我有一個數據框，里面有很多 json 數據。例如 ：{"serial": "000000001fb105ea", "sensorType": "acceleration", "data": [1603261123.328814, 0.171875, -0.9609375, 0.0234375]}{"serial": "000000001fb105ea", "sensorType": "acceleration", "data": [1603261125.0605137, 0.0859375, -0.984375, 0.0]}{"serial": "000000001fb105ea", "sensorType": "strain", "data": [1603261126.3532753, 0.9649793604217437]}{"serial": "000000001fb105ea", "sensorType": "acceleration", "data": [1603261127.6988888, 0.0390625, -1.0, 0.125]}{"serial": "000000001fb105ea", "sensorType": "acceleration", "data": [1603261128.8530502, 0.078125, -0.9921875, 0.0]}有兩種類型的數據：應變傳感器和加速度傳感器。我想解析這些json數據并轉換為正常形式。我只需要 json 對象的數據部分。結果我應該為數據中的每個值都有 4 列。Date: 21.20.2020:09:18:46 x:0.171875 y:-0.9609375 z:0.0234375我嘗試了 json_normalize 但出現此錯誤。AttributeError: 'str' object has no attribute 'itervalues'如何將數據部分解析為 4 列數據框？
1 回答
海綿寶寶撒
TA貢獻1809條經驗 獲得超8個贊
如果輸入數據在json文件中使用：
cols = ['Date','x','y','z']
df = pd.DataFrame(pd.read_json('json.json', lines=True)['data'].tolist(), columns=cols)
df['Date'] = pd.to_datetime(df['Date'], unit='s')
print (df)
Date x y z
0 2020-10-21 06:18:43.328814030 0.171875 -0.960938 0.023438
1 2020-10-21 06:18:45.060513735 0.085938 -0.984375 0.000000
2 2020-10-21 06:18:46.353275299 0.964979 NaN NaN
3 2020-10-21 06:18:47.698888779 0.039062 -1.000000 0.125000
4 2020-10-21 06:18:48.853050232 0.078125 -0.992188 0.000000
如果輸入DataFrame帶有列col：
cols = ['Date','x','y','z']
df = pd.DataFrame(pd.json_normalize(df['col'])['data'].tolist(), columns=cols)
df['Date'] = pd.to_datetime(df['Date'], unit='s')
print (df)
Date x y z
0 2020-10-21 06:18:43.328814030 0.171875 -0.960938 0.023438
1 2020-10-21 06:18:45.060513735 0.085938 -0.984375 0.000000
2 2020-10-21 06:18:46.353275299 0.964979 NaN NaN
3 2020-10-21 06:18:47.698888779 0.039062 -1.000000 0.125000
4 2020-10-21 06:18:48.853050232 0.078125 -0.992188 0.000000
編輯：
就個人而言，像這樣保存 csv.xls并不是一個好主意，因為這樣會read_excel引發奇怪的錯誤，但您可以使用：
import ast
df = pd.read_csv('15-10-2020-OO.xls')
cols = ['Date','x','y','z']
data = [x['data'] for x in df['Data'].apply(ast.literal_eval)]
df = pd.DataFrame(data, columns=cols)
df['Date'] = pd.to_datetime(df['Date'], unit='s')
print (df)
Date x y z
0 2020-10-15 07:21:16.159236193 0.085938 -0.972656 0.003906
1 2020-10-15 07:21:17.597931385 0.089844 -0.968750 0.003906
2 2020-10-15 07:21:18.838171959 0.089844 -0.972656 0.003906
3 2020-10-15 07:21:20.338105917 0.085938 -0.972656 0.003906
4 2020-10-15 07:21:21.768864155 0.089844 -0.984375 0.003906
... ... ... ...
8457 2020-10-15 08:59:57.907007933 0.085938 -0.972656 0.003906
8458 2020-10-15 08:59:58.371274233 0.089844 -0.976562 0.003906
8459 2020-10-15 08:59:58.833237648 0.085938 -0.976562 0.003906
8460 2020-10-15 08:59:59.313337088 1.517057 NaN NaN
8461 2020-10-15 08:59:59.863240004 0.089844 -0.968750 0.007812
[8462 rows x 4 columns]
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