Matplotlib is a powerful plotting library in Python used for 2D graphics. It allows users to create static, animated, and interactive visualizations in Python.

Whether you're doing data analysis or building dashboards, mastering Matplotlib is an essential skill.

What You Will Learn

• What is Matplotlib?

• Installing Matplotlib

• Basic Structure of a Plot

• Common Plot Types

• Customizing Plots

• Saving Plots

• Complete Code Example

• Tips and Common Pitfalls

Installing Matplotlib

If you haven’t installed Matplotlib yet, you can do so using pip:

pip install matplotlib

Or in Jupyter notebooks:

!pip install matplotlib

Basic Structure of a Plot

The core component of Matplotlib is pyplot, typically imported like this:

import matplotlib.pyplot as plt

A basic plot involves:

1. Defining data

2. Creating the plot

3. Customizing the appearance

4. Displaying the plot

Step-by-Step Example: A Simple Line Plot

import matplotlib.pyplot as plt

# Step 1: Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Step 2: Create the plot
plt.plot(x, y)

# Step 3: Customize
plt.title("Simple Line Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")

# Step 4: Show the plot
plt.show()

What Just Happened?

• plt.plot(x, y) creates a line chart.

• plt.title(), plt.xlabel(), and plt.ylabel() add context to your graph.

• plt.show() renders the plot.

Common Plot Types

1. Bar Chart

categories = ['A', 'B', 'C']
values = [5, 7, 3]

plt.bar(categories, values)
plt.title("Bar Chart")
plt.show()

2. Scatter Plot

x = [1, 2, 3, 4]
y = [10, 15, 13, 17]

plt.scatter(x, y)
plt.title("Scatter Plot")
plt.show()

3. Histogram

data = [1,2,2,3,3,3,4,4,4,4,5,5,5,5,5]

plt.hist(data, bins=5)
plt.title("Histogram")
plt.show()

4. Pie Chart

labels = ['Python', 'Java', 'C++', 'Ruby']
sizes = [215, 130, 245, 210]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("Programming Language Usage")
plt.show()

Customizing Plots

Change Line Style and Color

plt.plot(x, y, color='green', linestyle='--', marker='o')

Set Figure Size

plt.figure(figsize=(8, 4))
plt.plot(x, y)
plt.show()

Add Grid

plt.grid(True)

Add Legend

plt.plot(x, y, label='Prime Growth')
plt.legend()

Saving the Plot

plt.savefig('my_plot.png', dpi=300)

This saves the current figure to a file.

✅ Complete Code Example

Here’s a complete script that uses multiple features of Matplotlib:

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y1 = [1, 4, 9, 16, 25]
y2 = [1, 2, 3, 4, 5]

# Create figure and axes
plt.figure(figsize=(10, 6))

# Plot lines
plt.plot(x, y1, label='Squared', color='blue', linestyle='-', marker='o')
plt.plot(x, y2, label='Linear', color='red', linestyle='--', marker='x')

# Titles and labels
plt.title('Line Comparison')
plt.xlabel('X Values')
plt.ylabel('Y Values')

# Grid, Legend, Save
plt.grid(True)
plt.legend()
plt.savefig('comparison_plot.png', dpi=300)

# Display
plt.show()

Tips for Using Matplotlib

1. Always label your axes and title – It adds meaning to your plots.

2. Use plt.figure() when creating multiple plots – Helps avoid overlap.

3. Use plt.tight_layout() – Automatically adjusts subplot spacing.

4. Test in Jupyter Notebooks – The %matplotlib inline magic command renders plots inline.

5. Try Seaborn for advanced styling – It’s built on top of Matplotlib.

⚠️ Common Pitfalls

Conclusion

Matplotlib is a robust and flexible tool for data visualization in Python. Whether you're plotting simple lines or complex figures, knowing how to use Matplotlib will make your data analysis much more insightful.

Want to go further? Look into:

• plt.subplot() for multiple plots in one figure

• matplotlib.animation for animated plots

• Axes3D for 3D visualizations