def _load(data_loc: Union[str, PathLike,
Connection, Engine],
multiindex=True,
table_name: Optional[str] = None):
"""Load existing data from CSV or SQL."""
try:
if isinstance(data_loc, (Engine, Connection)):
if multiindex is True:
previous_data = sqlutil.read(con=data_loc,
table_name=table_name)
else:
previous_data = pd.read_sql(sql=table_name,
con=data_loc,
index_col="index",
parse_dates="index")
else:
if multiindex is True:
previous_data = pd.read_csv(data_loc, index_col=0,
parse_dates=True,
header=list(range(9)),
float_precision="high")
metadata._set(previous_data)
else:
previous_data = pd.read_csv(data_loc, index_col=0,
parse_dates=True,
float_precision="high")
except (ProgrammingError, OperationalError, FileNotFoundError):
print("Data does not exist. No data will be updated")
previous_data = pd.DataFrame()
return previous_data
def indicator_options(table):
if not table:
raise PreventUpdate
df = sqlutil.read(con=db.engine, table_name=table)
columns = df.columns.get_level_values(0)
return (
[{"label": "Todos los indicadores", "value": "*"}]
+ [{"label": v, "value": v} for v in columns]
), False
def test_sqlutil():
remove_clutter()
sqlutil.read(con=TEST_CON, command='SELECT * FROM nxr_daily')
sqlutil.read(con=TEST_CON, table_name="nxr_daily",
start_date="2011-01-14",
end_date="2012-01-15")
sqlutil.read(con=TEST_CON, table_name="nxr_daily",
start_date="2011-01-14")
sqlutil.read(con=TEST_CON, table_name="reserves_changes",
end_date="2012-01-01",
cols=["1. Compras netas de moneda extranjera"])
def store_query_data(table, indicator):
if not table or not indicator:
return {}, {}
if "*" in indicator:
indicator = "*"
data = sqlutil.read(con=db.engine, table_name=table, cols=indicator)
metadata = data.columns.to_frame()
data.columns = data.columns.get_level_values(0)
data.reset_index(inplace=True)
return data.to_dict("records"), metadata.to_dict("records")
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