Plotting in (mostly) Python*
PlotNine is a python package that has a lot of usage and syntax similarities to the popular ggplots package in R. Install following the link below. Installing the PlotNine Package
Note: If you’re using Python2, you might run into some trouble with the installation here. This has to do with some underlying dependencies. For users who are using Python 2.7 as part of their everyday workflow, you might consider using conda to create a new environment with Python3 following the instructions here. If you’re not sure what conda is, you can read more about that here: Conda installation/usage
Links to plotting tutorials/resources
https://lmicke.github.io/first-ramen-post.html https://lmicke.github.io/second-ramen-post.html#second-ramen-post
- Many different types of graphs and the code to make them (Python code):
https://python-graph-gallery.com/
- Useful Python libraries:
https://blog.modeanalytics.com/python-data-visualization-libraries/
- All python libraries (similar to R-cran)
- Lots of examples of ggplots2 graphs (R code):
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#Slope%20Chart
- Advanced data visualization ideas and examples (not necessarily tutorials and not exclusive to python)
- If you’re using jupyter notebooks, here are some tips: https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/
https://python-graph-gallery.com/