Json column in Pandas dataframe - Parsing and splitting - json

Question

I have an json dataframe with tedx talks as items (rows), that has a column 'ratings' in json format going like this. (The column depicts how the talk was described by audience)
[{"id": 7, "name": "Funny", "count": 19645}, {"id": 1, "name": "Beautiful", "count": 4573}, {"id": 9, "name": "Ingenious", "count": 6073}, ..........]
[{"id": 7, "name": "Funny", "count": 544}, {"id": 3, "name": "Courageous", "count": 139}, {"id": 2, "name": "Confusing", "count": 62}, {"id": 1, "name": "Beautiful", "count": 58}, ........]
Obviously the order of the descriptive words name is not standard/same for each item (tedx talk). Each word has an id(same for all talks) and a count respectively for each talk.
I am interested in manipulating the data and extracting three new integer columns regarding counts of: funny, inspiring, confusing, storing there the count for each of those words for the respective talks
Among other stuff, tried this
df['ratings'] = df['ratings'].map(lambda x: dict(eval(x)))
in return i get this error
File "C:/Users/Paul/Google Drive/WEEK4/ted-talks/w4e1.py", line 30, in
df['ratings'] = df['ratings'].map(lambda x: dict(eval(x)))
ValueError: dictionary update sequence element #0 has length 3; 2 is required
Been trying several different ways, but havent been able to even get values from the json formatted column properly. Any suggestions?

Answer 1

You can use list comprehension with flattening and convert string repr to list of dict by ast.literal_eval what is better solution like eval:
import pandas as pd
import ast
df = pd.DataFrame({'ratings': ['[{"id": 7, "name": "Funny", "count": 19645}, {"id": 1, "name": "Beautiful", "count": 4573}, {"id": 9, "name": "Ingenious", "count": 6073}]', '[{"id": 7, "name": "Funny", "count": 544}, {"id": 3, "name": "Courageous", "count": 139}, {"id": 2, "name": "Confusing", "count": 62}, {"id": 1, "name": "Beautiful", "count": 58}]']})
print (df)
ratings
0 [{"id": 7, "name": "Funny", "count": 19645}, {...
1 [{"id": 7, "name": "Funny", "count": 544}, {"i...
df1 = pd.DataFrame([y for x in df['ratings'] for y in ast.literal_eval(x)])
print (df1)
id name count
0 7 Funny 19645
1 1 Beautiful 4573
2 9 Ingenious 6073
3 7 Funny 544
4 3 Courageous 139
5 2 Confusing 62
6 1 Beautiful 58