15.5 Data visualization

Learning objectives

By the end of this section you should be able to

• Explain why visualization has an important role in data science.
• Choose appropriate visualization for a given task.
• Use Python visualization libraries to create data visualization.

Why visualization?

Data visualization has a crucial role in data science for understanding the data. Data visualization can be used in all steps of the data science life cycle to facilitate data exploration, identify anomalies, understand relationships and trends, and produce reports. Several visualization types are commonly used:

Data visualization tools

Many Python data visualization libraries exist that offer a range of capabilities and features to create different plot types. Some of the most commonly used frameworks are Matplotlib, Plotly, and Seaborn. Here, some useful functionalities of Matplotlib are summarized.

Table 15.8 Matplotlib functionalities.

Plot type	Method
Bar plot	The plt.bar(x, height) function takes in two inputs, x and height, and plots bars for each x value with the height given in the height variable.
Example	Output
import matplotlib.pyplot as plt # Data categories = ["Course A", "Course B", "Course C"] values = [25, 40, 30] # Create the bar chart fig = plt.bar(categories, values) # Customize the chart plt.title("Number of students in each course') plt.xlabel("Courses") plt.ylabel("Number of students") # Display the chart plt.show

Table 15.9 Matplotlib functionalities.

Plot type	Method
Line plot	The plt.plot(x, y) function takes in two inputs, x and y, and plots lines connecting pairs of (x, y) values.
Example	Output
import matplotlib.pyplot as plt # Data month = ["Jan", "Feb", "Mar", "Apr", "May"] inflation = [6.41, 6.04, 4.99, 4.93, 4.05] # Create the line chart plt.plot(month, inflation, marker="o", linestyle="-", color="blue") # Customize the chart plt.title("Inflation trend in 2023") plt.xlabel("Month") plt.ylabel("Inflation") # Display the chart plt.show

Table 15.10 Matplotlib functionalities.

Plot type	Method
Scatter plot	The plt.scatter(x, y) function takes in two inputs, x and y, and plots points representing (x, y) pairs.
Example	Output
import matplotlib.pyplot as plt # Data x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] y = [10, 8, 6, 4, 2, 5, 7, 9, 3, 1] # Create the scatter plot plt.scatter(x, y, marker="o", color="blue") # Customize the chart plt.title("Scatter Plot Example") plt.xlabel("X") plt.ylabel("Y") # Display the chart plt.show

Table 15.11 Matplotlib functionalities.

Plot type	Method
Histogram plot	The plt.hist(x) function takes in one input, x, and plots a histogram of values in x to show distribution or trend.
Example	Output
import matplotlib.pyplot as plt import numpy as np # Data: random 1000 samples data = np.random.randn(1000) # Create the histogram plt.hist(data, bins=30, edgecolor="black") # Customize the chart plt.title("Histogram of random values") plt.xlabel("Values") plt.ylabel("Frequency") # Display the chart plt.show

Table 15.12 Matplotlib functionalities.

Plot type	Method
Box plot	The plt.boxplot(x) function takes in one input, x, and represents minimum, maximum, first, second, and third quartiles, as well as outliers in x.
Example	Output
import matplotlib.pyplot as plt import numpy as np # Data: random 100 samples data = [np.random.normal(0, 5, 100)] # Create the box plot plt.boxplot(data) # Customize the chart plt.title("Box Plot of random values") plt.xlabel("Data Distribution") plt.ylabel("Values") # Display the chart plt.show

Programming practice with Google

Use the Google Colaboratory document below to practice a visualization task on a given dataset.

Google Colaboratory document