Python Data Science Handbook, 2nd Edition

Chapter 5. The Basics of NumPy Arrays

Data manipulation in Python is nearly synonymous with NumPy array manipulation: even newer tools like Pandas (Part III) are built around the NumPy array. This chapter will present several examples of using NumPy array manipulation to access data and subarrays, and to split, reshape, and join the arrays. While the types of operations shown here may seem a bit dry and pedantic, they comprise the building blocks of many other examples used throughout the book. Get to know them well!

We’ll cover a few categories of basic array manipulations here:

Attributes of arrays

Determining the size, shape, memory consumption, and data types of arrays

Indexing of arrays

Getting and setting the values of individual array elements

Slicing of arrays

Getting and setting smaller subarrays within a larger array

Reshaping of arrays

Changing the shape of a given array

Joining and splitting of arrays

Combining multiple arrays into one, and splitting one array into many

NumPy Array Attributes

First let’s discuss some useful array attributes. We’ll start by defining random arrays of one, two, and three dimensions. We’ll use NumPy’s random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run:

In [1]: import numpy as np
        rng = np.random.default_rng(seed=1701)  # seed for reproducibility

        x1 = rng.integers(10, size=6)  # one-dimensional array
        x2 = rng.integers(10, size=(