Python for Excel

Chapter 4. NumPy Foundations

As you may recall from Chapter 1, NumPy is the core package for scientific computing in Python, providing support for array-based calculations and linear algebra. As NumPy is the backbone of pandas, I am going to introduce its basics in this chapter: after explaining what a NumPy array is, we will look into vectorization and broadcasting, two important concepts that allow you to write concise mathematical code and that you will find again in pandas. After that, we’re going to see why NumPy offers special functions called universal functions before we wrap this chapter up by learning how to get and set values of an array and by explaining the difference between a view and a copy of a NumPy array. Even if we will hardly use NumPy directly in this book, knowing its basics will make it easier to learn pandas in the next chapter.

Getting Started with NumPy

In this section, we’ll learn about one- and two-dimensional NumPy arrays and what’s behind the technical terms vectorization, broadcasting, and universal function.

NumPy Array

To perform array-based calculations with nested lists, as we met them in the last chapter, you would have to write some sort of loop. For example, to add a number to every element in a nested list, you can use the following nested list comprehension:

In [1]: matrix = [[1, 2, 3],
                  [4, 5, 6],
                  [7, 8, 9]]

In [2]: [[i + 1 for i in row] for row in matrix]

Out[2]: [[2, 3, 4], [5, 6, 7], [8, 9, 10]]

This isn’t very readable and more importantly, ...