I converted a JSON into DataFrame and ended up with a column 'Structure_value' having below values as list of dictionary/dictionaries:
                   Structure_value
[{'Room': 6, 'Length': 7}, {'Room': 6, 'Length': 7}]
[{'Room': 6, 'Length': 22}]
[{'Room': 6, 'Length': 8}, {'Room': 6, 'Length': 9}]
Since it is an object so I guess it ended up in this format.
I need to split it into below four columns:
Structure_value_room_1
Structure_value_length_1
Structure_value_room_2
Structure_value_length_2  
All other solutions on StackOverflow only deal with converting Simple JSON into DataFrame and not the nested structure.
P.S.: I know I can do something by explicitly naming fields but I need a generic solution so that in future any JSON of this format can be handled
[Edit]: The output should look like this:
   Structure_value_room_1  Structure_value_length_1  Structure_value_room_2  \
0                       6                         7                     6.0   
1                       6                        22                     NaN   
2                       6                         8                     6.0   
   Structure_value_length_2  
0                       7.0  
1                       NaN  
2                       9.0  
                Pandas have a nice inbuilt function called json_normalize() to flatten the simple to moderately semi-structured nested JSON structures to flat tables. Parameters: data – dict or list of dicts. errors – {'raise', 'ignore'}, default 'raise'
Flatten a JSON object: var flatten = (function (isArray, wrapped) { return function (table) { return reduce("", {}, table); }; function reduce(path, accumulator, table) { if (isArray(table)) { var length = table.
Use list comprehension with nested dictionary comprehension with enumerate for deduplicate keys of dicts, last pass list of dictionaries to DataFrame constructor:
L = [ {f"{k}_{i}": v for i, y in enumerate(x, 1) 
                     for k, v in y.items()}
                     for x in df["Structure_value"] ]
df = pd.DataFrame(L)
print(df)
   Room_1  Length_1  Room_2  Length_2
0       6         7     6.0       7.0
1       6        22     NaN       NaN
2       6         8     6.0       9.0
For columns names from question use:
def json_to_df(df, column):
    L = [ {f"{column}_{k.lower()}_{i}": v for i, y in enumerate(x, 1) 
                         for k, v in y.items()}
                         for x in df[column] ]
    return pd.DataFrame(L)
df1 = json_to_df(df, 'Structure_value')
print(df1)
   Structure_value_room_1  Structure_value_length_1  Structure_value_room_2  \
0                       6                         7                     6.0   
1                       6                        22                     NaN   
2                       6                         8                     6.0   
   Structure_value_length_2  
0                       7.0  
1                       NaN  
2                       9.0  
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