How do I json_normalize() a specific field within a df and keep the other columns?

So here’s my simple example (the json field in my actual dataset is very nested so I’m unpacking things one level at a time). I need to keep certain columns on the dataset post json_normalize().

https://pandas.pydata.org/docs/reference/api/pandas.json_normalize.html

Start:

Expected (Excel mockup):

Actual:

import json

d = {'report_id': [100, 101, 102], 'start_date': ["2021-03-12", "2021-04-22", "2021-05-02"], 
     'report_json': ['{"name":"John", "age":30, "disease":"A-Pox"}', '{"name":"Mary", "age":22, "disease":"B-Pox"}', '{"name":"Karen", "age":42, "disease":"C-Pox"}']}

df = pd.DataFrame(data=d)
display(df)

df = pd.json_normalize(df['report_json'].apply(json.loads), max_level=0, meta=['report_id', 'start_date'])
display(df)

Looking at the documentation on json_normalize(), I think the meta parameter is what I need to keep the report_id and start_date but it doesn’t seem to be working as the expected fields to keep are not appearing on the final dataset.

Does anyone have advice? Thank you.

>Solution :

as you’re dealing with a pretty simple json along a structured index you can just normalize your frame then make use of .join to join along your axis.

from ast import literal_eval


df.join(
      pd.json_normalize(df['report_json'].map(literal_eval))
 ).drop('report_json',axis=1)


   report_id  start_date   name  age disease
0        100  2021-03-12   John   30   A-Pox
1        101  2021-04-22   Mary   22   B-Pox
2        102  2021-05-02  Karen   42   C-Pox