我正在尝试将下面的json文件标准化为4个表-“ content”，“ Modules”，“ Images”和“另一个表中的其他所有内容”
{
"id": "0000050a",
"revision": 1580225050941,
"slot": "product-description",
"type": "E",
"create_date": 1580225050941,
"modified_date": 1580225050941,
"creator": "Auto",
"modifier": "Auto",
"audit_info": {
"date": 1580225050941,
"source": "AutoService",
"username": "Auto"
},
"total_ID": 1,
"name": "Auto_A1AM78C64UM0Y8_B07JCJR5HW",
"content": [{
"ID": ["B01"],
"content_revision": 1580225050941,
"template": {
"module": [{
"id": "module-11",
"text": null,
"header": [{
"id": "title",
"value": null,
"decorators": []
}],
"paragraph": [{
"id": "description",
"value": [],
"decorators": []
}],
"image": [{
"id": "image",
"assetId": "/images/2cdabb786d10.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,970,300_PT0_SX970_V1__",
"src": "image.jpg",
"originalSrc": null
}],
"integer": null,
"chart": null,
"list": null,
"video": null,
"gallery": null,
"composite": null,
"collection": null,
"product": null
}, {
"id": "module-6",
"text": null,
"header": [{
"id": "title1",
"value": "Dest ",
"decorators": []
}, {
"id": "title2",
"value": "cc",
"decorators": []
}, {
"id": "title3",
"value": "Col",
"decorators": []
}, {
"id": "title4",
"value": "C",
"decorators": []
}, {
"id": "caption1",
"value": null,
"decorators": []
}, {
"id": "caption2",
"value": null,
"decorators": []
}, {
"id": "caption3",
"value": null,
"decorators": []
}, {
"id": "caption4",
"value": null,
"decorators": []
}],
"paragraph": [{
"id": "description1",
"value": [" Sport"],
"decorators": [
[]
]
}, {
"id": "description2",
"value": ["elements "],
"decorators": [
[]
]
}, {
"id": "description3",
"value": ["Film "],
"decorators": [
[]
]
}, {
"id": "description4",
"value": ["Our signature "],
"decorators": [
[]
]
}],
"image": [{
"id": "image1",
"assetId": "/images/dbbfc9873e31.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image2_.jpg",
"originalSrc": null
}, {
"id": "image2",
"assetId": "/images/f577ae005.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "test.jpg",
"originalSrc": null
}, {
"id": "image3",
"assetId": "/images/-0df21c5216d0.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image.jpg",
"originalSrc": null
}, {
"id": "image4",
"assetId": "/images/78d26b9c-408c-4299-8ea8-e9257f170320.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image.jpg",
"originalSrc": null
}, {
"id": "thumb1",
"assetId": "/images/-bbbfc9873e31.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image.jpg",
"originalSrc": null
}, {
"id": "thumb2",
"assetId": "/images/e56f577ae005.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image_.jpg",
"originalSrc": null
}, {
"id": "thumb3",
"assetId": "/images/0df21c5216d0.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image_.jpg",
"originalSrc": null
}, {
"id": "thumb4",
"assetId": "/images/-e9257f170320.jpg",
"alt": " ",
"viewLarger": null,
"styleCodes": "__CR0,0,300,300_PT0_SX300_V1__",
"src": "image.jpg",
"originalSrc": null
}],
"integer": null,
"chart": null,
"list": null,
"video": null,
"gallery": null,
"composite": null,
"collection": null,
"product": null
}],
"renderType": "VERTICAL"
},
"locale_data": {
"locale": "en_US",
"identified_by": "MACHINE_DETECT"
}
}],
"badges": []
}
我可以将标头作为JSON路径成功地将JSON展平为一个大数据框架。但我想将JSON标准化为单独的表。例如，“模块”表应具有ID，Text，Header_ID，Header_Value之类的列，依此类推。图像表应具有诸如Image_ID，Assest_ID，Src之类的列，依此类推。谁能帮助我将此JSON标准化为4个表格。
答案 0 :(得分：1)
您可以如下使用https://towardsdatascience.com/flattening-json-objects-in-python-f5343c794b10定义的函数，然后使用json_normalize：
import pandas as pd
import json
with open('test.json') as json_file:
data = json.load(json_file)
def flatten_json(y):
out = {}
def flatten(x, name=''):
if type(x) is dict:
for a in x:
flatten(x[a], name + a + '_')
elif type(x) is list:
i = 0
for a in x:
flatten(a, name + str(i) + '_')
i += 1
else:
out[name[:-1]] = x
flatten(y)
return out
module = flatten_json(data["content"][0])
module = pd.json_normalize(module)
然后，您要做的是根据您描述的四个类别选择列。 输出为：
ID_0 content_revision ... locale_data_locale locale_data_identified_by
0 B01 1580225050941 ... en_US MACHINE_DETECT
然后您选择以下内容，例如为您的模块和图像DataFrames：
module = df.loc[:,df.columns.str.contains("module")]
image = df.loc[:,df.columns.str.contains("image")]
例如，您获得模块的结果是：
template_module_0_id ... template_module_1_product
0 module-11 ... None
然后，我给出了转换DataFrame模块的示例，您只有两个模块，因此您可以在重命名列后执行concat：
module1 = module.loc[:,module.columns.str.contains("module_0")]
module1.columns = module1.columns.str.replace("_0","")
module2 = module.loc[:,module.columns.str.contains("module_1")]
module2.columns = module2.columns.str.replace("_1","")
modules = pd.concat([module1,
module2])
您会得到：
template_module_id ... template_module_image_7_originalSrc
0 module-11 ... NaN
0 module-6 ... None
如果元素更多，另一个选择是直接在所需的嵌套元素上使用flatten_json和json_normalize函数。