Python Pandas – 将嵌套字典转换为多级索引数据框架

首先，让我们创建一个嵌套字典

dictNested = {'Cricket': {'Boards': ['BCCI', 'CA', 'ECB'],'Country': ['India', 'Australia', 'England']},'Football': {'Boards': ['TFA', 'TCSA', 'GFA'],'Country': ['England', 'Canada', 'Germany']
   }}

现在，创建一个空字典

new_dict = {}

然后循环赋值

for outerKey, innerDict in dictNested.items():
   for innerKey, values in innerDict.items():
      new_dict[(outerKey, innerKey)] = values

转换为多级索引数据框架

pd.DataFrame(new_dict)

更多Pandas文章，请阅读：Pandas教程

示例

代码如下

import pandas as pd

# 创建嵌套字典
dictNested = {'Cricket': {'Boards': ['BCCI', 'CA', 'ECB'],'Country': ['India', 'Australia', 'England']},'Football': {'Boards': ['TFA', 'TCSA', 'GFA'],'Country': ['England', 'Canada', 'Germany']
   }}

print"\nNested Dictionary...\n",dictNested

new_dict = {}
for outerKey, innerDict in dictNested.items():
   for innerKey, values in innerDict.items():
      new_dict[(outerKey, innerKey)] = values

# 转换为多级索引数据框架
print"\nMulti-index DataFrame...\n",pd.DataFrame(new_dict)

输出结果

这将产生以下输出

Nested Dictionary...
{'Cricket': {'Country': ['India', 'Australia', 'England'], 'Boards': ['BCCI', 'CA', 'ECB']}, 'Football': {'Country': ['England', 'Canada', 'Germany'], 'Boards': ['TFA', 'TCSA', 'GFA']}}

Multi-index DataFrame...
    Cricket             Football
    Boards   Country   Boards Country
0    BCCI     India      TFA England
1      CA Australia     TCSA  Canada
2     ECB   England      GFA Germany