'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)
