python-tidytuesday
PydyTuesday is a Python-focused extension of the weekly TidyTuesday data science challenge, providing resources and guidance for exploring TidyTuesday datasets using Python instead of R. It helps Python users participate in the Data Science Learning Community’s weekly challenges by offering tutorials, setup guides, and examples for data exploration, visualization, modeling, and dashboard creation.
The repository solves the problem of Python users wanting to participate in TidyTuesday but lacking Python-specific resources and examples. It includes step-by-step video tutorials covering environment setup, library installation, data import workflows (particularly with Positron IDE), and deployment options. The project bridges the gap between R-centric TidyTuesday and the Python data science community, making the weekly challenges accessible to a broader audience.
Resources featuring python-tidytuesday#
Four steps for managing data teams | Toby Hall | 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 Toby Hall, Executive Vice President and CIO at Delta Dental of Michigan, to chat about all kinds of things, including: data science team management, AI governance and the ethical use of data in healthcare, insurance data types and use cases, and hiring for traits vs. skills in data science.
In this Hangout, among other topics, Toby mentions Delta Dental’s “guilty until proven innocent” approach to AI. This involves a two-stage approval process that critically examines use cases for ethical implications, data handling (ensuring data stays within dedicated tenants and doesn’t train public models), and tool appropriateness. The discussion also touches on the regulatory skepticism towards AI from departments of insurance and the broader environmental considerations of AI use.
Toby also gives loads of career advice, but our favorite was his four-step process for managing a data team:
- Put the right people in the right role
- Give them the right vision of what you’re trying to accomplish, ensuring it’s clear and everyone is aligned on “what that is and why we’re doing it”
- Give them the right culture to get it done
- Get out of their way!
Resources mentioned in the video and zoom chat: Posit Conference → https://posit.co/conference/ Delta Dental Tooth Fairy Poll → https://www.deltadental.com/us/en/tooth-fairy/the-original-poll.html TidyTuesday Data Repo (get your reps in!) → https://github.com/rfordatascience/tidytuesday Resources for doing TidyTuesday stuff in Python (PydyTuesday?) → https://github.com/posit-dev/python-tidytuesday
If you didn’t join live, one great discussion you missed from the zoom chat was about the Delta Dental Tooth Fairy data, including its outrageously expensive rates and the humorous debate around the inflation of self-reported flossing data. Let us know below if you’d like to hear more about this topic!
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00:00 Introduction 02:28 “Tell us a little bit about your day to day, but also the data teams at Delta Dental of Michigan, what is the type of data that they work with, and what is it that they solve with it, or what decisions are made based on it?” 06:04 “What about third party data?” 07:57 “How do you all decide which language to use and for what applications?” 10:37 “How do you make sure your AI pipeline is accurate in the SQL and the regulation because you are working with health health care data?” 23:22 “What are some tips for leaning into those challenge assignments as the IC, the individual contributor?” 23:31 “How do you identify the challenge assignments? Like, how do you know this is the right thing with the stakes that are right to give to somebody?” 25:54 “Do you share your data as an API or for research?” 36:45 “Coming from a core statistical background, how much would you expect from a fresh data science graduate about statistical knowledge if you were an interviewer for a modeling role? Any specific concepts that you might ask them about in the interview?” 38:17 “How do you make sure your team stays current with new data science tools or skills or methods?” 40:18 “Did you have a ranking of necessary roles that you wanted to hire first, like data engineer versus data scientist or analyst or maybe dedicated reporting staff? And what tools or platforms did you consider essential from day one?” 43:53 “How much of your background in teaching high school students has spilled over into managing your team?” 46:21 “Do you have any advice for how someone could maybe emphasize those traits on their resume?” 49:19 “Do you have a piece of career advice that has either really, really helped you or that you try to give to everybody?”
Open Source in Pharma | Harvey Lieberman | 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 Harvey Lieberman, Associate Director of Data Science at Novartis, to chat about R/Pharma, automating processes, career advice, and data science in drug discovery vs. development.
In this Hangout, Harvey talks about a lot of things, like the power of automating processes. He shares examples of how automating mundane tasks can save significant time and identify errors that humans might miss (we all know human error is a thing!). For instance, he automated the analysis of data from 48 Excel sheets that had previously taken a colleague about three months to process by hand; Harvey completed the automated analysis in one hour over lunch and found copying and pasting errors in the original manual process! Automating processes not only increases efficiency but can also help move people into more data-focused roles. Harvey suggests demonstrating that automation speeds things up and, most importantly, removes errors, which is when people start to pay attention and get interested.
Resources mentioned in the video and zoom chat: R/Pharma website → https://rinpharma.com/ Cecilia Baldoni’s scrollytelling project (on shrews!) → https://cecibaldoni.github.io/projects.html Advent of Code → https://adventofcode.com/ Pharmaverse.org (pharmaceutical R packages) → https://pharmaverse.org GSK’s Journey to R → https://www.youtube.com/watch?v=xDrt6txplek Roche’s Journey to R → https://www.youtube.com/watch?v=BlJNILSoZlM R/Pharma March 2025 newsletter (LinkedIn) → https://www.linkedin.com/pulse/rpharma-march-2025-newsletter-open-source-in-pharma-wmf5c/ ggplot2 extenders club → https://ggplot2-extenders.github.io/ggplot-extension-club/ Coursera: Making Data Science Work for Clinical Reporting Course → https://www.coursera.org/learn/making-data-science-work-for-clinical-reporting hiring.cafe (for finding R jobs) → https://hiring.cafe/ Posit’s PydyTuesday GitHub → https://github.com/posit-dev/python-tidytuesday Joy’s Law (management concept) Wikipedia → https://en.wikipedia.org/wiki/Joy%27s_law_(management)
If you didn’t join live, one great discussion you missed from the zoom chat was about the diverse backgrounds of attendees. Many participants shared that they came to data science “sideways,” holding degrees in fields such as sociology, psychology, mathematics, atmospheric science, education, history, chemistry, and various engineering disciplines, rather than traditional statistics or computational degrees. so many data scientists have non-traditional paths into the field! But we’re all better together.
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Thanks for hanging out with us!
People Analytics at Pinterest | Trevor Fry | 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 Trevor Fry, Lead Data Analyst at Pinterest, to chat about people analytics, bridging the data science practice gap, the future of the field including AI, and measuring the value of data teams. We also celebrated a birthday
In this Hangout, we explore bridging the data science practice gap—the challenge of effectively translating research findings and data insights back to business leaders and stakeholders. Trevor discusses how the nature of people data is particularly sensitive, requiring careful handling, and emphasizes the importance of adapting your communication and language to your specific audience. He also shares insights into the future of people analytics, especially the potential of AI and LLMs for analyzing unstructured data like survey comments, while noting the challenges of ensuring reliability for scientific applications. Trevor also touched upon the difficulty of measuring the return on investment (ROI) for people analytics functions and discussed common analytical methods used in the field, such as Structural Equation Modeling (SEM) and relative weight analysis (RWA).
Resources mentioned in the video and zoom chat: Storytelling with Data by Cole Nussbaumer Knaflic → https://www.storytellingwithdata.com/ Society of Industrial Organizational Psychology (SIOP) → https://www.siop.org/events/the-annual-conference/ Relative Weight Analysis (RWA) R package → https://martinctc.github.io/rwa/ People Analytics: Regression Modeling by Keith McNulty → https://peopleanalytics-regression-book.org/ Structural Equation Modeling (SEM) info → https://stats.oarc.ucla.edu/r/seminars/rsem/ Ollama (local LLMs) → https://ollama.com/ Posit blog: Secure AI-Assisted data science in R with Posit and Snowflake → https://posit.co/blog/ai-assisted-ai-posit-snowflake-cortex/ TidyTuesday GitHub repo → https://github.com/rfordatascience/tidytuesday TidyTuesday in Python GitHub repo → https://github.com/posit-dev/python-tidytuesday Forecasting: Principles and Practice (Python version) → https://otexts.com/fpp3/ (R version also mentioned) Causal Inference: The Mixtape by Scott Cunningham → https://mixtape.scunning.com/ Text Analysis using Quanteda (R package) YouTube video → https://www.youtube.com/watch?v=tf5FmXiwEQE
If you didn’t join live, one great discussion you missed from the zoom chat was about the definition of a “data scientist” and whether individuals feel like they are “really” data scientists, highlighting the varied backgrounds and nonlinear paths in the field. If you have any imposter syndrome around data work, you’re not alone!
► 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!
Projects to build your Python portfolio #pydytuesday #pythonprojects #datascience #dataanalysis
Github repo: https://github.com/posit-dev/python-tidytuesday #positshorts
Create a PydyTuesday Shiny app with Shiny Assistant
Watch Sara Altman quickly prototype a Shiny app for PydyTuesday with Shiny Assistant, an AI-powered tool designed to help you with Shiny. Shiny Assistant can create Shiny apps from scratch, debug or assist with existing apps, and answer general questions about Shiny.
Shiny Assistant: https://gallery.shinyapps.io/assistant/
Learn more:
- Shiny for Python: https://shiny.posit.co/py/
- The potential for AI-powered Shiny app prototyping with Shiny Assistant: https://posit.co/blog/ai-powered-shiny-app-prototyping/
- TidyTuesday project: https://github.com/rfordatascience/tidytuesday
- Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday
- Other videos in this PydyTuesday playlist: https://youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9&si=i09_CuKmjiV86D-9
#pythoncontent
Shiny Assistant - Prototype Shiny for Python Apps with AI
Ryan Johnson walks through how to use Shiny Assistant, an AI-powered tool for Shiny for Python app development. Learn how to quickly create and interact with a Shiny app based on your own description. Shiny Assistant is a powerful tool for rapid prototyping and iteration. You can also ask it questions about how to use Shiny.
Shiny Assistant: https://gallery.shinyapps.io/assistant/
Learn more:
- Shiny for Python: https://shiny.posit.co/py/
- The potential for AI-powered Shiny app prototyping with Shiny Assistant: https://posit.co/blog/ai-powered-shiny-app-prototyping/
- TidyTuesday project: https://github.com/rfordatascience/tidytuesday
- Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday
- Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
Deploy your work to Posit Connect Cloud
Deploy your data science content with Posit Connect Cloud! Daniel Chen walks you through deploying a Shiny for Python app to Connect Cloud, sharing your content, and updating it via GitHub.
While this video focuses on Shiny for Python, Posit Connect Cloud also supports Quarto, Streamlit, Jupyter, and more!
Use Posit Connect Cloud to share your PydyTuesday work with the world. Learn more here: https://github.com/posit-dev/python-tidytuesday .
Resources:
- Shiny app code: https://github.com/posit-dev/py-shiny-templates/tree/main/dashboard
- Posit Connect Cloud: https://connect.posit.cloud/
- Shiny for Python: https://shiny.posit.co/py/
- Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
PydyTuesday | Getting Data from the TidyTuesday Repo with Python
Let’s navigate the TidyTuesday repo and download some data! Libby Heeren walks you through what TidyTuesday is, how to navigate the GitHub repo, and how to pull in and use the data in Python using Positron. We hope you join us in participating in PydyTuesday!
Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create
Note: This video shows the pydytuesday package as “PyDyTuesday” - this was an early naming convention and has since been updated to just “pydytuesday”
Resources and Repos to star:
- TidyTuesday GitHub Repo: https://github.com/rfordatascience/tidytuesday
- Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-tidytuesday
- TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday
- Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
Create Quarto dashboards with Python
Learn how to create Quarto dashboards with Python! Senior Product Marketing Manager Isabella Velásquez walks through building a Quarto dashboard from scratch, using water insecurity data from the TidyTuesday project. You’ll learn how to structure a Quarto document, preview dashboards, and customize layouts and themes. See Isabella’s code here: https://github.com/ivelasq/water-insecurity-dashboard And the dashboard here: https://ivelasq-water-insecurity-dashboard.share.connect.posit.cloud/
Resources:
Quarto dashboards guide: https://quarto.org/docs/dashboards/ Posit Connect Cloud: connect.posit.cloud Hello, Dashboards!: https://www.youtube.com/watch?v=HW7QbqI4fH0&t=590s TidyTuesday project: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
TidyTuesday + Posit | PydyTuesday | Weekly Community Python Data Project
Posit software engineer Isabel Zimmerman discusses the TidyTuesday project and the Posit PydyTuesday Initiative. Learn how to participate in weekly TidyTuesday projects, watch Isabel explore Central Park squirrel data, and discover how to deploy your work to Posit Connect Cloud. Find her code here: https://github.com/isabelizimm/pydy-tuesday
Check out these repositories to join the TidyTuesday and the Posit PydyTuesday Initiative:
TidyTuesday repo with datasets: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday repo: https://github.com/posit-dev/python-tidytuesday
Learn more about Quarto and Connect Cloud:
Quarto website: https://quarto.org/ Posit Connect Cloud: https://connect.posit.cloud/ Other videos in this Posit PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9
#pythoncontent
