Python Resources

Basics

• Terminal commands cheatsheet
• Python basics cheatsheet
• Python interactive reference
• Python for R users

Python libraries we’ll use

When you’re working in Python it can be super helpful to regularly refer to these resources. Remember that you can always use any API reference link below to get a comprehensive list of all the functions and methods in a library - a bit nicer than only relying on ? in your notebook.

Throughout the course we’’ll make use of the following Python libraries in case you want quickly reference their documentation:

• polars
• seaborn
• matplotlib
• scipy
• numpy
• bossanova
• scikit-learn

polars - DataFrames & tidy data analysis

• Polars user guide
• Polars API reference
• Tidyverse and Polars side-by-side
• Polars Rgonomic patterns
• Pandas - alternative DataFrame library we’re NOT using

seaborn - high-level statistical visualizations

• Seaborn user guide
• Seaborn API
• Seaborn cheatsheet

matplotlib - lower-level plot customization

• Matplotlib user guide
• Matplotlib API reference
• Matplotlib tutorials
• Matplotlib cheatsheets

scipy - scientific functions & basic stats

• SciPy user guide
• SciPy API reference
• Summary statistics
• Resampling, i.e. montecarlo, bootstrap, permutation
• Hypothesis testing

numpy - arrays, matrices, and linear algebra

• Numpy tutorials
• Numpy API reference
• Numpy Cheatsheet
• Numpy for MATLAB users

bossanova - intuitive formula-based statistical modeling

• Documentation & tutorials

scikit-learn - machine-learning

• Scikit-learn cheatsheet
• Supervised learning
• Decomposition
• Model selection & evaluations