def get_bdf_aggregates(data_year = None):
assert data_year is not None
depenses = get_input_data_frame(data_year)
depenses_by_grosposte = pandas.DataFrame()
for grosposte in range(1, 13):
if depenses_by_grosposte is None:
depenses_by_grosposte = depenses['coicop12_{}'.format(grosposte)]
else:
depenses_by_grosposte = concat([depenses_by_grosposte, depenses['coicop12_{}'.format(grosposte)]], axis = 1)
depenses_by_grosposte = concat([depenses_by_grosposte, depenses['pondmen']], axis = 1)
grospostes = set(depenses_by_grosposte.columns)
grospostes.remove('pondmen')
bdf_aggregates_by_grosposte = pandas.DataFrame()
for grosposte in grospostes:
bdf_aggregates_by_grosposte.loc[grosposte, 'bdf_aggregates'] = (
depenses_by_grosposte[grosposte] * depenses_by_grosposte['pondmen']
).sum()
return bdf_aggregates_by_grosposte
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 19 10:27:32 2015
@author: thomas.douenne
"""
from __future__ import division
from openfisca_core.tools import assert_near
from openfisca_france_indirect_taxation.examples.utils_example import get_input_data_frame
for year in [2000, 2005, 2011]:
aggregates_data_frame = get_input_data_frame(year)
if year == 2000:
df = aggregates_data_frame[
['poste_coicop_722', 'coicop12_7', 'poste_coicop_711'] + [
'poste_coicop_712', 'poste_coicop_713', 'poste_coicop_721',
'poste_coicop_723'
] + [
'poste_coicop_724', 'poste_coicop_731', 'poste_coicop_732',
'poste_coicop_733'
] + ['poste_coicop_734', 'poste_coicop_736']].copy()
df['check'] = (df['poste_coicop_711'] + df['poste_coicop_712'] +
df['poste_coicop_713'] + df['poste_coicop_721'] +
df['poste_coicop_722'] + df['poste_coicop_723'] +
df['poste_coicop_724'] + df['poste_coicop_731'] +
df['poste_coicop_732'] + df['poste_coicop_733'] +
df['poste_coicop_734'] + df['poste_coicop_736'] -
df['coicop12_7'])
else:
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 19 10:27:32 2015
@author: thomas.douenne
"""
from __future__ import division
from openfisca_core.tools import assert_near
from openfisca_france_indirect_taxation.examples.utils_example import get_input_data_frame
for year in [2000, 2005, 2011]:
aggregates_data_frame = get_input_data_frame(year)
if year == 2000:
df = aggregates_data_frame[['poste_coicop_722', 'coicop12_7', 'poste_coicop_711'] +
['poste_coicop_712', 'poste_coicop_713', 'poste_coicop_721', 'poste_coicop_723'] +
['poste_coicop_724', 'poste_coicop_731', 'poste_coicop_732', 'poste_coicop_733'] +
['poste_coicop_734', 'poste_coicop_736']].copy()
df['check'] = (
df['poste_coicop_711'] + df['poste_coicop_712'] + df['poste_coicop_713'] + df['poste_coicop_721'] +
df['poste_coicop_722'] + df['poste_coicop_723'] + df['poste_coicop_724'] + df['poste_coicop_731'] +
df['poste_coicop_732'] + df['poste_coicop_733'] + df['poste_coicop_734'] + df['poste_coicop_736'] -
df['coicop12_7']
)
else:
df = aggregates_data_frame[['poste_coicop_722', 'coicop12_7', 'poste_coicop_711'] +
['poste_coicop_712', 'poste_coicop_735', 'poste_coicop_713', 'poste_coicop_721'] +
['poste_coicop_723', 'poste_coicop_724', 'poste_coicop_731', 'poste_coicop_732'] +
from __future__ import division
import pandas as pd
import numpy as np
from pandas import concat
from openfisca_france_indirect_taxation.examples.utils_example import get_input_data_frame
from openfisca_france_indirect_taxation.almost_ideal_demand_system.aids_price_index_builder import \
df_indice_prix_produit
# Now that we have our price indexes, we construct a dataframe with the rest of the information
data_frame_for_reg = None
for year in [2000, 2005, 2011]:
aggregates_data_frame = get_input_data_frame(2011)
aggregates_data_frame['depenses_tot'] = 0
for i in range(1, 13):
aggregates_data_frame['depenses_tot'] += aggregates_data_frame['coicop12_{}'.format(i)]
produits = [column for column in aggregates_data_frame.columns if column.isdigit()]
data = aggregates_data_frame[produits + ['vag']].copy()
data.index.name = 'ident_men'
data.reset_index(inplace = True)
df = pd.melt(data, id_vars = ['vag', 'ident_men'], value_vars=produits,
value_name = 'depense_bien', var_name = 'bien')
df_indice_prix_produit = df_indice_prix_produit[['indice_prix_produit'] + ['prix'] + ['temps'] + ['mois']]
