python - 扩展分层数据，根据列中的列表项创建新行

Question

我如何根据下面提供的关于嵌套组的条件来扩展数据框？

团体

Name                    Job        Group
[Matt,Adam,John,James]  Peon       Workers
[Sam,Andrew,John]       Boss       Leader
[Leader,Ian]            Owner      Owner

我怎样才能做出如下预期的输出:

预期输出:

Name    Job       Group
Matt    Peon      Workers
Adam    Peon      Workers
John    Peon      Workers
James   Peon      Workers
Sam     Boss      Leader
Andrew  Boss      Leader
John    Boss      Leader
Sam     Owner     Owner
Andrew  Owner     Owner
John    Owner     Owner
Ian     Owner     Owner

我当前的方法(不完全有效)提取所有用户，但不识别也等于组名的成员并为每个成员创建一个新条目。

groups.members.apply(lambda x: pd.Series(x)).stack().reset_index(level=1, drop=True).to_frame('members').join(groups[['Job', 'Group']], how='left')

Answer 1

我不确定这是否可以完全在 pandas 中完成。我在外部处理过相关数据，然后重新加入。

import pandas as pd

groups = pd.DataFrame({'Name': [['Matt','Adam','John','James'], ['Sam','Andrew','John', 'Boss']], 'Job': ['Peon', 'Owner'], 'Group': ['Boss', 'Leader']})

# Build a list of tuples with row to draw group and job from and name
x = [(idx, i) for idx, j in enumerate(groups['Name']) for i in j]

# Search the list for group names, if found resolve group 
# names to additional members of row where group was found
for i, j in x:
    if j in set(groups.Group):
            x.remove((i, j))
            for n in list(*list(groups['Name'][groups.Group == j])):
                x.append((i, n))

# Create new DataFrame
idx, names = zip(*x)
z = pd.DataFrame(list(names), index=list(idx))

# Join on the old one
groups = groups.drop('Name', axis=1).join(z)