📝 Introduction to Plotting in Python

• Python would serve you well as your primary tool for visualizing quantitative data for most classes you will take as well as in your career.

• Matplotlib is the most common used Python package for 2-dimensional graphics.

Data visualization

• Graphics display information using a coordinate system, numbers, lines, symbols, words, shading, and color.

• Graphics are instruments for reasoning about quantitative information.

• Valuable graphics allow large collections of data to be turned into actionable information.

• For engineers, making easy-to-interpret, honest graphical representations of information is often the most effective way of communicating scientific information.

• Graphics can be more informative than statistics!

What characterizes a valuable graphic?

• Presents data accurately, clearly, and efficiently

• Encourages the viewer to relate important pieces of information

Examples of data visualization

Famous dot map of deaths from Cholera in central London in September 1854 by Dr. John Snow

What could someone learn from reading this graphic?

Including Various Dimensions

• Most data have at least two dimensions, which are represented on an x-axis and y-axis.

• Color, size, and marker type are common ways to represent additional dimensions of data.

What are the dimensions of data shown here and which feature on the figure is used to show each one?

Principles of Graphical Excellence

• Presentation of data needs to consider substance, statistics, and design

• Complex ideas should be communicated with clarity, precision, and efficiency

• Graphics must not deceive the audience, intentionally or unintentionally.

What is wrong with this figure?

Guiding Principles

Appropriate visualization for the data

• Avoid if possible since people are not good at determining angles.

  

• A bar chart or a line chart is much more informative

  

Plots with multiple y-axes

• Multiple y-axes are possible when it is important to show the interrelationship of two variables with different absolute values.

• Be intentional and careful when choosing this strategy.

Make the y-value zero at the origin

• Most bar charts and figures with physical quantities are best represented with a value of zero at the origin.

• If the reference point and all of the values are greater than zero, it may be acceptable to have a non-zero value at the origin.

How to plot in Python

Plotting is easy using matplotlib.

import matplotlib.pyplot as plt

# some example data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# try to plot the data
try:
    plt.plot(x, y)
    plt.show()
except:
    print("Something went wrong with the plot!")

You might have noticed we used try and except this is called exception handling.

If a code in a try statement encounters an error it continues with the except statement.

• This is helpful in making code where it is not easy to define a branching statement for all cases.

• This is also the basis of error handeling, which makes your code fail gracefully.

import matplotlib.pyplot as plt

# some example data
x = [1, 2, 3, 4, plt]
y = [2, 4, 6, 8, 10]

# try to plot the data
try:
    plt.plot(x, y)
except:
    print("Something went wrong with the plot!")

Something went wrong with the plot!