I need help with converting a nested list of dictionaries with a nested list of dictionaries inside of it to a dataframe. At the end, I want something that looks like (the dots are for other columns in between):
id | isbn | isbn13 | .... | average_rating|
30278752 |1594634025|9781594634024| .... |3.92 |
34006942 |1501173219|9781501173219| .... |4.33 |
review_stat =[{'books': [{'id': 30278752,
'isbn': '1594634025',
'isbn13': '9781594634024',
'ratings_count': 4832,
'reviews_count': 8435,
'text_reviews_count': 417,
'work_ratings_count': 2081902,
'work_reviews_count': 3313007,
'work_text_reviews_count': 109912,
'average_rating': '3.92'}]},
{'books': [{'id': 34006942,
'isbn': '1501173219',
'isbn13': '9781501173219',
'ratings_count': 4373,
'reviews_count': 10741,
'text_reviews_count': 565,
'work_ratings_count': 1005504,
'work_reviews_count': 2142280,
'work_text_reviews_count': 75053,
'average_rating': '4.33'}]}]
If you key is always books
pd.concat([pd.DataFrame(i['books']) for i in review_stat])
id isbn isbn13 ratings_count reviews_count text_reviews_count work_ratings_count work_reviews_count work_text_reviews_count average_rating
0 30278752 1594634025 9781594634024 4832 8435 417 2081902 3313007 109912 3.92
0 34006942 1501173219 9781501173219 4373 10741 565 1005504 2142280 75053 4.33
You can always reset the index if you need
You can also use json_normalize
here:
df = pd.json_normalize(review_stat, 'books')
[out]
id isbn ... work_text_reviews_count average_rating
0 30278752 1594634025 ... 109912 3.92
1 34006942 1501173219 ... 75053 4.33
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