我有以下输出json，我尝试使用json_normalize将其转换为带有pandas的数据帧。我可以使用json_normalize（数据，[＆＃39;跑步者＆＃39;]）进入跑步者水平，但我可以达到前级水平。
[{
u 'status' : u 'OPEN',
u 'isMarketDataDelayed' : False,
u 'numberOfRunners' : 9,
u 'complete' : True,
u 'bspReconciled' : False,
u 'runnersVoidable' : False,
u 'betDelay' : 0,
u 'marketId' : u '1.123264244',
u 'crossMatching' : False,
u 'totalMatched' : 4.22,
u 'version' : 1241856317,
u 'lastMatchTime' : u '2016-02-25T10:32:25.704Z',
u 'numberOfWinners' : 1,
u 'inplay' : False,
u 'numberOfActiveRunners' : 9,
u 'totalAvailable' : 39.26,
u 'runners' : [{
u 'status' : u 'ACTIVE',
u 'handicap' : 0.0,
u 'selectionId' : 10861647,
u 'totalMatched' : 0.0,
u 'adjustmentFactor' : 16.631,
u 'ex' : {
u 'availableToBack' : [{
u 'price' : 1.02,
u 'size' : 2.15
}
],
u 'availableToLay' : [],
u 'tradedVolume' : []
}
}, {
u 'status' : u 'ACTIVE',
u 'handicap' : 0.0,
u 'selectionId' : 10861648,
u 'totalMatched' : 0.0,
u 'adjustmentFactor' : 13.237,
u 'ex' : {
u 'availableToBack' : [{
u 'price' : 1.01,
u 'size' : 7.11
}
],
u 'availableToLay' : [],
u 'tradedVolume' : []
}
},
使用其他数据我可以轻松地使用json_normalize（数据，[＆＃39;跑步者＆＃39;前＆＃39;]），但如果我在这种情况下这样做，我会得到< / p>
0
0 availableToBack
1 availableToLay
2 tradedVolume
3 availableToBack
4 availableToLay
5 tradedVolume
6 availableToBack
7 availableToLay
8 tradedVolume
9 availableToBack
10 availableToLay
11 tradedVolume
12 availableToBack
13 availableToLay
14 tradedVolume
15 availableToBack
16 availableToLay
17 tradedVolume
18 availableToBack
19 availableToLay
20 tradedVolume
21 availableToBack
22 availableToLay
23 tradedVolume
24 availableToBack
25 availableToLay
26 tradedVolume
27 availableToBack
28 availableToLay
29 tradedVolume
.. ...
你能帮我解决一下这个问题吗？
答案 0 :(得分：0)
data 是嵌套的 list 和 dictslists
'ex.availableToBack' 是 list 的 dicts，它被标准化为列 'price' 和 'size'。dataimport pandas as pd
# if you want all of data, load data into a dataframe
df = pd.json_normalize(data)
# runners is a list that needs to be exploded
df = df.explode('runners').reset_index(drop=True)
# runners is a column of dicts that need to be normalized
runners = pd.json_normalize(df.pop('runners'))
# there are a number of columns that are lists that must be exploded
runners = runners.apply(pd.Series.explode)
# flatten ex.availableToBack
runners = runners.join(pd.DataFrame(runners.pop('ex.availableToBack').values.tolist()))
# add a prefix to all the runners column names
runners.columns = [f'runners_{v}' for v in runners.columns]
# join df and runners
df = df.join(runners)
# extract the ex columns
ex_cols = df.iloc[:, -4:].copy()
# display(df)
betDelay bspReconciled complete crossMatching inplay isMarketDataDelayed lastMatchTime marketId numberOfActiveRunners numberOfRunners numberOfWinners runnersVoidable status totalAvailable totalMatched version runners_adjustmentFactor runners_handicap runners_selectionId runners_status runners_totalMatched runners_ex.availableToLay runners_ex.tradedVolume runners_price runners_size
0 0 False True False False False 2016-02-25T10:32:25.704Z 1.123264244 9 9 1 False OPEN 39.26 4.22 1241856317 16.631 0.0 10861647 ACTIVE 0.0 NaN NaN 1.02 2.15
1 0 False True False False False 2016-02-25T10:32:25.704Z 1.123264244 9 9 1 False OPEN 39.26 4.22 1241856317 13.237 0.0 10861648 ACTIVE 0.0 NaN NaN 1.01 7.11
# display(ex_cols)
runners_ex.availableToLay runners_ex.tradedVolume runners_price runners_size
0 NaN NaN 1.02 2.15
1 NaN NaN 1.01 7.11
keys 中的 'runners' 进行标准化# normalize the runners key to get ex
runners = pd.json_normalize(data, record_path=['runners'])
# there are a number of columns that are lists that must be exploded
runners = runners.apply(pd.Series.explode).reset_index(drop=True)
# flatten ex.availableToBack
runners = runners.join(pd.DataFrame(runners.pop('ex.availableToBack').values.tolist()))
# extract the ex columns
ex_cols = runners.iloc[:, -4:].copy()
# display(runners)
adjustmentFactor handicap selectionId status totalMatched ex.availableToLay ex.tradedVolume price size
0 16.631 0.0 10861647 ACTIVE 0.0 NaN NaN 1.02 2.15
1 13.237 0.0 10861648 ACTIVE 0.0 NaN NaN 1.01 7.11
# display(ex_cols)
ex.availableToLay ex.tradedVolume price size
0 NaN NaN 1.02 2.15
1 NaN NaN 1.01 7.11
datadata =\
[{'betDelay': 0,
'bspReconciled': False,
'complete': True,
'crossMatching': False,
'inplay': False,
'isMarketDataDelayed': False,
'lastMatchTime': '2016-02-25T10:32:25.704Z',
'marketId': '1.123264244',
'numberOfActiveRunners': 9,
'numberOfRunners': 9,
'numberOfWinners': 1,
'runners': [{'adjustmentFactor': 16.631,
'ex': {'availableToBack': [{'price': 1.02, 'size': 2.15}],
'availableToLay': [],
'tradedVolume': []},
'handicap': 0.0,
'selectionId': 10861647,
'status': 'ACTIVE',
'totalMatched': 0.0},
{'adjustmentFactor': 13.237,
'ex': {'availableToBack': [{'price': 1.01, 'size': 7.11}],
'availableToLay': [],
'tradedVolume': []},
'handicap': 0.0,
'selectionId': 10861648,
'status': 'ACTIVE',
'totalMatched': 0.0}],
'runnersVoidable': False,
'status': 'OPEN',
'totalAvailable': 39.26,
'totalMatched': 4.22,
'version': 1241856317}]