Bar Charts with Matplotlib & Pandas

import matplotlib.pyplot as plt

# 1. Prepare Categorical Data
categories = ['Electronics', 'Apparel', 'Home Goods', 'Beauty', 'Sports']
revenue = [840, 520, 410, 290, 180]  # in thousands ($k)

# 2. Instantiate Figure & Axes
fig, ax = plt.subplots(figsize=(8, 5))

# 3. Render Vertical Bar Chart
bars = ax.bar(categories, revenue, color='#6366f1', edgecolor='#4338ca', width=0.6)

# 4. Polish Titles & Readable Axes
ax.set_title('Q3 Department Revenue Performance ($k)', fontsize=14, weight='bold', pad=12)
ax.set_xlabel('Department', fontsize=11, labelpad=8)
ax.set_ylabel('Revenue ($k USD)', fontsize=11, labelpad=8)

# 5. Clean Grid & Baseline Enforcement
ax.grid(axis='y', linestyle='--', alpha=0.5)
ax.set_ylim(bottom=0)  # MANDATORY: Baseline must always start at 0

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

# Categories sorted ascending so highest sits at the top when inverted
categories = [
    'Outdoor, Fitness & Sporting Goods',
    'Personal Care, Health & Beauty',
    'Home Goods, Furniture & Kitchen',
    'Fashion, Footwear & Apparel',
    'Consumer Electronics & Gadgets'
]
revenue = [180, 290, 410, 520, 840]

fig, ax = plt.subplots(figsize=(9, 5))

# Plot horizontal bars: y = categories, width = values
bars = ax.barh(categories, revenue, color='#38bdf8', edgecolor='#0284c7', height=0.6)

# Invert Y-axis so #1 rank is at the top
ax.invert_yaxis()

# Direct value labels on the bars
ax.bar_label(bars, fmt='$%d k', padding=5, fontsize=10, weight='bold')

ax.set_title('Top Department Revenue Ranking ($k USD)', fontsize=14, weight='bold', pad=12)
ax.set_xlabel('Revenue ($k USD)', fontsize=11)
ax.grid(axis='x', linestyle='--', alpha=0.4)
ax.set_xlim(0, 1000)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

departments = ['Electronics', 'Apparel', 'Home Goods', 'Beauty']
sales_2024 = [780, 490, 390, 260]
sales_2025 = [840, 520, 410, 290]

x = np.arange(len(departments))  # [0, 1, 2, 3]
width = 0.35                      # Width of each individual bar

fig, ax = plt.subplots(figsize=(8, 5))

# Plot Series 1 shifted left (-width/2) and Series 2 shifted right (+width/2)
rects1 = ax.bar(x - width/2, sales_2024, width, label='2024 Actual', color='#94a3b8')
rects2 = ax.bar(x + width/2, sales_2025, width, label='2025 Actual', color='#6366f1')

# Set tick labels centered between the two bars
ax.set_xticks(x)
ax.set_xticklabels(departments)

ax.set_title('Year-Over-Year Sales Comparison by Department ($k)', fontsize=14, weight='bold')
ax.set_ylabel('Sales ($k USD)')
ax.legend(frameon=True)
ax.grid(axis='y', linestyle='--', alpha=0.4)
ax.set_ylim(bottom=0)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

regions = ['North America', 'Europe', 'Asia-Pacific', 'Latin America']
in_store_sales = [420, 310, 240, 110]
online_sales = [280, 210, 220, 80]

fig, ax = plt.subplots(figsize=(8, 5))

# Base segment (In-Store)
p1 = ax.bar(regions, in_store_sales, label='In-Store Sales', color='#6366f1', width=0.55)

# Stacked segment on top (Online) using bottom= parameter
p2 = ax.bar(regions, online_sales, bottom=in_store_sales, label='Online Sales', color='#38bdf8', width=0.55)

ax.set_title('Total Regional Sales Breakdown: In-Store vs Online ($k)', fontsize=13, weight='bold')
ax.set_ylabel('Total Sales ($k USD)')
ax.legend(loc='upper right')
ax.grid(axis='y', linestyle='--', alpha=0.4)
ax.set_ylim(bottom=0)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# Raw transactional DataFrame
data = {
    'Category': ['Electronics', 'Apparel', 'Electronics', 'Home Goods', 'Apparel', 'Beauty', 'Electronics'],
    'Sales': [450, 210, 390, 410, 310, 290, 120]
}
df = pd.DataFrame(data)

# Step 1: Aggregate and Sort
category_sales = (
    df.groupby('Category')['Sales']
    .sum()
    .sort_values(ascending=False)
)

# Step 2: Plot directly using Pandas integrated Matplotlib backend
fig, ax = plt.subplots(figsize=(8, 5))
category_sales.plot.bar(ax=ax, color='#6366f1', edgecolor='#4338ca', width=0.6)

ax.set_title('Total Category Revenue from Raw Transactions ($)', fontsize=13, weight='bold')
ax.set_ylabel('Total Sales ($ USD)')
ax.set_xlabel('Product Category')
plt.xticks(rotation=0)  # Keep category names straight
ax.grid(axis='y', linestyle='--', alpha=0.4)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

categories = ['Electronics', 'Apparel', 'Home Goods', 'Beauty']
sales = [840, 520, 410, 290]
target_quota = 500

fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(categories, sales, color='#6366f1', width=0.55)

# 1. Automatic Direct Value Labels (Matplotlib 3.4+)
ax.bar_label(bars, fmt='$%d k', padding=4, fontsize=10, weight='bold')

# 2. Benchmark Target Reference Line
ax.axhline(target_quota, color='#ef4444', linestyle='--', linewidth=1.5, label=f'Target Quota ($500k)')

# 3. Action-Driven Title
ax.set_title('Electronics & Apparel Surpass Q3 Sales Quota', fontsize=13, weight='bold', pad=14)
ax.set_ylabel('Sales ($k USD)')
ax.legend(loc='upper right')
ax.set_ylim(0, 1000)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# 1. Realistic Transactional Dataset
transactions = pd.DataFrame({
    'Category': ['Electronics', 'Apparel', 'Home Goods', 'Beauty', 'Office Supplies', 'Electronics', 'Apparel'],
    'Sales': [650, 420, 310, 190, 80, 240, 150],
    'Profit': [120, 85, -25, 45, -15, 60, 30]
})

# 2. Aggregation & Sorting
summary = (
    transactions.groupby('Category')[['Sales', 'Profit']]
    .sum()
    .sort_values(by='Sales', ascending=True)  # Ascending so top item appears at top after invert
)

# 3. Dynamic Conditional Color: Green for Profit, Red for Loss
bar_colors = ['#10b981' if p >= 0 else '#ef4444' for p in summary['Profit']]

# 4. Render Horizontal Ranking Chart
fig, ax = plt.subplots(figsize=(9, 5))
bars = ax.barh(summary.index, summary['Sales'], color=bar_colors, height=0.6)
ax.invert_yaxis()

# 5. Direct Value Labels
ax.bar_label(bars, fmt='$%d k', padding=5, fontsize=10, weight='bold')

# 6. Formatting & Action Title
ax.set_title('Category Revenue with Profit Status (Green = Profitable, Red = Loss)', fontsize=13, weight='bold')
ax.set_xlabel('Total Sales ($k USD)')
ax.grid(axis='x', linestyle='--', alpha=0.4)
ax.set_xlim(0, 1100)

plt.tight_layout()
plt.show()