Posit PBC

Join Joe Cheng as he dives into new functionality to support long-running background operations with your Shiny applications (R or Python). Introducing ExtendedTask. YouTube: https://buff.ly/3yvbGVr It’s been years since Shiny evolved to allow asynchronous operations within applications, improving scalability. The introduction of the {promises} package enabled concurrent processing between multiple Shiny sessions, a significant step forward in handling background tasks. However, this did not address the need for intra-session concurrency, where users expect to interact with the application while long-running calculations are executed in the background. Recently, the Shiny team added a new ExtendedTask feature to Shiny to allow for such intra-session concurrency. This new feature provides a different approach for developers to incorporate asynchronous tasks, enabling smoother user interactions during intensive computations. Alongside ExtendedTask, this talk will also discuss newer methods for launching asynchronous tasks, besides the usual {future} package. The focus will be on the practical application and integration of these features into Shiny applications. ExtendedTask with R Shiny: - Introducing ExtendedTask: https://buff.ly/3WW3yr3 - Task or computation that proceeds in the background: https://buff.ly/3KcrFKK ExtendedTask with Shiny for Python: - True non-blocking behavior with ExtendedTask: https://buff.ly/3wPWt0I

Great News! The table contest deadline has been extended to June 14, 2024. The Table Contest is a great way to show off your data storytelling skills with the community. 𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻 - We re-announced the table contest at PyCon last week, and wanted to give everyone an extra couple of weeks. 𝗪𝗵𝘆 𝗽𝗮𝗿𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗲? 𝗧𝗮𝗯𝗹𝗲𝘀 𝗮𝗿𝗲 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 - Data display tables are central to summarizing and communicating insights into data and our analytics. But compared to data visualizations and novel methodologies, the design and construction of great tables receives shockingly little attention. - Do you have a table you've always wanted to create & share? - Is there a great table you've always wanted to reproduce? - Do you have a favorite Python, R, JS, or other package for tables you want to provide a tutorial on? - Do you have table tricks & tips that others may benefit from? Please consider this your call to action! ⬡ Deadline for submissions is now June 14, 2024. ⬡ Submit at pos.it/table-contest ⬡ Need a place to host your table? Here’s a video on how to create a table in R or Python with Quarto and share it free on Quarto Pub; https://lnkd.in/g3TamvYs https://lnkd.in/gAcxpSqz

I've been enjoying playing with and visualizing baseball data with plotnine. And having learned that the plotnine package in Python is being developed by Posit PBC, I feel great about continuing to upskill in my ggplot skills! #python #datavisualization #ggplot2 #plotnine

Are you a Python user and into visualization? Are you a {ggplot2} user and want to dabble in Python? Give {plotnine} a chance and participate in the contest sponsored by Posit PBC ✌ {plotnine} is a Python package that attempts to bring {ggplot2} functionality and philosophy to Python users. I have created a comparison of the two on my blog: https://lnkd.in/edDbTwqA Why participate in the contest? There are fun prizes, the top examples will be included in Hassan Kibirige's docs website gallery & other docs. In addition, it's a lovely way to learn and share your {plotnine} tips and tricks and help expand the package's community of users 🤝 The deadline for submissions is July 12, 2024, more information is at the contest website: https://lnkd.in/eYKfmj2C I already have an idea for a submission and I'm curious to see whether {plotnine} offers all the capabilities that I need to implement my vision 🤔 #python #dataviz

Python will never be as good as R for data visualization. Prove me wrong. Show me what you've got here: https://lnkd.in/eg_GgHYE Curtis Kephart

TIL about the plotnine library- the grammar of graphics in Python 🚀 I had never heard about the Plotnine library until I came across the Posit Plotnine contest (see the link below). The plotnine is a Python implementation of a grammar of graphics based on the ggplot2 library. It follows the exact same syntax format as ggplot2, using code blocks and concatenating them with the + symbol. Therefore, theoretically, ggplot2 users should not feel the difference when creating plots with plotnine - with respect to ggplot2. Installation: 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘱𝘭𝘰𝘵𝘯𝘪𝘯𝘦 The library has additional extensions that you can install to extend its functionality. License: MIT 🦄 Resources 📚 Plotnine contest: https://lnkd.in/gSibZR-Z Source code: https://lnkd.in/gehdsW34 Documentation: https://plotnine.org #python #datascience #dataviz #rstats

Join us for Posit Conf - all things open source data science. And Act Now!, Early Bird Pricing Ends May 31. If you're a data scientists, Posit Conf is for you. Learn more and register at https://buff.ly/49NpZBW 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗧𝗮𝗹𝗸𝘀 - There are over 100 talks dedicated to tools & ideas that can seriously help your work. Below are just a few of the dozens of talk tracks: - Machine Learning And Statistical Modeling, https://buff.ly/4dPNqO8 - Beautiful And Effective Tables, https://buff.ly/3WRHbDc - Making Decisions With Data, https://buff.ly/4bR7uh1 - Automated Reporting With Quarto: Beyond Copy And Paste, https://buff.ly/4bSDeCI - What's New With Shiny For Python?, https://buff.ly/3R0LuIK - End-To-End Data Science With Real-World Impact, https://buff.ly/4bSdxSq - Data Science Case Studies, https://buff.ly/3Vh33H7 - Level Up Your Data Science Skills, https://buff.ly/3WQpyUr - Teaching Data Science, https://buff.ly/3ytzN6O - Data Visualizations: Idea > Process > Sharing, https://buff.ly/3WPFHcH - Drugs Not Bugs: Effective Use Of R & Python In Pharma, https://buff.ly/4bVsWlf 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽𝘀 - Sorry, but you'll only be able to select one! - Introduction to Shiny for Python with Garrett Grolemund & Andrie de Vries, https://buff.ly/4dKQN8X - Advanced Shiny for Python with Gordon Shotwell, https://buff.ly/4dM000K - Data Science Workflows with Posit Tools with Sam Edwardes & Gagandeep Singh, https://buff.ly/3VdwglY - Introduction to Machine Learning in Python with Scikit-learn with @Tiffany Timbers & Trevor Campbell, https://buff.ly/3wLri6N - Build-a-Dashboard Workshop (with Quarto, R and/or Python) with Mine Çetinkaya-Rundel, https://buff.ly/3QYy2VI - Making Tables with gt and Great Tables with Richard Iannone & Michael Chow, https://buff.ly/3R0cOqF - Intro to MLOps with vetiver with Julia Silge & Isabel Zimmerman, https://buff.ly/3VcM8Fv - DevOps for Data Scientists with Rika Gorn, https://buff.ly/4bSff6p - Introduction to Quarto with Andrew Bray , https://buff.ly/4bMFn2s - Quarto Websites with Charlotte Wickham & Emil Reinert Hvitfeldt, https://buff.ly/3VfqHUn 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 - You'll find over 1000 fellow statisticians, analysts, & data scientists all gathered in one place. There's a huge open-source developer community that supports our work, and rarely will you find so much of it all in one place, eager to learn & share. Learn more and register at https://buff.ly/49NpZBW

Wes McKinney was just on The Data Scientist Show with Daliana Liu! Wes talks about why he created Pandas, the future of data systems, why he left his CTO role to become a Principal Architect at Posit PBC Wes McKinney - The Data Scientist Show, no. 086 ▶️ YouTube: https://lnkd.in/gFezBjW2 🍎 Apple: https://lnkd.in/gNH2BuwY 🙉 Spotify: https://lnkd.in/gdq6XZ8v

We’ll be at the Databricks Data + AI Summit, will you? If you’re attending, we can’t wait to see you at the Posit Lounge (located by Theater 1), where we’ll be brewing up some fun activities like: ☕ The Posit Cafe: Grab freshly made lattes and teas – on the house! 🧱 Lego Building: Create your very own custom Lego Minifigure. 🌮 Taco Tuesday (with a twist): We’ll be scooping up some fun with freshly made ice cream tacos. Want to learn more about what we have cooking up at the Data + AI Summit? Check out our event page: https://lnkd.in/gudTxeC2

The April 2024 release of Posit Package Manager brings support for air-gapped PyPI repositories, more flexible curated CRAN repositories, performance improvements and more! • Package Manager enables data scientists working in restricted environments to access public packages from their own internal server, even when internet access is blocked. Users can select specific PyPI packages, download them using Package Manager's Offline Downloader, and update package information as needed, ensuring seamless functionality in air-gapped environments. • This release enhances flexibility for CRAN sources by allowing selection of specific package versions from different snapshots, offering more control over dependencies, and enabling deletion of packages from curated sources without requiring a complete rebuild. • With the latest update, the efficient format introduced for managing PyPI mirrors is extended to CRAN mirrors, eliminating long wait times for syncing package data. Now, package metadata is downloaded in the background as needed, providing nearly instant access without requiring any configuration changes, though there might be a slight delay during the initial migration after upgrading Package Manager. Learn more in the blog post: https://lnkd.in/dCz2RWZS