)
quantite_bdf = pd.DataFrame()
produits = ['carburants', 'diesel', 'essence']
for element in produits:
quantite = pd.DataFrame.from_csv(os.path.join(assets_directory,
'openfisca_france_indirect_taxation', 'assets', 'quantites',
'quantites_{}_consommees_bdf.csv'.format(element)), sep = ',', header = -1)
quantite.rename(columns = {1: '{} bdf'.format(element)}, inplace = True)
quantite.index = quantite.index.str.replace('en milliers de m3 en ', '')
quantite = quantite.sort_index()
quantite_bdf = concat([quantite, quantite_bdf], axis = 1)
# Import des fichiers csv donnant les quantités agrégées d'après la comptabilité nationale.
quantite_carbu_vp_france = pd.read_csv(os.path.join(assets_directory,
'openfisca_france_indirect_taxation', 'assets', 'quantites',
'quantite_carbu_vp_france.csv'), sep = ';')
quantite_carbu_vp_france['Unnamed: 0'] = quantite_carbu_vp_france['Unnamed: 0'].astype(str)
quantite_carbu_vp_france = quantite_carbu_vp_france.set_index('Unnamed: 0')
quantite_carbu_vp_france.rename(columns = {'essence': 'essence agregat'}, inplace = True)
quantite_carbu_vp_france.rename(columns = {'diesel': 'diesel agregat'}, inplace = True)
quantite_carbu_vp_france['carburants agregat'] = quantite_carbu_vp_france.sum(axis = 1)
comparaison_bdf_agregats = concat([quantite_carbu_vp_france, quantite_bdf], axis = 1)
comparaison_bdf_agregats = comparaison_bdf_agregats.dropna()
# Créer des graphiques pour comparer les consommations obtenues via Bdf vis-à-vis de la comptabilité nationale
graph_builder_line(comparaison_bdf_agregats[['essence agregat'] + ['essence bdf']])
graph_builder_line(comparaison_bdf_agregats[['diesel agregat'] + ['diesel bdf']])
graph_builder_line(comparaison_bdf_agregats[['carburants agregat'] + ['carburants bdf']])
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.rename(columns={
'ticpe_totale':
'ticpe totale {}'.format(year),
'diesel_ticpe':
'ticpe diesel {}'.format(year),
'essence_ticpe':
'ticpe essence {}'.format(year),
'depenses_carburants':
'depenses carburants {}'.format(year),
'depenses_diesel':
'depenses diesel {}'.format(year),
'depenses_essence':
'depenses essence {}'.format(year)
},
inplace=True)
if depenses is not None:
depenses = concat([depenses, df[element + '{}'.format(year)]],
axis=1)
else:
depenses = df[element + '{}'.format(year)]
graph_builder_line(depenses)
depenses_bdf = concat([depenses, depenses_bdf], axis=1)
depenses_bdf.index = depenses_bdf.index.astype(int)
# Import des fichiers csv donnant les montants agrégés des mêmes postes d'après la comptabilité nationale
parametres_fiscalite_file_path = os.path.join(
assets_directory, 'openfisca_france_indirect_taxation', 'assets',
'legislation', 'Parametres fiscalite indirecte.xls')
masses_cn_data_frame = pd.read_excel(parametres_fiscalite_file_path,
sheetname="consommation_CN")
masses_cn_carburants = masses_cn_data_frame[masses_cn_data_frame['Fonction'] ==
'Carburants et lubrifiants']
masses_cn_carburants = masses_cn_carburants.transpose()
masses_cn_carburants.rename(columns={76: 'carburants agregat'}, inplace=True)
masses_cn_transports = masses_cn_data_frame[masses_cn_data_frame['Fonction'] ==
'Transports']
masses_cn_transports = masses_cn_transports.transpose()
masses_cn_transports.rename(columns={69: 'transports agregat'}, inplace=True)
comparaison_bdf_agregats = concat(
[depenses_bdf, masses_cn_carburants, masses_cn_transports], axis=1)
comparaison_bdf_agregats = comparaison_bdf_agregats.dropna()
# Créer des graphiques pour comparer les consommations obtenues via Bdf vis-à-vis de la comptabilité nationale
graph_builder_line(comparaison_bdf_agregats[['carburants agregat'] +
['carburants bdf']])
graph_builder_line(comparaison_bdf_agregats[['transports agregat'] +
['transports bdf']])
depenses_ticpe_pour_1000_euros_carbu = pd.DataFrame(index = index, columns = columns)
for element in columns:
if element == 'si seulement vehicules essence':
dies = 0
else:
dies = 1
if element == 'si seulement vehicules diesel':
ess = 0
else:
ess = 1
for year in range(2000, 2014):
year = year
simulation = base.tax_benefit_system.new_scenario().init_single_entity(
period = year,
personne_de_reference = dict(
birth = datetime.date(year - 40, 1, 1),
),
menage = dict(
depenses_carburants = 1000,
veh_essence = ess,
veh_diesel = dies,
),
).new_simulation(debug = True)
depenses_ticpe_pour_1000_euros_carbu.loc[depenses_ticpe_pour_1000_euros_carbu.index == year, element] = \
simulation.calculate('ticpe_totale')
graph_builder_line(depenses_ticpe_pour_1000_euros_carbu)
save_dataframe_to_graph(depenses_ticpe_pour_1000_euros_carbu, 'cas_type_1000€_carbu.csv')
comparaison_vehicules["diesel bdf"] = 0
comparaison_vehicules["essence bdf"] = 0
comparaison_vehicules["total bdf"] = 0
# Sélection des données Budget des Familles utilisées et organisation de la dataframe
for year in [2000, 2005, 2011]:
aggregates_data_frame = get_input_data_frame(year)
df_nombre_vehicules_bdf = aggregates_data_frame[["veh_diesel"] + ["veh_essence"] + ["pondmen"]]
nombre_vehicules_diesel_bdf = (
df_nombre_vehicules_bdf["veh_diesel"] * df_nombre_vehicules_bdf["pondmen"]
).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, "diesel bdf"] = nombre_vehicules_diesel_bdf
nombre_vehicules_essence_bdf = (
df_nombre_vehicules_bdf["veh_essence"] * df_nombre_vehicules_bdf["pondmen"]
).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, "essence bdf"] = nombre_vehicules_essence_bdf
nombre_vehicules_total_bdf = (
(df_nombre_vehicules_bdf["veh_essence"] + df_nombre_vehicules_bdf["veh_diesel"])
* df_nombre_vehicules_bdf["pondmen"]
).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, "total bdf"] = nombre_vehicules_total_bdf
comparaison_vehicules = comparaison_vehicules[comparaison_vehicules["total bdf"] != 0]
# Réalisation de graphiques
graph_builder_line(comparaison_vehicules[["total bdf"] + ["total agregats"]])
graph_builder_line(comparaison_vehicules[["diesel bdf"] + ["diesel agregats"]])
graph_builder_line(comparaison_vehicules[["essence bdf"] + ["essence agregats"]])
depenses.index = depenses.index.str.replace('en ', '')
depenses = depenses.sort_index()
depenses_bdf = concat([depenses, depenses_bdf], axis = 1)
depenses_bdf.index = depenses_bdf.index.astype(int)
# Import des fichiers csv donnant les montants agrégés des mêmes postes d'après la comptabilité nationale
parametres_fiscalite_file_path = os.path.join(
assets_directory,
'openfisca_france_indirect_taxation',
'assets',
'legislation',
'Parametres fiscalite indirecte.xls'
)
masses_cn_data_frame = pd.read_excel(parametres_fiscalite_file_path, sheetname = "consommation_CN")
masses_cn_carburants = masses_cn_data_frame[masses_cn_data_frame['Fonction'] == 'Carburants et lubrifiants']
masses_cn_carburants = masses_cn_carburants.transpose()
masses_cn_carburants.rename(columns = {76: 'carburants agregat'}, inplace = True)
masses_cn_transports = masses_cn_data_frame[masses_cn_data_frame['Fonction'] == 'Transports']
masses_cn_transports = masses_cn_transports.transpose()
masses_cn_transports.rename(columns = {69: 'transports agregat'}, inplace = True)
comparaison_bdf_agregats = concat([depenses_bdf, masses_cn_carburants, masses_cn_transports], axis = 1)
comparaison_bdf_agregats = comparaison_bdf_agregats.dropna()
# Créer des graphiques pour comparer les consommations obtenues via Bdf vis-à-vis de la comptabilité nationale
graph_builder_line(comparaison_bdf_agregats[['carburants agregat'] + ['carburants bdf']])
graph_builder_line(comparaison_bdf_agregats[['transports agregat'] + ['transports bdf']])
part_revtot = pandas.DataFrame()
part_revtot['part ' + element.replace('_', ' ') + ' revtot {}'.format(year)] = \
df[element] / df['revtot']
part_rev_loyerimput = pandas.DataFrame()
part_rev_loyerimput['part ' + element.replace('_', ' ') + ' rev disp loyerimput {}'.format(year)] = \
df[element] / df['rev_disp_loyerimput']
part_depenses = pandas.DataFrame()
part_depenses['part ' + element.replace('_', ' ') + ' depenses {}'.format(year)] = \
df[element] / df['somme_coicop12_conso']
if revtot is not None:
revtot = concat([revtot, part_revtot], axis = 1)
else:
revtot = part_revtot
if rev_disp_loyerimput is not None:
rev_disp_loyerimput = concat([rev_disp_loyerimput, part_rev_loyerimput], axis = 1)
else:
rev_disp_loyerimput = part_rev_loyerimput
if depenses is not None:
depenses = concat([depenses, part_depenses], axis = 1)
else:
depenses = part_depenses
graph_builder_line(revtot)
graph_builder_line(rev_disp_loyerimput)
graph_builder_line(depenses)
# Le but est de construire un graphique représentant les 3 années pour chaque variable. On fait donc une boucle
# dans une boucle.
to_graph = ['ticpe totale ', 'ticpe diesel ', 'ticpe essence ', 'depenses carburants ', 'depenses diesel ',
'depenses essence ']
for element in to_graph:
depenses = None
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 = ['niveau_vie_decile'])
])
df = pivot_table.T
df.rename(columns = {'ticpe_totale': 'ticpe totale {}'.format(year),
'diesel_ticpe': 'ticpe diesel {}'.format(year),
'essence_ticpe': 'ticpe essence {}'.format(year),
'depenses_carburants': 'depenses carburants {}'.format(year),
'depenses_diesel': 'depenses diesel {}'.format(year),
'depenses_essence': 'depenses essence {}'.format(year)},
inplace = True)
if depenses is not None:
depenses = concat(
[depenses, df[element + '{}'.format(year)]], axis = 1)
else:
depenses = df[element + '{}'.format(year)]
graph_builder_line(depenses)
quantite.index = quantite.index.str.replace('en milliers de m3 en ', '')
quantite = quantite.sort_index()
quantite_bdf = concat([quantite, quantite_bdf], axis=1)
# Import des fichiers csv donnant les quantités agrégées d'après la comptabilité nationale.
quantite_carbu_vp_france = pd.read_csv(os.path.join(
assets_directory, 'openfisca_france_indirect_taxation', 'assets',
'quantites', 'quantite_carbu_vp_france.csv'),
sep=';')
quantite_carbu_vp_france['Unnamed: 0'] = quantite_carbu_vp_france[
'Unnamed: 0'].astype(str)
quantite_carbu_vp_france = quantite_carbu_vp_france.set_index('Unnamed: 0')
quantite_carbu_vp_france.rename(columns={'essence': 'essence agregat'},
inplace=True)
quantite_carbu_vp_france.rename(columns={'diesel': 'diesel agregat'},
inplace=True)
quantite_carbu_vp_france['carburants agregat'] = quantite_carbu_vp_france.sum(
axis=1)
comparaison_bdf_agregats = concat([quantite_carbu_vp_france, quantite_bdf],
axis=1)
comparaison_bdf_agregats = comparaison_bdf_agregats.dropna()
# Créer des graphiques pour comparer les consommations obtenues via Bdf vis-à-vis de la comptabilité nationale
graph_builder_line(comparaison_bdf_agregats[['essence agregat'] +
['essence bdf']])
graph_builder_line(comparaison_bdf_agregats[['diesel agregat'] +
['diesel bdf']])
graph_builder_line(comparaison_bdf_agregats[['carburants agregat'] +
['carburants bdf']])
df_nombre_vehicules_bdf = aggregates_data_frame[['veh_diesel'] +
['veh_essence'] +
['pondmen']]
nombre_vehicules_diesel_bdf = (
df_nombre_vehicules_bdf['veh_diesel'] *
df_nombre_vehicules_bdf['pondmen']).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, 'diesel bdf'] = \
nombre_vehicules_diesel_bdf
nombre_vehicules_essence_bdf = (
df_nombre_vehicules_bdf['veh_essence'] *
df_nombre_vehicules_bdf['pondmen']).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, 'essence bdf'] = \
nombre_vehicules_essence_bdf
nombre_vehicules_total_bdf = (
(df_nombre_vehicules_bdf['veh_essence'] +
df_nombre_vehicules_bdf['veh_diesel']) *
df_nombre_vehicules_bdf['pondmen']).sum() / 1000
comparaison_vehicules.loc[comparaison_vehicules.index == year, 'total bdf'] = \
nombre_vehicules_total_bdf
comparaison_vehicules = comparaison_vehicules[
comparaison_vehicules['total bdf'] != 0]
# Réalisation de graphiques
graph_builder_line(comparaison_vehicules[['total bdf'] + ['total agregats']])
graph_builder_line(comparaison_vehicules[['diesel bdf'] + ['diesel agregats']])
graph_builder_line(comparaison_vehicules[['essence bdf'] +
['essence agregats']])
