I’m trying to learn Python, and a recent short course (Data Scientist’s Workflow: EDA and Statistical Modeling with Python in Jupyter Noteboos workshop, taght by Chris Holdgraf, David Liu, Nathan Taback and Nathaniel Stevens) has helped me to organize some of the basic resources that a statistician would need in Python. The short course materials are available on a github repository.
- Altair. A Python library for data visualization based on “The Grammar of Graphics.” Available in html format
- binder. This is a common way to take a Jupyter notebook and make it accessible easily to others. You only need a decent web browser. Available in html format
- Folium. A Python library that creates maps using the leaflet.js library. Available in html format
- The Littlest jupyterhub. This is another common way to distribute Jupyter notesbooks. Available in html format
- matplotlib. A Python library with a basic set of visualization tools. Available in html format
- NumPy. A Python library for arrays with lots of data wrangling tools. Available in html format
- scikit-learn. A Python library for machine learning models. Available in html format
- seaborn. A Python library that sits on top of matplotlib and allows flexible customization. Available in html format
- statsmodels. A Python library for many commonly used statistical models. Available in html format
An earlier version of this page was published on new.pmean.com.