Python for Data Analysis

Appendix A. Python Language Essentials

Knowledge is a treasure, but practice is the key to it.

—Thomas Fuller

People often ask me about good resources for learning Python for data-centric applications. While there are many excellent Python language books, I am usually hesitant to recommend some of them as they are intended for a general audience rather than tailored for someone who wants to load in some data sets, do some computations, and plot some of the results. There are actually a couple of books on “scientific programming in Python”, but they are geared toward numerical computing and engineering applications: solving differential equations, computing integrals, doing Monte Carlo simulations, and various topics that are more mathematically-oriented rather than being about data analysis and statistics. As this is a book about becoming proficient at working with data in Python, I think it is valuable to spend some time highlighting the most important features of Python’s built-in data structures and libraries from the perspective of processing and manipulating structured and unstructured data. As such, I will only present roughly enough information to enable you to follow along with the rest of the book.

This chapter is not intended to be an exhaustive introduction to the Python language but rather a biased, no-frills overview of features which are used repeatedly throughout this book. For new Python programmers, I recommend that you supplement this chapter with the official Python ...