This is actually a seaborn plot in the RStudio IDE lower right quadrant! This is super useful so I don’t have to scroll away from my code to see the help documenation and function examples. Help documentation shows up in the Help Window. Directory paths, function completion, even function arguments are supported. Scripting can now be done efficiently with CTRL + Enter sending code to the Console. Here’s what we liked about the new RStudio IDE Python Integration: py file) and worked interactively with the RStudio IDE’s console, help documentation, and plotting panel performing basic operations that a data scientist will be doing quite frequently. With the rollout of the Python Integration - a major new feature in RStudio - We did a product review of the RStudio IDE from the perspective of data scientist using Python. RStudio is making the case for a powerful mult-language IDE designed for Data Science. Let’s take a look at how the Python integration works. With this release, RStudio is making a case for a powerful, all-in-one R + Python Data Science IDE. The RStudio Version 1.2 Preview Edition comes with support for Python and several other data science languages including SQL and Stan. Until now - RStudio is making the case for a powerful mult-language IDE designed for Data Science. Most data scientists write their code in separate places - Python is written in Jupyter Notebooks, and R is written in the RStudio IDE. The Python language has the Jupyter Notebook (and more recently Jupyter Lab) that provides a web-based notebook. The R language has the RStudio IDE, which is a great IDE for data science because of its feature rich setup for efficiently developing analyses.

The two major data science languages, Python and R, have historically taken two separate paths when it comes to where data scientists are doing the coding. Python Integration in RStudio - Data Science IDE Review

Rstudio for python