如何用星期和小时数据的平均值来填充缺失值？

import pandas as pd
import numpy as np

# 创建示例数据集
data = {
    'Weekday': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] * 4,
    'Hour': [i % 24 for i in range(28)],
    'Value': [np.random.randint(0, 100) if i % 10 != 0 else np.nan for i in range(28)]
}

df = pd.DataFrame(data)

# 计算每个星期和小时的平均值
mean_values = df.groupby(['Weekday', 'Hour']).mean().reset_index()

# 创建一个字典来存储平均值
mean_dict = {(row['Weekday'], row['Hour']): row['Value'] for _, row in mean_values.iterrows()}

# 使用平均值填充缺失值
df['Value'] = df.apply(lambda row: mean_dict.get((row['Weekday'], row['Hour'])), axis=1)

print(df)