In [1]:

    

import pandas as pd
import numpy as np
import json


    

In [2]:

    

xls = pd.ExcelFile('../CDPdata/Investor CDP 2014_Public Data.xlsx')


    

In [4]:

    

sheet_names = pd.Series(xls.sheet_names)


    

In [5]:

    

sheet_names[30:40]


    

    
Out[5]:





30    CC7. Emissions Methodology
31                         CC7.1
32                         CC7.2
33                         CC7.3
34                         CC7.4
35           CC8. Emissions Data
36                        CC8.4a
37                         CC8.5
38                        CC8.6a
39                        CC8.6b
dtype: object

In [3]:

    

p = xls.parse(43)


    

In [5]:

    

# pickle some data?
p.to_pickle('../CDPdata/sheet43_2014.pkl')


    

In [3]:

    

p = pd.read_pickle('../CDPdata/sheet43_2014.pkl')


    

In [4]:

    

pcols = p.columns.values
p[pcols[14:25]].head()


    

    
Out[4]:






  

    

      

      
Reporting Period
From
      
Reporting Period
To
      
CC9.1a. Please break down your total gross global Scope 1 emissions by country/region

CC9.1a. Country/Region
      
CC9.1a. Scope 1 metric tonnes CO2e  
    
  
  

    

      
0
      
 1 Jan 2013
      
 31 Dec 2013
      
              Argentina
      
  34591
    
    

      
1
      
 1 Jan 2013
      
 31 Dec 2013
      
                Belgium
      
  25267
    
    

      
2
      
 1 Jan 2013
      
 31 Dec 2013
      
 Bosnia and Herzegovina
      
    268
    
    

      
3
      
 1 Jan 2013
      
 31 Dec 2013
      
                 Brazil
      
  80239
    
    

      
4
      
 1 Jan 2013
      
 31 Dec 2013
      
                  China
      
 422748
    
  

In [7]:

    

# Emissions are not relevant, No emissions from this source, Emissions excluded due to a recent acquisition 
p[pcols[16]].value_counts()
# excuses_s12 = p[["Organisation"]+pcols[17:19].tolist()]


    

In [20]:

    

excuses_s12.to_pickle("../CDPdata/excuses_s12.pkl")


    

In [4]:

    

import ghgmosh as gm


    

In [803]:

    

reload(gm)


    

    
Out[803]:





<module 'ghgmosh' from 'ghgmosh.py'>

In [689]:

    

pscope1 = gm.get_scope1or2(p, 1, 2010)
# scope1totals = gm.get_totals(pscope1, "Country", "GICS Sector", 20)


    

In [440]:

    

pscope2 = pscope2[1:]
# pscope2.head()


    

In [670]:

    

# get scope2 totals by country and sector
pscope2 = gm.get_scope1or2(p, 2, 2010)
# scope2totals = gm.get_totals(pscope2, "Country", "GICS Sector", 20)


    

In [108]:

    

# pickle some data
scope1totals.to_pickle('mydata/2014scope1totals.pkl')
scope2totals.to_pickle('mydata/2014scope2totals.pkl')


    

In [176]:

    

scope1total = pd.read_pickle('mydata/2013scope1totals.pkl')
scope2total = pd.read_pickle('mydata/2013scope2totals.pkl')


    

In [539]:

    

scope2total.head()


    

    
Out[539]:






  

    

      

      

      
Scope 2
    
    

      
Country
      
GICS Sector
      

    
  
  

    

      
Australia
      
Consumer Discretionary
      
 0.294324
    
    

      
Consumer Staples
      
 5.398313
    
    

      
Energy
      
 0.596422
    
    

      
Financials
      
 2.022157
    
    

      
Health Care
      
 0.148036
    
  

In [560]:

    

s1data = gm.write_json(scope1total, "scope1countrysector.json")
s2data = gm.write_json(scope2total, "scope2countrysector.json")


    

In [671]:

    

p2 = xls.parse(49)


    

In [3]:

    

# p2.to_pickle('../CDPdata/sheet49_2010.pkl')
p2 = pd.read_pickle("../CDPdata/sheet68_2014.pkl")


    

In [30]:

    

catgroups = p2.groupby([p2.columns[gm.scopecols[2014][3]["cat"]],'GICS Sector'])
cats = catgroups.groups.keys()
# catgroups.head()


    

In [50]:

    

cg_sums = catgroups.sum()
cg_sums.reset_index(inplace=True)
cg_sums = cg_sums.rename( columns = { cg_sums.columns[0]: "Scope3Cat", "GICS Sector": "Sector",
                                    cg_sums.columns[4]: "CO2eTons" } )
cg_sums = cg_sums[["Scope3Cat", "Sector", "CO2eTons"]]


    

In [87]:

    

categories = list(set(cg_sums.Scope3Cat.values.tolist()))
sectors = list(set(cg_sums.Sector.values.tolist()))
# categories
cgs = cg_sums.set_index(['Scope3Cat', 'Sector'])


    

In [55]:

    

from collections import OrderedDict

from bokeh.charts import Donut
from bokeh.plotting import show, output_file
bk.output_notebook()


    

In [89]:

    

sectorsScope3 = OrderedDict()
for s in sectors:
    sectorsScope3[s]  = []
    for c in categories:
         sectorsScope3[s].append(cgs.loc[c,s]["CO2eTons"])


    

In [94]:

    

donut = Donut(sectorsScope3, categories, filename="Scope3CatsSectors.html")
donut.show()


    

    




Wrote donut.html

In [673]:

    

pscope3 = gm.get_scope3(p2, 2010)