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

📖 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:** - 📓 **[Open in JupyterLite](../../jupyterlite/lab/index.html?path=projects/uv/04-matplotlib-viz.ipynb)** - Full Jupyter environment in your browser - ▶️ **Run code directly below** - All code cells on this page are editable and runnable - 📥 **[Download Notebook (Challenge)](../../files/projects/uv/data-analysis-challenge.ipynb)** - For use in local Jupyter or Google Colab ::: ## 📖 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**: [1.3 Data Magic with Pandas](03-pandas-basics.qmd) | **Next**: [1.5 Real World Data Analysis Project](05-complete-project.qmd) :::