import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris

# 加载Iris数据集并计算平均值
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['Species'] = [iris.target_names[t] for t in iris.target]
df_mean = df.groupby('Species').mean().reset_index()

# 数据整理为长格式
df_melt = df_mean.melt(id_vars='Species', var_name='Measurement', value_name='Average')

# 设置可视化参数
measurements = ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
species = df_mean['Species'].tolist()
colors = ['#4e79a7', '#f28e2b', '#e15759']  # 专业色盲友好配色
bar_width = 0.25

# 创建画布
plt.figure(figsize=(14, 8), dpi=100)
x = range(len(measurements))

# 绘制分组柱状图
for i, specie in enumerate(species):
    offsets = [val + bar_width*i for val in x]
    values = df_melt[df_melt['Species'] == specie]['Average']
    plt.bar(offsets, values, width=bar_width, color=colors[i], label=specie, edgecolor='white', linewidth=0.5)

# 添加数据标签
for i in range(len(measurements)):
    for j, specie in enumerate(species):
        value = df_mean.loc[j, measurements[i]]
        plt.text(x[i] + bar_width*j, value + 0.05, f'{value:.1f}',
                 ha='center', va='bottom', fontsize=9)

# 图表装饰
plt.title('Average Sepal and Petal Dimensions by Iris Species', pad=20, fontsize=14, fontweight='bold')
plt.xlabel('Measurement', labelpad=15, fontsize=12)
plt.ylabel('Average Dimension (cm)', labelpad=15, fontsize=12)
plt.xticks([r + bar_width for r in range(len(measurements))], 
           [m.replace(' (cm)', '').title() for m in measurements], 
           rotation=45, ha='right')
plt.ylim(0, df_melt['Average'].max()*1.3)
plt.grid(axis='y', linestyle='--', alpha=0.4)
plt.legend(title='Species', bbox_to_anchor=(1.02, 1), loc='upper left')

# 优化布局
plt.tight_layout()
plt.show()