我正在使用的MY json：
{
"lastMonth":{
"aotalCost":4120.6241,
"cpu_avg_partition":{
"10":{
"count":1,
"cost":0.0,
"value":10
},
"100":{
"count":0,
"cost":0.0,
"value":100
},
"5":{
"count":16,
"cost":0.0,
"value":5
},
"50":{
"count":0,
"cost":0.0,
"value":50
},
"90":{
"count":0,
"cost":0.0,
"value":90
}
},
"providers":{
"azure":{
"cost":1844.134
},
"aws":{
"cost":2276.4901
}
},
"period":"lastMonth",
"resourceTypes":{
"db":{
"cost":842.8003
},
"bucket":{
"cost":362.2997
},
"server":{
"cost":976.9376
},
"volume":{
"cost":349.5705
},
"instance":{
"cost":868.4003
},
"others":{
"cost":199.1993
},
"null":{
"cost":521.4163
}
},
"accounts":{
"188f6226-59d8-4a7c-9fdb-f32f4f0885ff":{
"cost":19.3469
},
"19d2b0d2-d947-4e81-96aa-3b7f9583178a":{
"cost":543.6707
},
"c658a818-b96e-48b4-8a2e-c77404c58af6":{
"cost":1281.1164
},
"628455167342":{
"cost":2276.4901
}
},
"running_hours_partition":{
"60":{
"count":0,
"cost":0.0,
"value":60
},
"80":{
"count":0,
"cost":0.0,
"value":80
},
"20":{
"count":0,
"cost":0.0,
"value":20
},
"100":{
"count":5,
"cost":1008.16,
"value":100
},
"40":{
"count":0,
"cost":0.0,
"value":40
}
},
"projects":{
"813":{
"cost":4120.6241
}
}
},
"currency_code":"GBP",
"idmap":{
"accounts":{
"188f6226-59d8-4a7c-9fdb-f32f4f0885ff":"AzureForPM",
"19d2b0d2-d947-4e81-96aa-3b7f9583178a":"AzureForPM",
"c658a818-b96e-48b4-8a2e-c77404c58af6":"AzureForPM",
"628455167342":"628455167342"
},
"projects":{
"813":"ACP PM Work"
}
},
"currentMonth":{
"totalCost":1769.9801,
"cpu_avg_partition":{
"10":{
"count":0,
"cost":0.0,
"value":10
},
"100":{
"count":0,
"cost":0.0,
"value":100
},
"5":{
"count":10,
"cost":0.0,
"value":5
},
"50":{
"count":0,
"cost":0.0,
"value":50
},
"90":{
"count":0,
"cost":0.0,
"value":90
}
},
"providers":{
"azure":{
"cost":756.614
},
"aws":{
"cost":1013.3661
}
},
"period":"currentMonth",
"resourceTypes":{
"db":{
"cost":376.3396
},
"bucket":{
"cost":151.4477
},
"server":{
"cost":389.8187
},
"volume":{
"cost":152.1401
},
"instance":{
"cost":369.6178
},
"others":{
"cost":91.6273
},
"null":{
"cost":238.9888
}
},
"accounts":{
"188f6226-59d8-4a7c-9fdb-f32f4f0885ff":{
"cost":11.8057
},
"19d2b0d2-d947-4e81-96aa-3b7f9583178a":{
"cost":224.0437
},
"c658a818-b96e-48b4-8a2e-c77404c58af6":{
"cost":520.7645
},
"628455167342":{
"cost":1013.3661
}
},
"running_hours_partition":{
"60":{
"count":0,
"cost":0.0,
"value":60
},
"80":{
"count":0,
"cost":0.0,
"value":80
},
"20":{
"count":1,
"cost":3.08,
"value":20
},
"100":{
"count":5,
"cost":407.314,
"value":100
},
"40":{
"count":0,
"cost":0.0,
"value":40
}
},
"projects":{
"813":{
"cost":1769.98
}
}
我的python代码如下所示，我试图获取csv文件
import json
from pandas.io.json import json_normalize
def loading_file():
#File path
file_path =
'C：/Python27/robotscripts/Analytics_new/MonthlycostCSV/monthly_costformatter_data.json'
#Loading json file
json_data = open(file_path)
data = json.load(json_data)
return data
#Storing avaliable keys
def data_keys(data):
values = {}
for i in data["lastMonth"]:
for k in i.str():
values[k] = 1
values = values.values()
#Excluding nested arrays from keys - hard coded -> IMPROVE
new_keys = [x for x in values if
x != 'lastMonth' and
x != 'cpu_avg_partition']
return new_keys
#Excluding nested arrays from json dictionary
def new_data(data, values):
new_data = []
for i in range(0, len(data)):
x = {k:v for (k,v) in data[i].items() if k in values }
new_data.append(x)
return new_data
def csv_out(data):
data.to_csv('out.csv',encoding='utf-8')
def main():
data_file = loading_file()
values = data_keys(data_file)
table = new_data(data_file, values)
csv_out(json_normalize(table))
main()
我收到错误值
Traceback（最近一次调用最后一次）：文件 “C：\ Python27 \ robotscripts \ Analytics_new \ MonthlycostC main（）文件“C：\ Python27 \ robotscripts \ Analytics_new \ MonthlycostC values = data_keys（data_file）文件“C：\ Python27 \ robotscripts \ Analytics_new \ MonthlycostC 对于i.values（）中的k：AttributeError：'unicode'对象没有属性'values'
我还尝试过其他程序：
import json
import csv
f = open('monthly_costformatter_data.json')
data = json.load(f)
s=csv.writer(open('m1.csv','w'))
s.writerow(["aotalCost","cpu_avg_partition","providers"])
for i in data["lastMonth"]:
s.writerow([i["aotalCost"],i["cpu_avg_partition"],i["providers"]])
错误：
C：\ Python27 \ robotscripts \ Analytics_new \ MonthlycostCSV＆GT; monthlycostcsv4.py Traceback（最近一次调用最后一次）：文件 “C：\ Python27 \ robotscripts \ Analytics_new \ MonthlycostCSV \ monthlycostcsv4.py” 第13行，在 s.writerow（[i [“aotalCost”]，i [“cpu_avg_partition”]，i [“providers”]]）TypeError：字符串索引必须是整数
答案 0 :(得分：0)
对于第二个解决方案，data["lastMonth"]是一个字典，您可以直接使用键来查找值，不需要使用for loop，如下所示：
import json ,ast
import csv
f = open('monthly_costformatter_data.json')
data = json.load(f)
with open('m1.csv','w') as opfile:
s=csv.writer(opfile)
s.writerow(["aotalCost","cpu_avg_partition","providers"])
lastmonth = data["lastMonth"]
cpu_avg_partition = ast.literal_eval(json.dumps(lastmonth["cpu_avg_partition"]))
providers = ast.literal_eval(json.dumps(lastmonth["providers"]))
write_list = [lastmonth["aotalCost"],cpu_avg_partition,providers]
s.writerow(write_list)
f.close()
请注意，lastmonth["cpu_avg_partition"]和lastmonth["providers"]也是字典。
如果你想加载其他字典，例如：currentMonth和idmap，你可以直接使用加载的json数据，就像你对lastMonth所做的那样：
currentMonth= data["currentMonth"]
idmap= data["idmap"]
<强>更新
使用pandas，您可以将json文件加载到数据帧df，然后将df保存到csv文件中，如下所示：
import pandas as pd
df=pd.read_json("monthly_costformatter_data.json")
new_df = df.transpose()
column_name = new_df.columns.values
print column_name #print column name here
new_df[['totalCost','cpu_avg_partition']].to_csv('test.csv',index=False) #here you can select specific columns you want like 'totalCost','cpu_avg_partition' to write to csv file.