def compute(self):
"""
Compute inequality dataframe
"""
final_df = None
simulation = self.survey_scenario.new_simulation()
column_by_name = simulation.tax_benefit_system.column_by_name
# amounts and beneficiaries from current data and default data if exists
# Build weights for each entity
from openfisca_france_data import FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity_key_plural in entities:
column = column_by_name[varname]
weight_name = self.survey_scenario.weight_column_name_by_entity_key_plural[
column.entity_key_plural]
filter_by = FILTERING_VARS[0]
filter_by_name = FILTERING_VARS[0]
if column.entity_key_plural is not 'menages':
filter_by_name = "{}_{}".format(filter_by,
column.entity_key_plural)
val = simulation.calculate(varname)
weights = simulation.calculate(weight_name)
filter_var = simulation.calculate(filter_by_name)
items = []
# Compute mean
moy = (weights * filter_var * val).sum() / (weights *
filter_var).sum()
items.append(("Moyenne", [moy]))
# Compute deciles
labels = range(1, 11)
method = 2
decile, values = mark_weighted_percentiles(val,
labels,
weights * filter_var,
method,
return_quantiles=True)
labels = ['D' + str(d) for d in range(1, 11)]
del decile
for l, v in zip(labels[:-1], values[1:-1]):
items.append((l, [v]))
# Compute Gini
gini_coeff = gini(val, weights * filter_var)
items.append((_("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient='index', columns=[varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (
final_df['nivvie_net'] -
final_df['nivvie_ini']) / final_df['nivvie_ini']
final_df[u"Net à disponible"] = (
final_df['nivvie'] -
final_df['nivvie_net']) / final_df['nivvie_net']
final_df = final_df[[
'index', 'nivvie_ini', u"Initial à net", 'nivvie_net',
u"Net à disponible", 'nivvie'
]]
self.inequality_data_frame = final_df
# Poverty
poverty = dict()
varname = "nivvie"
for percentage in [40, 50, 60]:
varname = "pauvre{}".format(percentage)
column = column_by_name[varname]
weight_name = self.survey_scenario.weight_column_name_by_entity_key_plural[
column.entity_key_plural]
filter_by_name = FILTERING_VARS[0]
if column.entity_key_plural is not 'menages':
filter_by_name = "{}_{}".format(filter_by,
column.entity_key_plural)
val = simulation.calculate(varname)
weights = simulation.calculate(weight_name)
filter_var = simulation.calculate(filter_by_name)
poverty[percentage] = (weights * filter_var *
val).sum() / (weights * filter_var).sum()
self.poverty = poverty
def compute(self):
"""
Compute inequality dataframe
"""
output = self.simulation.output_table
final_df = None
WEIGHT = model.WEIGHT
FILTERING_VARS = model.FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity in entities:
#idx = output.index[entity]
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var= output._inputs.get_value(filter_var_name, entity)
items = []
# Compute mean
moy = (weights*filter_var*val).sum()/(weights*filter_var).sum()
items.append( ("Moyenne", [moy]))
# Compute deciles
labels = range(1,11)
method = 2
decile, values = mark_weighted_percentiles(val, labels, weights*filter_var, method, return_quantiles=True)
labels = [ 'D'+str(d) for d in range(1,11)]
del decile
for l, v in zip(labels[:-1],values[1:-1]):
items.append( (l, [v]))
# Compute Gini
gini_coeff = gini(val, weights*filter_var)
items.append( ( _("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient = 'index', columns = [varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (final_df['nivvie_net']-final_df['nivvie_ini'])/final_df['nivvie_ini']
final_df[u"Net à disponible"] = (final_df['nivvie']-final_df['nivvie_net'])/final_df['nivvie_net']
final_df = final_df[['index','nivvie_ini', u"Initial à net", 'nivvie_net',u"Net à disponible",'nivvie']]
self.inequality_dataframe = final_df
# poverty
poverty = dict()
entity = "men"
varname = "nivvie"
for percentage in [ 40, 50, 60]:
# idx = output.index[entity]
varname = "pauvre" + str(percentage)
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var= output._inputs.get_value(filter_var_name, entity)
poverty[percentage] = (weights*filter_var*val).sum()/(weights*filter_var).sum()
self.poverty = poverty
def compute(self):
"""
Compute inequality dataframe
"""
output = self.simulation.output_table
final_df = None
WEIGHT = model.WEIGHT
FILTERING_VARS = model.FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity in entities:
#idx = output.index[entity]
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var = output._inputs.get_value(filter_var_name, entity)
items = []
# Compute mean
moy = (weights * filter_var * val).sum() / (weights *
filter_var).sum()
items.append(("Moyenne", [moy]))
# Compute deciles
labels = range(1, 11)
method = 2
decile, values = mark_weighted_percentiles(val,
labels,
weights * filter_var,
method,
return_quantiles=True)
labels = ['D' + str(d) for d in range(1, 11)]
del decile
for l, v in zip(labels[:-1], values[1:-1]):
items.append((l, [v]))
# Compute Gini
gini_coeff = gini(val, weights * filter_var)
items.append((_("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient='index', columns=[varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (
final_df['nivvie_net'] -
final_df['nivvie_ini']) / final_df['nivvie_ini']
final_df[u"Net à disponible"] = (
final_df['nivvie'] -
final_df['nivvie_net']) / final_df['nivvie_net']
final_df = final_df[[
'index', 'nivvie_ini', u"Initial à net", 'nivvie_net',
u"Net à disponible", 'nivvie'
]]
self.inequality_dataframe = final_df
# poverty
poverty = dict()
entity = "men"
varname = "nivvie"
for percentage in [40, 50, 60]:
# idx = output.index[entity]
varname = "pauvre" + str(percentage)
val = output.get_value(varname, entity)
weights = output._inputs.get_value(WEIGHT, entity)
filter_var_name = FILTERING_VARS[0]
filter_var = output._inputs.get_value(filter_var_name, entity)
poverty[percentage] = (weights * filter_var *
val).sum() / (weights * filter_var).sum()
self.poverty = poverty
def compute(self):
"""
Compute inequality dataframe
"""
final_df = None
simulation = self.survey_scenario.new_simulation()
column_by_name = simulation.tax_benefit_system.column_by_name
# amounts and beneficiaries from current data and default data if exists
# Build weights for each entity
from openfisca_france_data import FILTERING_VARS
for varname, entities in self.vars.iteritems():
for entity_key_plural in entities:
column = column_by_name[varname]
weight_name = self.survey_scenario.weight_column_name_by_entity_key_plural[column.entity_key_plural]
filter_by = FILTERING_VARS[0]
filter_by_name = FILTERING_VARS[0]
if column.entity_key_plural is not 'menages':
filter_by_name = "{}_{}".format(filter_by, column.entity_key_plural)
val = simulation.calculate(varname)
weights = simulation.calculate(weight_name)
filter_var = simulation.calculate(filter_by_name)
items = []
# Compute mean
moy = (weights * filter_var * val).sum() / (weights * filter_var).sum()
items.append(("Moyenne", [moy]))
# Compute deciles
labels = range(1, 11)
method = 2
decile, values = mark_weighted_percentiles(val, labels, weights * filter_var,
method, return_quantiles = True)
labels = ['D' + str(d) for d in range(1, 11)]
del decile
for l, v in zip(labels[:-1], values[1:-1]):
items.append((l, [v]))
# Compute Gini
gini_coeff = gini(val, weights * filter_var)
items.append((_("Gini index"), [gini_coeff]))
df = DataFrame.from_items(items, orient = 'index', columns = [varname])
df = df.reset_index()
if final_df is None:
final_df = df
else:
final_df = final_df.merge(df, on='index')
final_df[u"Initial à net"] = (final_df['nivvie_net'] - final_df['nivvie_ini']) / final_df['nivvie_ini']
final_df[u"Net à disponible"] = (final_df['nivvie'] - final_df['nivvie_net']) / final_df['nivvie_net']
final_df = final_df[['index', 'nivvie_ini', u"Initial à net", 'nivvie_net', u"Net à disponible", 'nivvie']]
self.inequality_data_frame = final_df
# Poverty
poverty = dict()
varname = "nivvie"
for percentage in [40, 50, 60]:
varname = "pauvre{}".format(percentage)
column = column_by_name[varname]
weight_name = self.survey_scenario.weight_column_name_by_entity_key_plural[column.entity_key_plural]
filter_by_name = FILTERING_VARS[0]
if column.entity_key_plural is not 'menages':
filter_by_name = "{}_{}".format(filter_by, column.entity_key_plural)
val = simulation.calculate(varname)
weights = simulation.calculate(weight_name)
filter_var = simulation.calculate(filter_by_name)
poverty[percentage] = (weights * filter_var * val).sum() / (weights * filter_var).sum()
self.poverty = poverty