Elegant SciPy

Chapter 1. Elegant NumPy: The Foundation of Scientific Python

[NumPy] is everywhere. It is all around us. Even now, in this very room. You can see it when you look out your window or when you turn on your television. You can feel it when you go to work…when you go to church…when you pay your taxes.

Morpheus, The Matrix

This chapter touches on some statistical functions in SciPy, but more than that, it focuses on exploring the NumPy array, a data structure that underlies almost all numerical scientific computation in Python. We will see how NumPy array operations enable concise and efficient code for manipulating numerical data.

Our use case is using gene expression data from The Cancer Genome Atlas (TCGA) project to predict mortality in skin cancer patients. We will be working toward this goal throughout this chapter and Chapter 2, learning about some key SciPy concepts along the way. Before we can predict mortality, we will need to normalize the expression data using a method called RPKM normalization. This allows the comparison of measurements between different samples and genes. (We will unpack what “gene expression” means in just a moment.)

Let’s start with a code snippet to tantalize you and introduce the ideas in this chapter. As we will do in each chapter, we open with a code sample that we believe epitomizes the elegance and power of a particular function from the SciPy ecosystem. In this case, we want to highlight NumPy’s vectorization and broadcasting rules, which ...