Chapter 1.4: Visualizing Data with Matplotlib

Turn Numbers into Beautiful, Insightful Charts

🚀 PROJECT 1.4 | Difficulty: Intermediate | Time: 20 minutes

📊 Complexity Level: Intermediate ⭐⭐

Learn to create professional visualizations! Matplotlib is the foundation of data visualization in Python.

💻 Interactive Options:

📓 Open in JupyterLite - Full Jupyter environment in your browser
▶️ Run code directly below - All code cells on this page are editable and runnable
📥 Download Notebook (Challenge) - For use in local Jupyter or Google Colab

Note

Special Note: This project is designed for students who have completed the first chapter of CS Quests and are familiar with Python basics. If you’re new to programming, consider starting with the earlier lessons before diving into this project.

Another Special Note: This project requires a local Python environment for full functionality. While we provide examples and code snippets here, some features may not run directly in the browser. For the best experience, consider setting up the project on your own computer.

📖 Introduction: Why Visualize Data?

Numbers tell a story, but visualizations make that story instantly understandable. Compare these:

Numbers: “The average GPA for CS students is 3.45, Math is 3.52, Biology is 3.38”
Visualization: A colorful bar chart showing all majors at a glance! 📊

🎯 Real-World Use: When data scientists at Netflix want to show how viewing patterns change throughout the day, they don’t present a table of millions of numbers—they create a visualization that executives can understand in seconds!

🎨 Introduction to Matplotlib

Matplotlib is Python’s most popular plotting library. It can create:

📊 Bar charts
📈 Line graphs
🎯 Scatter plots
🥧 Pie charts
And much more!

Let’s start simple:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

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

# Create a basic line plot
plt.figure(figsize=(8, 5))
plt.plot(x, y, marker='o', color='blue', linewidth=2)
plt.title('My First Matplotlib Plot', fontsize=16, fontweight='bold')
plt.xlabel('X Values')
plt.ylabel('Y Values')
plt.grid(True, alpha=0.3)
plt.show()

print("📊 Your first plot is ready!")

📊 Different Types of Plots

Bar Chart

Perfect for comparing categories:

# Student enrollment by major
majors = ['CS', 'Math', 'Biology', 'Physics', 'Engineering']
enrollments = [45, 32, 38, 28, 41]

plt.figure(figsize=(10, 6))
colors = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12', '#9b59b6']
plt.bar(majors, enrollments, color=colors, edgecolor='black', linewidth=1.5)
plt.title('Student Enrollment by Major', fontsize=16, fontweight='bold')
plt.xlabel('Major', fontsize=12)
plt.ylabel('Number of Students', fontsize=12)
plt.ylim(0, 50)

# Add value labels on top of bars
for i, v in enumerate(enrollments):
    plt.text(i, v + 1, str(v), ha='center', fontweight='bold')

plt.show()
print("📊 Bar chart shows which majors are most popular!")

Line Graph

Great for showing trends over time:

# Student GPA over semesters
semesters = ['Fall 2023', 'Spring 2024', 'Fall 2024', 'Spring 2025']
alice_gpa = [3.4, 3.6, 3.7, 3.8]
bob_gpa = [3.2, 3.3, 3.5, 3.6]
charlie_gpa = [3.8, 3.7, 3.9, 3.9]

plt.figure(figsize=(10, 6))
plt.plot(semesters, alice_gpa, marker='o', linewidth=2, label='Alice', color='#3498db')
plt.plot(semesters, bob_gpa, marker='s', linewidth=2, label='Bob', color='#e74c3c')
plt.plot(semesters, charlie_gpa, marker='^', linewidth=2, label='Charlie', color='#2ecc71')

plt.title('Student GPA Progress Over Time', fontsize=16, fontweight='bold')
plt.xlabel('Semester', fontsize=12)
plt.ylabel('GPA', fontsize=12)
plt.ylim(3.0, 4.0)
plt.legend(fontsize=11)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

print("📈 Line graph shows how students improve over time!")

Scatter Plot

Perfect for showing relationships between two variables:

# Relationship between study hours and GPA
np.random.seed(42)
study_hours = np.random.uniform(5, 30, 50)
gpa = 2.5 + (study_hours * 0.04) + np.random.normal(0, 0.15, 50)
gpa = np.clip(gpa, 2.0, 4.0)  # Keep GPA in valid range

plt.figure(figsize=(10, 6))
plt.scatter(study_hours, gpa, s=100, alpha=0.6, c=gpa, cmap='viridis', edgecolors='black')
plt.colorbar(label='GPA')
plt.title('Relationship: Study Hours vs GPA', fontsize=16, fontweight='bold')
plt.xlabel('Weekly Study Hours', fontsize=12)
plt.ylabel('GPA', fontsize=12)
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

print("🎯 Scatter plot reveals: more study hours → higher GPA!")

Histogram

Shows the distribution of values:

# Distribution of student ages
ages = np.random.normal(21, 1.5, 200).astype(int)
ages = np.clip(ages, 18, 25)

plt.figure(figsize=(10, 6))
plt.hist(ages, bins=range(18, 26), edgecolor='black', color='#3498db', alpha=0.7)
plt.title('Distribution of Student Ages', fontsize=16, fontweight='bold')
plt.xlabel('Age', fontsize=12)
plt.ylabel('Number of Students', fontsize=12)
plt.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
plt.show()

print("📊 Most students are around 21 years old!")

🎨 Customization: Make It Beautiful!

Multiple Subplots

Create a dashboard of multiple charts:

# Create sample data
np.random.seed(42)
students_df = pd.DataFrame({
    'Major': np.random.choice(['CS', 'Math', 'Biology', 'Physics'], 100),
    'GPA': np.random.uniform(2.5, 4.0, 100),
    'Study_Hours': np.random.uniform(5, 30, 100),
    'Age': np.random.randint(18, 25, 100)
})

# Create a 2x2 grid of plots
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Student Data Dashboard', fontsize=18, fontweight='bold')

# Plot 1: Major distribution
major_counts = students_df['Major'].value_counts()
axes[0, 0].bar(major_counts.index, major_counts.values, color='#3498db')
axes[0, 0].set_title('Students by Major')
axes[0, 0].set_ylabel('Count')

# Plot 2: GPA distribution
axes[0, 1].hist(students_df['GPA'], bins=15, color='#2ecc71', edgecolor='black')
axes[0, 1].set_title('GPA Distribution')
axes[0, 1].set_xlabel('GPA')
axes[0, 1].set_ylabel('Frequency')

# Plot 3: Study Hours vs GPA
axes[1, 0].scatter(students_df['Study_Hours'], students_df['GPA'], alpha=0.5, color='#e74c3c')
axes[1, 0].set_title('Study Hours vs GPA')
axes[1, 0].set_xlabel('Weekly Study Hours')
axes[1, 0].set_ylabel('GPA')

# Plot 4: Average GPA by Major
avg_gpa = students_df.groupby('Major')['GPA'].mean().sort_values()
axes[1, 1].barh(avg_gpa.index, avg_gpa.values, color='#9b59b6')
axes[1, 1].set_title('Average GPA by Major')
axes[1, 1].set_xlabel('Average GPA')

plt.tight_layout()
plt.show()

print("🎨 A complete dashboard in one view!")

💡 Styling and Themes

# Using different styles
styles = ['default', 'seaborn-v0_8-darkgrid', 'ggplot', 'bmh']

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Same Data, Different Styles', fontsize=16, fontweight='bold')

x = np.linspace(0, 10, 100)
y = np.sin(x)

for idx, (ax, style) in enumerate(zip(axes.flat, styles)):
    with plt.style.context(style):
        ax.plot(x, y, linewidth=2)
        ax.set_title(f'Style: {style}')
        ax.grid(True)

plt.tight_layout()
plt.show()

print("🎨 Different styles change the entire look!")

📊 Real-World Example: Complete Analysis

Let’s create a complete analysis with pandas and matplotlib:

# Create realistic student dataset
np.random.seed(42)
n_students = 100

student_data = pd.DataFrame({
    'StudentID': range(1, n_students + 1),
    'Major': np.random.choice(['CS', 'Math', 'Biology', 'Physics', 'Engineering'], n_students),
    'GPA': np.round(np.random.uniform(2.5, 4.0, n_students), 2),
    'Study_Hours': np.random.randint(5, 35, n_students),
    'Projects': np.random.randint(0, 10, n_students),
    'Scholarship': np.random.choice([True, False], n_students, p=[0.3, 0.7])
})

# Analysis and visualization
fig, axes = plt.subplots(1, 3, figsize=(16, 5))
fig.suptitle('🎓 Complete Student Performance Analysis', fontsize=16, fontweight='bold')

# Chart 1: GPA by Major
major_gpa = student_data.groupby('Major')['GPA'].mean().sort_values()
axes[0].barh(major_gpa.index, major_gpa.values, color='#3498db')
axes[0].set_xlabel('Average GPA')
axes[0].set_title('Average GPA by Major')
axes[0].grid(True, alpha=0.3, axis='x')

# Chart 2: Scholarship vs Non-Scholarship GPA
scholarship_gpa = student_data.groupby('Scholarship')['GPA'].mean()
labels = ['No Scholarship', 'Scholarship']
colors = ['#e74c3c', '#2ecc71']
axes[1].bar(labels, scholarship_gpa.values, color=colors, edgecolor='black')
axes[1].set_ylabel('Average GPA')
axes[1].set_title('GPA: Scholarship Impact')
axes[1].grid(True, alpha=0.3, axis='y')

# Chart 3: Projects vs GPA
axes[2].scatter(student_data['Projects'], student_data['GPA'], 
                alpha=0.5, s=50, c='#9b59b6', edgecolors='black')
axes[2].set_xlabel('Number of Projects')
axes[2].set_ylabel('GPA')
axes[2].set_title('Projects Completed vs GPA')
axes[2].grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

print("📊 Complete analysis shows multiple insights at once!")

🎮 Practice Challenges

🏆 Challenge 1: Create a Visualization

Create a bar chart showing:

Your top 5 favorite movies/games/books
Rate each from 1-10
Color bars based on rating (low=red, high=green)

# Your code here!
# Example solution:
items = ['Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5']
ratings = [8, 9, 7, 10, 8]

plt.figure(figsize=(10, 6))
colors_map = ['#e74c3c' if r < 7 else '#f39c12' if r < 9 else '#2ecc71' for r in ratings]
plt.bar(items, ratings, color=colors_map, edgecolor='black')
plt.title('My Top 5 Favorites', fontsize=14, fontweight='bold')
plt.ylabel('Rating (out of 10)')
plt.ylim(0, 10)
plt.show()

🏆 Challenge 2: Multi-Plot Dashboard

Create a 2x2 grid showing:

Your weekly schedule (bar chart of hours per activity)
Your mood over a week (line chart)
Any scatter plot of your choice
A histogram of something interesting

🚀 What’s Next?

You now know both pandas and matplotlib! In the next slide, we’ll:

Combine everything into a real data analysis project
Load data from CSV files
Clean, analyze, and visualize real-world data
Build a complete mini data science project!

--- title: "Chapter 1.4: Visualizing Data with Matplotlib" subtitle: "Turn Numbers into Beautiful, Insightful Charts" format: live-html: code-tools: true execute: eval: false --- ::: {.quest-badge} 🚀 PROJECT 1.4 | Difficulty: Intermediate | Time: 20 minutes ::: ::: {.concept-box} **📊 Complexity Level: Intermediate ⭐⭐** Learn to create professional visualizations! Matplotlib is the foundation of data visualization in Python. ::: ::: {.tip-box} **💻 Interactive Options:** - 📓 **<a href="../../jupyterlite/lab/index.html?path=projects/uv/04-matplotlib-viz.ipynb" target="_blank">Open in JupyterLite</a>** - Full Jupyter environment in your browser - ▶️ **Run code directly below** - All code cells on this page are editable and runnable - 📥 **<a href="../../files/projects/uv/04-matplotlib-viz.ipynb" target="_blank">Download Notebook (Challenge)</a>** - For use in local Jupyter or Google Colab ::: ::: {.callout-note} Special Note: This project is designed for students who have completed the first chapter of CS Quests and are familiar with Python basics. If you're new to programming, consider starting with the earlier lessons before diving into this project. Another Special Note: This project requires a local Python environment for full functionality. While we provide examples and code snippets here, some features may not run directly in the browser. For the best experience, consider setting up the project on your own computer. ::: ## 📖 Introduction: Why Visualize Data? Numbers tell a story, but visualizations make that story **instantly understandable**. Compare these: - **Numbers**: "The average GPA for CS students is 3.45, Math is 3.52, Biology is 3.38" - **Visualization**: A colorful bar chart showing all majors at a glance! 📊 ::: {.story-box} **🎯 Real-World Use**: When data scientists at Netflix want to show how viewing patterns change throughout the day, they don't present a table of millions of numbers—they create a visualization that executives can understand in seconds! ::: ## 🎨 Introduction to Matplotlib **Matplotlib** is Python's most popular plotting library. It can create: - 📊 Bar charts - 📈 Line graphs - 🎯 Scatter plots - 🥧 Pie charts - And much more! Let's start simple: ```{python} import matplotlib.pyplot as plt import numpy as np import pandas as pd # Create simple data x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] # Create a basic line plot plt.figure(figsize=(8, 5)) plt.plot(x, y, marker='o', color='blue', linewidth=2) plt.title('My First Matplotlib Plot', fontsize=16, fontweight='bold') plt.xlabel('X Values') plt.ylabel('Y Values') plt.grid(True, alpha=0.3) plt.show() print("📊 Your first plot is ready!") ``` ## 📊 Different Types of Plots ### Bar Chart Perfect for comparing categories: ```{python} # Student enrollment by major majors = ['CS', 'Math', 'Biology', 'Physics', 'Engineering'] enrollments = [45, 32, 38, 28, 41] plt.figure(figsize=(10, 6)) colors = ['#3498db', '#e74c3c', '#2ecc71', '#f39c12', '#9b59b6'] plt.bar(majors, enrollments, color=colors, edgecolor='black', linewidth=1.5) plt.title('Student Enrollment by Major', fontsize=16, fontweight='bold') plt.xlabel('Major', fontsize=12) plt.ylabel('Number of Students', fontsize=12) plt.ylim(0, 50) # Add value labels on top of bars for i, v in enumerate(enrollments): plt.text(i, v + 1, str(v), ha='center', fontweight='bold') plt.show() print("📊 Bar chart shows which majors are most popular!") ``` ### Line Graph Great for showing trends over time: ```{python} # Student GPA over semesters semesters = ['Fall 2023', 'Spring 2024', 'Fall 2024', 'Spring 2025'] alice_gpa = [3.4, 3.6, 3.7, 3.8] bob_gpa = [3.2, 3.3, 3.5, 3.6] charlie_gpa = [3.8, 3.7, 3.9, 3.9] plt.figure(figsize=(10, 6)) plt.plot(semesters, alice_gpa, marker='o', linewidth=2, label='Alice', color='#3498db') plt.plot(semesters, bob_gpa, marker='s', linewidth=2, label='Bob', color='#e74c3c') plt.plot(semesters, charlie_gpa, marker='^', linewidth=2, label='Charlie', color='#2ecc71') plt.title('Student GPA Progress Over Time', fontsize=16, fontweight='bold') plt.xlabel('Semester', fontsize=12) plt.ylabel('GPA', fontsize=12) plt.ylim(3.0, 4.0) plt.legend(fontsize=11) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show() print("📈 Line graph shows how students improve over time!") ``` ### Scatter Plot Perfect for showing relationships between two variables: ```{python} # Relationship between study hours and GPA np.random.seed(42) study_hours = np.random.uniform(5, 30, 50) gpa = 2.5 + (study_hours * 0.04) + np.random.normal(0, 0.15, 50) gpa = np.clip(gpa, 2.0, 4.0) # Keep GPA in valid range plt.figure(figsize=(10, 6)) plt.scatter(study_hours, gpa, s=100, alpha=0.6, c=gpa, cmap='viridis', edgecolors='black') plt.colorbar(label='GPA') plt.title('Relationship: Study Hours vs GPA', fontsize=16, fontweight='bold') plt.xlabel('Weekly Study Hours', fontsize=12) plt.ylabel('GPA', fontsize=12) plt.grid(True, alpha=0.3) plt.tight_layout() plt.show() print("🎯 Scatter plot reveals: more study hours → higher GPA!") ``` ### Histogram Shows the distribution of values: ```{python} # Distribution of student ages ages = np.random.normal(21, 1.5, 200).astype(int) ages = np.clip(ages, 18, 25) plt.figure(figsize=(10, 6)) plt.hist(ages, bins=range(18, 26), edgecolor='black', color='#3498db', alpha=0.7) plt.title('Distribution of Student Ages', fontsize=16, fontweight='bold') plt.xlabel('Age', fontsize=12) plt.ylabel('Number of Students', fontsize=12) plt.grid(True, alpha=0.3, axis='y') plt.tight_layout() plt.show() print("📊 Most students are around 21 years old!") ``` ## 🎨 Customization: Make It Beautiful! ### Multiple Subplots Create a dashboard of multiple charts: ```{python} # Create sample data np.random.seed(42) students_df = pd.DataFrame({ 'Major': np.random.choice(['CS', 'Math', 'Biology', 'Physics'], 100), 'GPA': np.random.uniform(2.5, 4.0, 100), 'Study_Hours': np.random.uniform(5, 30, 100), 'Age': np.random.randint(18, 25, 100) }) # Create a 2x2 grid of plots fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle('Student Data Dashboard', fontsize=18, fontweight='bold') # Plot 1: Major distribution major_counts = students_df['Major'].value_counts() axes[0, 0].bar(major_counts.index, major_counts.values, color='#3498db') axes[0, 0].set_title('Students by Major') axes[0, 0].set_ylabel('Count') # Plot 2: GPA distribution axes[0, 1].hist(students_df['GPA'], bins=15, color='#2ecc71', edgecolor='black') axes[0, 1].set_title('GPA Distribution') axes[0, 1].set_xlabel('GPA') axes[0, 1].set_ylabel('Frequency') # Plot 3: Study Hours vs GPA axes[1, 0].scatter(students_df['Study_Hours'], students_df['GPA'], alpha=0.5, color='#e74c3c') axes[1, 0].set_title('Study Hours vs GPA') axes[1, 0].set_xlabel('Weekly Study Hours') axes[1, 0].set_ylabel('GPA') # Plot 4: Average GPA by Major avg_gpa = students_df.groupby('Major')['GPA'].mean().sort_values() axes[1, 1].barh(avg_gpa.index, avg_gpa.values, color='#9b59b6') axes[1, 1].set_title('Average GPA by Major') axes[1, 1].set_xlabel('Average GPA') plt.tight_layout() plt.show() print("🎨 A complete dashboard in one view!") ``` ## 💡 Styling and Themes ```{python} # Using different styles styles = ['default', 'seaborn-v0_8-darkgrid', 'ggplot', 'bmh'] fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle('Same Data, Different Styles', fontsize=16, fontweight='bold') x = np.linspace(0, 10, 100) y = np.sin(x) for idx, (ax, style) in enumerate(zip(axes.flat, styles)): with plt.style.context(style): ax.plot(x, y, linewidth=2) ax.set_title(f'Style: {style}') ax.grid(True) plt.tight_layout() plt.show() print("🎨 Different styles change the entire look!") ``` ## 📊 Real-World Example: Complete Analysis Let's create a complete analysis with pandas and matplotlib: ```{python} # Create realistic student dataset np.random.seed(42) n_students = 100 student_data = pd.DataFrame({ 'StudentID': range(1, n_students + 1), 'Major': np.random.choice(['CS', 'Math', 'Biology', 'Physics', 'Engineering'], n_students), 'GPA': np.round(np.random.uniform(2.5, 4.0, n_students), 2), 'Study_Hours': np.random.randint(5, 35, n_students), 'Projects': np.random.randint(0, 10, n_students), 'Scholarship': np.random.choice([True, False], n_students, p=[0.3, 0.7]) }) # Analysis and visualization fig, axes = plt.subplots(1, 3, figsize=(16, 5)) fig.suptitle('🎓 Complete Student Performance Analysis', fontsize=16, fontweight='bold') # Chart 1: GPA by Major major_gpa = student_data.groupby('Major')['GPA'].mean().sort_values() axes[0].barh(major_gpa.index, major_gpa.values, color='#3498db') axes[0].set_xlabel('Average GPA') axes[0].set_title('Average GPA by Major') axes[0].grid(True, alpha=0.3, axis='x') # Chart 2: Scholarship vs Non-Scholarship GPA scholarship_gpa = student_data.groupby('Scholarship')['GPA'].mean() labels = ['No Scholarship', 'Scholarship'] colors = ['#e74c3c', '#2ecc71'] axes[1].bar(labels, scholarship_gpa.values, color=colors, edgecolor='black') axes[1].set_ylabel('Average GPA') axes[1].set_title('GPA: Scholarship Impact') axes[1].grid(True, alpha=0.3, axis='y') # Chart 3: Projects vs GPA axes[2].scatter(student_data['Projects'], student_data['GPA'], alpha=0.5, s=50, c='#9b59b6', edgecolors='black') axes[2].set_xlabel('Number of Projects') axes[2].set_ylabel('GPA') axes[2].set_title('Projects Completed vs GPA') axes[2].grid(True, alpha=0.3) plt.tight_layout() plt.show() print("📊 Complete analysis shows multiple insights at once!") ``` ## 🎮 Practice Challenges ::: {.challenge-box} **🏆 Challenge 1: Create a Visualization** Create a bar chart showing: - Your top 5 favorite movies/games/books - Rate each from 1-10 - Color bars based on rating (low=red, high=green) ```{python} # Your code here! # Example solution: items = ['Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5'] ratings = [8, 9, 7, 10, 8] plt.figure(figsize=(10, 6)) colors_map = ['#e74c3c' if r < 7 else '#f39c12' if r < 9 else '#2ecc71' for r in ratings] plt.bar(items, ratings, color=colors_map, edgecolor='black') plt.title('My Top 5 Favorites', fontsize=14, fontweight='bold') plt.ylabel('Rating (out of 10)') plt.ylim(0, 10) plt.show() ``` ::: ::: {.challenge-box} **🏆 Challenge 2: Multi-Plot Dashboard** Create a 2x2 grid showing: 1. Your weekly schedule (bar chart of hours per activity) 2. Your mood over a week (line chart) 3. Any scatter plot of your choice 4. A histogram of something interesting ::: ## 🚀 What's Next? You now know both pandas and matplotlib! In the next slide, we'll: - Combine everything into a real data analysis project - Load data from CSV files - Clean, analyze, and visualize real-world data - Build a complete mini data science project! --- ::: {.navigation-box} **Previous**: <a href="03-pandas-basics.qmd" target="_blank">1.3 Data Magic with Pandas</a> | **Next**: <a href="05-complete-project.qmd" target="_blank">1.5 Real World Data Analysis Project</a> :::