我正在嘗試將 api 響應從 json 轉換為 Pandas 中的數據幀。我遇到的問題是 de 數據嵌套在 json 格式中，而我的數據框中沒有得到正確的列。數據是從具有以下格式的 api 收集的：{ "data": [ { "timestamp": "2019-04-10T11:40:13.437Z", "score": 87, "sensors": [ { "comp": "temp", "value": 20.010000228881836 }, { "comp": "humid", "value": 34.4900016784668 }, { "comp": "co2", "value": 418 }, { "comp": "voc", "value": 166 }, { "comp": "pm25", "value": 4 }, { "comp": "lux", "value": 961.4000244140625 }, { "comp": "spl_a", "value": 45.70000076293945 } ], "indices": [ { "comp": "temp", "value": -1 }, { "comp": "humid", "value": -2 }, { "comp": "co2", "value": 0 }, { "comp": "voc", "value": 0 }, { "comp": "pm25", "value": 0 } ] }, { "timestamp": "2019-04-10T11:40:03.413Z", "score": 87, "sensors": [ { "comp": "temp", "value": 20.040000915527344 }, { "comp": "humid", "value": 34.630001068115234 },我需要的結果如下： 結果有人可以幫助我嗎？
1 回答
Smart貓小萌
TA貢獻1911條經驗 獲得超7個贊
你可以重塑你的作品數據：
data = {
"data": [
{
"timestamp": "2019-04-10T11:40:13.437Z",
"score": 87,
"sensors": [
{
"comp": "temp",
"value": 20.010000228881836
},
{
"comp": "humid",
"value": 34.4900016784668
},
{
"comp": "co2",
"value": 418
},
{
"comp": "voc",
"value": 166
},
{
"comp": "pm25",
"value": 4
},
{
"comp": "lux",
"value": 961.4000244140625
},
{
"comp": "spl_a",
"value": 45.70000076293945
}
],
"indices": [
{
"comp": "temp",
"value": -1
},
{
"comp": "humid",
"value": -2
},
{
"comp": "co2",
"value": 0
},
{
"comp": "voc",
"value": 0
},
{
"comp": "pm25",
"value": 0
}
]
},
{
"timestamp": "2019-04-10T11:40:03.413Z",
"score": 87,
"sensors": [
{
"comp": "temp",
"value": 20.040000915527344
},
{
"comp": "humid",
"value": 34.630001068115234
},
{
"comp": "co2",
"value": 418
},
{
"comp": "voc",
"value": 169
},
{
"comp": "pm25",
"value": 5
},
{
"comp": "lux",
"value": 960.2000122070312
},
{
"comp": "spl_a",
"value": 46
}
],
"indices": [
{
"comp": "temp",
"value": -1
},
{
"comp": "humid",
"value": -1
},
{
"comp": "co2",
"value": 0
},
{
"comp": "voc",
"value": 0
},
{
"comp": "pm25",
"value": 0
}
]
}]}
from pandas.io.json import json_normalize
import pandas as pd
df = pd.DataFrame()
for each in data['data']:
timestamp = each['timestamp']
temp_df = json_normalize(data=each, record_path=['sensors']).T
columns = list(temp_df.iloc[0])
data_values = list(temp_df.iloc[1,:])
temp_df = pd.DataFrame([data_values + [timestamp]], columns=columns + ['timestamp'])
df = df.append(temp_df).reset_index(drop=True)
print(df)
輸出：
print(df)
temp humid co2 ... lux spl_a timestamp
0 20.010000 34.490002 418.0 ... 961.400024 45.700001 2019-04-10T11:40:13.437Z
1 20.040001 34.630001 418.0 ... 960.200012 46.000000 2019-04-10T11:40:03.413Z
[2 rows x 8 columns]
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