Bar Charts and Categorical Data

import matplotlib.pyplot as plt
import numpy as np

products = ['Laptop', 'Phone', 'Monitor', 'Tablet', 'Headphones', 'Camera']
sales    = [1850, 3200, 890, 1540, 2100, 620]

fig, ax = plt.subplots(figsize=(10, 6))

# Color bars by value rank
colors = ['#1565C0' if s == max(sales) else '#42A5F5' for s in sales]
bars = ax.bar(products, sales, color=colors, edgecolor='white', linewidth=0.5, width=0.6)

# Add value labels on top of bars
for bar, val in zip(bars, sales):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 30,
            f'{val:,}', ha='center', va='bottom', fontsize=10, fontweight='bold')

ax.set_title('Product Sales Volume — Q4 2024', fontsize=14, fontweight='bold')
ax.set_xlabel('Product')
ax.set_ylabel('Units Sold')
ax.set_ylim(0, max(sales) * 1.15)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, axis='y', alpha=0.3)
plt.tight_layout()
plt.savefig('vertical_bar.png', dpi=150)
plt.show()

regions = ['North America', 'Europe', 'Asia Pacific', 'Latin America',
           'Middle East', 'Africa', 'South Asia']
revenue = [2850000, 2100000, 1980000, 890000, 740000, 580000, 420000]

fig, ax = plt.subplots(figsize=(10, 6))

# Sort for easier reading
sorted_pairs = sorted(zip(revenue, regions))
revenue_s, regions_s = zip(*sorted_pairs)

colors = ['#1565C0' if r == max(revenue_s) else '#90CAF9' for r in revenue_s]
bars = ax.barh(regions_s, revenue_s, color=colors, edgecolor='white', height=0.6)

for bar, val in zip(bars, revenue_s):
    ax.text(val + 20000, bar.get_y() + bar.get_height()/2,
            f'${val/1e6:.2f}M', va='center', fontsize=10, fontweight='bold')

ax.set_title('Revenue by Region — 2024', fontsize=14, fontweight='bold')
ax.set_xlabel('Revenue (USD)')
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1e6:.1f}M'))
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, axis='x', alpha=0.3)
plt.tight_layout()
plt.savefig('horizontal_bar.png', dpi=150)
plt.show()

categories = ['Electronics', 'Furniture', 'Clothing', 'Books', 'Sports']
q3_sales = [45000, 28000, 62000, 18000, 35000]
q4_sales = [72000, 31000, 89000, 22000, 41000]

x = np.arange(len(categories))
width = 0.35

fig, ax = plt.subplots(figsize=(11, 6))
bars1 = ax.bar(x - width/2, q3_sales, width, label='Q3 2024', color='#90CAF9', edgecolor='white')
bars2 = ax.bar(x + width/2, q4_sales, width, label='Q4 2024', color='#1565C0', edgecolor='white')

for bars in [bars1, bars2]:
    for bar in bars:
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 300,
                f'${bar.get_height()/1000:.0f}K', ha='center', va='bottom', fontsize=9)

ax.set_title('Quarterly Sales Comparison by Category', fontsize=14, fontweight='bold')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.set_ylabel('Sales ($)')
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, axis='y', alpha=0.3)
plt.tight_layout()
plt.savefig('grouped_bar.png', dpi=150)
plt.show()

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
online   = [28000, 32000, 38000, 35000, 42000, 48000]
instore  = [45000, 41000, 47000, 44000, 50000, 52000]
wholesale= [12000, 14000, 16000, 15000, 18000, 20000]

fig, ax = plt.subplots(figsize=(11, 6))
ax.bar(months, online,    label='Online',    color='#1565C0', edgecolor='white')
ax.bar(months, instore,   label='In-Store',  color='#42A5F5', bottom=online, edgecolor='white')
ax.bar(months, wholesale, label='Wholesale', color='#90CAF9',
       bottom=[o+i for o,i in zip(online, instore)], edgecolor='white')

# Total labels
totals = [o+i+w for o,i,w in zip(online, instore, wholesale)]
for i, (month, total) in enumerate(zip(months, totals)):
    ax.text(i, total + 500, f'${total/1000:.0f}K', ha='center', fontsize=10, fontweight='bold')

ax.set_title('Monthly Sales by Channel', fontsize=14, fontweight='bold')
ax.set_ylabel('Revenue ($)')
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
ax.legend(loc='upper left')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('stacked_bar.png', dpi=150)
plt.show()