We’re thrilled to announce the release of Tapyr 0.2, a major update that incorporates valuable feedback from the Shiny for Python developer community. Learn how Tapyr enhances Shiny for Python in our Open Source Spotlight. See how it simplifies deployment for Shiny apps. This version focuses on improving the developer ...

Web developers love JavaScript. And who can blame them, it’s a fantastic do-it-all language for making stunning websites. But what about Shiny and Shiny for Python? As it turns out, you can add JavaScript scripts to your Shiny for Python dashboards without too much trouble. Today you’ll learn ...

Think of Shiny for Python custom modules as reusable components. You write the logic once, make it as tweakable as possible, and then reuse it throughout your application. As it turns out, creating custom modules in Shiny for Python is simple enough, but there are some new concepts you need ...

PyShiny (Shiny for Python) represents a significant advancement in the field of data dashboarding, setting new standards for design, maintainability, and scalability. This framework is not just an extension of R Shiny to Python but a refined approach that encapsulates the best practices learned from years of dashboard development: Modular ...

In an era where data drives critical decisions, interactive dashboards and data applications have ascended as indispensable tools across sectors like business, scientific research, and more. This is especially true in fields like life sciences, where the need for accessible data analytics and visualization is critical for research and healthcare ...

We’re happy to announce a new data science in Python course by Appsilon has been released and is available to everyone for free, as open-source material! Check out the course repository on GitHub!  The course is a great way to introduce yourself to the Data Science world. It does ...

In a recent post, we discussed how to use Quarto with Python and VS Code. However, as a Python user, I’m not that familiar with .Rmd/.qmd files; I use ipynb notebooks most often. And in this post, I’ll show why you might consider using Jupyter Notebooks and ...

Quarto, the successor of R Markdown, is a powerful tool for creating reports and presentations. In this blog post, I’ll show you how easy it is to use Quarto in Visual Studio Code (VS Code) together with Python. Make your coding experience a breeze and learn useful techniques with ...

Are you maximizing the benefit of templates for your machine learning or data science projects? At Appsilon, we’ve built numerous R Shiny dashboards and machine learning projects for data science teams at Fortune 500s. Over the years, we’ve recognized the value of templates for quickly building and, equally ...

Streamlit Tutorial on RStudio Connect At Appsilon we specialize in R and Shiny, but as data scientists and ML engineers we also know our way around Python. In our recent Streamlit tutorial series, we created a Streamlit application from scratch. If you completed the tutorial you might’ve noticed it ...