Example use with random data

Here, we will load test MRIO data. We have defined a .yaml file with all the require instructions, to load the data.

import os
import logging
import mrio_toolbox as mrio

# get the current workbook directory
workbookDir = os.getcwd()

# Load the data
test = mrio.MRIO(file="loading_instructions.yaml")

All raw data tables will be stored in an mrio object, which offers several functions for indexing and manipulating the data.

First, let’s check which countries and sectors are in our mrio object.

print(test.labels["countries"])
print(test.labels["sectors"])

['Austria', 'Belgium', 'Bulgaria', 'Cyprus', 'Czech Republic', 'ROW']
['Agriculture', 'Manufacturing', 'Services']

Our MRIO object contains an inter-industry table, noted t:

print(test.parts)

{'t': <mrio_toolbox._parts._Part.Part object at 0x7f3a065c5be0>}

This where the actual data is stored. We can check its dimensions and its shape

print(test.t.get_dimensions())
print(test.t.shape)

[['countries', 'sectors'], ['countries', 'sectors']]
(18, 18)

Now, we can look at the exports from Czech republic to Austria. (Note that this is all random data!)

In order to nicely print our outputs, we can transform it into a Pandas DataFrame.

cz_exports = test.t[["Czech Republic"],["Austria"]]
cz_exports.to_pandas()

	countries	Austria
	sectors	Agriculture	Manufacturing	Services
countries	sectors
Czech Republic	Agriculture	8	38	36
	Manufacturing	96	48	31
	Services	70	10	75

In the groupings.yaml file, we have defined groupings. We have grouped the EU countries together and put our RoW “country” into a RoW group.

We can use those groupings to look at agricultural exports of the EU group to the rest of the world.

test.t.aggregate(on = "countries")

<mrio_toolbox._parts._Part.Part at 0x7f3a065e1230>

test.t[["EU","Agriculture"],"ROW"].to_pandas()

	countries	ROW
	sectors	Agriculture	Manufacturing	Services
countries	sectors
Austria	Agriculture	5	36	95
Belgium	Agriculture	84	24	77
Bulgaria	Agriculture	58	47	21
Cyprus	Agriculture	11	86	95
Czech Republic	Agriculture	56	52	57

We can also output a simplified table, where all EU countries are grouped together:

test.t.aggregate(on="countries").to_pandas()

	countries	EU			ROW
	sectors	Agriculture	Manufacturing	Services	Agriculture	Manufacturing	Services
countries	sectors
EU	Agriculture	1142.0	1191.0	1333.0	214.0	245.0	345.0
	Manufacturing	1340.0	1253.0	1363.0	248.0	279.0	304.0
	Services	1140.0	1290.0	1446.0	288.0	209.0	167.0
ROW	Agriculture	235.0	251.0	267.0	12.0	40.0	97.0
	Manufacturing	267.0	275.0	271.0	77.0	15.0	63.0
	Services	220.0	321.0	300.0	34.0	35.0	62.0

Now, we want to remove intra-sectoral trade. The “extract” method will do the job.

extracted = test.t.extraction(dimensions="sectors",domestic_only=True)
extracted[[("Austria","Belgium"),"all"],[("Austria","Belgium"),"all"]].to_pandas()

	countries	Austria			Belgium
	sectors	Agriculture	Manufacturing	Services	Agriculture	Manufacturing	Services
countries	sectors
Austria	Agriculture	0	4	29	0	14	80
	Manufacturing	30	0	63	35	0	2
	Services	49	60	0	5	60	0
Belgium	Agriculture	0	94	94	0	31	11
	Manufacturing	91	0	83	21	0	86
	Services	32	9	0	13	46	0

test.groupings

{'countries': {'EU': ['Austria',
   'Belgium',
   'Bulgaria',
   'Cyprus',
   'Czech Republic'],
  'ROW': ['ROW']},
 'sectors': {'primary': ['Agriculture'],
  'secondary': ['Manufacturing'],
  'tertiary': ['Services']}}

In the previous prompt, I added the “all” keyword. This makes sure I select Austria and Belgium on the “countries” axis; and “all” sectors on the “sectors” axis.

Note that the package does not support numpy-like ellipsis (:), nor slice objects.