Population Pyramid
Chart overview
Population pyramids display age-sex distribution with males on one side and females on the other.
Key points
- The shape reveals demographic patterns: expansive (young population), constrictive (aging population), or stationary.
- They are essential for demographic analysis, healthcare planning, and policy development.
Python Tutorial
How to create a population pyramid in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a population pyramid showing the 'Age Distribution' of Japan's population by gender. Generate realistic demographic data for 18 age groups (0-4, 5-9, ..., 80-84, 85+) in millions: younger groups smaller (reflecting low birth rate), bulge at 45-54 (baby boomers), and significant 65+ population. Males on left (blue), Females on right (pink). X-axis shows population in millions (symmetric scale). Y-axis shows age groups. Add a vertical center line. Highlight the 'working age' population (15-64) with a subtle background shade. Include annotations for median age (48.4 years), dependency ratio, and total population. Title: 'Japan Population Pyramid 2023'."
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Python Code Example
# === IMPORTS ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# === USER-EDITABLE PARAMETERS ===
title = "Japan Population Pyramid 2023"
figsize = (12, 10)
# === EXAMPLE DATASET ===
# Age groups and population data (in millions)
age_groups = ['0-4', '5-9', '10-14', '15-19', '20-24', '25-29', '30-34', '35-39',
'40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74',
'75-79', '80-84', '85+']
# Japan-style pyramid: aging population with bulge at 45-54
male_pop = [2.4, 2.5, 2.7, 2.8, 3.0, 3.2, 3.3, 3.5,
4.2, 4.8, 4.5, 4.0, 3.8, 3.5, 4.0,
3.2, 2.5, 1.8]
female_pop = [2.3, 2.4, 2.6, 2.7, 2.9, 3.1, 3.2, 3.4,
4.1, 4.7, 4.4, 3.9, 3.9, 3.7, 4.3,
3.8, 3.2, 3.0]
# Print summary
total_pop = sum(male_pop) + sum(female_pop)
working_age_male = sum(male_pop[3:13]) # 15-64
working_age_female = sum(female_pop[3:13])
elderly_pop = sum(male_pop[13:]) + sum(female_pop[13:])
print("=== Japan Population Statistics ===")
print(f"\nTotal Population: {total_pop:.1f}M")
print(f"Male: {sum(male_pop):.1f}M | Female: {sum(female_pop):.1f}M")
print(f"Working Age (15-64): {working_age_male + working_age_female:.1f}M")
print(f"Elderly (65+): {elderly_pop:.1f}M ({elderly_pop/total_pop*100:.1f}%)")
print(f"Dependency Ratio: {(total_pop - working_age_male - working_age_female) / (working_age_male + working_age_female) * 100:.1f}%")
# === CREATE POPULATION PYRAMID ===
fig, ax = plt.subplots(figsize=figsize)
y_pos = np.arange(len(age_groups))
# Male bars (left side - negative values)
ax.barh(y_pos, [-m for m in male_pop], color='#3498db', edgecolor='white',
label='Male', height=0.8)
# Female bars (right side - positive values)
ax.barh(y_pos, female_pop, color='#e91e63', edgecolor='white',
label='Female', height=0.8)
# Center line
ax.axvline(x=0, color='black', linewidth=1)
# Highlight working age (15-64) with background
ax.axhspan(2.5, 12.5, alpha=0.1, color='green', label='Working Age (15-64)')
# Labels
ax.set_yticks(y_pos)
ax.set_yticklabels(age_groups)
ax.set_xlabel('Population (Millions)', fontsize=12, fontweight='bold')
ax.set_ylabel('Age Group', fontsize=12, fontweight='bold')
ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
# X-axis: show absolute values
ax.set_xlim(-6, 6)
ax.set_xticks([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])
ax.set_xticklabels(['5M', '4M', '3M', '2M', '1M', '0', '1M', '2M', '3M', '4M', '5M'])
# Legend
ax.legend(loc='lower right', framealpha=0.9)
# Add annotations
ax.annotate(f'Total: {total_pop:.1f}M\nMedian Age: 48.4 years',
xy=(0.02, 0.98), xycoords='axes fraction',
fontsize=11, verticalalignment='top',
bbox=dict(boxstyle='round', facecolor='white', alpha=0.9))
ax.grid(True, alpha=0.3, axis='x')
plt.tight_layout()
plt.show()
# END-OF-CODE
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Console Output
Total Population: 115.0 million Male Population: 57.0M (49.6%) Female Population: 58.0M (50.4%) Median Age: 48.4 years Dependency Ratio: 73.8%
Common Use Cases
- 1Census data visualization
- 2Healthcare resource planning
- 3Market demographic analysis
- 4Social policy development
Pro Tips
Use consistent age brackets (5 or 10 years)
Mirror the axes for symmetry
Add median age line for context
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
seaborn
Fastest path to statistically-aware defaults and tidy-data workflows, especially for grouped and distribution-focused population-pyramid views.
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for population-pyramid.
Scientific Chart Selection Cheat Sheet
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