I have a pandas DataFrame containing one column with multiple JSON data items as list of dicts. I want to normalize the JSON column and duplicate the non-JSON columns:

# creating dataframe
df_actions = pd.DataFrame(columns=['id', 'actions'])
rows = [[12,json.loads('[{"type": "a","value": "17"},{"type": "b","value": "19"}]')],
   [15, json.loads('[{"type": "a","value": "1"},{"type": "b","value": "3"},{"type": "c","value": "5"}]')]]
df_actions.loc[0] = rows[0]
df_actions.loc[1] = rows[1]

>>>df_actions
   id                                            actions
0  12  [{'type': 'a', 'value': '17'}, {'type': 'b', '...
1  15  [{'type': 'a', 'value': '1'}, {'type': 'b', 'v...

I want

>>>df_actions_parsed
   id      type    value
   12      a        17
   12      b        19
   15      a        1
   15      b        3
   15      c        5

I can normalize JSON data using:

pd.concat([pd.DataFrame(json_normalize(x)) for x in df_actions['actions']],ignore_index=True)

but I don't know how to join that back to the id column of the original DataFrame.

You can use concat with dict comprehension with pop for extract column, remove second level and join to original:

df1 = (pd.concat({i: pd.DataFrame(x) for i, x in df_actions.pop('actions').items()})
         .reset_index(level=1, drop=True)
         .join(df_actions)
         .reset_index(drop=True))

What is same as:

df1 = (pd.concat({i: json_normalize(x) for i, x in df_actions.pop('actions').items()})
         .reset_index(level=1, drop=True)
         .join(df_actions)
         .reset_index(drop=True))

print (df1)
  type value  id
0    a    17  12
1    b    19  12
2    a     1  15
3    b     3  15
4    c     5  15