Data-Science-For-Beginners/examples/04_basic_visualization.py

"""
Basic Data Visualization

Learn how to create simple, effective visualizations to communicate your findings.
Visualizations help you and others understand data at a glance.

What you'll learn:
- How to create bar charts
- How to create line plots
- How to create pie charts
- How to customize and save your visualizations

Prerequisites: 
- pandas library (install with: pip install pandas)
- matplotlib library (install with: pip install matplotlib)
"""

import pandas as pd
import matplotlib.pyplot as plt

print("=" * 70)
print("Basic Data Visualization Tutorial")
print("=" * 70)
print()

# Load data
print("📂 Loading honey production data...")
data = pd.read_csv('../data/honey.csv')
print("✅ Data loaded!\n")

# For better display, we'll use a subset of the data
# Let's focus on a few states in recent years
if 'state' in data.columns and 'year' in data.columns:
    # Get data for a few states in recent years
    states_to_show = ['CA', 'FL', 'ND', 'SD', 'MT']
    recent_data = data[(data['year'] >= 2010) & (data['state'].isin(states_to_show))]

# VISUALIZATION 1: Bar Chart
print("=" * 70)
print("VISUALIZATION 1: BAR CHART")
print("=" * 70)
print()

if 'state' in data.columns and 'totalprod' in data.columns:
    # Calculate average production by state (for top 10 states)
    state_avg = data.groupby('state')['totalprod'].mean().sort_values(ascending=False).head(10)
    
    print("Creating a bar chart of average honey production by state...")
    print()
    
    # Create the figure and axis
    plt.figure(figsize=(12, 6))  # Width: 12 inches, Height: 6 inches
    
    # Create the bar chart
    plt.bar(state_avg.index, state_avg.values, color='gold', edgecolor='orange')
    
    # Add labels and title
    plt.xlabel('State', fontsize=12)
    plt.ylabel('Average Production (pounds)', fontsize=12)
    plt.title('Top 10 States by Average Honey Production', fontsize=14, fontweight='bold')
    
    # Rotate x-axis labels for better readability
    plt.xticks(rotation=45)
    
    # Add a grid for easier reading (behind the bars)
    plt.grid(axis='y', alpha=0.3, linestyle='--')
    
    # Adjust layout to prevent label cutoff
    plt.tight_layout()
    
    # Save the figure
    plt.savefig('bar_chart_example.png', dpi=300, bbox_inches='tight')
    print("✅ Bar chart saved as 'bar_chart_example.png'")
    plt.close()  # Close to free memory
    print()

# VISUALIZATION 2: Line Plot
print("=" * 70)
print("VISUALIZATION 2: LINE PLOT")
print("=" * 70)
print()

if 'year' in data.columns and 'totalprod' in data.columns:
    # Calculate total production by year
    yearly_production = data.groupby('year')['totalprod'].sum()
    
    print("Creating a line plot of honey production over time...")
    print()
    
    plt.figure(figsize=(12, 6))
    
    # Create the line plot
    plt.plot(yearly_production.index, yearly_production.values, 
             marker='o',           # Add circular markers at each data point
             linewidth=2,          # Line thickness
             color='darkorange',   # Line color
             markersize=6,         # Size of markers
             markerfacecolor='gold')  # Fill color of markers
    
    # Add labels and title
    plt.xlabel('Year', fontsize=12)
    plt.ylabel('Total Production (pounds)', fontsize=12)
    plt.title('Honey Production Over Time', fontsize=14, fontweight='bold')
    
    # Add a grid
    plt.grid(True, alpha=0.3, linestyle='--')
    
    plt.tight_layout()
    plt.savefig('line_plot_example.png', dpi=300, bbox_inches='tight')
    print("✅ Line plot saved as 'line_plot_example.png'")
    plt.close()
    print()

# VISUALIZATION 3: Pie Chart
print("=" * 70)
print("VISUALIZATION 3: PIE CHART")
print("=" * 70)
print()

if 'state' in data.columns and 'totalprod' in data.columns:
    # Get total production for top 5 states
    top5_states = data.groupby('state')['totalprod'].sum().sort_values(ascending=False).head(5)
    
    print("Creating a pie chart of production share (top 5 states)...")
    print()
    
    plt.figure(figsize=(10, 8))
    
    # Create the pie chart
    colors = ['gold', 'orange', 'lightsalmon', 'lightcoral', 'peachpuff']
    plt.pie(top5_states.values, 
            labels=top5_states.index,     # State names
            autopct='%1.1f%%',            # Show percentages
            startangle=90,                # Start from top
            colors=colors,
            explode=(0.1, 0, 0, 0, 0))   # Slightly separate the first slice
    
    plt.title('Top 5 States Share of Total Honey Production', 
              fontsize=14, fontweight='bold', pad=20)
    
    plt.savefig('pie_chart_example.png', dpi=300, bbox_inches='tight')
    print("✅ Pie chart saved as 'pie_chart_example.png'")
    plt.close()
    print()

# VISUALIZATION 4: Multiple Lines on One Plot
print("=" * 70)
print("VISUALIZATION 4: COMPARING MULTIPLE SERIES")
print("=" * 70)
print()

if 'year' in data.columns and 'totalprod' in data.columns and 'state' in data.columns:
    # Compare production trends for a few states
    states_to_compare = ['CA', 'ND', 'SD']
    
    print(f"Creating a comparison plot for states: {', '.join(states_to_compare)}...")
    print()
    
    plt.figure(figsize=(12, 6))
    
    # Plot a line for each state
    colors_map = {'CA': 'blue', 'ND': 'green', 'SD': 'red'}
    
    for state in states_to_compare:
        if state in data['state'].values:
            state_data = data[data['state'] == state].groupby('year')['totalprod'].sum()
            plt.plot(state_data.index, state_data.values, 
                    marker='o', 
                    label=state,  # This will appear in the legend
                    linewidth=2,
                    color=colors_map.get(state, 'gray'))
    
    plt.xlabel('Year', fontsize=12)
    plt.ylabel('Total Production (pounds)', fontsize=12)
    plt.title('Honey Production Comparison by State', fontsize=14, fontweight='bold')
    plt.legend(title='State')  # Add a legend
    plt.grid(True, alpha=0.3, linestyle='--')
    
    plt.tight_layout()
    plt.savefig('comparison_plot_example.png', dpi=300, bbox_inches='tight')
    print("✅ Comparison plot saved as 'comparison_plot_example.png'")
    plt.close()
    print()

# Summary
print("=" * 70)
print("CONGRATULATIONS!")
print("=" * 70)
print("You've learned how to:")
print("  ✓ Create bar charts to compare categories")
print("  ✓ Create line plots to show trends over time")
print("  ✓ Create pie charts to show proportions")
print("  ✓ Plot multiple data series on one chart")
print("  ✓ Customize colors, labels, and titles")
print("  ✓ Save your visualizations as image files")
print()
print("Your visualizations have been saved in the examples/ folder!")
print()
print("Try this yourself:")
print("  • Change the colors of your charts")
print("  • Add more states to the comparison plot")
print("  • Create a horizontal bar chart")
print("  • Experiment with different chart styles")
print()
print("Pro tip: Always choose the right chart type for your data:")
print("  • Bar charts: Compare categories")
print("  • Line plots: Show trends over time")
print("  • Pie charts: Show parts of a whole")
print("  • Scatter plots: Show relationships between variables")
print("=" * 70)