Introduction

Data visualization is a powerful tool that allows users to digest large amounts of data very quickly. There are different types of plots that serve various purposes. In business, line plots and bar graphs are very common to display trends over time and compare metrics across groups, respectively. Statisticians, on the other hand, may be more interested in checking correlations between variables using a scatterplot or correlation matrix. They may also use histograms to check the distribution of a variable or boxplots to check for outliers. In politics, pie charts are widely used for comparing the total data between or among categories. Data visualizations can be very intricate and creative, being limited only by one's imagination.

The Python library Matplotlib is a well-documented, two-dimensional plotting library that can be used to create a variety of powerful data visualizations and aims to "...make easy things easy and hard things possible" (https://matplotlib...

Functional Approach

The functional approach to plotting in Matplotlib is a way of quickly generating a single-axis plot. Often, this is the approach taught to beginners. The functional approach allows the user to customize and save plots as image files in a chosen directory. In the following exercises and activities, you will learn how to build line plots, bar plots, histograms, box-and-whisker plots, and scatterplots using the functional approach.

Exercise 13: Functional Approach – Line Plot

To get started with Matplotlib, we will begin by creating a line plot and go on to customize it:

1. Generate an array of numbers for the horizontal axis ranging from 0 to 10 in 20 evenly spaced values using the following code:

   import numpy as np

   x = np.linspace(0, 10, 20)

2. Create an array and save it as object y. The snippet of the following code cubes the values of x and saves it to the array, y:

   y = x**3

3. Create the plot as follows:

   import matplotlib.pyplot as plt

   plt.plot(x, y)

   plt.show()

   See...

Object-Oriented Approach Using Subplots

Using the functional approach of plotting in Matplotlib does not allow the user to save the plot as an object in our environment. In the object-oriented approach, we create a figure object that acts as an empty canvas and then we add a set of axes, or subplots, to it. The figure object is callable and, if called, will return the figure to the console. We will demonstrate how this works by plotting the same x and y objects as we did in Exercise 13.

Exercise 19: Single Line Plot using Subplots

When we learned about the functional approach of plotting in Matplotlib, we began by creating and customizing a line plot. In this exercise, we will create and style a line plot using the functional plotting approach:

1. Save x as an array ranging from 0 to 10 in 20 linearly spaced steps as follows:

   import numpy as np

   x = np.linspace(0, 10, 20)

   Save y as x cubed using the following:

   y = x**3

2. Create a figure and a set of axes as follows:

   import matplotlib.pyplot as...

Summary

In this chapter, we used the Python plotting library Matplotlib to create, customize, and save plots using the functional approach. We then covered the importance of a descriptive title and created our own descriptive, programmatic titles. However, the functional approach does not create a callable figure object and it does not return subplots. Thus, to create a callable figure object with the potential of numerous subplots, we created, customized, and saved our plots using the object-oriented approach. Plotting needs can vary analysis to analysis, so covering every possible plot in this chapter is not practical. To create powerful plots that meet the needs of each individual analysis, it is imperative to become familiar with the documentation and examples found on the Matplotlib documentation page.

In the subsequent chapter, we will apply some of these plotting techniques as we dive into machine learning using scikit-learn.