一对多熊猫以JSON形式加入数据爆炸，而不是熊猫数据爆炸

I have 2 pandas dataframes:

我有两只熊猫dataframes:

dept = pd.DataFrame({'dep_id': [1,2], 'dep_name':['shoes', 'giraffes']})
emp = pd.DataFrame({'dep_id': [1,1,2], 'emp_name': ['joe', 'bo', 'gigi']})

joining them duplicates dept rows for every row in emp , as customary in relational joins:

按照关系联接的惯例，将emp中的每一行重复dept行:

pd.merge(dept, emp, on = 'dep_id')

dep_id  dep_name emp_name
0       1     shoes      joe
1       1     shoes       bo
2       2  giraffes     gigi

instead, I would like to create a hierarchical JSON: e.g.

相反，我想创建一个分层的JSON:例如。

[ 
{ dep_name: 'shoes', emps: [{emp_name: 'joe'}, {emp_name: 'bo'}]},
{ dep_name: 'giraffes', emps: [{emp_name: 'gigi'}]}
]

what's an elegant way to do it? I can join and then groupby, but then its impossible to tell which columns go to the outer dep and which go to the emps...

有什么优雅的方法吗?我可以加入，也可以加入groupby，但这样就不可能知道哪些列是到外部dep的，哪些是到emps的……

1 个解决方案

#1

One possible solution is define columns to emps list of DataFrames in apply:

一种可能的解决方案是将列定义为emps中应用的DataFrames列表:

d = (pd.merge(dept, emp, on = 'dep_id')
      .groupby('dep_name').apply(lambda x: x[['emp_name']]
      .to_dict('r'))
      .reset_index(name='emps'))

print (d)
   dep_name                                       emps
0  giraffes                     [{'emp_name': 'gigi'}]
1     shoes  [{'emp_name': 'joe'}, {'emp_name': 'bo'}]


j = d.to_json(orient='records')
print (j)
[{"dep_name":"giraffes","emps":[{"emp_name":"gigi"}]},
  {"dep_name":"shoes","emps":[{"emp_name":"joe"},{"emp_name":"bo"}]}]

d = (pd.merge(dept, emp, on = 'dep_id')
      .groupby('dep_name').apply(lambda x: x[['emp_name', 'dep_id']]
      .to_dict('r'))
      .reset_index(name='emps'))

print (d)
   dep_name                                               emps
0  giraffes                [{'dep_id': 2, 'emp_name': 'gigi'}]
1     shoes  [{'dep_id': 1, 'emp_name': 'joe'}, {'dep_id': ...

j = d.to_json(orient='records')
print (j)
[{"dep_name":"giraffes","emps":[{"dep_id":2,"emp_name":"gigi"}]},
  {"dep_name":"shoes","emps":[{"dep_id":1,"emp_name":"joe"},{"dep_id":1,"emp_name":"bo"}]}]

EDIT1:

I think for all columns converted out of nested json need:

我认为对于所有从嵌套json转换而来的列都需要:

dept = pd.DataFrame({'dep_id': [1,2], 'dep_name':['shoes', 'giraffes'], 'def_size':[4,5]})
emp = pd.DataFrame({'dep_id': [1,1,2], 'emp_name': ['joe', 'bo', 'gigi']})

df = pd.merge(dept, emp, on = 'dep_id')
#single columns def_size and dep_name
d = (df.groupby(['def_size','dep_name']).apply(lambda x: x[['emp_name']]
      .to_dict('r'))
      .reset_index(name='emps'))
print (d)
   def_size  dep_name                                       emps
0         4     shoes  [{'emp_name': 'joe'}, {'emp_name': 'bo'}]
1         5  giraffes                     [{'emp_name': 'gigi'}]

j = d.to_json(orient='records')
print (j)
[{"def_size":4,"dep_name":"shoes","emps":[{"emp_name":"joe"},{"emp_name":"bo"}]},
  {"def_size":5,"dep_name":"giraffes","emps":[{"emp_name":"gigi"}]}]

#1