![]() The list of supported Python content types has grown steadily over the last two years. With this RStudio Connect release, you can deploy, manage, and scale Python APIs built with FastAPI and several other ASGI-compliant frameworks. For an overview of all the ways our pro products support data science teams using R and Python, check out our Single Home for R and Python page.ĪPIs are a key to integrating your data science results into other applications and processes ( see this blog post for more on APIs). Frameworks like Shiny, Dash, Streamlit, plumber, Flask, and R Markdown allow data scientists to focus on communication regardless of the language they use.At RStudio we know that many data science teams leverage both R and Python in their work, so it’s important that we build products to support the best tools available in both languages. By supporting both languages, teams have access to more tools for distributing work and making an impact. Optimize your team’s impact, not the language they use: Data science teams are most effective when they are sharing work with their fellow team members and with their key stakeholders, as was discussed in this recent panel webinar with leaders of data science teams. For example, RStudio Workbench ( recently renamed from RStudio Server Pro) allows data science teams to use the RStudio IDE, Jupyter or VS Code on the same infrastructure, so data scientists can use their IDE of choice without putting an additional burden on IT. Video: Recent improvements to Python integrations in the RStudio 1.4 release.Ĭommon infrastructure can support multiple languages and reduce support costs: By using a platform that supports both R and Python, such as RStudio Team, DevOps and IT teams can enable data scientists to use their preferred languages and development environments, while supporting a single infrastructure for both development and deployment. Teams sometimes believe that they must standardize on one or the other for efficiency, at the cost of forcing individual data scientists to give up their preferred, most productive language. RStudio’s professional products deliver a platform on which to centralize, secure and scale your data science, but there are two prominent choices for open source, code-first environments: R and Python.
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