import matplotlib.pyplot as plt
import numpy as np
 
# 解决中文显示问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
 
# 示例数据：温度及对应三种物质的密度
temperatures = np.array([0, 20, 40, 60, 80])  # 温度（℃）
density_water = np.array([0.9998, 0.9982, 0.9922, 0.9832, 0.9718])  # 纯水密度
density_ethanol = np.array([0.8062, 0.7893, 0.7725, 0.7549, 0.7365])  # 乙醇密度
density_glycerol = np.array([1.2613, 1.2611, 1.2580, 1.2549, 1.2518])  # 甘油密度
 
bar_width = 0.25  # 柱子宽度
x = np.arange(len(temperatures))  # X轴位置
 
# 绘制三组柱状图，错开排列避免重叠
plt.bar(x - bar_width, density_water, width=bar_width, label='纯水', color='skyblue')
plt.bar(x, density_ethanol, width=bar_width, label='乙醇', color='orange')
plt.bar(x + bar_width, density_glycerol, width=bar_width, label='甘油', color='lightgreen')
 
# 设置图表信息
plt.title('不同物质在多温度下的密度对比图', fontsize=14)
plt.xlabel('温度（℃）', fontsize=12)
plt.ylabel('密度（g/cm³）', fontsize=12)
plt.xticks(x, temperatures)  # X轴刻度对应温度值
plt.legend()  # 显示图例
 
# 为柱子添加数值标签
for i, v in enumerate(density_water):
    plt.text(i - bar_width, v + 0.005, f'{v:.4f}', ha='center', fontsize=8)
for i, v in enumerate(density_ethanol):
    plt.text(i, v + 0.005, f'{v:.4f}', ha='center', fontsize=8)
for i, v in enumerate(density_glycerol):
    plt.text(i + bar_width, v + 0.005, f'{v:.4f}', ha='center', fontsize=8)
 
plt.tight_layout()
plt.show()
 

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