An Introduction to Plotting in Python

The Jupyter Notebook in this repository (i.e., Plotting_in_Python.ipynb in the code subfolder) provides a high-level overview of how to generate basic charts—from scatterplots to heatmaps—using the seaborn and plotnine libraries in Python. Along the way, you’ll be using methods from pandas, matplotlib and cognate libraries to modify your data, customize your plotting aesthetics and export your visualizations.¹

In addition, this repository includes code that briefly details how to use reticulate as a portal to Python from R. To work your way though this example (in the code.R script file), make sure to:

1. Clone, fork or download the contents of this repository.

2. Open python_plotting.Rproj in RStudio.

3. Retrieve code.R from the code subfolder.

4. Run renv::restore() in R before executing the rest of the script file.

Footnotes

1. If you choose to run this notebook locally, do not use pip to install or update packages if you generally use conda to manage your Python libraries. ↩