[Python] How to use json_normalize to parse json [closed]

Here is my json file looks like:

data = [
{"Ver":0,
"Num":[
{"0":{"A":1,"color": "red", "C": 9,"date": "2024-12-28"}},
{"1":{"A":5,"color": "blue", "C": 9,"date": "2024-12-29"}},
{"2":{"A":8,"color": "white","C": 9,"date": "2024-12-30"}}
]}
]


I tried to use json_normalize as below:

0.A 0.color 0.C 0.date 1.A 1.color 1.C 1.date 2.A 2.color \
0 1.0 red 9.0 2024-12-28 NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN 5.0 blue 9.0 2024-12-29 NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN 8.0 white

2.C 2.date
0 NaN NaN
1 NaN NaN
2 9.0 2024-12-30


Expected result

Num A color C date
0 1 red 9 2024-12-28
1 5 blue 9 2024-12-29
2 5 white 9 2024-12-30


How do I use it? thanks

if data[] info change to color.json

{"Ver":0,
"Num":[
{"0":{"A":1,"color": "red", "C": 9,"date": "2024-12-28"}},
{"1":{"A":5,"color": "blue", "C": 9,"date": "2024-12-29"}},
{"2":{"A":8,"color": "white","C": 9,"date": "2024-12-30"}}
}


It give me an error

import json
import pandas as pd

with open('test\\color.json', 'r') as file:
data = json.load(file)
#print(data)
flattened_data = []
for index, item in enumerate(data[0]['Num']):
for key, value in item.items():
flattened_data.append({'Num': int(key), **value})

# Convert to a DataFrame
df = pd.DataFrame(flattened_data)


How can I modify it?

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