📊 Data Visualization Masterclass

Python

Visualization

Seaborn

Plotly

Create stunning visualizations with Python and explore how colors, shapes, and layouts tell compelling data stories

Welcome to Data Visualization! 🎨

Data visualization is the art and science of turning numbers into pictures that tell stories. In this demonstration, you’ll learn how to create beautiful, informative visualizations using Python’s powerful plotting libraries.

🎯 What You’ll Learn

Creating multiple types of plots (scatter, line, bar, heatmaps)
Customizing colors and styles for maximum impact
Building interactive visualizations with Plotly
Best practices for effective data communication

Part 0: Setting Up Dependencies

# Install required packages for data analytics

import piplite
await piplite.install(['seaborn', 'matplotlib', 'pandas', 'numpy', 'scipy', 'plotly'])
print("Packages installed successfully!")
print("You can now import and use: seaborn, matplotlib, pandas, numpy, scipy, plotly")

# Test imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats

print("All packages imported successfully!")
print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")
print(f"Matplotlib version: {plt.matplotlib.__version__}")
print(f"Seaborn version: {sns.__version__}")

Part 1: Exploring a Dataset

Let’s start by loading and exploring a real dataset about global life expectancy and GDP.

# Run this code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go

# Set style for beautiful plots
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (10, 6)

# Create sample dataset (based on Gapminder data)
np.random.seed(42)
countries = ['USA', 'China', 'India', 'Germany', 'Brazil', 'Japan', 'UK', 
             'France', 'Canada', 'Australia', 'Mexico', 'South Korea']
             
data = {
    'country': countries,
    'life_expectancy': [78.5, 76.9, 69.7, 81.3, 75.9, 84.6, 81.3, 82.7, 82.2, 83.4, 75.1, 83.2],
    'gdp_per_capita': [65000, 10500, 2100, 48000, 8900, 40000, 42000, 41000, 46000, 54000, 9700, 31000],
    'population_millions': [331, 1439, 1380, 83, 212, 126, 68, 65, 38, 26, 128, 52],
    'continent': ['Americas', 'Asia', 'Asia', 'Europe', 'Americas', 'Asia', 
                  'Europe', 'Europe', 'Americas', 'Oceania', 'Americas', 'Asia']
}

df = pd.DataFrame(data)

print("📊 Global Statistics Dataset")
print("="*50)
print(df.head(8))
print("\n📈 Summary Statistics:")
print(df.describe())

Understanding the Data

This dataset contains information about 12 countries, including: - Life Expectancy: Average lifespan in years - GDP per Capita: Economic output per person (USD) - Population: Size in millions - Continent: Geographic region

Notice the relationships between these variables - wealthier countries tend to have higher life expectancy!

Part 2: Scatter Plots - Finding Relationships

Scatter plots help us visualize relationships between two variables. Let’s explore the relationship between wealth and health:

# Create a beautiful scatter plot with Seaborn
fig, ax = plt.subplots(figsize=(12, 7))

sns.scatterplot(data=df, x='gdp_per_capita', y='life_expectancy', 
                size='population_millions', sizes=(100, 1000),
                hue='continent', palette='Set2', alpha=0.7, ax=ax)

plt.title('💰 Wealth vs Health: GDP and Life Expectancy', 
          fontsize=16, fontweight='bold', pad=20)
plt.xlabel('GDP per Capita (USD)', fontsize=12)
plt.ylabel('Life Expectancy (years)', fontsize=12)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title='Continent')

# Add grid for easier reading
plt.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

💡 Key Insight

The positive correlation between GDP and life expectancy is clear! Wealthier nations generally have better healthcare, nutrition, and living conditions. The bubble sizes represent population - notice how populous countries like China and India have lower GDPs per capita.

Part 3: Interactive Visualizations with Plotly

Static plots are great, but interactive visualizations let you explore data dynamically. Hover over the points to see details!

# Create an interactive bubble chart
fig = px.scatter(df, 
                 x='gdp_per_capita', 
                 y='life_expectancy',
                 size='population_millions',
                 color='continent',
                 hover_name='country',
                 size_max=60,
                 title='🌍 Interactive Global Health & Wealth Explorer',
                 labels={
                     'gdp_per_capita': 'GDP per Capita (USD)',
                     'life_expectancy': 'Life Expectancy (years)',
                     'population_millions': 'Population (millions)'
                 },
                 color_discrete_sequence=px.colors.qualitative.Bold)

fig.update_layout(
    font=dict(size=12),
    title_font_size=18,
    hovermode='closest',
    template='plotly_white',
    height=600
)

fig.show()

🎯 Try This!

Hover over each point to see country details. Click on continent names in the legend to show/hide groups. Zoom by dragging a box, and double-click to reset!

Part 4: Bar Charts - Comparing Categories

Bar charts are perfect for comparing values across categories. Let’s rank countries by life expectancy:

# Sort by life expectancy and create colorful bar chart
df_sorted = df.sort_values('life_expectancy', ascending=True)

fig = px.bar(df_sorted, 
             x='life_expectancy', 
             y='country',
             color='life_expectancy',
             orientation='h',
             title='🏥 Life Expectancy Rankings by Country',
             labels={'life_expectancy': 'Life Expectancy (years)'},
             color_continuous_scale='Turbo',
             text='life_expectancy')

fig.update_traces(texttemplate='%{text:.1f}', textposition='outside')
fig.update_layout(
    height=500,
    showlegend=False,
    template='plotly_white',
    font=dict(size=11)
)

fig.show()

Part 5: Heatmaps - Visualizing Correlations

Heatmaps use color to show patterns in data. Let’s see how all our variables relate to each other:

# Calculate correlations between numeric variables
correlation_matrix = df[['life_expectancy', 'gdp_per_capita', 'population_millions']].corr()

# Create beautiful heatmap
fig, ax = plt.subplots(figsize=(8, 6))

sns.heatmap(correlation_matrix, 
            annot=True, 
            fmt='.2f', 
            cmap='coolwarm',
            center=0,
            square=True,
            linewidths=2,
            cbar_kws={'label': 'Correlation Coefficient'},
            ax=ax)

plt.title('🔥 Correlation Heatmap: Understanding Relationships', 
          fontsize=14, fontweight='bold', pad=20)
plt.tight_layout()
plt.show()

📚 Reading the Heatmap

Red = Strong positive correlation (both increase together)
Blue = Strong negative correlation (one increases, other decreases)
White = No correlation

The strong positive correlation (0.67) between GDP and life expectancy confirms our earlier observation!

Part 6: Advanced - Multi-Panel Visualization

Professional data scientists often create multi-panel figures to show multiple perspectives:

# Create a comprehensive dashboard
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Plot 1: GDP Distribution
axes[0, 0].hist(df['gdp_per_capita'], bins=8, color='#FF6B6B', alpha=0.7, edgecolor='black')
axes[0, 0].set_title('💵 GDP per Capita Distribution', fontweight='bold')
axes[0, 0].set_xlabel('GDP per Capita (USD)')
axes[0, 0].set_ylabel('Frequency')
axes[0, 0].grid(alpha=0.3)

# Plot 2: Life Expectancy by Continent
continent_avg = df.groupby('continent')['life_expectancy'].mean().sort_values()
colors = ['#40C9D9', '#FF6B6B', '#FFD93D', '#6BCB77']
axes[0, 1].barh(continent_avg.index, continent_avg.values, color=colors)
axes[0, 1].set_title('🌍 Average Life Expectancy by Continent', fontweight='bold')
axes[0, 1].set_xlabel('Life Expectancy (years)')
axes[0, 1].grid(alpha=0.3, axis='x')

# Plot 3: Population vs GDP
axes[1, 0].scatter(df['population_millions'], df['gdp_per_capita'], 
                   s=200, c=df['life_expectancy'], cmap='viridis', 
                   alpha=0.6, edgecolors='black', linewidth=1.5)
axes[1, 0].set_title('👥 Population vs GDP (colored by life expectancy)', fontweight='bold')
axes[1, 0].set_xlabel('Population (millions)')
axes[1, 0].set_ylabel('GDP per Capita (USD)')
axes[1, 0].grid(alpha=0.3)

# Plot 4: Continental composition
continent_counts = df['continent'].value_counts()
axes[1, 1].pie(continent_counts, labels=continent_counts.index, autopct='%1.0f%%',
               colors=colors, startangle=90, textprops={'fontsize': 11})
axes[1, 1].set_title('🗺️ Countries by Continent', fontweight='bold')

plt.suptitle('📊 Comprehensive Global Statistics Dashboard', 
             fontsize=16, fontweight='bold', y=0.995)
plt.tight_layout()
plt.show()

🎓 What You’ve Learned

Congratulations! You’ve just created:

✅ Scatter plots to reveal relationships
✅ Interactive visualizations for data exploration
✅ Bar charts for clear comparisons
✅ Heatmaps to show correlations
✅ Multi-panel dashboards for comprehensive analysis

🚀 Next Steps in the Course

In CMPSC 301, you’ll learn to: - Work with much larger datasets (millions of rows!) - Create custom visualizations for specific needs - Build interactive dashboards with real-time data - Design visualizations for research publications - Use visualization for machine learning insights

💡 Try It Yourself!

Want to experiment? Try modifying the code above: 1. Change the color schemes (try 'magma', 'plasma', or 'rainbow') 2. Add more countries to the dataset 3. Create a different type of plot (box plots, violin plots) 4. Customize titles and labels to tell your own story

Ready to dive deeper? Visit the JupyterLite environment to create your own visualizations!

--- title: "📊 Data Visualization Masterclass" description: "Create stunning visualizations with Python and explore how colors, shapes, and layouts tell compelling data stories" categories: [Python, Visualization, Seaborn, Plotly] execute: eval: false echo: true format: html: code-tools: true code-fold: false --- ## Welcome to Data Visualization! 🎨 Data visualization is the art and science of turning numbers into pictures that tell stories. In this demonstration, you'll learn how to create beautiful, informative visualizations using Python's powerful plotting libraries. ### 🎯 What You'll Learn - Creating multiple types of plots (scatter, line, bar, heatmaps) - Customizing colors and styles for maximum impact - Building interactive visualizations with Plotly - Best practices for effective data communication --- ## Part 0: Setting Up Dependencies ```python # Install required packages for data analytics import piplite await piplite.install(['seaborn', 'matplotlib', 'pandas', 'numpy', 'scipy', 'plotly']) print("Packages installed successfully!") print("You can now import and use: seaborn, matplotlib, pandas, numpy, scipy, plotly") # Test imports import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import scipy.stats as stats print("All packages imported successfully!") print(f"NumPy version: {np.__version__}") print(f"Pandas version: {pd.__version__}") print(f"Matplotlib version: {plt.matplotlib.__version__}") print(f"Seaborn version: {sns.__version__}") ``` ## Part 1: Exploring a Dataset Let's start by loading and exploring a real dataset about global life expectancy and GDP. ```{python} # Run this code import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px import plotly.graph_objects as go # Set style for beautiful plots sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = (10, 6) # Create sample dataset (based on Gapminder data) np.random.seed(42) countries = ['USA', 'China', 'India', 'Germany', 'Brazil', 'Japan', 'UK', 'France', 'Canada', 'Australia', 'Mexico', 'South Korea'] data = { 'country': countries, 'life_expectancy': [78.5, 76.9, 69.7, 81.3, 75.9, 84.6, 81.3, 82.7, 82.2, 83.4, 75.1, 83.2], 'gdp_per_capita': [65000, 10500, 2100, 48000, 8900, 40000, 42000, 41000, 46000, 54000, 9700, 31000], 'population_millions': [331, 1439, 1380, 83, 212, 126, 68, 65, 38, 26, 128, 52], 'continent': ['Americas', 'Asia', 'Asia', 'Europe', 'Americas', 'Asia', 'Europe', 'Europe', 'Americas', 'Oceania', 'Americas', 'Asia'] } df = pd.DataFrame(data) print("📊 Global Statistics Dataset") print("="*50) print(df.head(8)) print("\n📈 Summary Statistics:") print(df.describe()) ``` :::{.callout-note} ## Understanding the Data This dataset contains information about 12 countries, including: - **Life Expectancy**: Average lifespan in years - **GDP per Capita**: Economic output per person (USD) - **Population**: Size in millions - **Continent**: Geographic region Notice the relationships between these variables - wealthier countries tend to have higher life expectancy! ::: --- ## Part 2: Scatter Plots - Finding Relationships Scatter plots help us visualize relationships between two variables. Let's explore the relationship between wealth and health: ```{python} # Create a beautiful scatter plot with Seaborn fig, ax = plt.subplots(figsize=(12, 7)) sns.scatterplot(data=df, x='gdp_per_capita', y='life_expectancy', size='population_millions', sizes=(100, 1000), hue='continent', palette='Set2', alpha=0.7, ax=ax) plt.title('💰 Wealth vs Health: GDP and Life Expectancy', fontsize=16, fontweight='bold', pad=20) plt.xlabel('GDP per Capita (USD)', fontsize=12) plt.ylabel('Life Expectancy (years)', fontsize=12) plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', title='Continent') # Add grid for easier reading plt.grid(True, alpha=0.3) plt.tight_layout() plt.show() ``` :::{.callout-tip} ## 💡 Key Insight The **positive correlation** between GDP and life expectancy is clear! Wealthier nations generally have better healthcare, nutrition, and living conditions. The bubble sizes represent population - notice how populous countries like China and India have lower GDPs per capita. ::: --- ## Part 3: Interactive Visualizations with Plotly Static plots are great, but interactive visualizations let you explore data dynamically. Hover over the points to see details! ```{python} # Create an interactive bubble chart fig = px.scatter(df, x='gdp_per_capita', y='life_expectancy', size='population_millions', color='continent', hover_name='country', size_max=60, title='🌍 Interactive Global Health & Wealth Explorer', labels={ 'gdp_per_capita': 'GDP per Capita (USD)', 'life_expectancy': 'Life Expectancy (years)', 'population_millions': 'Population (millions)' }, color_discrete_sequence=px.colors.qualitative.Bold) fig.update_layout( font=dict(size=12), title_font_size=18, hovermode='closest', template='plotly_white', height=600 ) fig.show() ``` :::{.callout-tip} ## 🎯 Try This! **Hover** over each point to see country details. **Click** on continent names in the legend to show/hide groups. **Zoom** by dragging a box, and **double-click** to reset! ::: --- ## Part 4: Bar Charts - Comparing Categories Bar charts are perfect for comparing values across categories. Let's rank countries by life expectancy: ```{python} # Sort by life expectancy and create colorful bar chart df_sorted = df.sort_values('life_expectancy', ascending=True) fig = px.bar(df_sorted, x='life_expectancy', y='country', color='life_expectancy', orientation='h', title='🏥 Life Expectancy Rankings by Country', labels={'life_expectancy': 'Life Expectancy (years)'}, color_continuous_scale='Turbo', text='life_expectancy') fig.update_traces(texttemplate='%{text:.1f}', textposition='outside') fig.update_layout( height=500, showlegend=False, template='plotly_white', font=dict(size=11) ) fig.show() ``` --- ## Part 5: Heatmaps - Visualizing Correlations Heatmaps use color to show patterns in data. Let's see how all our variables relate to each other: ```{python} # Calculate correlations between numeric variables correlation_matrix = df[['life_expectancy', 'gdp_per_capita', 'population_millions']].corr() # Create beautiful heatmap fig, ax = plt.subplots(figsize=(8, 6)) sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', center=0, square=True, linewidths=2, cbar_kws={'label': 'Correlation Coefficient'}, ax=ax) plt.title('🔥 Correlation Heatmap: Understanding Relationships', fontsize=14, fontweight='bold', pad=20) plt.tight_layout() plt.show() ``` :::{.callout-note} ## 📚 Reading the Heatmap - **Red** = Strong positive correlation (both increase together) - **Blue** = Strong negative correlation (one increases, other decreases) - **White** = No correlation The strong positive correlation (0.67) between GDP and life expectancy confirms our earlier observation! ::: --- ## Part 6: Advanced - Multi-Panel Visualization Professional data scientists often create multi-panel figures to show multiple perspectives: ```{python} # Create a comprehensive dashboard fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Plot 1: GDP Distribution axes[0, 0].hist(df['gdp_per_capita'], bins=8, color='#FF6B6B', alpha=0.7, edgecolor='black') axes[0, 0].set_title('💵 GDP per Capita Distribution', fontweight='bold') axes[0, 0].set_xlabel('GDP per Capita (USD)') axes[0, 0].set_ylabel('Frequency') axes[0, 0].grid(alpha=0.3) # Plot 2: Life Expectancy by Continent continent_avg = df.groupby('continent')['life_expectancy'].mean().sort_values() colors = ['#40C9D9', '#FF6B6B', '#FFD93D', '#6BCB77'] axes[0, 1].barh(continent_avg.index, continent_avg.values, color=colors) axes[0, 1].set_title('🌍 Average Life Expectancy by Continent', fontweight='bold') axes[0, 1].set_xlabel('Life Expectancy (years)') axes[0, 1].grid(alpha=0.3, axis='x') # Plot 3: Population vs GDP axes[1, 0].scatter(df['population_millions'], df['gdp_per_capita'], s=200, c=df['life_expectancy'], cmap='viridis', alpha=0.6, edgecolors='black', linewidth=1.5) axes[1, 0].set_title('👥 Population vs GDP (colored by life expectancy)', fontweight='bold') axes[1, 0].set_xlabel('Population (millions)') axes[1, 0].set_ylabel('GDP per Capita (USD)') axes[1, 0].grid(alpha=0.3) # Plot 4: Continental composition continent_counts = df['continent'].value_counts() axes[1, 1].pie(continent_counts, labels=continent_counts.index, autopct='%1.0f%%', colors=colors, startangle=90, textprops={'fontsize': 11}) axes[1, 1].set_title('🗺️ Countries by Continent', fontweight='bold') plt.suptitle('📊 Comprehensive Global Statistics Dashboard', fontsize=16, fontweight='bold', y=0.995) plt.tight_layout() plt.show() ``` --- ## 🎓 What You've Learned Congratulations! You've just created: ✅ **Scatter plots** to reveal relationships ✅ **Interactive visualizations** for data exploration ✅ **Bar charts** for clear comparisons ✅ **Heatmaps** to show correlations ✅ **Multi-panel dashboards** for comprehensive analysis ### 🚀 Next Steps in the Course In CMPSC 301, you'll learn to: - Work with much larger datasets (millions of rows!) - Create custom visualizations for specific needs - Build interactive dashboards with real-time data - Design visualizations for research publications - Use visualization for machine learning insights --- ## 💡 Try It Yourself! Want to experiment? Try modifying the code above: 1. Change the color schemes (try `'magma'`, `'plasma'`, or `'rainbow'`) 2. Add more countries to the dataset 3. Create a different type of plot (box plots, violin plots) 4. Customize titles and labels to tell your own story **Ready to dive deeper?** Visit the [JupyterLite](/live/) environment to create your own visualizations!