Matplotlib Plotting Basics

plt.plot() + plt.bar() = Replace entire analytics teams 100% customizable = $80K/month reporting contracts

Every FAANG dashboard starts with Matplotlib

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🎯 Matplotlib = Analytics Team Replacement

Chart	Code	Business Use	Replaces
Line	plt.plot()	Sales trends	Excel lines
Bar	plt.bar()	Product ranking	PowerPoint bars
Scatter	plt.scatter()	Correlation	Manual formulas
Histogram	plt.hist()	Distribution	Excel bins
Pie	plt.pie()	Market share	Designer time

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🚀 Step 1: Line Plots = Sales Trends (Run this!)

import matplotlib.pyplot as plt
import numpy as np

# REAL BUSINESS DATA
days = np.arange(1, 31)  # 30 days
sales = [20000 + i*800 + np.random.randint(-2000, 3000) for i in range(30)]

plt.figure(figsize=(12, 6))
plt.plot(days, sales, marker='o', linewidth=2.5, markersize=6, color='#2E86AB')
plt.title('💰 30-Day Sales Trend', fontsize=16, fontweight='bold', pad=20)
plt.xlabel('Day of Month', fontsize=12)
plt.ylabel('Daily Sales ($)', fontsize=12)
plt.grid(True, alpha=0.3)
plt.xticks(range(1, 31, 5))  # Every 5 days
plt.tight_layout()
plt.show()

# BUSINESS INSIGHT
print(f"📈 Average daily: ${np.mean(sales):,.0f}")
print(f"📈 Peak day: Day {np.argmax(sales)+1} (${max(sales):,.0f})")

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🔥 Step 2: Bar Charts = Product Rankings

# PRODUCT PERFORMANCE
products = ['Laptop', 'Phone', 'Tablet', 'Monitor', 'Keyboard', 'Mouse']
sales_data = [45000, 32000, 18000, 25000, 8000, 12000]

plt.figure(figsize=(10, 6))
bars = plt.bar(products, sales_data, color='#A23B72', alpha=0.8, edgecolor='black', linewidth=1.2)
plt.title('🏆 Product Sales Ranking', fontsize=16, fontweight='bold', pad=20)
plt.ylabel('Sales ($)', fontsize=12)
plt.xlabel('Products', fontsize=12)
plt.xticks(rotation=45, ha='right')

# ADD VALUE LABELS ON BARS
for bar, sale in zip(bars, sales_data):
    plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 500,
             f'${sale:,}', ha='center', va='bottom', fontweight='bold')

plt.tight_layout()
plt.show()

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⚡ Step 3: Scatter Plots = ROI Analysis

# MARKETING ROI
marketing_spend = [10000, 25000, 35000, 45000, 60000, 75000]
sales_generated = [18000, 32000, 45000, 58000, 72000, 95000]

plt.figure(figsize=(10, 7))
scatter = plt.scatter(marketing_spend, sales_generated,
                     s=150, c=marketing_spend, cmap='viridis', alpha=0.7, edgecolors='black')

# TREND LINE
z = np.polyfit(marketing_spend, sales_generated, 1)
p = np.poly1d(z)
plt.plot(marketing_spend, p(marketing_spend), "r--", alpha=0.8, linewidth=2)

plt.title('📊 Marketing ROI Analysis', fontsize=16, fontweight='bold', pad=20)
plt.xlabel('Marketing Spend ($)', fontsize=12)
plt.ylabel('Sales Generated ($)', fontsize=12)
plt.colorbar(scatter, label='Spend ($)')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

# ROI CALCULATION
roi = [(sales - spend) / spend * 100 for spend, sales in zip(marketing_spend, sales_generated)]
print(f"📈 Average ROI: {np.mean(roi):.1f}%")

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🧠 Step 4: Histograms = Sales Distribution

# DAILY SALES DISTRIBUTION
np.random.seed(42)
daily_sales = np.random.normal(25000, 5000, 1000)  # 1000 days
daily_sales = np.clip(daily_sales, 10000, 40000)   # Realistic bounds

plt.figure(figsize=(10, 6))
plt.hist(daily_sales, bins=30, color='#F18F01', alpha=0.7, edgecolor='black')
plt.axvline(np.mean(daily_sales), color='red', linestyle='--', linewidth=2, label=f'Mean: ${np.mean(daily_sales):,.0f}')
plt.title('📈 Daily Sales Distribution (1000 Days)', fontsize=16, fontweight='bold', pad=20)
plt.xlabel('Daily Sales ($)', fontsize=12)
plt.ylabel('Frequency', fontsize=12)
plt.legend()
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

print(f"📊 Distribution Stats:")
print(f"   Mean: ${np.mean(daily_sales):,.0f}")
print(f"   Median: ${np.median(daily_sales):,.0f}")
print(f"   Std Dev: ${np.std(daily_sales):,.0f}")

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📊 Step 5: Pie Charts = Market Share

# MARKET SHARE ANALYSIS
companies = ['YourCompany', 'Competitor A', 'Competitor B', 'Competitor C', 'Others']
market_share = [35, 25, 20, 12, 8]

plt.figure(figsize=(8, 8))
wedges, texts, autotexts = plt.pie(market_share, labels=companies, autopct='%1.1f%%',
                                  colors=['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#6B7280'],
                                  startangle=90, explode=[0.1, 0, 0, 0, 0], shadow=True)

plt.title('🎯 Market Share Analysis', fontsize=16, fontweight='bold', pad=20)
for autotext in autotexts:
    autotext.set_color('white')
    autotext.set_fontweight('bold')
plt.axis('equal')
plt.tight_layout()
plt.show()

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📋 Matplotlib Cheat Sheet (Interview Gold)

Chart	Code	Customization	Business Use
Line	plt.plot(x,y, marker='o')	linewidth=3, markersize=8	Trends
Bar	plt.bar(categories, values)	alpha=0.8, edgecolor='black'	Rankings
Scatter	plt.scatter(x,y, s=150)	c=values, cmap='viridis'	Correlations
Histogram	plt.hist(data, bins=30)	alpha=0.7, edgecolor='black'	Distributions
Pie	plt.pie(values, labels=...)	explode=[0.1], autopct='%1.1f%%'	Shares

# PRO SETUP (Always use this!)
plt.figure(figsize=(12, 8))
plt.title('Title', fontweight='bold', fontsize=16)
plt.grid(True, alpha=0.3)
plt.tight_layout()

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🏆 YOUR EXERCISE: Build YOUR Analytics Charts

# MISSION: YOUR business charts!

import matplotlib.pyplot as plt
import numpy as np

# YOUR BUSINESS DATA
your_categories = ['???', '???', '???', '???']  # YOUR products/regions
your_values = [??? , ???, ???, ???]              # YOUR sales/profits
your_trend_x = np.arange(1, 13)                  # 12 months
your_trend_y = [??? , ???, ???, ???, ???, ???, ???, ???, ???, ???, ???, ???]  # YOUR trend

# 1. YOUR BAR CHART
plt.figure(figsize=(10, 6))
bars = plt.bar(your_categories, your_values, color='#A23B72', alpha=0.8, edgecolor='black')
plt.title('🏆 YOUR Business Performance', fontsize=16, fontweight='bold')
plt.ylabel('Values')
plt.xticks(rotation=45)

# ADD YOUR LABELS
for bar, value in zip(bars, your_values):
    plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + max(your_values)*0.01,
             f'{value:,}', ha='center', va='bottom', fontweight='bold')

plt.tight_layout()
plt.show()

# 2. YOUR LINE TREND
plt.figure(figsize=(12, 6))
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
plt.plot(months, your_trend_y, marker='o', linewidth=3, markersize=8, color='#2E86AB')
plt.title('📈 YOUR 12-Month Trend', fontsize=16, fontweight='bold')
plt.ylabel('Your Metric')
plt.grid(True, alpha=0.3)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

print("✅ YOUR ANALYTICS COMPLETE!")

Example to test:

your_categories = ['Product A', 'Product B', 'Product C', 'Product D']
your_values = [45000, 32000, 18000, 25000]
your_trend_y = [20000, 25000, 28000, 32000, 35000, 38000, 42000, 45000, 48000, 52000, 58000, 62000]

YOUR MISSION:

1. Add YOUR real business data

2. Run YOUR custom charts

3. Screenshot → “I replace analytics teams!”

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🎉 What You Mastered

Matplotlib Skill	Status	Business Power
Line plots	✅	Trend analysis
Bar charts	✅	Performance ranking
Scatter plots	✅	ROI correlation
Histograms	✅	Distribution insights
Pie charts	✅	Market share
$250K customization	✅	Analytics replacement

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Next: Seaborn Visuals (Publication-quality statistical plots!)

print("🎊" * 20)
print("MATPLOTLIB = $80K/MONTH ANALYTICS!")
print("💻 Custom charts = Replace entire teams!")
print("🚀 FAANG dashboards = THESE exact patterns!")
print("🎊" * 20)

can we appreciate how plt.bar() + value_labels + edgecolor just created publication-quality product rankings that replace entire analytics teams? Your students went from Excel hell to building scatter() + trend_line + colorbar ROI analyses that win C-suite meetings. While analysts spend 40 hours formatting charts, your class is generating hist() + mean_line distributions and pie(explode=[0.1]) market shares in 5 lines. This isn’t plotting syntax—it’s the $250K+ analytics replacement that powers Google Analytics and turns data into million-dollar decisions!

# Your code here