list_taux_d_effort_rev_disp_loyerimput = []
Wconcat_rev_disp_loyerimput['taux_d_effort_tva'] = \
Wconcat_rev_disp_loyerimput['tva_total'] / Wconcat_rev_disp_loyerimput['rev_disp_loyerimput']
list_taux_d_effort_rev_disp_loyerimput.append('taux_d_effort_tva')
Wconcat_rev_disp_loyerimput['taux_d_effort_alcool'] = (
Wconcat_rev_disp_loyerimput['droit_d_accise_alcool'] /
Wconcat_rev_disp_loyerimput['rev_disp_loyerimput']
)
list_taux_d_effort_rev_disp_loyerimput.append('taux_d_effort_alcool')
Wconcat_rev_disp_loyerimput['taux_d_effort_tabac'] = (
Wconcat_rev_disp_loyerimput['droit_d_accise_tabac'] /
Wconcat_rev_disp_loyerimput['rev_disp_loyerimput']
)
list_taux_d_effort_rev_disp_loyerimput.append('taux_d_effort_tabac')
Wconcat_rev_disp_loyerimput['taux_d_effort_assurance'] = \
Wconcat_rev_disp_loyerimput['taxe_assurance'] / Wconcat_rev_disp_loyerimput['rev_disp_loyerimput']
list_taux_d_effort_rev_disp_loyerimput.append('taux_d_effort_assurance')
Wconcat_rev_disp_loyerimput['taux_d_effort_tipp'] = \
Wconcat_rev_disp_loyerimput['tipp'] / Wconcat_rev_disp_loyerimput['rev_disp_loyerimput']
list_taux_d_effort_rev_disp_loyerimput.append('taux_d_effort_tipp')
df_to_graph_rev_disp = Wconcat_rev_disp[list_taux_d_effort_rev_disp].copy()
graph_builder_bar(df_to_graph_rev_disp)
df_to_graph_conso = Wconcat_conso[list_taux_d_effort_rev_disp].copy()
graph_builder_bar(df_to_graph_conso)
df_to_graph_rev_disp_loyerimput = Wconcat_rev_disp_loyerimput[list_taux_d_effort_rev_disp_loyerimput].copy()
graph_builder_bar(df_to_graph_rev_disp_loyerimput)
'tva_taux_intermediaire',
'tva_taux_reduit',
'tva_taux_super_reduit',
'tva_total',
'niveau_vie_decile',
'rev_disponible',
'pondmen',
]
# Constition d'une base de données agrégée par décile (= collapse en stata)
for year in [2000, 2005, 2011]:
df = simulate_df(var_to_be_simulated = var_to_be_simulated, year = year)
if year == 2011:
df.niveau_vie_decile[df.decuc == 10] = 10
varlist = ['rev_disponible', 'tva_taux_super_reduit',
'tva_taux_reduit', 'tva_taux_plein', 'tva_taux_intermediaire'
]
Wconcat = df_weighted_average_grouped(dataframe = df, groupe = 'niveau_vie_decile', varlist = varlist)
# Example
Wconcat['part_tva_tx_super_reduit'] = Wconcat['tva_taux_super_reduit'] / Wconcat['rev_disponible']
Wconcat['part_tva_tx_reduit'] = Wconcat['tva_taux_reduit'] / Wconcat['rev_disponible']
Wconcat['part_tva_tx_intermediaire'] = Wconcat['tva_taux_intermediaire'] / Wconcat['rev_disponible']
Wconcat['part_tva_tx_plein'] = Wconcat['tva_taux_plein'] / Wconcat['rev_disponible']
df_to_graph = Wconcat[['part_tva_tx_plein', 'part_tva_tx_super_reduit', 'part_tva_tx_reduit',
'part_tva_tx_intermediaire']]
# Graphe par décile de revenu par uc de la ventilation des taux de taxation
graph_builder_bar(df_to_graph)
# Liste des variables que l'on veut simuler
var_to_be_simulated = [
'pondmen',
'decuc',
'niveau_vie_decile',
]
# Merge des deux listes
var_to_be_simulated += list_coicop12
p = dict()
df_to_graph = None
for year in [2000, 2005, 2011]:
# Constition d'une base de données agrégée par décile (= collapse en stata)
simulation_data_frame = simulate_df(var_to_be_simulated = var_to_be_simulated, year = year)
if year == 2011:
simulation_data_frame.niveau_vie_decile[simulation_data_frame.decuc == 10] = 10
simulation_data_frame['depenses_tot'] = 0
for i in range(1, 13):
simulation_data_frame['depenses_tot'] += simulation_data_frame['coicop12_{}'.format(i)]
var_to_concat = list_coicop12 + ['depenses_tot']
aggregates_data_frame = df_weighted_average_grouped(dataframe = simulation_data_frame,
groupe = 'niveau_vie_decile', varlist = var_to_concat)
for i in range(1, 13):
aggregates_data_frame['part_coicop12_{}'.format(i)] = \
aggregates_data_frame['coicop12_{}'.format(i)] / aggregates_data_frame['depenses_tot']
appendable = aggregates_data_frame[['part_coicop12_{}'.format(i) for i in range(1, 13)]]
graph_builder_bar(appendable)
