使用嵌套列表和字典解析数据框？

Question

I'm pretty new to pandas/python and currently stuck doing this parsing. Parsing Dataframe with nested lists and dicts

DF:

    a       b       c             d         e        f          

0   1       2   {'county':        3         4       [{'name': 'essex,england','locality': None,'region':'harlow','subregion':None}]
               'cleveland', 
             'country': 
                'england'}       

1   4       5   {'county':        6         7       [{'name': 'hampsire,england','locality': None,'region':'alton','subregion':None}]
               'hamphsire', 
             'country': 
                'england'}

Output should be:

     a      b    county     country      d       e      name        locality       region         subregion 

0   1      2   cleveland    england      3       4      essex,england      None      harlow         None


1   4      5   hamphsire    england      6        7     hampsire,england   None      alton         None

What i have tried/Known: I parsed the df if only a single row present and been successful, but two rows i couldn't do it. How i parsed a single row : create a function and expand it then merge into df

Original dict if needed:

F column:

   [{'name': 'essex, england',
'locality': None,
'region': 'harlow',
'subregion': None,

c column

'location': {0: {'county': 'cleveland',
                   'country': 'england'} 

Answer 1

I am not sure if I understand what you mean, but df.from_dict() should solve your problem.

You can find the documentation here: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.from_dict.html

try something like:

data = {dict with the data}
keys = [list with the name of the columns]
my_english_dataframe = pd.DataFrame.from_dict(data, orient='index', columns=keys)

Pandas allow for some really powerful operations, you just have to get used with how it works.

Answer 2

following your logic:

import pandas as pd

df = pd.DataFrame({'a':[1,4], 
                   'b':[2,5],
                   'c':[{'county':'cleveland','country':'england'},
                        {'county':'hamphsire','country':'england'}],
                   'd':[3,6], 
                   'e':[4,7],
                   'f':[{'name': 'essex,england','locality': 
None,'region':'harlow','subregion':None},
                        {'name': 'hampsire,england','locality': 
None,'region':'alton','subregion':None}]})

df1 = df['c'].apply(pd.Series)
df2 = df['f'].apply(pd.Series)
result = pd.concat([df.filter(['a', 'b']), df1,df.filter(['d', 'e']), df2] , axis= 1)

print(result)

output:

   a  b     county  country  d  e              name locality  region subregion
0  1  2  cleveland  england  3  4     essex,england     None  harlow      None
1  4  5  hamphsire  england  6  7  hampsire,england     None   alton      None