def build_demand(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("natacc_gas_con_nsa_long")
# p.chg_diff(period="inter")
demand = p.dataset
demand.columns = demand.columns.get_level_values(0)
demand = (
demand.div(demand["Producto bruto interno"], axis=0).shift(4).mul(
demand.pct_change(4), axis=0) * 100)
demand["Importaciones de bienes y servicios"] = (
demand["Importaciones de bienes y servicios"] * -1)
demand_plot = build_chart(
demand,
y=[
"Gasto de consumo: hogares",
"Gasto de consumo: gobierno y ISFLH",
"Formación bruta de capital",
"Exportaciones de bienes y servicios",
"Importaciones de bienes y servicios",
],
title="Cuentas nacionales, demanda",
subtitle="Contribución al crecimiento interanual",
kind="bar",
start=start,
end=end,
extra_trace="Producto bruto interno",
)
return demand_plot
def build_taxes(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("tax_revenue")
p.convert(flavor="real")
p.chg_diff(period="inter")
tax = p.dataset
tax[[
"IRAE - Rentas de Actividades Económicas",
"IRPF Cat II - Rentas de las Personas Físicas",
]] = tax[[
"IRAE - Rentas de Actividades Económicas",
"IRPF Cat II - Rentas de las Personas Físicas",
]].mask(tax.index.to_series() < "2009-01-01")
tax.columns = tax.columns.get_level_values(0)
tax_plot = build_chart(
tax,
title="Recaudación impositiva",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
y=[
"IRAE - Rentas de Actividades Económicas",
"IRPF Cat II - Rentas de las Personas Físicas",
"IVA - Valor Agregado",
"Recaudación Total de la DGI",
],
)
return tax_plot
def build_ubi(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("sovereign_risk")
ubi = p.dataset
ubi.columns = ubi.columns.get_level_values(0)
ubi_plot = build_chart(
ubi,
title="Uruguay Bond Index",
subtitle="Spread con respecto Treasury 10Y",
kind="area",
start=start,
end=end,
)
return ubi_plot
def build_bonds(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("bonds")
bonds = p.dataset
bonds.columns = bonds.columns.get_level_values(0)
bonds_plot = build_chart(
bonds,
title="Rendimiento de bonos soberanos",
subtitle="Puntos básicos",
kind="line",
start=start,
end=end,
)
return bonds_plot
def build_regional_nxr(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("regional_nxr")
nxr = p.dataset.pct_change(30) * 100
nxr.columns = nxr.columns.get_level_values(0)
rxr_plot = build_chart(
nxr,
title="Tipo de cambio nominal",
subtitle="Variación 30 días",
kind="line",
start=start,
end=end,
)
return rxr_plot
def build_nxr(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("nxr_daily")
nxr_daily = p.dataset
nxr_daily.columns = nxr_daily.columns.get_level_values(0)
nxr_plot = build_chart(
nxr_daily,
title="Tipo de cambio",
subtitle="Cable",
kind="area",
start=start,
end=end,
)
return nxr_plot
def build_industrial(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("core_industrial")
p.chg_diff(period="inter")
industrial = p.dataset
industrial.columns = industrial.columns.get_level_values(0)
industrial_plot = build_chart(
industrial,
title="Producción industrial",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return industrial_plot
def build_wages(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("real_wages")
p.chg_diff(period="inter")
wages = p.dataset
wages.columns = wages.columns.get_level_values(0)
wages_plot = build_chart(
wages,
title="Salario real",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return wages_plot
def build_debt(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("net_public_debt")
p.convert(flavor="gdp")
debt = p.dataset
debt.columns = debt.columns.get_level_values(0)
debt_plot = build_chart(
debt,
title="Deuda neta del sector público global",
subtitle="% del PBI",
kind="area",
start=start,
end=end,
)
return debt_plot
def build_cpi(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("cpi")
p.chg_diff(period="inter")
cpi = p.dataset
cpi.columns = cpi.columns.get_level_values(0)
cpi_plot = build_chart(
cpi,
title="IPC",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return cpi_plot
def build_commodity(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("commodity_index")
p.chg_diff(period="inter")
commodity = p.dataset
commodity.columns = commodity.columns.get_level_values(0)
commodity_plot = build_chart(
commodity,
title="Índice de precios de commodities",
subtitle="Variación interanual",
kind="area",
start=start,
end=end,
)
return commodity_plot
def build_tot(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("terms_of_trade")
p.chg_diff(period="inter")
tot = p.dataset
tot.columns = tot.columns.get_level_values(0)
tot_plot = build_chart(
tot,
title="Términos de intercambio",
subtitle="Variación interanual",
kind="area",
start=start,
end=end,
)
return tot_plot
def build_gdp(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("gdp_con_idx_sa_long")
p.chg_diff(period="last")
gdp = p.dataset
gdp.columns = gdp.columns.get_level_values(0)
gdp_plot = build_chart(
gdp,
title="PBI real",
subtitle="Desestacionalizado, variación trimestral",
kind="bar",
start=start,
end=end,
)
return gdp_plot
def build_global_nxr(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("global_nxr")
nxr = p.dataset[["Índice Dólar", "Euro", "Renminbi"
]].pct_change(30) * 100
nxr.columns = nxr.columns.get_level_values(0)
nxr_plot = build_chart(
nxr,
title="Tipo de cambio nominal",
subtitle="Variación 30 días",
kind="line",
start=start,
end=end,
)
return nxr_plot
def build_global_gdp(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("global_gdp")
p.chg_diff(period="inter")
gdp = p.dataset[["Estados Unidos", "Unión Europea", "China"]]
gdp.columns = gdp.columns.get_level_values(0)
gdp_plot = build_chart(
gdp,
title="PBI real",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return gdp_plot
def build_regional_gdp(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("regional_monthly_gdp")
p.chg_diff(period="inter")
gdp = p.dataset
gdp.columns = gdp.columns.get_level_values(0)
gdp_plot = build_chart(
gdp,
title="PBI mensual",
subtitle="Variación interanual",
kind="line",
start=start,
end=end,
)
return gdp_plot
def build_rxr(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("rxr_custom")
p.rebase(start_date=p.dataset.index.min(),
end_date=p.dataset.index.max())
rxr = p.dataset
rxr.columns = rxr.columns.get_level_values(0)
rxr_plot = build_chart(
rxr,
title=f"Tipo de cambio real",
subtitle=f"1980-{dt.date.today().year}=100",
kind="line",
start=start,
end=end,
)
return rxr_plot
def build_cpi_measures(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("cpi_measures")
p.chg_diff(period="inter")
cpi_measures = p.dataset
cpi_measures.columns = cpi_measures.columns.get_level_values(0)
cpi_measures_plot = build_chart(
cpi_measures,
title="IPC transable, no transable y subyacente",
subtitle="Variación interanual",
kind="line",
y=cpi_measures.columns[:-2],
start=start,
end=end,
height=460,
)
return cpi_measures_plot
def build_supply(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("natacc_ind_con_nsa_long")
# p.chg_diff(period="inter")
supply = p.dataset
supply.columns = supply.columns.get_level_values(0)
supply = (
supply.div(supply["Producto bruto interno"], axis=0).shift(4).mul(
supply.pct_change(4), axis=0) * 100)
supply_plot = build_chart(
supply,
y=supply.columns[:-1],
title="Cuentas nacionales, oferta",
subtitle="Contribución al crecimiento interanual",
kind="bar",
start=start,
end=end,
extra_trace="Producto bruto interno",
height=470,
)
return supply_plot
def build_labor_rates(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("labor_rates_people")
nsa = p.dataset
nsa.columns = nsa.columns.get_level_values(0)
trends = sqlutil.read(con=db.engine,
table_name="labor_rates_people_seas")
trends.columns = trends.columns.get_level_values(0) + [
" (tendencia-ciclo)"
]
data = pd.concat([nsa, trends], axis=1)
activity_employment_plot = build_chart(
data,
title="Actividad y empleo",
subtitle="Tasa",
kind="line",
start=start,
end=end,
y=[
"Tasa de actividad",
"Tasa de actividad (tendencia-ciclo)",
"Tasa de empleo",
"Tasa de empleo (tendencia-ciclo)",
],
height=460,
)
unemployment_plot = build_chart(
data,
title="Desempleo",
subtitle="Tasa",
kind="line",
start=start,
end=end,
y=["Tasa de desempleo", "Tasa de desempleo (tendencia-ciclo)"],
)
return activity_employment_plot, unemployment_plot
def build_fiscal_balance(start, end):
p = Pipeline(location=db.engine, download=False)
p.get("balance_summary")
p.convert(flavor="gdp")
balance = p.dataset
balance.columns = balance.columns.get_level_values(0)
balance_plot = build_chart(
balance,
title="Resultado fiscal del sector público consolidado",
subtitle="% del PBI",
kind="line",
start=start,
end=end,
y=[
"Resultado: Primario SPC ex FSS",
"Resultado: Primario SPC",
"Resultado: Global SPC ex FSS",
"Resultado: Global SPC",
],
)
balance_sectors_plot = build_chart(
balance,
title="Resultado global por sector",
subtitle="% del PBI",
kind="bar",
start=start,
end=end,
y=[
"Resultado: Global GC-BPS ex FSS",
"Resultado: Global EEPP",
"Resultado: Global intendencias",
"Resultado: Global BSE",
"Resultado: Global BCU",
],
)
return balance_plot, balance_sectors_plot
