Last week, we introduced RStudio’s new visual markdown editor. Today, we’re excited to introduce some of the expanded support for Python in the next release of RStudio.
The RStudio 1.4 release introduces a number of features that will further improve the Python editing experience in RStudio:
The default Python interpreter to be used by RStudio /
reticulate can now be customized in the Global Options pane,
The Environment pane now displays a summary of Python objects available in the main module when the
reticulate REPL is active,
Python objects can now be viewed and explored within the RStudio data viewer and object explorer,
matplotlib plots are now displayed within the Plots pane when
show() is called.
When working with
reticulate, one normally selects a Python interpreter using
reticulate functions – for example, via
reticulate::use_python(…, required = TRUE) or by setting the
RETICULATE_PYTHON environment variable. (Or, alternatively, they trust
reticulate to find and activate an appropriate version of Python as available on their system.)
However, one might want to control the version of Python without explicitly using
reticulate to configure the active Python session. RStudio now provides a Python options pane, available both globally (via
Tools -> Global Options…), or per-project (via
Tools -> Project Options…), which can be used to configure the default version of Python to be used in RStudio.
Within the Python preferences pane, the default Python interpreter to be used by RStudio can be viewed and modified:
Select… button is pressed, RStudio will find and display the available Python interpreters and environments:
RStudio will display system interpreters, Python virtual environments (created by either the Python
venv modules), and Anaconda environments (if Anaconda is installed). Once an environment has been selected, RStudio will instruct
reticulate to use that environment by default for future Python sessions.
Note that the
RETICULATE_PYTHON environment variable still takes precedence over the default interpreter set here. If you’d like to use RStudio to configure the default version of Python, but are setting
RETICULATE_PYTHON within your
.Rprofile startup files, you may need to unset it.
The RStudio environment pane is now capable of displaying the contents of Python modules when the
reticulate REPL is active. By default, the contents of the main module are displayed.
Similar to how R environments are displayed within the Environment pane, one can also view the contents of other loaded Python modules.
In addition, pandas
DataFrame objects can be opened and viewed similarly to R
data.frame objects, and other Python objects can be viewed in the object explorer.
Python objects can be explored either by calling the
View() function from the
reticulate REPL, or by using the associated right-most buttons in the Environment pane.
matplotlib is a popular Python module, used to create visualizations in Python. With RStudio 1.4, the IDE can now also display
matplotlib plots within the Plots pane.
Data scientists using Python might also be familiar with the
seaborn module, which provides a higher-level interface on top of
matplotlib for producing high quality data visualizations. RStudio can also render plots generated by the
Currently, only static (non-interactive) plots are supported – we hope to support interactive graphics in a future release of RStudio.
In this series, we walk through lesser-known tips and tricks to help you work more effectively and efficiently in R Markdown. This third post focuses on features that save you time and trouble.
The RStudio IDE includes a visual markdown editor that displays changes in real-time and provides support for technical writing. This post walks through several features of visual editing mode.