Radar Chart

# === IMPORTS ===
import pandas as pd
import matplotlib.pyplot as plt
import squarify

# === USER-EDITABLE PARAMETERS ===
# Change: Modify these lists to update countries and their land areas (in km²)
countries = [
    'Russia', 'Canada', 'United States', 'China', 'Brazil', 'Australia',
    'India', 'Argentina', 'Kazakhstan', 'Algeria'
]
land_areas = [
    17098242, 9984670, 9833517, 9596961, 8515767, 7692024,
    3287263, 2780400, 2724902, 2381741
]

# Change: Customize figure size (width, height in inches)
figsize = (12, 8)

# Change: List of hex colors for categorical groups (add more if needed, limit to 10 for visibility)
colors = [
    '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
    '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]

# Change: Font sizes for title (larger) and general elements
title_fontsize = 18
label_fontsize = 16  # Used for any text labels if added

# Change: Toggle to show percentages in labels (True/False)
show_percentages = True

# Change: Alpha (transparency) for squares, 0.6-0.9 recommended for overlaps
alpha = 0.8

# Data preparation
# Create DataFrame from example data
df = pd.DataFrame({'Countries': countries, 'Land Area': land_areas})

# Sort by land area descending for proper largest-first layout
df = df.sort_values('Land Area', ascending=False).reset_index(drop=True)

# Calculate total and percentages for insights
total_area = df['Land Area'].sum()
df['Percentage'] = (df['Land Area'] / total_area * 100).round(1)

# Print relevant data values
print("Top 10 countries by land area (km²) and share of total:")
print(df[['Countries', 'Land Area', 'Percentage']].round({'Land Area': 0}))
print(f"\nTotal land area in dataset: {total_area:,.0f} km²")
print(f"Largest country ({df['Countries'].iloc[0]}) share: {df['Percentage'].iloc[0]:.1f}%")

# Create insight-driven title
largest_country = df['Countries'].iloc[0]
largest_share = df['Percentage'].iloc[0]
insight_title = f"{largest_country} dominates with {largest_share:.1f}% of total land area among top 10 countries"

# Create plot
fig, ax = plt.subplots(figsize=figsize)

# Prepare labels: country name + optional percentage
if show_percentages:
    labels = [f"{country}\n{perc:.1f}%" for country, perc in zip(df['Countries'], df['Percentage'])]
else:
    labels = df['Countries'].tolist()

# Generate proportional squares using squarify (areas exactly proportional to land area)
squarify.plot(
    sizes=df['Land Area'],
    label=labels,
    color=colors[:len(df)],
    alpha=alpha,
    ax=ax,
    bar_kwargs={'linewidth': 2, 'edgecolor': 'white'}  # Change: Add white edges for separation
)

# Customize title with insight
ax.set_title(insight_title, fontsize=title_fontsize, pad=20, weight='bold')

# Remove axes for clean proportional area view
plt.axis('off')

# Adjust layout to prevent clipping (title, labels)
plt.subplots_adjust(top=0.92)
plt.tight_layout()

# Assign final plot to fig (required)
fig

# Display the plot
plt.show()
# END-OF-CODE

Comparison Summary:
  Average Score - Tesla: 85.7
  Average Score - BMW: 77.5
  Tesla Strongest: Technology
  BMW Strongest: Interior Quality