Introduction to Matplotlib

Matplotlib is a popular Python library for creating visualizations, including line plots, bar charts, scatter plots, and more.

In this lesson, you’ll learn how to use Matplotlib to create and customize various types of plots.

Installing Matplotlib

To install Matplotlib, use the following command in your terminal:

pip install matplotlib

Once installed, you can import it in your Python scripts:

# Importing matplotlib
import matplotlib.pyplot as plt

print("Matplotlib imported successfully")

Creating a Simple Line Plot

You can create a basic line plot using Matplotlib’s plot() function:

# Creating a simple line plot
import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.plot(x, y)
plt.title("Simple Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Customizing Plots

Matplotlib allows you to customize plots with titles, labels, legends, and different line styles:

# Customizing a plot
plt.plot(x, y, label="Line 1", linestyle="--", color="red")
plt.legend()
plt.title("Customized Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Creating Bar Charts

Bar charts are useful for comparing categories. Use the bar() function to create a bar chart:

# Creating a bar chart
categories = ["A", "B", "C"]
values = [10, 15, 7]

plt.bar(categories, values, color="blue")
plt.title("Bar Chart")
plt.xlabel("Categories")
plt.ylabel("Values")
plt.show()

Creating Scatter Plots

Scatter plots are useful for showing relationships between two variables. Use the scatter() function:

# Creating a scatter plot
x = [5, 7, 8, 7]
y = [3, 8, 6, 4]

plt.scatter(x, y, color="green")
plt.title("Scatter Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Creating Histograms

Histograms show the distribution of a dataset. Use the hist() function:

# Creating a histogram
data = [1, 1, 2, 3, 3, 3, 4, 4, 5]

plt.hist(data, bins=5, color="purple")
plt.title("Histogram")
plt.xlabel("Bins")
plt.ylabel("Frequency")
plt.show()

Adding Multiple Plots

You can add multiple plots to the same figure using the subplot() function:

# Adding multiple plots
plt.subplot(2, 1, 1)
plt.plot([1, 2, 3], [4, 5, 6])
plt.title("First Plot")

plt.subplot(2, 1, 2)
plt.bar([1, 2, 3], [7, 8, 9])
plt.title("Second Plot")

plt.tight_layout()
plt.show()

Common Mistakes with Matplotlib

Here are some mistakes to avoid:

• Forgetting to call plt.show() to display the plot.
• Using overlapping elements without adjusting the layout.
• Not labeling axes and plots for clarity.

What Did We Learn?

In this lesson, you learned:

• How to install and import Matplotlib.
• How to create line plots, bar charts, scatter plots, and histograms.
• How to customize plots with labels, legends, and multiple plots.

Great job! Let’s move to the next topic.