References

This reader covers a variety of intermediate Python topics, but there’s always more to learn, and seeing other perspectives will help you understand the concepts better. Many Python and data science learning resources are available for free online or through the library.

Some references I recommend are:

• Python for Data Analysis (2nd edition) by McKinney. An introduction to using Python for data science, by the creator of the Pandas package.

• Python Data Science Handbook by VanderPlas.

• Python Basics, the reader from DataLab’s introductory Python workshop series.

• My own teaching notes from several years of teaching statistical computing.

Section 1

Section 1 is a deep dive into how indexing works in the pandas package. While developing the chapter, I relied heavily on the excellent pandas documentation.

Section 2

Section 4 describes strategies to improve the organization and reproducibility of your computing projects. The chapter was informed by the following references:

• DataLab’s README, Write Me! Reader

• The Turing Way

• The Hitchhiker’s Guide to Python

• Jenny Bryan’s presentation How to Name Files

• Cookiecutter Data Science

• This chapter of DataLab’s Introduction to Remote Computing Reader

Section 3

Section 2 is a deep dive into how Python’s iterators and generators work. The chapter is based on the following references:

• Generator Tricks for Systems Programmers, v3.0 by Beazley.

• The official Python 3 Documentation.

• The Scaling to Large Datasets chapter of the pandas documentation.