import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# 生成示例数据
np.random.seed(42)
num_countries = 100
data = pd.DataFrame({
'Country': [f'Country {i}' for i in range(num_countries)],
'GDP per Capita': np.random.lognormal(mean=9, sigma=1, size=num_countries),
'Life Expectancy': np.random.uniform(low=50, high=80, size=num_countries),
'Population': np.random.lognormal(mean=16, sigma=1, size=num_countries),
'Region': np.random.choice(['country1', 'country2', 'country3', 'country4', 'country5'], num_countries)
})
# 设置颜色
palette = sns.color_palette("hls", len(data['Region'].unique()))
# 创建图表
plt.figure(figsize=(14, 10))
# 使用log scale展示GDP per Capita
plt.xscale('log')
# 绘制气泡图
sns.scatterplot(
data=data,
x='GDP per Capita',
y='Life Expectancy',
size='Population',
hue='Region',
palette=palette,
sizes=(20, 2000),
alpha=0.6,
edgecolor='w',
linewidth=0.5
)
# 添加图例和标签
plt.legend(title='World Regions', loc='upper left', bbox_to_anchor=(1, 1), borderaxespad=0)
plt.xlabel('GDP per Capita (log scale)')
plt.ylabel('Life Expectancy at Birth (years)')
plt.title('Gapminder World Map 2030\nMore money often leads to longer lives (i.e. better health).')
# 展示图表
plt.grid(True, which="both", ls="--", linewidth=0.5)
plt.show()
