Parsing using pd.DataFrame

In [1]:

from mario import Database
import pandas as pd
import numpy as np

IOT Database¶

In [2]:

# Creating indeces according to mario format
regions  = ['reg.1']
Z_levels = ['Sector']
sectors  = ['sec.1','sec.2']

factors   = ['Labor']
satellite = ['CO2'] 


Y_level = ['Consumption category']
demands = ['Households']

Z_index   = pd.MultiIndex.from_product([regions,Z_levels,sectors])
Y_columns = pd.MultiIndex.from_product([regions,Y_level,demands])

In [3]:

# creating matrices
Z = pd.DataFrame(
    data =  np.array([
            [10,70],
            [50,10]]),
    index = Z_index,
    columns= Z_index
)
Y = pd.DataFrame(
    data =  np.array([
            [200],
            [80]]),
    index = Z_index,
    columns= Y_columns,
)
E = pd.DataFrame(
    data =  np.array([
            [30,20]]),
    index = satellite,
    columns= Z_index,
)
V = pd.DataFrame(
    data =  np.array([
            [220,60]]),
    index = factors,
    columns= Z_index,
)
EY = pd.DataFrame(
    data =  np.array([8]),
    index = satellite,
    columns= Y_columns,
)

In [4]:

Z

Out[4]:

			reg.1
			Sector
			sec.1	sec.2
reg.1	Sector	sec.1	10	70
		sec.2	50	10

In [5]:

Y

Out[5]:

			reg.1
			Consumption category
			Households
reg.1	Sector	sec.1	200
		sec.2	80

In [6]:

V

Out[6]:

	reg.1
	Sector
	sec.1	sec.2
Labor	220	60

In [7]:

E

Out[7]:

	reg.1
	Sector
	sec.1	sec.2
CO2	30	20

In [8]:

EY

Out[8]:

	reg.1
	Consumption category
	Households
CO2	8

In [9]:

# units as a dict of pd.DataFrames
units= {'Sector':pd.DataFrame('EUR',index=sectors,columns=['unit']),
       'Satellite account':pd.DataFrame('Ton',index=satellite,columns=['unit']),
       'Factor of production': pd.DataFrame('EUR',index=factors,columns=['unit'])}

In [10]:

# Creating a mario database
data = Database(Z=Z,
                Y=Y,
                E=E,
                V=V,
                EY=EY,
                table='IOT',
                units=units,
                name='iot test'
               )

In [11]:

print(data)

name = iot test
table = IOT
scenarios = ['baseline']
Factor of production = 1
Satellite account = 1
Consumption category = 1
Region = 1
Sector = 2

In [12]:

# Calculating matrices needed
data.calc_all(['p'])

Database: to calculate p following matrices are need.
['w'].Trying to calculate dependencies.

In [13]:

# Accessing to matrices 
data.p
# or when a specific scenario is needed
data['baseline']['p']

Out[13]:

			price index
Region	Level	Item
reg.1	Sector	sec.1	1.0
		sec.2	1.0

SUT Database¶

In [14]:

# Creating indeces according to mario format
regions  = ['reg.1']

activity_level  = ['Activity']
commodity_level = ['Commodity']

activities  = ['act.1']
commodities = ['com.1']

activity_index  = pd.MultiIndex.from_product([regions,activity_level,activities])
commodity_index = pd.MultiIndex.from_product([regions,commodity_level,commodities])

Z_index = activity_index.append(commodity_index)

factors   = ['Labor']
satellite = ['CO2'] 


Y_level = ['Consumption category']
demands = ['Households']

Y_columns = pd.MultiIndex.from_product([regions,Y_level,demands])

In [15]:

# creating matrices
Z = pd.DataFrame(
    data =  np.array([
            [0,70],
            [50,0]]),
    index = Z_index,
    columns= Z_index
)
Y = pd.DataFrame(
    data =  np.array([
            [0],
            [20]]),
    index = Z_index,
    columns= Y_columns,
)
E = pd.DataFrame(
    data =  np.array([
            [30,0]]),
    index = satellite,
    columns= Z_index,
)
V = pd.DataFrame(
    data =  np.array([
            [20,0]]),
    index = factors,
    columns= Z_index,
)
EY = pd.DataFrame(
    data =  np.array([8]),
    index = satellite,
    columns= Y_columns,
)

In [16]:

Z

Out[16]:

			reg.1
			Activity	Commodity
			act.1	com.1
reg.1	Activity	act.1	0	70
	Commodity	com.1	50	0

In [17]:

Y

Out[17]:

			reg.1
			Consumption category
			Households
reg.1	Activity	act.1	0
	Commodity	com.1	20

In [18]:

V

Out[18]:

	reg.1
	Activity	Commodity
	act.1	com.1
Labor	20	0

In [19]:

E

Out[19]:

	reg.1
	Activity	Commodity
	act.1	com.1
CO2	30	0

In [20]:

EY

Out[20]:

	reg.1
	Consumption category
	Households
CO2	8

In [21]:

# units as a dict of pd.DataFrames
units= {'Activity':pd.DataFrame('EUR',index=activities,columns=['unit']),
        'Commodity':pd.DataFrame('EUR',index=commodities,columns=['unit']),
       'Satellite account':pd.DataFrame('Ton',index=satellite,columns=['unit']),
       'Factor of production': pd.DataFrame('EUR',index=factors,columns=['unit'])}

In [22]:

# Creating a mario database
data = Database(Z=Z,
                Y=Y,
                E=E,
                V=V,
                EY=EY,
                table='SUT',
                units=units,
                name='sut test'
               )

In [23]:

print(data)

name = sut test
table = SUT
scenarios = ['baseline']
Activity = 1
Commodity = 1
Factor of production = 1
Satellite account = 1
Consumption category = 1
Region = 1

In [24]:

#Looking at supply matrix
data.s

Out[24]:

		Region	reg.1
		Level	Commodity
		Item	com.1
Region	Level	Item
reg.1	Activity	act.1	1.0

In [25]:

#Looking at supply matrix
data.u

Out[25]:

		Region	reg.1
		Level	Activity
		Item	act.1
Region	Level	Item
reg.1	Commodity	com.1	0.714286

In [ ]: