Unit 9 • Lesson 7

Data Visualization with Matplotlib

Overview

Graphs and charts make data easier to understand. You'll learn to create simple line, bar, and scatter plots using matplotlib, customizing colors, labels, and titles to make your visualizations clear and informative, presenting data visually.

Intermediate 25–30 min

Next: Basic Automation with Python All Units

What You Will Learn in This Lesson

By the end of this lesson, you will know:

Why visualize data: Making data easier to understand.
matplotlib basics: Creating simple plots and charts.
Plot types: Line plots, bar charts, scatter plots, histograms.
Customization: Adding labels, titles, and colors.
Displaying plots: Showing and saving visualizations.

Why Visualize Data?

Visualizations make data easier to understand:

Benefits of Visualization

Makes patterns and trends visible
Easier to understand than raw numbers
Helps communicate findings
Enables data-driven decisions
Identifies outliers and anomalies

Creating Your First Plot

Basic steps to create a plot with matplotlib:

Simple Line Plot

import matplotlib.pyplot as plt

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

# Create plot
plt.plot(x, y)

# Add labels and title
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('My First Plot')

# Display plot
plt.show()

Installing matplotlib

First, install matplotlib using pip:

Installation

# In your terminal:
pip install matplotlib

# Or for Python 3:
pip3 install matplotlib

Import Convention

Most Python programmers import matplotlib.pyplot as plt for convenience: import matplotlib.pyplot as plt

Common Plot Types

Different plots for different data. Choose the right plot type for your data:

Line Plot - Trends Over Time

import matplotlib.pyplot as plt

# Data
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May']
sales = [100, 120, 140, 110, 150]

# Create line plot
plt.plot(months, sales, marker='o', color='blue', linewidth=2)
plt.xlabel('Month')
plt.ylabel('Sales')
plt.title('Monthly Sales Trend')
plt.grid(True)
plt.show()

Bar Chart - Comparing Categories

import matplotlib.pyplot as plt

# Data
categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]

# Create bar chart
plt.bar(categories, values, color=['red', 'blue', 'green', 'orange'])
plt.xlabel('Category')
plt.ylabel('Value')
plt.title('Category Comparison')
plt.show()

Scatter Plot - Relationships

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [2, 4, 5, 7, 8, 9, 11, 12, 13, 15]

# Create scatter plot
plt.scatter(x, y, s=100, alpha=0.6, color='purple')
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.title('Relationship Between X and Y')
plt.show()

Histogram - Distribution

import matplotlib.pyplot as plt

# Data
scores = [65, 70, 75, 80, 85, 90, 95, 100, 85, 90, 75, 80, 85]

# Create histogram
plt.hist(scores, bins=10, color='skyblue', edgecolor='black')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.title('Score Distribution')
plt.show()

Choosing the Right Plot

Line Plot: Use for trends over time or continuous data
Bar Chart: Use for comparing discrete categories
Scatter Plot: Use to show relationships between two variables
Histogram: Use to show the distribution of a single variable

Customizing Plots

Make your plots more informative and visually appealing:

Customization Example

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 3, 5, 7, 9]

# Create figure with custom size
plt.figure(figsize=(10, 6))

# Plot multiple lines
plt.plot(x, y1, label='Series 1', marker='o', linewidth=2)
plt.plot(x, y2, label='Series 2', marker='s', linewidth=2)

# Customize
plt.xlabel('X Axis Label', fontsize=12)
plt.ylabel('Y Axis Label', fontsize=12)
plt.title('Customized Plot', fontsize=14, fontweight='bold')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Common Customizations

Colors: Use color='red' or hex codes like color='#FF5733'
Markers: Use marker='o' (circle), 's' (square), '^' (triangle)
Line width: Use linewidth=2 for thicker lines
Transparency: Use alpha=0.5 for semi-transparent plots
Figure size: Use plt.figure(figsize=(10, 6)) for custom dimensions

Practice: Creating Plots

Try It Yourself

Try creating a simple plot:

# Simulate matplotlib plot
# In real code: import matplotlib.pyplot as plt

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

print("Creating plot with data:")
for i in range(len(x)):
    print(f"  Point ({x[i]}, {y[i]})")
print("\nIn real matplotlib, this would display a line plot!")

Press Run to see output

Note: This is a simulation. In a real program with matplotlib installed, plt.plot(x, y) followed by plt.show() would display a visual plot!

Advanced Customization

matplotlib offers extensive customization options:

Multiple Plots in One Figure

import matplotlib.pyplot as plt

# Create figure with subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# First subplot: Line plot
x1 = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
ax1.plot(x1, y1, marker='o')
ax1.set_title('Line Plot')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')

# Second subplot: Bar chart
categories = ['A', 'B', 'C']
values = [10, 20, 15]
ax2.bar(categories, values, color=['red', 'blue', 'green'])
ax2.set_title('Bar Chart')
ax2.set_xlabel('Category')
ax2.set_ylabel('Value')

plt.tight_layout()  # Adjust spacing
plt.show()

Saving Plots to Files

import matplotlib.pyplot as plt

# Create a plot
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.plot(x, y, marker='o')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('My Plot')

# Save to file (instead of showing)
plt.savefig('my_plot.png', dpi=300, bbox_inches='tight')
# Formats: PNG, PDF, SVG, JPG

Common Customization Options

Colors: Use color names ('red', 'blue') or hex codes ('#FF5733')
Line styles: Use linestyle='--' for dashed, '-.' for dash-dot
Markers: Use marker='o' for circles, 's' for squares
Figure size: Use figsize=(width, height) in inches
Legends: Use plt.legend() to show what each line/bar represents
Grid: Use plt.grid(True) to add grid lines

Summary

In this lesson, you learned:

Visualization: Makes data easier to understand and identify patterns
matplotlib: Python library for creating plots and charts
Plot types: Line plots, bar charts, scatter plots, histograms
Customization: Colors, labels, titles, markers, grid lines
Display: Use plt.show() to display plots or plt.savefig() to save them
Subplots: Create multiple plots in one figure

Remember

Good visualizations tell a story! Choose the right plot type for your data, always add clear labels and titles, and use colors and styles that make your data easy to understand.

End-of-Lesson Exercises

Think about these questions to reinforce what you've learned:

Exercise 1: Data Visualization

Why is data visualization important? What types of plots would you use for different situations?

Exercise 2: matplotlib Basics

What are the basic steps to create a plot with matplotlib?

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