Machine Learning with Python Cookbook

Chapter 1. Vectors, Matrices, and Arrays

1.0 Introduction

NumPy is the foundation of the Python machine learning stack. NumPy allows for efficient operations on the data structures often used in machine learning: vectors, matrices, and tensors. While NumPy is not the focus of this book, it will show up frequently throughout the following chapters. This chapter covers the most common NumPy operations we are likely to run into while working on machine learning workflows.

1.1 Creating a Vector

Problem

You need to create a vector.

Solution

Use NumPy to create a one-dimensional array:

# Load library
import numpy as np

# Create a vector as a row
vector_row = np.array([1, 2, 3])

# Create a vector as a column
vector_column = np.array([[1],
                          [2],
                          [3]])

Discussion

NumPy’s main data structure is the multidimensional array. To create a vector, we simply create a one-dimensional array. Just like vectors, these arrays can be represented horizontally (i.e., rows) or vertically (i.e., columns).

See Also

• Vectors, Math Is Fun

• Euclidean vector, Wikipedia

1.2 Creating a Matrix

Problem

You need to create a matrix.

Solution

Use NumPy to create a two-dimensional array:

# Load library
import numpy as np

# Create a matrix
matrix = np.array([[1, 2],
                   [1, 2],
                   [1, 2]])

Discussion

To create a matrix we can use a NumPy two-dimensional array. In our solution, the matrix contains three rows and two columns (a column of 1s and a column of 2s).

NumPy actually has a dedicated matrix data structure: ...