json to pandas dataframe with first column value to be same across all rows

Question

I want to have two columns empname and empjosn, the empjson contains employee history in the json format. I want to convert this empjson column to normal data with all the details in a separate column along with empname column.

When I try this code I getting output only for one row, I want this to be for all the values.

df[['alias','deptid','empid','mgnme','salary']] = pd.DataFrame.from_records(df['empjson'][0])

DataFrame:

| empname | empjson |
| David   | [{'alias': 'xxxxx', 'deptid':   'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias':   'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary':   20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme':   'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid':   10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid':   'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias':   'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary':   20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme':   'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid':   10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid':   'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias':   'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary':   20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme':   'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid':   10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid':   'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias':   'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme': 'hhhhh', 'salary':   20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid': 10749, 'mgnme':   'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid': 'P1021', 'empid':   10749, 'mgnme': 'hhhhh', 'salary': 20123}, {'alias': 'xxxxx', 'deptid':   'P1021', 'empid': 1829, 'mgnme': 'hhhhh', 'salary': 1061}] |

Required Output:

| empname | alias | deptid | empid | mgnme | salary |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |
| David   | xxxxx | P1021  | 10749 | yyyyy | 20123  |

Answer 1

import pandas as pd

df = pd.read_csv(r'read_file.csv')

js =df.iloc[0,1].replace("[{","").replace("}]","").split("}, {")

lst = []
for i in range(len(js)):
    lt = []
    dict = eval("{" + js[i] + "}")
    lt.append(df.iloc[0,0])
    lt.append(dict['alias'])
    lt.append(dict['deptid'])
    lt.append(dict['empid'])
    lt.append(dict['mgnme'])
    lt.append(dict['salary'])
    lst.append(lt)
    
final = pd.DataFrame(lst)
final.columns = ['name','alias', 'deptid', 'empid', 'mgnme', 'salary']