In [1]:

    

import pandas as pd


    

In [2]:

    

cy = pd.read_pickle("WRIdata/CountryGHGs.pkl")


    

In [25]:

    

cy2011 = cy[cy["Year"] == 2011]
cy2011.set_index("Country", inplace=True)
cy2011.head()


    

    
Out[25]:






  

    

      

      
Year
      
Total GHG Emissions Excluding Land-Use Change and Forestry (MtCO2e)
      
Total GHG Emissions Including Land-Use Change and Forestry (MtCO2e)
      
Total CO? Excluding Land-Use Change and Forestry (MtCO2)
      
Total CH4 (MtCO2e)
      
Total N2O (MtCO2e)
      
Total F-Gas (MtCO2e)
      
Energy (MtCO2e)
      
Industrial Processes (MtCO2e)
      
Agriculture (MtCO2e)
      
Waste (MtCO2e)
      
LUCF (MtCO2)
      
Bunker Fuels (MtCO2)
      
Electricity/Heat (MtCO2)
      
Manufacturing/Construction (MtCO2)
      
Transportation (MtCO2)
      
Other Fuel Combustion (MtCO2e)
      
Fugitive Emissions (MtCO2e)
    
    

      
Country
      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
Afghanistan
      
 2011
      
  25.3140
      
  25.3140
      
   6.5892
      
  14.918806
      
  3.555945
      
 0.250075
      
   0.000000
      
  0.000000
      
  10.649049
      
 7.807114
      
  0.00000
      
 0.00
      
  0.00
      
  0.00
      
  0.00
      
  0.000000
      
  0.018588
    
    

      
Albania
      
 2011
      
   6.6421
      
   6.4350
      
   3.8700
      
   2.042046
      
  0.709250
      
 0.020793
      
   4.178090
      
  0.000000
      
   1.815239
      
 0.626808
      
 -0.20709
      
 0.06
      
  0.13
      
  0.87
      
  2.32
      
  0.813442
      
  0.044648
    
    

      
Algeria
      
 2011
      
 171.0483
      
 172.5160
      
 121.3781
      
  43.355179
      
  2.738071
      
 3.576946
      
 140.423879
      
 14.253527
      
   6.605581
      
 9.755265
      
  1.46775
      
 2.23
      
 39.60
      
 13.51
      
 32.34
      
 18.847913
      
 36.125966
    
    

      
Angola
      
 2011
      
 223.3533
      
 263.4348
      
  28.4594
      
 125.905947
      
 68.879997
      
 0.107982
      
 116.460058
      
  0.000000
      
 104.617518
      
 2.167738
      
 40.08147
      
 1.16
      
  2.40
      
  2.71
      
  6.82
      
  5.439277
      
 99.090781
    
    

      
Antigua & Barbuda
      
 2011
      
   1.2977
      
   1.2977
      
   0.7316
      
   0.176359
      
  0.045035
      
 0.344739
      
   0.000000
      
  0.000000
      
   0.068951
      
 0.151450
      
  0.00000
      
 0.00
      
  0.00
      
  0.00
      
  0.00
      
  0.000000
      
  0.000000
    
  

In [9]:

    

orginfos = pd.read_pickle("../CDPdata/orginfos.pkl")
orginfos = orginfos.reset_index().set_index("Organisation")


    

In [12]:

    

cdp2011 = pd.read_pickle("../CDPdata/2011scope12.pkl")
cdp2011orgs = cdp2011.join(orginfos[["Country", "GICS Sector", "GICS Industry Group", "GICS Industry"]])
cdp2011country = cdp2011orgs.groupby("Country").sum()/1000000


    

In [33]:

    

# cdp2011country.loc["USA"]


    

    
Out[33]:





Scope 1    733.665351
Scope 2    200.645159
Name: USA, dtype: float64

In [42]:

    

# cy2011.loc["United States of America"]
cy2011[[cy2011.columns[1]]].sort(cy2011.columns[1], ascending = 0)[0:21]


    

    
Out[42]:






  

    

      

      
Total GHG Emissions Excluding Land-Use Change and Forestry (MtCO2e)
    
    

      
Country
      

    
  
  

    

      
World
      
 43816.7343
    
    

      
China
      
 10552.6054
    
    

      
United States of America
      
  6550.0981
    
    

      
European Union (28)
      
  4540.9445
    
    

      
European Union (15)
      
  3611.0495
    
    

      
India
      
  2486.1713
    
    

      
Russian Federation
      
  2374.3143
    
    

      
Japan
      
  1307.4082
    
    

      
Brazil
      
  1131.1022
    
    

      
Germany
      
   882.9341
    
    

      
Indonesia
      
   834.5754
    
    

      
Canada
      
   716.2074
    
    

      
Iran
      
   715.5275
    
    

      
Mexico
      
   699.0501
    
    

      
Korea (South)
      
   687.7381
    
    

      
Australia
      
   563.4540
    
    

      
United Kingdom
      
   543.5536
    
    

      
Saudi Arabia
      
   532.8904
    
    

      
Italy
      
   491.0664
    
    

      
France
      
   487.3909
    
    

      
South Africa
      
   456.8534
    
  

In [37]:

    

cdp2011country.sort("Scope 1", ascending =0)[0:20]


    

    
Out[37]:






  

    

      

      
Scope 1
      
Scope 2
    
    

      
Country
      

      

    
  
  

    

      
Germany
      
 984.913104
      
  56.747364
    
    

      
USA
      
 733.665351
      
 200.645159
    
    

      
Canada
      
 461.843479
      
   3.635726
    
    

      
United Kingdom
      
 240.951168
      
  78.134328
    
    

      
France
      
 190.085999
      
  28.803831
    
    

      
Luxembourg
      
 165.226000
      
  19.599000
    
    

      
South Korea
      
  80.813559
      
  11.677886
    
    

      
Netherlands
      
  75.105620
      
  10.249494
    
    

      
Japan
      
  49.391950
      
  11.997287
    
    

      
India
      
  43.998342
      
   1.864853
    
    

      
Italy
      
  40.078773
      
   5.413656
    
    

      
Spain
      
  39.086450
      
   3.338354
    
    

      
Portugal
      
  14.744282
      
   1.027109
    
    

      
South Africa
      
  11.615124
      
  19.307781
    
    

      
Brazil
      
   6.521813
      
   0.473788
    
    

      
Finland
      
   6.313675
      
   0.660746
    
    

      
Norway
      
   5.912968
      
   5.417774
    
    

      
Belgium
      
   4.070731
      
   3.285823
    
    

      
Austria
      
   3.246066
      
   0.441521
    
    

      
Switzerland
      
   3.153211
      
   2.624117
    
  

In [ ]: