CHAPTER 07 Beginner

Pie Charts and Distribution Visualization

Updated: May 18, 2026

5 min read

# CHAPTER 7

Pie Charts and Distribution Visualization

1. Chapter Introduction

Pie charts are the most debated chart type — powerful when used correctly, misleading when misused. This chapter teaches proper use of pie/donut charts, their limitations, and better alternatives for when categories exceed 5.

2. Pie Chart Fundamentals

python

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import matplotlib.pyplot as plt
import numpy as np

labels = [&#039;Google', 'Facebook', 'Amazon', 'Other']
sizes  = [38.5, 19.2, 10.1, 32.2]
explode = (0.05, 0, 0, 0)   # Slightly pop out Google
colors = [&#039;#4285F4', '#1877F2', '#FF9900', '#9E9E9E']

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Basic pie
wedges, texts, autotexts = axes[0].pie(
    sizes, labels=labels, autopct=&#039;%1.1f%%',
    colors=colors, explode=explode,
    startangle=90,          # Start from top
    pctdistance=0.8,        # Label position (0=center, 1=edge)
    textprops={&#039;fontsize': 11}
)
for at in autotexts:
    at.set_fontweight(&#039;bold')
    at.set_color(&#039;white')
axes[0].set_title(&#039;US Digital Ad Market Share 2024', fontsize=13, fontweight='bold')

# Donut chart (more modern, shows KPI in center)
wedges, texts = axes[1].pie(
    sizes, labels=None, colors=colors,
    startangle=90, pctdistance=0.85,
    wedgeprops={&#039;width': 0.5, 'edgecolor': 'white', 'linewidth': 2}
)
axes[1].text(0, 0, f&#039;Total\n$285B', ha='center', va='center',
             fontsize=14, fontweight=&#039;bold', color='#333')
axes[1].legend(wedges, [f&#039;{l}: {s}%' for l, s in zip(labels, sizes)],
               loc=&#039;lower center', bbox_to_anchor=(0.5, -0.1), ncol=2)
axes[1].set_title(&#039;Donut Chart Style', fontsize=13, fontweight='bold')

plt.suptitle(&#039;Part-of-Whole Visualization Styles', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(&#039;pie_donut.png', dpi=150)
plt.show()

3. When to Use Pie Charts (and When NOT To)

text

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✅ USE PIE CHARTS WHEN:
  - You have ≤5 categories
  - Proportions clearly differ (not 22% vs 24%)
  - The whole = meaningful total (100% market share, 100% budget)
  - You want to show one dominant segment

❌ DON&#039;T USE PIE CHARTS WHEN:
  - More than 5-6 categories (use bar chart or treemap)
  - Values are similar (hard to judge equal-ish angles)
  - You need to compare two pies side-by-side
  - Exact values matter (bar chart is more accurate)
  - You have negative values

Better Alternatives:
  - Bar chart → More accurate comparison
  - Treemap   → Better for many categories
  - Waffle    → Shows "out of 100" clearly
  - Donut     → Modern, allows center KPI text

python

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import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np

# Smartphone market data
brands = [&#039;Samsung', 'Apple', 'Xiaomi', 'OPPO', 'Vivo', 'Others']
share  = [20.1, 17.3, 12.5, 10.2, 8.6, 31.3]
colors = [&#039;#1428A0', '#555555', '#FF6900', '#1F7A1F', '#415FFF', '#90A4AE']

fig = plt.figure(figsize=(15, 6))
gs = gridspec.GridSpec(1, 3, figure=fig)

# Left: Donut chart
ax1 = fig.add_subplot(gs[0])
wedges, _ = ax1.pie(share, colors=colors, startangle=90,
                     wedgeprops={&#039;width': 0.5, 'edgecolor': 'white', 'linewidth': 2})
ax1.text(0, 0.05, &#039;Global', ha='center', fontsize=10, color='#666')
ax1.text(0, -0.15, &#039;Smartphone', ha='center', fontsize=10, color='#666')
ax1.text(0, -0.35, &#039;Market', ha='center', fontsize=10, color='#666')
ax1.set_title(&#039;Market Share Q4 2024', fontsize=12, fontweight='bold')

# Middle: Sorted bar chart
ax2 = fig.add_subplot(gs[1])
sorted_pairs = sorted(zip(share, brands, colors), reverse=True)
s_sorted, b_sorted, c_sorted = zip(*sorted_pairs)
bars = ax2.barh(b_sorted, s_sorted, color=c_sorted, edgecolor=&#039;white', height=0.6)
for bar, val in zip(bars, s_sorted):
    ax2.text(val + 0.3, bar.get_y() + bar.get_height()/2,
             f&#039;{val}%', va='center', fontsize=10, fontweight='bold')
ax2.set_xlabel(&#039;Market Share (%)')
ax2.set_title(&#039;Ranked Comparison', fontsize=12, fontweight='bold')
ax2.spines[&#039;top'].set_visible(False)
ax2.spines[&#039;right'].set_visible(False)

# Right: Trend (simulated)
ax3 = fig.add_subplot(gs[2])
quarters = [&#039;Q1', 'Q2', 'Q3', 'Q4']
top3 = {&#039;Samsung': [21.5, 20.8, 20.3, 20.1],
         &#039;Apple': [15.2, 16.1, 16.8, 17.3],
         &#039;Xiaomi': [13.1, 12.8, 12.6, 12.5]}
for brand, data in top3.items():
    idx = brands.index(brand)
    ax3.plot(quarters, data, &#039;o-', label=brand, color=colors[idx], linewidth=2, markersize=7)
ax3.set_title(&#039;Top 3 Trend', fontsize=12, fontweight='bold')
ax3.set_ylabel(&#039;Share (%)')
ax3.legend(fontsize=9)
ax3.grid(True, alpha=0.3)
ax3.spines[&#039;top'].set_visible(False)
ax3.spines[&#039;right'].set_visible(False)

plt.suptitle(&#039;Smartphone Market Analysis Dashboard', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig(&#039;market_share.png', dpi=150, bbox_inches='tight')
plt.show()

5. Common Mistakes

Too many slices: A pie with 10 categories has slices too thin to read. Consolidate into "Other" or switch to a bar chart.

3D pie charts: Perspective distortion makes near slices look larger than they are. Never use 3D pie charts.

6. MCQs

Question 1

Pie charts work best with?

Question 2

`wedgeprops={'width': 0.5}` creates?

Question 3

`startangle=90` in pie chart?

Question 4

`autopct='%1.1f%%'` shows?

Question 5

When pie slices are similar size (e.g., 23% vs 24%)?

Question 6

Donut chart advantage over pie?

Question 7

`explode=(0.05, 0, 0)` does?

Question 8

Best alternative to pie for many categories?

Question 9

3D pie charts are problematic because?

Question 10

`pctdistance=0.8` controls?

7. Interview Questions

Q: When would you choose a bar chart over a pie chart?

Q: What is a donut chart and when is it preferred over a pie chart?

8. Summary

Pie charts: use for ≤5 clearly differentiated categories showing part-of-whole. Donut charts add modern style and center text space. For >5 categories, use sorted bar charts or treemaps. Avoid 3D, avoid too-similar slices, avoid side-by-side pies. The bar chart is more accurate for most comparison tasks.

9. Next Chapter Recommendation

In Chapter 8: Scatter Plots and Correlation Analysis, we visualize relationships between two numeric variables and detect correlation patterns.

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Pie Charts and Distribution Visualization #

1. Chapter Introduction #

2. Pie Chart Fundamentals #

3. When to Use Pie Charts (and When NOT To) #

4. Mini Project: Market Share Visualization #

5. Common Mistakes #

6. MCQs #

Pie charts work best with?

wedgeprops={'width': 0.5} creates?

startangle=90 in pie chart?

autopct='%1.1f%%' shows?

When pie slices are similar size (e.g., 23% vs 24%)?

Donut chart advantage over pie?

explode=(0.05, 0, 0) does?

Best alternative to pie for many categories?

3D pie charts are problematic because?

pctdistance=0.8 controls?

7. Interview Questions #

8. Summary #

9. Next Chapter Recommendation #

Finish this Chapter

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Pie Charts and Distribution Visualization

1. Chapter Introduction

2. Pie Chart Fundamentals

3. When to Use Pie Charts (and When NOT To)

4. Mini Project: Market Share Visualization

5. Common Mistakes

6. MCQs

`wedgeprops={'width': 0.5}` creates?

`startangle=90` in pie chart?

`autopct='%1.1f%%'` shows?

`explode=(0.05, 0, 0)` does?

`pctdistance=0.8` controls?

7. Interview Questions

8. Summary

9. Next Chapter Recommendation