Iterating through Nested Dictionaries to create dataframes-CodePudding

I am trying to Output 4 data tables and get the Expected Output below? I want to use the Outcomes dictionary and iter and format it in such a way that it will enable me to put all the list values in option 1 and option 2 and all the row values row 1, row 2.... How can I modify the for loop in the code so that I get the Expected Output?

Code:

import pandas as pd

def Pandas(infos, title):
  display(pd.DataFrame(infos).style.set_caption(title).set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'red'),
        ('font-size', '16px'),
        ('text-align', 'center')
        ]
    }]))  


for id, info in Outcomes.items():
    for k in info:
        infos = {{f'{x}:': info[k][x]} if isinstance(info[k][x],list) else info[k][x] for x in info[k]}
        Pandas(infos, k)

Dictionary:

Outcomes = {
    'Values':{
        'First': {
            'option 1': [12,345,5412],
            'option 2': [2315,32,1],
            'Additional Option': {'row 1': [232,3,1,3],
                         'row 2': [3,4,5,11],
                         'row 3': [15,6,12,34]}
        },
        'Second': {
            'option 1': [1,4,5,6,2],
            'option 2': [5,6,3,2,1],
            'Additional Option': {'row 1': [-5,3,1,2],
                         'row 2': [4,4,12,11],
                         'row 3': [67,6,5,34]}
        }
    },
    'Values 2':{
        'First': {
            'option 1': [12,345345,512412],
            'option 2': [2315,4,3],
            'Mega':{'row 1': [-45,12,33,1.3],
                    'row 2': [3.5,4.8,5,11]}
        }
    }
}

CodePudding user response：

You could use a recursion to go through your dictionary and create a flatten dictionary of DataFrames:

def get_nested_df(dic, concat_key="", df_dic=dict()):
   rows = {k:v for k,v in dic.items() if not isinstance(v, dict)}
   if rows:
      df_dic.update({concat_key: pd.DataFrame.from_dict(rows)})
   for k,v in dic.items():
      if isinstance(v, dict):
         get_nested_df(v, f"{concat_key} {k}", df_dic)
   return df_dic

df_dic = get_nested_df(Outcomes)

for k,v in df_dic.items():
   print(f"{k}\n{v}\n")

Output:

 Values First
   option 1  option 2
0        12      2315
1       345        32
2      5412         1

 Values First Additional Option
   row 1  row 2  row 3
0    232      3     15
1      3      4      6
2      1      5     12
3      3     11     34

 Values Second
   option 1  option 2
0         1         5
1         4         6
2         5         3
3         6         2
4         2         1

 Values Second Additional Option
   row 1  row 2  row 3
0     -5      4     67
1      3      4      6
2      1     12      5
3      2     11     34

 Values 2 First
   option 1  option 2
0        12      2315
1    345345         4
2    512412         3

 Values 2 First Mega
   row 1  row 2
0  -45.0    3.5
1   12.0    4.8
2   33.0    5.0
3    1.3   11.0