我已经详尽地审查/尝试了与这一挑战相对应的所有其他问题的实现,但尚未找到解决方案。
问题:如何将员工和主管对转换为分层 JSON 结构以用于 D3 可视化?级别数量未知,因此它必须是动态的。
我有一个包含五列的数据框(是的,我意识到这不是 Office 的实际层次结构):
Employee_FN Employee_LN Supervisor_FN Supervisor_LN Level
0 Michael Scott None None 0
1 Jim Halpert Michael Scott 1
2 Dwight Schrute Michael Scott 1
3 Stanley Hudson Jim Halpert 2
4 Pam Beasley Jim Halpert 2
5 Ryan Howard Pam Beasley 3
6 Kelly Kapoor Ryan Howard 4
7 Meredith Palmer Ryan Howard 4
所需的输出快照:
{
"Employee_FN": "Michael",
"Employee_LN": "Scott",
"Level": "0",
"Reports": [{
"Employee_FN": "Jim",
"Employee_LN": "Halpert",
"Level": "1",
"Reports": [{
"Employee_FN": "Stanley",
"Employee_LN": "Hudson",
"Level": "2",
}, {
"Employee_FN": "Pam",
"Employee_LN": "Beasley",
"Level": "2",
}]
}]
}
当前状态:
j = (df.groupby(['Level','Employee_FN','Employee_LN'], as_index=False)
.apply(lambda x: x[['Level','Employee_FN','Employee_LN']].to_dict('r'))
.reset_index()
.rename(columns={0:'Reports'})
.to_json(orient='records'))
print(json.dumps(json.loads(j), indent=2, sort_keys=True))
电流输出:
[
{
"Employee_FN": "Michael",
"Employee_LN": "Scott",
"Level": 0,
"Reports": [
{
"Employee_FN": "Michael",
"Employee_LN": "Scott",
"Level": 0
}
]
},
{
"Employee_FN": "Dwight",
"Employee_LN": "Schrute",
"Level": 1,
"Reports": [
{
"Employee_FN": "Dwight",
"Employee_LN": "Schrute",
"Level": 1
}
]
},
{
"Employee_FN": "Jim",
"Employee_LN": "Halpert",
"Level": 1,
"Reports": [
{
"Employee_FN": "Jim",
"Employee_LN": "Halpert",
"Level": 1
}
]
},
{
"Employee_FN": "Pam",
"Employee_LN": "Beasley",
"Level": 2,
"Reports": [
{
"Employee_FN": "Pam",
"Employee_LN": "Beasley",
"Level": 2
}
]
},
{
"Employee_FN": "Stanley",
"Employee_LN": "Hudson",
"Level": 2,
"Reports": [
{
"Employee_FN": "Stanley",
"Employee_LN": "Hudson",
"Level": 2
}
]
},
{
"Employee_FN": "Ryan",
"Employee_LN": "Howard",
"Level": 3,
"Reports": [
{
"Employee_FN": "Ryan",
"Employee_LN": "Howard",
"Level": 3
}
]
},
{
"Employee_FN": "Kelly",
"Employee_LN": "Kapoor",
"Level": 4,
"Reports": [
{
"Employee_FN": "Kelly",
"Employee_LN": "Kapoor",
"Level": 4
}
]
},
{
"Employee_FN": "Meredith",
"Employee_LN": "Palmer",
"Level": 4,
"Reports": [
{
"Employee_FN": "Meredith",
"Employee_LN": "Palmer",
"Level": 4
}
]
}
]
问题:
- 每个人都只有小时候的自己
- 整个 JSON 结构似乎在一个字典中 - 我相信它必须用 {} 括起来才能可读
我尝试过切换groupby
and lambda
各种配置的元件也能达到所需的输出。任何和所有的见解将不胜感激!谢谢你!
Update:
我将代码块更改为:
j = (df.groupby(['Level','Supervisor_FN','Supervisor_LN'], as_index=False)
.apply(lambda x: x[['Level','Employee_FN','Employee_LN']].to_dict('r'))
.reset_index()
.rename(columns={0:'Reports'})
.rename(columns={'Supervisor_FN':'Employee_FN'})
.rename(columns={'Supervisor_LN':'Employee_LN'})
.to_json(orient='records'))
print(json.dumps(json.loads(j), indent=2, sort_keys=True))
新的输出是这样的:
[
{
"Employee_FN": "Michael",
"Employee_LN": "Scott",
"Level": 1,
"Reports": [
{
"Employee_FN": "Jim",
"Employee_LN": "Halpert",
"Level": 1
},
{
"Employee_FN": "Dwight",
"Employee_LN": "Schrute",
"Level": 1
}
]
},
{
"Employee_FN": "Jim",
"Employee_LN": "Halpert",
"Level": 2,
"Reports": [
{
"Employee_FN": "Stanley",
"Employee_LN": "Hudson",
"Level": 2
},
{
"Employee_FN": "Pam",
"Employee_LN": "Beasley",
"Level": 2
}
]
},
{
"Employee_FN": "Pam",
"Employee_LN": "Beasley",
"Level": 3,
"Reports": [
{
"Employee_FN": "Ryan",
"Employee_LN": "Howard",
"Level": 3
}
]
},
{
"Employee_FN": "Ryan",
"Employee_LN": "Howard",
"Level": 4,
"Reports": [
{
"Employee_FN": "Kelly",
"Employee_LN": "Kapoor",
"Level": 4
},
{
"Employee_FN": "Meredith",
"Employee_LN": "Palmer",
"Level": 4
}
]
}
]
问题:
- The
Level
为底层员工和主管匹配底层员工
- 嵌套仅深一层