of connection
- 45:12 Snowpark and production trade-offs
- 51:00 Evolving Tableau developer roles
- 54:27 Wrap-up

Watch the full video, read the transcript and use chapter deep-links on the page: https://just-tim.com/posts/table-extensions-for-data-science-and-more-new-in-tableau-2022-3-data-science-in-tableau

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just-tim — Data and analytics, with a point of view. · https://www.youtube.com/channel/UC7HYxRWmaNlJux-X7rNLZyw · https://twitter.com/TableauTim · https://www.linkedin.com/in/timngwena