Advanced Code Example — Data Visualization#

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

# ── Data ─────────────────────────────────────────────────────────────
regional_data = pd.DataFrame({
    'region':    ['Northwest', 'Southwest', 'Southeast', 'Northeast'],
    'revenue':   [48250.75, 36890.50, 22140.25, 41600.00],
    'customers': [85, 62, 41, 73]
})

monthly_data = pd.DataFrame({
    'month':   ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],
    'revenue': [28500, 31200, 29800, 35600, 38900, 42100,
                 44300, 43100, 46800, 51200, 58900, 63400]
})

customer_data = pd.DataFrame({
    'customer':        ['Alice J.', 'Bob M.', 'Carol C.', 'David K.',
                        'Eve T.', 'Frank L.', 'Grace P.', 'Helen S.'],
    'total_spent':     [1257, 430, 890, 125, 1450, 675, 980, 310],
    'purchase_count':  [12, 4, 9, 2, 15, 7, 11, 3],
    'tier':            ['Platinum', 'Silver', 'Gold', 'Standard',
                        'Platinum', 'Gold', 'Gold', 'Silver']
})

tier_counts = customer_data['tier'].value_counts()

# ── Gonzaga-inspired color palette ───────────────────────────────────
NAVY   = '#041E42'
RED    = '#C8102E'
SILVER = '#8B9BAA'
LIGHT  = '#E8EFF8'

# ── Figure Layout ─────────────────────────────────────────────────────
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Customer Analytics Dashboard', fontsize=16, fontweight='bold', color=NAVY, y=0.98)
fig.patch.set_facecolor(LIGHT)

for ax in axes.flat:
    ax.set_facecolor('#ffffff')
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# ── Plot 1: Revenue by Region (bar) ───────────────────────────────────
ax1 = axes[0, 0]
bars = ax1.bar(regional_data['region'], regional_data['revenue'],
               color=[NAVY, RED, SILVER, NAVY], edgecolor='white', linewidth=0.8)
ax1.set_title('Revenue by Region', fontweight='bold', color=NAVY)
ax1.set_ylabel('Revenue ($)', color=NAVY)
ax1.tick_params(axis='x', rotation=10)
ax1.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))

# Add value labels on bars
for bar in bars:
    h = bar.get_height()
    ax1.text(bar.get_x() + bar.get_width()/2, h + 300,
             f'${h/1000:.1f}K', ha='center', va='bottom', fontsize=9, color=NAVY)

# ── Plot 2: Monthly Revenue Trend (line) ─────────────────────────────
ax2 = axes[0, 1]
ax2.plot(monthly_data['month'], monthly_data['revenue'],
         color=RED, linewidth=2.5, marker='o', markersize=5, markerfacecolor=NAVY)
ax2.fill_between(range(len(monthly_data)), monthly_data['revenue'],
                 alpha=0.1, color=RED)
ax2.set_title('Monthly Revenue Trend', fontweight='bold', color=NAVY)
ax2.set_ylabel('Revenue ($)', color=NAVY)
ax2.tick_params(axis='x', rotation=20)
ax2.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
ax2.set_xticks(range(len(monthly_data)))
ax2.set_xticklabels(monthly_data['month'])

# Annotate peak month
peak_idx = monthly_data['revenue'].idxmax()
ax2.annotate(f"Peak: ${monthly_data['revenue'][peak_idx]/1000:.1f}K",
             xy=(peak_idx, monthly_data['revenue'][peak_idx]),
             xytext=(peak_idx - 2, monthly_data['revenue'][peak_idx] - 5000),
             arrowprops=dict(arrowstyle='->', color=RED),
             color=RED, fontsize=9)

# ── Plot 3: Spend vs. Frequency Scatter ──────────────────────────────
ax3 = axes[1, 0]
tier_colors = {'Platinum': NAVY, 'Gold': '#D4A017', 'Silver': SILVER, 'Standard': '#aaaaaa'}
colors = customer_data['tier'].map(tier_colors)

scatter = ax3.scatter(customer_data['purchase_count'], customer_data['total_spent'],
                      c=colors, s=120, edgecolors='white', linewidths=0.8, zorder=3)
ax3.set_title('Spend vs. Purchase Frequency', fontweight='bold', color=NAVY)
ax3.set_xlabel('Purchase Count', color=NAVY)
ax3.set_ylabel('Total Spent ($)', color=NAVY)

for _, row in customer_data.iterrows():
    ax3.annotate(row['customer'],
                 xy=(row['purchase_count'], row['total_spent']),
                 xytext=(4, 4), textcoords='offset points',
                 fontsize=7.5, color='#333333')

# Legend for tiers
from matplotlib.patches import Patch
legend_elements = [Patch(facecolor=c, label=t) for t, c in tier_colors.items()]
ax3.legend(handles=legend_elements, fontsize=8, title='Tier', title_fontsize=8)

# ── Plot 4: Tier Distribution (horizontal bar) ───────────────────────
ax4 = axes[1, 1]
tier_order = ['Platinum', 'Gold', 'Silver', 'Standard']
tier_vals = [tier_counts.get(t, 0) for t in tier_order]
bar_colors = [tier_colors[t] for t in tier_order]

hbars = ax4.barh(tier_order, tier_vals, color=bar_colors, edgecolor='white')
ax4.set_title('Customer Count by Tier', fontweight='bold', color=NAVY)
ax4.set_xlabel('Number of Customers', color=NAVY)
ax4.invert_yaxis()

for bar, val in zip(hbars, tier_vals):
    ax4.text(bar.get_width() + 0.05, bar.get_y() + bar.get_height()/2,
             f' {val}', va='center', fontsize=10, color=NAVY, fontweight='bold')

plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.savefig('dashboard.png', dpi=150, bbox_inches='tight', facecolor=LIGHT)
plt.show()
print("Dashboard saved to dashboard.png")