In [1]:

    

import pandas as pd


    

In [3]:

    

unem = pd.read_json("Unemployment.json")
wage = pd.read_json("Wages.json")


    

In [4]:

    

unem


    

    
Out[4]:




<class 'pandas.core.frame.DataFrame'>
Int64Index: 26650 entries, 0 to 26649
Data columns (total 5 columns):
Copyright     26650  non-null values
LastUpdate    26650  non-null values
Notes         26650  non-null values
Rows          26650  non-null values
Source        26650  non-null values
dtypes: object(5)

In [5]:

    

wage


    

    
Out[5]:




<class 'pandas.core.frame.DataFrame'>
Int64Index: 650 entries, 0 to 649
Data columns (total 5 columns):
Copyright     650  non-null values
LastUpdate    650  non-null values
Notes         650  non-null values
Rows          650  non-null values
Source        650  non-null values
dtypes: object(5)

In [15]:

    

unem.Rows[3]


    

    
Out[15]:





{u'constituencyname': u'Blyth Valley',
 u'countryid': u'K02000001',
 u'countryname': u'UK',
 u'dateofdataset': u'2010-05-01T00:00:00',
 u'datethisupdate': u'2013-10-16T00:00:00',
 u'onsconstid': u'E14000575',
 u'regionid': u'E15000001',
 u'regionname': u'North East',
 u'unempconstcountryrank': 0.0,
 u'unempconstnumber': 2248.0,
 u'unempconstrate': 0.055068737521804,
 u'unempcountrynumber': 1502155.0,
 u'unempcountryrate': 0.047924075944367005,
 u'unempregionnumber': 80105.0,
 u'unempregionrate': 0.064243841286268}

In [19]:

    

wage.Rows[0]


    

    
Out[19]:





{u'constituencyname': u'Berwick-upon-Tweed',
 u'countryid': u'K02000001',
 u'countryname': u'UK',
 u'dateofdataset': u'2012-01-01T00:00:00',
 u'dateofthisupdate': u'2013-03-13T00:00:00',
 u'onsconstid': u'E14000554',
 u'regionid': u'E15000001',
 u'regionname': u'North East',
 u'wageconstcountryrank': 420.0,
 u'wagemedianconst': 440.0,
 u'wagemediancountry': 510.0,
 u'wagemedianregion': 460.0}

In [ ]: