Great Tables

Make awesome display tables using Python

Python

Great Tables is a Python package for creating publication-quality tables from Pandas or Polars DataFrames. It provides a composable system of table components (headers, footers, stubs, spanners, column labels) that you can mix and match to build display tables for notebooks, Quarto documents, or HTML/image output.

The package emphasizes simplicity for common tasks while offering power for complex formatting needs. It includes built-in methods for formatting currency, dates, and numbers, along with extensive customization options for styling and layout. The package includes 16 sample datasets for testing and learning, and it’s designed specifically for display tables rather than interactive data exploration.

Contributors#

Events featuring Great Tables#

Resources featuring Great Tables#

Great Tables 1: Structure, Format, and Style

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 2: Introducing Units Notation

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables 3: Data Color and Polishing

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: 'How could I do that?' Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He's been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

We want GREAT tables! | Richard Iannone & Michael Chow | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Rich Iannone and Michael Chow, software engineers at Posit, to chat about their experiences building GT and Great Tables, how they drive community engagement around their packages, and their career advice for package developers. GT and Great Tables are R and Python packages for creating static tables in R and Python. They were created to fill a need for a good, maintained solution for generating tables with different output types from data frames. They've gained loads of popularity, and have an active community of users! Resources mentioned in the video and zoom chat: Great Tables Blog → https://posit-dev.github.io/great-tables/blog/ 2024 Table Contest Winners → https://posit.co/blog/2024-table-contest-winners/ Contributing to Public Transit Data Analysis and Tooling → https://posit-dev.github.io/great-tables/blog/open-transit-tools/ Tables as Powerful Representational Tools → https://www.researchgate.net/publication/363345970_Tables_as_Powerful_Representational_Tools The MockUp - 10+ Guidelines for Better Tables in R → https://themockup.blog/posts/2021-01-13-10-guidelines-for-better-tables-in-r/ Show Me the Numbers by Stephen Few → https://analyticspress.com/smtn.php Excel spreadsheets to gt package called {forgts} → https://github.com/luisDVA/forgts What They Forgot to Teach You About R → https://rstats.wtf/ David Robinson Talk called "The unreasonable effectiveness of public work" → https://www.youtube.com/watch?v=th79W4rv67g Publication quality tables in 2024 → https://posit.co/blog/what-we-did-with-publication-quality-tables-in-2024/ Great Tables Design Philosophy → https://posit-dev.github.io/great-tables/blog/design-philosophy/ gtsummary-to-excel → https://www.pipinghotdata.com/posts/2024-07-26-gtsummary-to-excel/ happy git with R → https://happygitwithr.com/ freeCodeCamp - how to contribute to open source → https://github.com/freeCodeCamp/how-to-contribute-to-open-source/ If you didn’t join live, one great discussion you missed from the zoom chat was about how people manage their personal and work GitHub accounts, and whether to have one account for all their work or separate accounts for each employer. Let us know YOUR thoughts on this topic below! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! #pythoncontent

Michael Chow, Rich Iannone

Polars, pandas, and Narwhals, oh my! | Marco Gorelli | Data Science Hangout

Welcome back to the Data Science Hangout! This week, we're joined by Marco Gorelli, a senior software engineer at Quansight Labs and a core developer of Pandas, Polars, and the author of Narwhals. Marco shares his insights on the evolving landscape of data frame libraries in Python, focusing on Polars, its advantages over Pandas, and the role of Narwhals in creating data frame agnostic tools. Marco discusses his journey into open source, highlighting the importance of finding your niche and contributing to areas that others might find "boring" but are essential for a project's success. He also emphasizes the significance of fostering welcoming and inclusive open source communities. This episode explores several key topics: Polars: We learn about Polars' key features like lazy execution, its performance benefits, and the power of its expressions API. Marco explains how lazy execution can lead to significant performance gains and answers questions about its relevance even when working with smaller datasets. Narwhals: We discover how Narwhals enables developers to write tools that work seamlessly with various data frame libraries, promoting interoperability and simplifying development workflows. Open Source Challenges: Marco addresses the challenges of maintaining work-life balance while being deeply involved in open-source projects. He offers practical advice on prioritization and managing the constant influx of tasks and requests. Resources mentioned in the chat: Pandas: https://pandas.pydata.org/ Polars: https://pola.rs/ Narwhals: https://narwhals-dev.github.io/narwhals/ Awesome Polars: https://github.com/ddotta/awesome-polars Ibis: https://ibis-project.org/ Polars Plugins Tutorial: https://marcogorelli.github.io/polars-plugins-tutorial/ Understanding Polars Expressions: https://www.youtube.com/watch?v=E7cHgN9rd9c Great Tables: https://posit-dev.github.io/great-tables/articles/intro.html Great Tables Blog - Polars Styling: https://posit-dev.github.io/great-tables/blog/polars-styling/ Great Tables Blog - BYODF: https://posit-dev.github.io/great-tables/blog/bring-your-own-df/ Ruff: https://astral.sh/ruff UV: https://docs.astral.sh/uv/ R/Pharma 2024: https://rinpharma.com/ PyladiesCon: https://conference.pyladies.com/ Beeminder: https://www.beeminder.com/ Todoist: https://todoist.com/ Nyctography: https://en.wikipedia.org/wiki/Nyctography This episode offers valuable insights for anyone working with data in Python, particularly those interested in exploring the benefits of Polars and the power of Narwhals for building data-frame-agnostic tools. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We'd love to see you!) Thanks for hanging out with us!

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It's nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of 'display tables' in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We'll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by "publication ready tables" 1:29 Overview of what we'll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07 .fmt_nanoplot(): Small plots within table cells 19:08 .data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

Build Captivating Display Tables in Python With Great Tables | Real Python Podcast #214

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library. Links from the show: https://realpython.com/podcasts/rpp/214/ Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library. We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday. This episode is sponsored by Mailtrap. Topics: - 00:00:00 -- Introduction - 00:02:00 -- Michael's background in open source - 00:04:07 -- Rich's background in open source - 00:05:27 -- Advice for someone starting out - 00:08:55 -- What do you mean by the term "display" table - 00:11:32 -- What components were missing from other tables? - 00:13:31 -- Using examples to explain features - 00:16:09 -- Why was there an absence of this functionality in Python? - 00:19:35 -- A progressive approach and the grammar of tables - 00:21:26 -- Sponsor: Mailtrap - 00:22:01 -- The design philosophy of great tables - 00:25:31 -- Nanoplots, spark lines, and column spanners - 00:27:06 -- Building a gallery of examples - 00:28:56 -- Heat mapping cells and automatically adjusting text color - 00:32:54 -- Output formats for the tables - 00:34:46 -- Building in accessibility - 00:36:55 -- Dependencies - 00:37:42 -- What is the common workflow? - 00:41:39 -- Video Course Spotlight - 00:43:15 -- Adding graphics - 00:46:41 -- Using a table contest to get examples - 00:49:47 -- quartodoc and documenting the project - 00:55:00 -- Tidy Tuesday and data science community - 01:00:29 -- What are you excited about in the world of Python? - 01:03:46 -- What do you want to learn next? - 01:08:05 -- How can people follow the work you do online? - 01:09:57 -- Thanks and goodbye Links from the show: https://realpython.com/podcasts/rpp/214/

Michael Chow

Great Tables

Contributors#

Rich Iannone

Michael Chow

Jules Walzer-Goldfeld

Isabel Zimmerman

Carlos Scheidegger

Events featuring Great Tables#

R/Pharma 2025

Resources featuring Great Tables#

Great Tables

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration

Michael Chow: From psychology and Python to constrained creativity

Making Things Nice in Python (Rich Iannone, Posit) | posit::conf(2025)

Exploring Web APIs | PydyTuesday Uncut #1

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

Great Tables 1: Structure, Format, and Style

Great Tables 2: Introducing Units Notation

Great Tables 3: Data Color and Polishing

Tables in Python with Great Tables

Build a Python table in under 1 minute using Great Tables

We want GREAT tables! | Richard Iannone & Michael Chow | Data Science Hangout

Polars, pandas, and Narwhals, oh my! | Marco Gorelli | Data Science Hangout

Richard Iannone - Adequate Tables? No, We Want Great Tables

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Richard Iannone - Great Tables for Everyone | SciPy 2024

Build Captivating Display Tables in Python With Great Tables | Real Python Podcast #214