Posit PBC

Check out our video on parameterized report automation. Generate customized data-driven reports in minutes with Python!  This is Part 2 of a multi-part video series on Quarto & Python, in collaboration with Keith Galli. Subscribe to Posit's YouTube channel to get them all. In this video, you'll find a practical guide to generating hundreds of customized reports using Quarto and Python. Learn how to leverage Quarto's parameter system to create PDFs and HTML reports at scale. Using a movie dataset example, we'll cover: - How to automate report generation with Python and Quarto - Create dynamic templates with data visualization and formatting - Adapt parameters to work with both Python and R code - Build scalable reporting workflows for any dataset This tutorial demonstrates how to transform what could be hours of manual reporting work into an efficient automated process. https://lnkd.in/gVaE4XYQ

Quarto. Shiny. Streamlit. Dash. Bokeh. Jupyter. Deploy them all with ease using Connect Cloud. In three minutes (or less), you can go from a public GitHub repository to a sharable data application or document. ✨ Try Connect Cloud for free, and let us know what you think → https://lnkd.in/gi6Ws2jh 📚 Need help getting started? Check out our library of how-to guides → https://lnkd.in/gsCUCsg4

We are proud to introduce the mall package for running multiple LLM predictions against a data frame in R or Python! With mall, you can use a local large language model (LLM) to run natural language processing (NLP) operations, such as sentiment, summarization, and translation, across a data frame. mall has been simultaneusly released to CRAN and PyPi (as an extension to Polars). This blog post by Edgar Ruiz walks through the motivation and how to use mall seamlessly in your workflows: https://lnkd.in/g_mqJSfV This package is inspired by the SQL AI functions now offered by vendors such as Databricks and Snowflake. mall uses Ollama to interact with LLMs installed locally. The idea is to integrate seamlessly with how data analysts use their preferred language on a daily basis. So, you can search for positive reviews in R using the pipe like so: ```r reviews |> llm_sentiment(review) |> filter(.sentiment == "positive") |> select(review) ``` Try it out today!

Do you want to create a polished and professional website that can directly include live code, outputs, and visualizations? Charlotte Wickham and Emil Hvitfeldt demonstrate the steps to create a powerful and customizable website with Quarto, from using available templates to get started to making it your own with custom fonts/themes, navigation, and listings. Videos in this series include: 1. Build your homepage: Use a functional and attractive template to begin your homepage. Learn to customize and publish it. https://lnkd.in/g4BcdsW7 2. Add pages and navigation: Add pages to your navigation and make them easy to find. https://lnkd.in/gj-mQQ8R 3. Customize appearance with CSS/SCSS: Learn the basics of CSS and SCSS and how to make good design choices, then apply them to your website. https://lnkd.in/gkvafuYZ 4. Add lists of content with listings: Create a listing page with either a Quarto document or a YAML file. https://lnkd.in/gWnQQ3qB We can't wait to see what you create. Enjoy!

What do data science managers really look for when hiring? After years of hearing this question answered at the Data Science Hangout, the top trait isn’t just coding skills or stats. It’s curiosity. In our latest blog post, Libby Heeren shares how curiosity doesn’t just get you hired, it’s how you grow and flourish in your data career. Thank you to Libby for her profound insights and to the Data Science Hangout community for sharing their wisdom and experience. Read the full blog post here: https://lnkd.in/gRN8WJj9

If you manage R models and need a way to streamline deployment while boosting performance, join our demo on November 27th at 11 a.m. ET! We’ll show how you can: 🛠️ Fit and deploy R models directly in Snowflake ⚡ Use Snowflake’s compute to drastically reduce model runtime 🤝 Save and share models across your team, making them accessible through tools like Snowsight and Python This end-to-end workflow makes it easier to implement models and scale insights across your organization using Snowflake and Posit. Learn more about the workflow before the event by checking out our blog → https://lnkd.in/gWhtfdNb Add the event to your calendar → https://lnkd.in/gwqwETWH

We are excited to announce that plotnine v0.14.0 is now out! Plotnine brings the grammar of graphics to Python. In addition to an amazing new hex logo 🛸 , this latest release introduces: • The removal of print as a way to render plot objects • Conversion of scales to dataclasses and leveraged finer controls over keyword-only parameters • Easier ways of working with Datetimes and Timedeltas • Recognition of figure options specified in the meta section of a Quarto document • Font aesthetics, where attributes of a text are now aesthetics Read more in the blog post! https://lnkd.in/gPysXTsx

Check out Part 1 of our Quarto Crash Course with Python! Whether you're a complete beginner or an experienced user, this tutorial covers the topics you need to know to get up and running with Quarto with Python. We've collaborated with YouTuber creator Keith Galli to create this video series. Keith specializes in long-form technical video tutorials. And in this 97 minute quick start tutorial, Keith guides you through getting Quarto up and running with VS Code, explores styling and formatting options, and publish a Quarto doc HTML, as slides, a PDF report, a dashboard, and a complete website. Subscribe to the Posit YouTube channel to follow the full series. https://lnkd.in/gbjDrBXm

How can you take the data application or document currently on your laptop and make it easily shareable with a single URL? Connect Cloud. With Connect Cloud, you can go from a public GitHub repository to a published data product in three minutes (or less). Need help getting started? Check out the community forum and user guides curated by the Connect Cloud team. 💫 Try Connect Cloud (for free) → https://lnkd.in/gi6Ws2jh 📚 Follow step-by-step guides to get started → https://lnkd.in/gkVHBUKz 🧑🏽💻 Visit the community forum → https://lnkd.in/ghmqeaWW

How do you create a Shiny app that interacts with a database? We'll show you in our new blog post ✨ In a previous post, we explained how to connect to a database from R and Python and manipulate tables using DuckDB, Databricks, and Snowflake as examples. But what if you want to connect to a database from a Shiny app? In this blog post, we’ll cover how to connect to, read from, and write to a database from a Shiny app in both R and Python. We’ll use the example of an app that reads from a database table, visualizes the data, and allows the user to flag suspicious values and write back to the database. Explore the post → https://lnkd.in/gACnwHx6