Computer Science
Grade 10
20 min
Introduction to Matplotlib: Creating Basic Plots
Introduce the Matplotlib library for creating basic plots (e.g., line plots, scatter plots, bar charts).
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1
Introduction & Learning Objectives
Learning Objectives
Import the Matplotlib library and its pyplot module using a standard alias.
Create a simple line plot from two lists of numerical data.
Create a basic scatter plot to visualize the relationship between two variables.
Add essential labels, including a title, x-axis label, and y-axis label, to a plot.
Use the `plt.show()` command to display their generated plots.
Explain the purpose of data visualization in the context of data science.
Ever wonder how your favorite app shows your screen time trends or how scientists track climate change? 📈 It all starts with turning raw numbers into pictures!
This tutorial introduces Matplotlib, a powerful Python library for creating static, animated, and interactive visualizations. You will learn how to transform lists o...
2
Key Concepts & Vocabulary
TermDefinitionExample
MatplotlibA comprehensive library for creating static, animated, and interactive visualizations in Python. It's the foundation for many other plotting libraries.Using Matplotlib to create a bar chart showing the population of different cities.
PyplotA module within Matplotlib that provides a simple interface for creating plots, similar to MATLAB. We typically import it with the alias 'plt'.import matplotlib.pyplot as plt
Data VisualizationThe practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from.Instead of a table of 1000 numbers, you create a line graph to see the trend instantly.
Line PlotA type of chart which displays information as a series of...
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Core Syntax & Patterns
The Standard Import
import matplotlib.pyplot as plt
This is the conventional way to import the pyplot module. It gives you access to all plotting functions using the short alias 'plt', which saves typing and is universally understood by Python programmers.
The Basic Plotting Workflow
1. Prepare Data (e.g., lists)
2. Create Plot (e.g., plt.plot(x, y))
3. Customize Plot (e.g., plt.title('My Plot'))
4. Show Plot (plt.show())
This four-step sequence is the fundamental pattern for creating any plot. You must always call `plt.show()` at the end to actually display the visualization you've built.
Plot Labeling Syntax
plt.title('Title Text')
plt.xlabel('X-Axis Label Text')
plt.ylabel('Y-Axis Label Text')
Use these three...
4 more steps in this tutorial
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Challenging
A teacher wants to visualize if there is a relationship between the number of absences a student has and their final grade. What is the most appropriate plot type, and what data should be on each axis?
A.Line plot, with student names on the x-axis and grades on the y-axis.
B.Scatter plot, with number of absences on the x-axis and final grade on the y-axis.
C.Line plot, with number of absences on the x-axis and final grade on the y-axis.
D.Scatter plot, with student names on the x-axis and absences on the y-axis.
Challenging
A line plot is created with the data points (1, 5), (2, 10), and (4, 20). What does the straight line segment between (2, 10) and (4, 20) implicitly assume about the data value when x=3?
A.It assumes a linear progression, suggesting the value at x=3 would be 15.
B.It assumes there is no data at x=3.
C.It assumes the value at x=3 is the average of 10 and 20.
D.It assumes the value at x=3 is the same as the value at x=2.
Challenging
You have the following code that creates a line plot:
import matplotlib.pyplot as plt
x_vals = [0, 1, 2, 3, 4]
y_vals = [0, 1, 4, 9, 16]
plt.plot(x_vals, y_vals)
plt.title('Square Numbers')
plt.show()
What single line of code must be changed to convert this into a scatter plot showing the same five data points?
A.Change `import matplotlib.pyplot as plt` to `import matplotlib.scatter as plt`
B.Add the line `plt.style('scatter')` before `plt.plot()`
C.Change `plt.show()` to `plt.show('scatter')`
D.Change `plt.plot(x_vals, y_vals)` to `plt.scatter(x_vals, y_vals)`
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