Interpretation of Data Visualizations

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:

Table 15.7 Common visualization types.

Checkpoint

Visualization types

Concepts in Practice

Comparing visualization methods

1. Which of the following plot types is best suited for comparing the distribution of a continuous variable?

1. scatter plot

2. histogram plot

3. line plot

2. Which of the following plot types is best suited for visualizing outliers and quartiles of a continuous variable?

1. histogram plot

2. bar plot

3. box plot

3. Which of the following plot types is effective for displaying trends and changes over time?

1. line plot

2. bar plot

3. histogram plot

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.

Bar Plot: Method, Example, and Output

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 15.8 Matplotlib functionalities. Bar plot.

Line Plot: Method, Example, and Output

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 15.9 Matplotlib functionalities. Line plot.

Scatter Plot: Method, Example, and Output

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 Matplotlib functionalities. Scatter plot.

Histogram Plot: Method, Example, and Output

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 15.11 Matplotlib functionalities. Histogram plot.

Box Plot: Method, Example, and Output

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()

Table 15.12 Matplotlib functionalities. Box plot.

Concepts in Practice

Matplotlib methods

4. Given the following code, which of the function calls is appropriate in showing association between x and y?

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [10, 15, 12, 18, 20]

1. plt.boxplot(x)
2. plt.hist(y)
3. plt.scatter(x, y)

5. What is being plotted using the code below?

import matplotlib.pyplot as plt

# Data
categories = ['A', 'B', 'C', 'D']
values = [10, 15, 12, 18]
plt.bar(categories, values)

1. a histogram plot
2. a bar plot
3. a line plot

6. Which library in Python is more commonly used for creating interactive visualizations?

1. Matplotlib
2. Plotly
3. Pandas

Source: Rice University, https://openstax.org/books/introduction-python-programming/pages/15-5-data-visualization
This work is licensed under a Creative Commons Attribution 4.0 License.