Python 如何使用具有相同列名的熊猫规范化JSON时间序列

我想使用具有以下格式的时间序列数据的api：

...
"value":[
{
"Key":"bt386",
"ReferenceDate":"2019-07-27T00:00:00Z",
"TargetDate":"2019-07-28T00:00:00Z",
"PublicationDate":null,
"ChangedOn":"2019-07-27T09:36:03.9727098+01:00",
"ValidUntil":"9999-12- 
31T23:59:59.9999999Z",
"ValueColumnsNumber":[ 
{"Key":"FreshSnowDepth","Value":0.000000000}, 
{"Key":"Precipitation","Value":0.000000000}, 
{"Key":"RainSnowMelt","Value":0.000000000}, 
{"Key":"Runoff","Value":31.800000000}, 
{"Key":"SnowDepth","Value":0.000000000}, 
{"Key":"SnowDepthNormalPerc","Value":0.000000000}, 
{"Key":"SnowMelt","Value":0.000000000}, 
{"Key":"SnowWaterEquivalents","Value":0.000000000}, 
{"Key":"Temperature","Value":18.450000000}],"ValueColumnsText": 
[],"ValueColumnsDateTime":[]},
{
"Key":"bt386",
"ReferenceDate":"2019-07-27T00:00:00Z",
"TargetDate":"2019-07-29T00:00:00Z",
"PublicationDate":null,
"ChangedOn":"2019-07- 
27T09:36:03.9727098+01:00",
"ValidUntil":"9999-12-31T23:59:59.9999999Z",
"ValueColumnsNumber":[ 
{"Key":"FreshSnowDepth","Value":0.000000000}, 
{"Key":"Precipitation","Value":0.000000000}, 
{"Key":"RainSnowMelt","Value":0.000000000}, 
{"Key":"Runoff","Value":28.400000000}, 
{"Key":"SnowDepth","Value":0.000000000}, 
{"Key":"SnowDepthNormalPerc","Value":0.000000000}, 
{"Key":"SnowMelt","Value":0.000000000}, 
{"Key":"SnowWaterEquivalents","Value":0.000000000}, 
{"Key":"Temperature","Value":18.750000000}],
"ValueColumnsText": 
[],
"ValueColumnsDateTime":[]
}
]

我尝试了以下代码：

d = json.loads(response.content)
timeSeries = json_normalize(data=d['value'], 
record_path='ValueColumnsNumber',
meta=['ReferenceDate', 'TargetDate'])

table = timeSeries.pivot_table('Value', ['ReferenceDate', 'TargetDate'], 
'Key')
table.reset_index(drop=False, inplace=True)
pd.set_option('display.max_columns', None)

print(table.head(3))

Key         ReferenceDate            TargetDate  FreshSnowDepth
0    2017-03-22T00:00:00Z  2017-03-23T00:00:00Z             2.8   
1    2017-03-22T00:00:00Z  2017-03-24T00:00:00Z             7.6   
2    2017-03-22T00:00:00Z  2017-03-25T00:00:00Z             0.3   

我需要的是还包括字母数字键

Key       CurveKey       ReferenceDate            TargetDate  FreshSnowDepth
0         bt386   2017-03-22T00:00:00Z  2017-03-23T00:00:00Z             2.8   
1         bt386   2017-03-22T00:00:00Z  2017-03-24T00:00:00Z             7.6   
2         abcde   2017-03-22T00:00:00Z  2017-03-25T00:00:00Z             0.3  

 timeSeries = json_normalize(data=d['value'], 
 record_path='ValueColumnsNumber',
 meta=['Key', 'ReferenceDate', 'TargetDate'])

更改

json\u normalize（）

函数时，出现以下错误：

ValueError:元数据名称键冲突，需要区分前缀

要将json转换为所需的格式，我需要做什么？

试试以下方法：

table = pd.io.json.json_normalize(d, ['value', 'ValueColumnsNumber'], meta=[
    ['value', 'Key'],
    ['value', 'ReferenceDate'],
    ['value', 'TargetDate'],
])

记录路径

应该是要循环的最深级别

meta

包含您想要抓住的较浅层次的任何内容

结果:

              Key  Value value.Key   value.ReferenceDate      value.TargetDate
0  FreshSnowDepth    0.0     bt386  2019-07-27T00:00:00Z  2019-07-28T00:00:00Z
1   Precipitation    0.0     bt386  2019-07-27T00:00:00Z  2019-07-28T00:00:00Z
2    RainSnowMelt    0.0     bt386  2019-07-27T00:00:00Z  2019-07-28T00:00:00Z
3          Runoff   31.8     bt386  2019-07-27T00:00:00Z  2019-07-28T00:00:00Z
4       SnowDepth    0.0     bt386  2019-07-27T00:00:00Z  2019-07-28T00:00:00Z