Parsing the Eora26 EE MRIO database

Getting Eora26

The Eora 26 database is available at http://www.worldmrio.com . You need to register there and can then download the files from http://www.worldmrio.com/simplified .

Parse

To parse a single year do:



In [1]:

    
import pymrio



In [2]:

    
eora_storage = '/tmp/mrios/eora26'



In [3]:

    
eora = pymrio.parse_eora26(year=2005, path=eora_storage)









    



/home/konstans/bin/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:3947: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  new_axis = axis.drop(labels, errors=errors)

Explore

Eora includes (almost) all countries:



In [4]:

    
eora.get_regions()









    Out[4]:





Index(['AFG', 'ALB', 'DZA', 'AND', 'AGO', 'ATG', 'ARG', 'ARM', 'ABW', 'AUS',
       ...
       'TZA', 'USA', 'URY', 'UZB', 'VUT', 'VEN', 'VNM', 'YEM', 'ZMB', 'ZWE'],
      dtype='object', name='region', length=189)

This can easily be aggregated to, for example, the OECD/NON_OECD countries with the help of the country converter coco.



In [5]:

    
import country_converter as coco



In [6]:

    
eora.aggregate(region_agg = coco.agg_conc(original_countries='Eora',
                                          aggregates=['OECD'],
                                          missing_countries='NON_OECD')
              )









    Out[6]:





<pymrio.core.mriosystem.IOSystem at 0x7f16d80985d0>



In [7]:

    
eora.get_regions()









    Out[7]:





Index(['NON_OECD', 'OECD'], dtype='object', name='region')



In [8]:

    
eora.calc_all()









    Out[8]:





<pymrio.core.mriosystem.IOSystem at 0x7f16d80985d0>



In [9]:

    
import matplotlib.pyplot as plt
with plt.style.context('ggplot'):
    eora.Q.plot_account(('Total cropland area', 'Total'), figsize=(8,5))
    plt.show()

See the other notebooks for further information on aggregation and file io.