遇到了一个复杂Python JSON对象到自定义词典的转换问题，JSON对象的数据结构复杂，涉及多层嵌套，需要对嵌套的数据进行递归遍历和转换。
解决方案
- 数据预处理：
- 首先使用
deepcopy()函数创建JSON数据的副本，防止在转换过程中修改原始数据。
- 首先使用
- 递归遍历：
- 接下来，使用自定义函数
walk_fun_lim()对JSON数据进行递归遍历。该函数可以递归遍历嵌套的数据结构，并对每个字典元素执行指定的转换操作。
- 接下来，使用自定义函数
- 转换操作：
- 在
transformers_robots_in_disguise()函数中，对每个字典元素进行转换操作。- 提取
PerfList元素，并将其转换为自定义格式。 - 提取
Resource元素，并将其转换为自定义格式。
- 提取
- 在
- 转换结果：
- 转换完成后，将得到一个新的字典数据结构，其中包含了转换后的数据。
代码例子
import json
from copy import deepcopy
from pprint import pprint
from StringIO import StringIO
# JSON数据
json_str = \
'''
{
"Resource": [
{
"@name": "Bravo",
"@signature": "h#Bravo",
"@type": "ESX_5.x",
"@typeDisplayName": "ESX Server",
"PerfList": {
"@attrId": "cpuUsage",
"@attrName": "Usage",
"Data": [
{
"@data": "26.00",
"@end": "01:05:00",
"@interval": "60",
"@start": "01:04:00"
},
{
"@data": "24.00",
"@end": "01:04:00",
"@interval": "60",
"@start": "01:03:00"
},
{
"@data": "36.00",
"@end": "01:03:00",
"@interval": "60",
"@start": "01:02:00"
},
{
"@data": "38.00",
"@end": "01:02:00",
"@interval": "60",
"@start": "01:01:00"
},
{
"@data": "37.00",
"@end": "01:01:00",
"@interval": "60",
"@start": "01:00:00"
}
]
},
"Resource": [
{
"@name": "Tango",
"@signature": "vm#Tango",
"@type": "vm",
"@typeDisplayName": "Virtual Machine",
"PerfList": {
"@attrId": "cpuUsage",
"@attrName": "Usage",
"Data": {
"@data": "12.00",
"@end": "04:05:00",
"@interval": "60",
"@start": "04:04:00"
}
}
},
{
"@name": "Charlie",
"@signature": "vm#Charlie",
"@type": "vm",
"@typeDisplayName": "Virtual Machine",
"PerfList": {
"@attrId": "cpuUsage",
"@attrName": "Usage",
"Data": [
{
"@data": "12.00",
"@end": "04:20:00",
"@interval": "60",
"@start": "04:19:00"
},
{
"@data": "12.00",
"@end": "04:19:00",
"@interval": "60",
"@start": "04:18:00"
}
]
}
}
]
},
{
"@name": "Alpha",
"@signature": "h#Alpha",
"@type": "ESX_5.x",
"@typeDisplayName": "ESX Server",
"PerfList": [
{
"@attrId": "cpuUsage",
"@attrName": "Usage",
"Data": {
"@data": "9",
"@end": "06:10:00",
"@interval": "60",
"@start": "06:09:00"
}
},
{
"@attrId": "cpuUsagemhz",
"@attrName": "Usage MHz",
"Data": {
"@data": "479",
"@end": "06:10:00",
"@interval": "60",
"@start": "06:09:00"
}
}
]
}
]
}
'''
# 数据预处理
json_data = json.load(StringIO(json_str))
data_copy = deepcopy(json_data)
# 递归遍历和转换
def walk_fun_lim(ilist, func=None):
tlist = []
ttlist = []
if(isinstance(ilist, list)):
ttlist = filter(lambda x: x, func(filter(lambda x: isinstance(x, dict), ilist)))
if(ttlist):
tlist += ttlist
for q in ilist:
ttlist = filter(lambda x: x, walk_fun_lim_helper(q, func, count+1))
if(ttlist):
tlist += ttlist
elif(isinstance(ilist, dict)):
ttlist = filter(lambda x: x, func([ilist]))
if(ttlist):
tlist += ttlist
for q in ilist:
ttlist = filter(lambda x: x, walk_fun_lim_helper(ilist[q], func, count+1))
if(ttlist):
tlist += ttlist
return [tlist] if(count != 0) else tlist
def transformers_robots_in_disguise(x):
for idict in x:
plist = idict.pop("PerfList", [])
plist = plist if(isinstance(plist, list)) else [plist]
for sub_dict in plist:
sub_name = sub_dict.pop("@attrId")
dlist = sub_dict.pop("Data", [])
dlist = dlist if(isinstance(dlist, list)) else [dlist]
new_dict = {}
for sub_dict in dlist:
new_dict["from_%(@start)s_to_%(@end)s" % sub_dict] = sub_dict["@data"]
new_dict["@interval"] = sub_dict["@interval"]
idict[sub_name] = new_dict
rlist = idict.pop("Resource", [])
rlist = rlist if(isinstance(rlist, list)) else [rlist]
for sub_dict in rlist:
sub_type = sub_dict.pop("@type")
sub_name = sub_dict.pop("@name")
idict.setdefault(sub_type, {})[sub_name] = sub_dict
return []
walk_fun_lim(data_copy, transformers_robots_in_disguise)
# 转换结果
pprint(data_copy)
这个解决方案使用walk_fun_lim()函数对JSON数据进行递归遍历，并在遍历过程中执行transformers_robots_in_disguise()函数进行转换操作。最后得到的data_copy就是转换后的结果。
