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我正在尝试规范化以下JSON：
{
"href": "url1",
"items": [
{
"count": 6,
"items": [
{
"href": "url2",
"count": 2,
"items": [
{
"href": "url3",
"description": {
"year": 2017,
"make": "ACURA",
"model": "ILX",
"trim": "4D SEDAN ACURAWATCH PLUS",
"subSeries": "ACURAWATCH"
},
"adjustedPricing": {
"wholesale": {
"above": 18050,
"average": 15650,
"below": 13250
},
"adjustedBy": {
"Odometer": "30352"
}
},
"wholesale": {
"above": 17600,
"average": 15200,
"below": 12800
},
"averageOdometer": 36939,
"odometerUnits": "miles",
"averageGrade": 43,
"currency": "USD",
"sampleSize": "6"
},
{
"href": "url4",
"description": {
"year": 2017,
"make": "ACURA",
"model": "ILX",
"trim": "4D SEDAN",
"subSeries": "NONE"
},
"adjustedPricing": {
"wholesale": {
"above": 17950,
"average": 16600,
"below": 15250
},
"adjustedBy": {
"Odometer": "30352"
}
},
"wholesale": {
"above": 17900,
"average": 16550,
"below": 15200
},
"averageOdometer": 31235,
"odometerUnits": "miles",
"averageGrade": 37,
"currency": "USD",
"sampleSize": "47"
}
]
},
{
"href": "url5",
"count": 2,
"items": [
{
"href": "url6",
"description": {
"year": 2017,
"make": "ACURA",
"model": "ILX",
"trim": "4D SEDAN PREMIUM",
"subSeries": "PREMIUM"
},
"adjustedPricing": {
"wholesale": {
"above": 16200,
"average": 14700,
"below": 13200
},
"adjustedBy": {
"Odometer": "53824"
}
},
"wholesale": {
"above": 18500,
"average": 16950,
"below": 15450
},
"averageOdometer": 30090,
"odometerUnits": "miles",
"averageGrade": 36,
"currency": "USD",
"sampleSize": "15"
},
{
"href": "url7",
"description": {
"year": 2017,
"make": "ACURA",
"model": "ILX",
"trim": "4D SEDAN TECH PKG",
"subSeries": "TECH PKG"
},
"adjustedPricing": {
"wholesale": {
"above": 17850,
"average": 15950,
"below": 14050
},
"adjustedBy": {
"Odometer": "53824"
}
},
"wholesale": {
"above": 19800,
"average": 17850,
"below": 15950
},
"averageOdometer": 27247,
"odometerUnits": "miles",
"averageGrade": 37,
"currency": "USD",
"sampleSize": "21"
}
]
},
{
"href": "url8",
"count": 1,
"items": [
{
"href": "url9",
"description": {
"year": 2017,
"make": "ACURA",
"model": "MDX AWD",
"trim": "4D SUV 3.5L",
"subSeries": "NONE"
},
"adjustedPricing": {
"wholesale": {
"above": 28900,
"average": 27400,
"below": 25900
},
"adjustedBy": {
"Odometer": "23753"
}
},
"wholesale": {
"above": 28000,
"average": 26500,
"below": 25000
},
"averageOdometer": 34427,
"odometerUnits": "miles",
"averageGrade": 36,
"currency": "USD",
"sampleSize": "79",
"bestMatch": true
}
]
},
{
"href": "url10",
"count": 1,
"items": [
{
"href": "url11",
"description": {
"year": 2017,
"make": "ACURA",
"model": "MDX AWD",
"trim": "4D SUV 3.5L",
"subSeries": "NONE"
},
"adjustedPricing": {
"wholesale": {
"above": 27200,
"average": 25700,
"below": 24200
},
"adjustedBy": {
"Odometer": "43735"
}
},
"wholesale": {
"above": 28000,
"average": 26500,
"below": 25000
},
"averageOdometer": 34427,
"odometerUnits": "miles",
"averageGrade": 36,
"currency": "USD",
"sampleSize": "79",
"bestMatch": true
}
]
},
{
"href": "url12",
"count": 1,
"items": [
{
"href": "url13",
"description": {
"year": 2017,
"make": "ACURA",
"model": "MDX AWD",
"trim": "4D SUV 3.5L",
"subSeries": "NONE"
},
"adjustedPricing": {
"wholesale": {
"above": 27700,
"average": 26200,
"below": 24700
},
"adjustedBy": {
"Odometer": "38097"
}
},
"wholesale": {
"above": 28000,
"average": 26500,
"below": 25000
},
"averageOdometer": 34427,
"odometerUnits": "miles",
"averageGrade": 36,
"currency": "USD",
"sampleSize": "79",
"bestMatch": true
}
]
},
{
"href": "url14",
"count": 1,
"items": [
{
"href": "url15",
"description": {
"year": 2017,
"make": "ACURA",
"model": "MDX AWD",
"trim": "4D SUV 3.5L",
"subSeries": "NONE"
},
"adjustedPricing": {
"wholesale": {
"above": 28100,
"average": 26500,
"below": 25000
},
"adjustedBy": {
"Odometer": "33627"
}
},
"wholesale": {
"above": 28000,
"average": 26500,
"below": 25000
},
"averageOdometer": 34427,
"odometerUnits": "miles",
"averageGrade": 36,
"currency": "USD",
"sampleSize": "79",
"bestMatch": true
}
]
}
]
}
],
"errors": [
{
"count": 0,
"errors": []
}
]
}
我尝试使用Pandas的json_normalize函数，该函数返回1行3列（href、items、errors）。我还尝试将其插入Normalize a deeply nested json in pandas中的每个解决方案中，但它最终返回一个包含数百列的数据帧。有人知道如何正确地展平这个JSON，以便将它输入SQL吗
Tags： makemodelcountitemsdescriptionyearbelowabove
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对于这样的递归结构，可以使用递归将其展平
js是您提供的示例JSON（必须将true更改为True）pop()将其删除，因此它不会附加到正在构建的list。对列表中的所有元素执行递归调用。不使用项键将其附加到列表中list样本输出
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