Comparing Product Line Performance

VISUALIZATION RECOMMENDATION

📊 Your Data: 3 product lines × 4 quarters (12 data points total)
🎯 Your Goal: Compare quarterly performance across product lines

RECOMMENDED: Grouped Bar Chart
✅ Why this works:
- Perfect for comparing multiple categories (products) across time periods (quarters)
- Side-by-side bars make direct comparisons immediate and obvious
- Quarterly progression visible within each product line
- Clear visual hierarchy shows both individual quarter performance and trends
- Easy to spot winners, losers, and growth patterns at a glance

❌ NOT Recommended:
- Line chart: Could work but bars better emphasize discrete quarterly comparisons
- Stacked bar chart: Would obscure individual product performance (hard to compare middle/bottom segments)
- Multiple pie charts: Terrible for trend analysis and comparisons
- Heat map: Overkill for this data size, less intuitive for stakeholders

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

data = pd.read_csv('sales_data.csv')

# Create grouped bar chart
fig, ax = plt.subplots(figsize=(12, 7))

quarters = ['Q1', 'Q2', 'Q3', 'Q4']
product_lines = ['Product_A', 'Product_B', 'Product_C']
colors = ['#2E86AB', '#A23B72', '#F18F01']  # Colorblind-friendly palette

x = np.arange(len(quarters))
width = 0.25

for i, product in enumerate(product_lines):
    product_data = data[data['Product_Line'] == product].sort_values('Quarter')
    sales = product_data['Sales'].values
    offset = (i - 1) * width
    bars = ax.bar(x + offset, sales, width, label=product.replace('_', ' '), color=colors[i], alpha=0.85)
    
    # Add value labels on bars
    for j, (bar, value) in enumerate(zip(bars, sales)):
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width()/2., height,
                f'${value/1000:.0f}K',
                ha='center', va='bottom', fontsize=9, fontweight='bold')

ax.set_xlabel('Quarter', fontsize=13, fontweight='bold')
ax.set_ylabel('Sales ($)', fontsize=13, fontweight='bold')
ax.set_title('Quarterly Sales Performance by Product Line', fontsize=15, fontweight='bold', pad=20)
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(loc='upper left', fontsize=11)
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${int(x/1000)}K'))
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()
plt.savefig('quarterly_sales_comparison.png', dpi=300, bbox_inches='tight')
plt.show()

KEY INSIGHTS

Performance Summary

Product A - Market Leader

• Total Sales: $1,144,000 (45% of total revenue)
• Q1→Q4 Growth: +21.6%
• Peak Quarter: Q3 ($312,000)
• Strong upward trajectory with slight Q4 softening

Product B - Steady Performer

• Total Sales: $813,000 (32% of total revenue)
• Q1→Q4 Growth: +19.3%
• Peak Quarter: Q4 ($223,000)
• Consistent growth every quarter, accelerating into Q4

Product C - Declining

• Total Sales: $585,000 (23% of total revenue)
• Q1→Q4 Growth: -4.5% (ONLY product line with negative growth)
• Peak Quarter: Q1 ($156,000)
• Concerning downward trend throughout the year

Critical Observations

✓ Products A & B show healthy growth (both ~20% Q1→Q4)
✓ Product A dominates with nearly half of total revenue
⚠ Product C is struggling - consecutive quarterly declines after Q1
⚠ Product A’s Q3→Q4 dip (-4.5%) warrants investigation

Recommended Actions

1. Investigate Product C decline - Is this market shift, pricing issue, or competitive pressure?
2. Analyze Product A’s Q4 softening - Seasonal or early warning sign?
3. Study Product B’s success - What’s driving its acceleration?
4. Consider resource reallocation - Product C’s trend suggests strategic review needed