Data Visualization with Matplotlib

Turning numbers into pictures

← Lab 8: pandas Lab 9 of 10 Lab 10: Capstone →

⏳ Loading Python + matplotlib... (this may take 15–20 seconds)

📊 This lab loads pandas, numpy, and matplotlib — allow extra time on first load. Charts will appear below each Run button.

📖 Concept Recap

matplotlib is Python’s foundational plotting library.

Figure & Axes: fig, ax = plt.subplots(figsize=(8, 5))
Chart types: ax.bar(), ax.plot(), ax.scatter(), ax.hist()
Labels: ax.set_title(), ax.set_xlabel(), ax.set_ylabel()
Legend: add label= to each plot call, then ax.legend()
Multiple panels: fig, axes = plt.subplots(2, 2, figsize=(12, 8))
Always call: plt.tight_layout() before plt.show() to prevent overlap
In this browser lab: include matplotlib.use('Agg') at the top of every code block

👀 Worked Example

A labeled bar chart — run this to see matplotlib in action:

import matplotlib.pyplot as plt import matplotlib matplotlib.use('Agg') categories = ['Python', 'SQL', 'Tableau', 'Excel', 'R'] popularity = [85, 78, 62, 71, 45] fig, ax = plt.subplots(figsize=(8, 5)) bars = ax.bar(categories, popularity, color=['#1565C0','#2E7D32','#6A1B9A','#E65100','#00695C']) ax.set_title('Data Analyst Skills by Job Posting Frequency', fontsize=14, fontweight='bold') ax.set_ylabel('% of Job Postings', fontsize=12) ax.set_ylim(0, 100) for bar, val in zip(bars, popularity): ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1, f'{val}%', ha='center', fontweight='bold') plt.tight_layout() plt.show()

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')

categories = ['Python', 'SQL', 'Tableau', 'Excel', 'R']
popularity = [85, 78, 62, 71, 45]

fig, ax = plt.subplots(figsize=(8, 5))
bars = ax.bar(categories, popularity,
              color=['#1565C0','#2E7D32','#6A1B9A','#E65100','#00695C'])
ax.set_title('Data Analyst Skills by Job Posting Frequency',
             fontsize=14, fontweight='bold')
ax.set_ylabel('% of Job Postings', fontsize=12)
ax.set_ylim(0, 100)
for bar, val in zip(bars, popularity):
    ax.text(bar.get_x() + bar.get_width()/2,
            bar.get_height() + 1,
            f'{val}%', ha='center', fontweight='bold')
plt.tight_layout()
plt.show()

Output will appear here...

✏️ Guided

Exercise 1 — Monthly Sales Line Chart

Complete the line chart by filling in the blanks. Add a marker symbol and titles.

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
team_a = [12000, 15000, 13500, 18000, 16500, 21000]
team_b = [10000, 11000, 14000, 13000, 17000, 16000]

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(months, team_a, marker='o', color='#1565C0', linewidth=2, label='Team A')
ax.plot(months, team_b, marker=___, color='#2E7D32', linewidth=2, label='Team B')
ax.set_title(___)            # add a descriptive title
ax.set_ylabel(___)           # label the y axis
ax.set_xlabel('Month')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()

Output will appear here...

💡 Hint: Marker options: 's' (square), '^' (triangle), 'D' (diamond). Title and ylabel should be descriptive strings in quotes.

💪 Independent

Exercise 2 — Study Hours vs. Exam Scores

Create a scatter plot for 10 students. Make up realistic data for hours studied (2–10) and exam scores (40–100). Color the dots by pass/fail (score ≥ 60 = green pass, red fail). Add axis labels and a title.

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')

# Make up your 10 students' data here
study_hours = [2, 4, 5, 3, 7, 8, 6, 9, 1, 5]
exam_scores  = [45, 62, 70, 52, 85, 90, 75, 95, 38, 68]

# Color each point by pass/fail
colors = ['#2E7D32' if score >= 60 else '#C62828' for score in exam_scores]

# Your scatter plot here

Output will appear here...

💡 Hint: ax.scatter(study_hours, exam_scores, c=colors, s=100, alpha=0.8). Add a horizontal line at y=60 with ax.axhline(60, color='gray', linestyle='--', alpha=0.5, label='Pass line').

🔥 Challenge

Exercise 3 — 2×2 Dashboard

Create a 2×2 subplot grid with four different chart types:

Top-left: Bar chart — monthly sales by category (Electronics, Clothing, Food)
Top-right: Line chart — monthly revenue trend over 6 months
Bottom-left: Histogram — distribution of test scores (use: [72,85,91,68,77,83,90,64,88,75,79,82,95,71,86,69,93,78,84,73])
Bottom-right: Horizontal bar chart — top 5 products by sales

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')

fig, axes = plt.subplots(2, 2, figsize=(12, 8))
fig.suptitle('Sales & Performance Dashboard', fontsize=14, fontweight='bold')

# Top-left: Bar chart - category sales
ax1 = axes[0, 0]
categories = ['Electronics', 'Clothing', 'Food']
sales = [42000, 28000, 19000]
ax1.bar(categories, sales, color=['#1565C0', '#2E7D32', '#E65100'])
ax1.set_title('Revenue by Category')
ax1.set_ylabel('Revenue ($)')

# Top-right: Line chart - monthly trend
ax2 = axes[0, 1]
months = ['Jan','Feb','Mar','Apr','May','Jun']
revenue = [85000, 92000, 88000, 105000, 98000, 115000]
# Your line chart here

# Bottom-left: Histogram
ax3 = axes[1, 0]
scores = [72,85,91,68,77,83,90,64,88,75,79,82,95,71,86,69,93,78,84,73]
# Your histogram here

# Bottom-right: Horizontal bar
ax4 = axes[1, 1]
products = ['Laptop', 'Phone', 'Tablet', 'Watch', 'Earbuds']
prod_sales = [320, 285, 195, 162, 140]
# Your horizontal bar chart here

plt.tight_layout()
plt.show()

Output will appear here...

💡 Hint: Line: ax2.plot(months, revenue, marker='o', color='#1565C0'). Histogram: ax3.hist(scores, bins=6, color='#6A1B9A', edgecolor='white'). Horizontal bar: ax4.barh(products, prod_sales).

🏆 Mini Project

Mini Project — Q1 Management Report

You are presenting Q1 results to management. Create a professional 2-row figure:

Top (full width): Line chart of weekly revenue for 12 weeks (make up the data — include a realistic dip and recovery)
Bottom-left: Bar chart of revenue by region (West, East, South, North)
Bottom-right: A text summary box using ax.text() showing: Total Revenue, Best Week, Top Region, Weeks Above Average

Use plt.subplots(2, 2) and merge the top row with fig.add_subplot(2, 1, 1) approach, or use gridspec. Consistent colors and proper labels required.

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('Agg')
import matplotlib.gridspec as gridspec

# Weekly revenue data (12 weeks)
weeks = [f'W{i}' for i in range(1, 13)]
weekly_revenue = [88000, 92000, 85000, 78000, 95000, 102000,
                  98000, 88000, 105000, 112000, 108000, 118000]

# Regional revenue
regions = ['West', 'East', 'South', 'North']
regional_revenue = [245000, 198000, 167000, 143000]

# Calculate key metrics
total = sum(weekly_revenue)
best_week = weeks[weekly_revenue.index(max(weekly_revenue))]
top_region = regions[regional_revenue.index(max(regional_revenue))]
avg = total / len(weekly_revenue)
above_avg = sum(1 for r in weekly_revenue if r > avg)

# Create the figure
fig = plt.figure(figsize=(12, 8))
gs = gridspec.GridSpec(2, 2, figure=fig)

ax_top = fig.add_subplot(gs[0, :])   # spans both columns on top
ax_bl  = fig.add_subplot(gs[1, 0])   # bottom-left
ax_br  = fig.add_subplot(gs[1, 1])   # bottom-right

fig.suptitle('Q1 2024 Executive Summary', fontsize=16, fontweight='bold')

# Top: weekly line chart
ax_top.plot(weeks, weekly_revenue, marker='o', color='#1565C0',
            linewidth=2.5, markersize=7)
ax_top.fill_between(weeks, weekly_revenue, alpha=0.1, color='#1565C0')
ax_top.axhline(avg, color='#E65100', linestyle='--', alpha=0.7, label=f'Avg: ${avg:,.0f}')
ax_top.set_title('Weekly Revenue', fontweight='bold')
ax_top.set_ylabel('Revenue ($)')
ax_top.legend()
ax_top.grid(True, alpha=0.3)

# Bottom-left: regional bar chart
colors_r = ['#1565C0', '#2E7D32', '#6A1B9A', '#E65100']
ax_bl.bar(regions, regional_revenue, color=colors_r)
ax_bl.set_title('Revenue by Region', fontweight='bold')
ax_bl.set_ylabel('Revenue ($)')
for i, (r, v) in enumerate(zip(regions, regional_revenue)):
    ax_bl.text(i, v + 2000, f'${v:,}', ha='center', fontsize=9)

# Bottom-right: summary text box
ax_br.axis('off')
summary = (
    f"Q1 2024 KEY METRICS\n"
    f"{'─'*28}\n"
    f"Total Revenue:    ${total:,.0f}\n"
    f"Best Week:        {best_week} (${max(weekly_revenue):,})\n"
    f"Top Region:       {top_region}\n"
    f"Weeks Above Avg:  {above_avg} of {len(weeks)}\n"
    f"Avg Weekly Rev:   ${avg:,.0f}"
)
ax_br.text(0.1, 0.5, summary, transform=ax_br.transAxes,
           fontsize=12, verticalalignment='center',
           fontfamily='monospace',
           bbox=dict(boxstyle='round', facecolor='#E3F2FD', alpha=0.8))

plt.tight_layout()
plt.show()

Output will appear here...

💡 Hint: The starter code is nearly complete — run it and study how gridspec works. Then customize colors, add more data labels, or change the metrics shown in the summary box.

✅ Lab 9 Complete!

You’ve built bar charts, line charts, scatter plots, histograms, multi-panel dashboards, and a full management report. Visualization is the final step that makes your analysis communicable.

Continue to Lab 10: Capstone Project →

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