将多级索引数据帧转换为嵌套字典

aggregated_data = df.groupby(['A', 'B']).agg({'C': 'sum'})

nested_dict = {}

for index, value in aggregated_data.iterrows():
    # 获取每个层次索引的值
    key1, key2 = index
    
    # 判断第一层次索引是否已存在于嵌套字典中，若不存在则创建
    if key1 not in nested_dict:
        nested_dict[key1] = {}
    
    # 将第二层次索引和对应的数据添加到嵌套字典中
    nested_dict[key1][key2] = value['C']

import pandas as pd

# 创建示例多级索引数据帧
data = {'A': ['A1', 'A1', 'A2', 'A2'],
        'B': ['B1', 'B2', 'B1', 'B2'],
        'C': [1, 2, 3, 4]}
df = pd.DataFrame(data).set_index(['A', 'B'])

# 分组和聚合操作
aggregated_data = df.groupby(['A', 'B']).agg({'C': 'sum'})

# 转换为嵌套字典
nested_dict = {}
for index, value in aggregated_data.iterrows():
    key1, key2 = index
    if key1 not in nested_dict:
        nested_dict[key1] = {}
    nested_dict[key1][key2] = value['C']

# 输出转换后的嵌套字典
print(nested_dict)

{'A1': {'B1': 1, 'B2': 2}, 'A2': {'B1': 3, 'B2': 4}}