如何将这个JSON数据读入Python

Question

我以JSON格式调用了来自世界卫生组织的一些数据，我想将其读入一个Pandas数据框架。你知道吗

我在这一页上称之为： WHO Measles First Dose Rate

{u'dimension': [{u'display': u'Indicator', u'label': u'GHO'},
  {u'display': u'PUBLISH STATES', u'label': u'PUBLISHSTATE'},
  {u'display': u'Year', u'label': u'YEAR'},
  {u'display': u'WHO region', u'label': u'REGION'},
  {u'display': u'World Bank income group', u'label': u'WORLDBANKINCOMEGROUP'},
  {u'display': u'Country', u'label': u'COUNTRY'}],
 u'fact': [{u'Value': u'25',
   u'dim': {u'COUNTRY': u'Afghanistan',
    u'GHO': u'Measles-containing-vaccine first-dose (MCV1) immunization coverage among 1-year-olds (%)',
    u'PUBLISHSTATE': u'Published',
    u'REGION': u'Eastern Mediterranean',
    u'WORLDBANKINCOMEGROUP': u'Low-income',
    u'YEAR': u'1993'}},
  {u'Value': u'57',
   u'dim': {u'COUNTRY': u'Afghanistan',
    u'GHO': u'Measles-containing-vaccine first-dose (MCV1) immunization coverage among 1-year-olds (%)',
    u'PUBLISHSTATE': u'Published',
    u'REGION': u'Eastern Mediterranean',
    u'WORLDBANKINCOMEGROUP': u'Low-income',
    u'YEAR': u'2013'}},
  {u'Value': u'62',
   u'dim': {u'COUNTRY': u'Angola',
    u'GHO': u'Measles-containing-vaccine first-dose (MCV1) immunization coverage among 1-year-olds (%)',
    u'PUBLISHSTATE': u'Published',
    u'REGION': u'Africa',
    u'WORLDBANKINCOMEGROUP': u'Upper-middle-income',
    u'YEAR': u'1996'}},
  {u'Value': u'94',
   u'dim': {u'COUNTRY': u'Andorra',
    u'GHO': u'Measles-containing-vaccine first-dose (MCV1) immunization coverage among 1-year-olds (%)',
    u'PUBLISHSTATE': u'Published',
    u'REGION': u'Europe',
    u'WORLDBANKINCOMEGROUP': u'High-income',
    u'YEAR': u'2005'}},
  {u'Value': u'34',
   u'dim': {u'COUNTRY': u'United Arab Emirates',
    u'GHO': u'Measles-containing-vaccine first-dose (MCV1) immunization coverage among 1-year-olds (%)',
    u'PUBLISHSTATE': u'Published',
    u'REGION': u'Eastern Mediterranean',
    u'WORLDBANKINCOMEGROUP': u'High-income',
    u'YEAR': u'1980'}},

我试过了

#Setting Up and loading JSON into object ready to turn into dataframe
url = "http://apps.who.int/gho/athena/data/GHO/WHS8_110.json?profile=simple&filter=COUNTRY:*"
response = requests.get(url)
response_json = response.content
json.loads(response_json)
whoDataSetVaccinationRate = json.loads(response_json)    

#Attempt to load JSON Data into Pandas Dataframe
whoDataSetVaccinationRateDF = pd.DataFrame(whoDataSetVaccinationRate['fact']
, columns=['COUNTRY', 'YEAR','REGION'])
whoDataSetVaccinationRateDF

看起来是这样的-但我只在国家和年份的数据框中得到NaN值：

我意识到我希望它在数据帧中有不同的布局——我不知道该怎么称呼它。我希望我的数据帧是这样的：

Answer 1

将^{}与^{}一起使用：

from pandas.io.json import json_normalize   
import urllib.request, json 

#https://stackoverflow.com/a/12965254
url = "http://apps.who.int/gho/athena/data/GHO/WHS8_110.json?profile=simple&filter=COUNTRY:*"

with urllib.request.urlopen(url) as url:
    data = json.loads(url.read().decode())

df = json_normalize(data['fact']).pivot('dim.COUNTRY','dim.YEAR','Value').astype(float)
print (df.head())

dim.YEAR     1980  1981  1982  1983  1984  1985  1986  1987  1988  1989  ...   \
dim.COUNTRY                                                              ...    
Afghanistan  11.0   NaN   8.0   9.0  14.0  14.0  14.0  31.0  34.0  22.0  ...    
Albania      90.0  90.0  93.0  96.0  96.0  96.0  96.0  96.0  96.0  96.0  ...    
Algeria       NaN   NaN   NaN   NaN   NaN  68.0  67.0  73.0  81.0  82.0  ...    
Andorra       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    
Angola        NaN   NaN   NaN  26.0  35.0  44.0  44.0  55.0  56.0  48.0  ...    

dim.YEAR     2007  2008  2009  2010  2011  2012  2013  2014  2015  2016  
dim.COUNTRY                                                              
Afghanistan  55.0  59.0  60.0  62.0  64.0  59.0  57.0  60.0  62.0  62.0  
Albania      97.0  98.0  97.0  99.0  99.0  98.0  99.0  98.0  97.0  96.0  
Algeria      92.0  88.0  92.0  95.0  95.0  95.0  95.0  95.0  95.0  94.0  
Andorra      94.0  98.0  98.0  99.0  99.0  98.0  95.0  96.0  96.0  97.0  
Angola       71.0  61.0  57.0  72.0  64.0  72.0  66.0  60.0  55.0  49.0  

[5 rows x 37 columns]