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)