'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)
for coicop12_index in range(1, 13): list_coicop12.append('coicop12_{}'.format(coicop12_index)) simulated_variables = [ 'pondmen', 'niveau_vie_decile', 'somme_coicop12', ] simulated_variables += list_coicop12 p = dict() df_to_graph = None for year in [2000, 2005, 2011]: simulation_data_frame = simulate( simulated_variables=simulated_variables, year=year) aggregates_data_frame = df_weighted_average_grouped( dataframe=simulation_data_frame, groupe='niveau_vie_decile', varlist=simulated_variables) aggregates_data_frame[year] = aggregates_data_frame[ 'coicop12_4'] / aggregates_data_frame['somme_coicop12'] appendable = aggregates_data_frame[year] if df_to_graph is not None: df_to_graph = concat([df_to_graph, appendable], axis=1) else: df_to_graph = appendable graph_builder_line_percent(df_to_graph, 1, 1)
'tva_total', 'revtot', 'somme_coicop12_conso', 'ocde10', ] simulated_variables += list_coicop12 # Constition d'une base de données agrégée par décile for year in [2000, 2005, 2011]: df = simulate(simulated_variables=simulated_variables, year=year) var_to_concat = [ 'tva_taux_plein', 'tva_taux_intermediaire', 'tva_taux_reduit', 'tva_taux_super_reduit', 'tva_total' ] aggregates_data_frame = df_weighted_average_grouped( dataframe=df, groupe='niveau_vie_decile', varlist=var_to_concat) list_part_TVA = [] aggregates_data_frame['part_tva_taux_plein'] = \ aggregates_data_frame['tva_taux_plein'] / aggregates_data_frame['tva_total'] list_part_TVA.append('part_tva_taux_plein') aggregates_data_frame['part_tva_taux_intermediaire'] = \ aggregates_data_frame['tva_taux_intermediaire'] / aggregates_data_frame['tva_total'] list_part_TVA.append('part_tva_taux_intermediaire') aggregates_data_frame['part_tva_taux_reduit'] = \ aggregates_data_frame['tva_taux_reduit'] / aggregates_data_frame['tva_total'] list_part_TVA.append('part_tva_taux_reduit') aggregates_data_frame['part_tva_taux_super_reduit'] = \ aggregates_data_frame['tva_taux_super_reduit'] / aggregates_data_frame['tva_total'] list_part_TVA.append('part_tva_taux_super_reduit')
] simulated_variables += list_coicop12 # Constition d'une base de données agrégée par décile for year in [2000, 2005, 2011]: df = simulate(simulated_variables=simulated_variables, year=year) var_to_concat = [ "tva_taux_plein", "tva_taux_intermediaire", "tva_taux_reduit", "tva_taux_super_reduit", "tva_total", ] aggregates_data_frame = df_weighted_average_grouped( dataframe=df, groupe="niveau_vie_decile", varlist=var_to_concat ) list_part_TVA = [] aggregates_data_frame["part_tva_taux_plein"] = ( aggregates_data_frame["tva_taux_plein"] / aggregates_data_frame["tva_total"] ) list_part_TVA.append("part_tva_taux_plein") aggregates_data_frame["part_tva_taux_intermediaire"] = ( aggregates_data_frame["tva_taux_intermediaire"] / aggregates_data_frame["tva_total"] ) list_part_TVA.append("part_tva_taux_intermediaire") aggregates_data_frame["part_tva_taux_reduit"] = ( aggregates_data_frame["tva_taux_reduit"] / aggregates_data_frame["tva_total"] ) list_part_TVA.append("part_tva_taux_reduit")
'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' ]
import sys logging.basicConfig(level = logging.INFO, stream = sys.stdout) list_coicop12 = [] for coicop12_index in range(1, 13): list_coicop12.append('coicop12_{}'.format(coicop12_index)) simulated_variables = [ 'pondmen', 'niveau_vie_decile', 'somme_coicop12', ] simulated_variables += list_coicop12 p = dict() df_to_graph = None for year in [2000, 2005, 2011]: simulation_data_frame = simulate(simulated_variables = simulated_variables, year = year) aggregates_data_frame = df_weighted_average_grouped(dataframe = simulation_data_frame, groupe = 'niveau_vie_decile', varlist = simulated_variables) aggregates_data_frame[year] = aggregates_data_frame['coicop12_4'] / aggregates_data_frame['somme_coicop12'] appendable = aggregates_data_frame[year] if df_to_graph is not None: df_to_graph = concat([df_to_graph, appendable], axis = 1) else: df_to_graph = appendable graph_builder_line_percent(df_to_graph, 1, 1)