def test():
# inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = ['coicop12_{}'.format(coicop12_index) for coicop12_index in range(1, 13)]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = None # dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities = elasticities,
inflation_kwargs = inflation_kwargs,
year = year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
print elasticite_1
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i) for i in range(1, 13)]].sum(axis = 1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
return toto
def test():
# inflators_by_year = get_inflators_by_year()
# Liste des coicop agrégées en 12 postes
simulated_variables = [
'coicop12_{}'.format(coicop12_index)
for coicop12_index in range(1, 13)
]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = None # dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities=elasticities,
inflation_kwargs=inflation_kwargs,
year=year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
print elasticite_1
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values=[values],
columns=['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i)
for i in range(1, 13)]].sum(axis=1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
return toto
import seaborn
# Import de modules spécifiques à Openfisca
from openfisca_france_indirect_taxation.examples.utils_example import graph_builder_bar
from openfisca_france_indirect_taxation.surveys import SurveyScenario
# Import d'une nouvelle palette de couleurs
seaborn.set_palette(seaborn.color_palette("Set2", 12))
if __name__ == '__main__':
# Sélection des variables utilisées pour la simulation
simulated_variables = ['depenses_carburants', 'depenses_essence', 'depenses_diesel', 'revtot']
for year in [2000, 2005, 2011]:
survey_scenario = SurveyScenario.create(year = year)
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['strate'])
])
df = pivot_table.T
# Réalisation de graphiques
for element in simulated_variables:
if element == 'revtot':
continue
df['part_{}_revtot'.format(element)] = \
df['{}'.format(element)] / df['revtot']
graph_builder_bar(df[['part_{}_revtot'.format(element)]])
'rev_disponible', 'depenses_cigarettes', 'depenses_cigares',
'depenses_tabac_a_rouler', 'depenses_alcools_forts', 'depenses_vin',
'depenses_biere'
]
# Merge des deux listes
simulated_variables += list_coicop12
for year in [2000, 2005, 2011]:
# Constition d'une base de données agrégée par décile (= collapse en stata)
df = simulate(simulated_variables=simulated_variables, year=year)
if year == 2011:
df.niveau_vie_decile[df.decuc == 10] = 10
var_to_concat = list_coicop12 + ['rev_disponible']
Wconcat = df_weighted_average_grouped(dataframe=df,
groupe='niveau_vie_decile',
varlist=simulated_variables)
list_alcool_tabac = []
Wconcat['part_alcool'] = \
(Wconcat['depenses_alcools_forts'] + Wconcat['depenses_vin'] + Wconcat['depenses_biere']) \
/ Wconcat['rev_disponible']
list_alcool_tabac.append('part_alcool')
Wconcat['part_tabac'] = \
(Wconcat['depenses_cigarettes'] + Wconcat['depenses_cigares'] +
Wconcat['depenses_tabac_a_rouler']) / Wconcat['rev_disponible']
list_alcool_tabac.append('part_tabac')
df_to_graph = Wconcat[list_alcool_tabac].copy()
df_to_graph.columns = ['Alcool', 'Tabac']
graph_builder_bar(df_to_graph)
taxe_indirectes['alcools_forts_droit_d_accise']
).copy()
taxe_indirectes[u'Taxes assurances'] = (
taxe_indirectes['assurance_sante_taxe'] +
taxe_indirectes['assurance_transport_taxe'] +
taxe_indirectes['autres_assurances_taxe']
).copy()
taxe_indirectes[u'Taxes tabacs'] = (
taxe_indirectes['cigarette_droit_d_accise'] +
taxe_indirectes['cigares_droit_d_accise'] +
taxe_indirectes['tabac_a_rouler_droit_d_accise']
).copy()
taxe_indirectes = taxe_indirectes.rename(columns = {'revtot': u'revenu total',
'rev_disponible': u'revenu disponible', 'somme_coicop12': u'depenses totales',
'taxes_indirectes_total': u'toutes les taxes indirectes'})
for revenu in [u'revenu total', u'revenu disponible', u'depenses totales', u'toutes les taxes indirectes']:
list_part_taxes = []
for taxe in ['TVA', 'TICPE', u'Taxes alcools', u'Taxes assurances', u'Taxes tabacs']:
taxe_indirectes[u'part ' + taxe] = (
taxe_indirectes[taxe] / taxe_indirectes[revenu]
)
'list_part_taxes_{}'.format(taxe)
list_part_taxes.append(u'part ' + taxe)
df_to_graph = taxe_indirectes[list_part_taxes]
print '''Contributions aux différentes taxes indirectes en part de {0},
par décile de revenu en {1}'''.format(revenu, year)
graph_builder_bar(df_to_graph)
# Liste des coicop agrégées en 12 postes
simulated_variables = ['coicop12_{}'.format(coicop12_index) for coicop12_index in range(1, 13)]
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = dict(inflator_by_variable = inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities = elasticities,
inflation_kwargs = inflation_kwargs,
year = year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
# boum
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values = [values], columns = ['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i) for i in range(1, 13)]].sum(axis = 1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
from openfisca_france_indirect_taxation.examples.utils_example import graph_builder_bar
from openfisca_france_indirect_taxation.surveys import SurveyScenario
# Import d'une nouvelle palette de couleurs
seaborn.set_palette(seaborn.color_palette("Set2", 12))
if __name__ == '__main__':
# Sélection des variables utilisées pour la simulation
simulated_variables = [
'depenses_carburants', 'depenses_essence', 'depenses_diesel', 'revtot'
]
for year in [2000, 2005, 2011]:
survey_scenario = SurveyScenario.create(year=year)
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values=[values],
columns=['strate'])
])
df = pivot_table.T
# Réalisation de graphiques
for element in simulated_variables:
if element == 'revtot':
continue
df['part_{}_revtot'.format(element)] = \
df['{}'.format(element)] / df['revtot']
graph_builder_bar(df[['part_{}_revtot'.format(element)]])
year = 2011
elasticities = get_elasticities(year)
inflation_kwargs = dict(inflator_by_variable=inflators_by_year[year])
survey_scenario = SurveyScenario.create(
elasticities=elasticities,
inflation_kwargs=inflation_kwargs,
year=year,
)
coicop12_1 = survey_scenario.simulation.calculate('coicop12_1')
elasticite_1 = survey_scenario.simulation.calculate('elas_exp_1')
# boum
pivot_table = pandas.DataFrame()
for values in simulated_variables:
pivot_table = pandas.concat([
pivot_table,
survey_scenario.compute_pivot_table(values=[values],
columns=['niveau_vie_decile'])
])
df = pivot_table.T
df['depenses_tot'] = df[['coicop12_{}'.format(i)
for i in range(1, 13)]].sum(axis=1)
for i in range(1, 13):
df['part_coicop12_{}'.format(i)] = \
df['coicop12_{}'.format(i)] / df['depenses_tot']
graph_builder_bar(df[['part_coicop12_{}'.format(i) for i in range(1, 13)]])
