Matplotlib Fundamentals - Programming for Machine Learning and Business

Building Core Static Charts for Analysis and Reporting¶

Notebook Guide¶

This notebook retains the original Matplotlib walkthrough and adds a compact framing section.

Learning focus¶

understand figure and axes concepts
create foundational business charts
adjust titles, labels, legends, and styles clearly
know when static plots are sufficient and effective

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

Every FAANG dashboard starts with Matplotlib

🎯 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

🚀 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})")

🔥 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()

⚡ 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}%")

🧠 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}")

## 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()

📋 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()

🏆 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:

Add YOUR real business data
Run YOUR custom charts
Screenshot → “I replace analytics teams!”

🎉 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

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

Exercises¶

Exercise 1¶

Write plot_data_summary(data) that returns a dict with min, max, and mean values for a numeric list (no actual plotting in pyodide-cell).

Exercise 2¶

Create normalize_series(series) that scales numbers to 0-1 range returning list.

Exercise 3¶

Implement bin_counts(data, bins) that returns counts per bin using simple integer floor division for numeric ranges.

Summary¶

Matplotlib is the foundation beneath much of Python plotting. The original notebook content is most useful when you treat each example as a reusable pattern for communicating one specific comparison or trend.

8. Interactive Code¶

9. Guided Practice¶

What is Matplotlib commonly used for?¶

Managing SQL transactions onlyMatplotlib is primarily a plotting library.

Creating charts and plots in PythonCorrect. It is a foundational visualization library.

Building linked listsThat is unrelated.

Replacing NumPy arraysMatplotlib works with data; it does not replace array libraries.

What is the revenue range in the example?¶

15Check the difference between max and min again.

20Correct. $140 - 120 = 20$.

25That is too high for these values.

140That is the maximum, not the range.