def chg_diff(self, operation: str = "chg", period_op: str = "last"):
"""
Calculate pct change or difference.
See Also
--------
:func:`~econuy.transform.chg_diff`
"""
if isinstance(self.dataset, dict):
output = {}
for key, value in self.dataset.items():
table = transform.chg_diff(value, operation=operation,
period_op=period_op)
output.update({key: table})
else:
output = transform.chg_diff(self.dataset, operation=operation,
period_op=period_op)
self.logger.info(f"Applied 'chg_diff' transformation with "
f"'{operation}' operation and '{period_op}' period.")
if self.inplace is True:
self.dataset = output
return self
else:
return Session(location=self.location,
revise_rows=self.revise_rows,
only_get=self.only_get,
dataset=output,
logger=self.logger,
inplace=self.inplace)
def test_chg():
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m, inplace=True)
trf_last = session.chg_diff(operation="chg", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.pct_change(periods=1).multiply(100))
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m, inplace=True)
trf_last = session.chg_diff(operation="chg", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.pct_change(periods=1).multiply(100))
data_q1 = dummy_df(freq="Q-DEC")
data_q2 = dummy_df(freq="Q-DEC")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict)
trf_inter = session.chg_diff(operation="chg", period_op="inter").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.pct_change(periods=4).
multiply(100))
assert trf_inter["data_q2"].equals(data_q2.pct_change(periods=4).
multiply(100))
data_a = dummy_df(freq="A", ts_type="Flow")
trf_annual = transform.chg_diff(data_a, operation="chg", period_op="last")
trf_annual.columns = data_a.columns
assert trf_annual.equals(data_a.pct_change(periods=1).multiply(100))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Flujo")
trf_q_annual = transform.chg_diff(data_q_annual, operation="chg",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.
rolling(window=4, min_periods=4).
sum().
pct_change(periods=4).multiply(100))
def test_diff():
data_m = dummy_df(freq="M")
session = Session(location=TEST_CON, dataset=data_m)
trf_last = session.chg_diff(operation="diff", period_op="last").dataset
trf_last.columns = data_m.columns
assert trf_last.equals(data_m.diff(periods=1))
data_q1 = dummy_df(freq="Q-DEC")
data_q2 = dummy_df(freq="Q-DEC")
data_dict = {"data_q1": data_q1, "data_q2": data_q2}
session = Session(location=TEST_CON, dataset=data_dict, inplace=True)
trf_inter = session.chg_diff(operation="diff", period_op="inter").dataset
trf_inter["data_q1"].columns = trf_inter[
"data_q2"].columns = data_q1.columns
assert trf_inter["data_q1"].equals(data_q1.diff(periods=4))
assert trf_inter["data_q2"].equals(data_q2.diff(periods=4))
data_a = dummy_df(freq="A", ts_type="Flow")
trf_annual = transform.chg_diff(data_a, operation="diff", period_op="last")
trf_annual.columns = data_a.columns
assert trf_annual.equals(data_a.diff(periods=1))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Flujo")
trf_q_annual = transform.chg_diff(data_q_annual, operation="diff",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.
rolling(window=4, min_periods=4).
sum().
diff(periods=4))
data_q_annual = dummy_df(freq="Q-DEC", ts_type="Stock")
trf_q_annual = transform.chg_diff(data_q_annual, operation="diff",
period_op="annual")
trf_q_annual.columns = data_q_annual.columns
assert trf_q_annual.equals(data_q_annual.diff(periods=4))
with pytest.raises(ValueError):
data_wrong = data_m.iloc[np.random.randint(0, 200, 100)]
transform.chg_diff(data_wrong)
def store_transformed_data(
real_start,
real_end,
resample_freq,
resample_operation,
rolling_periods,
rolling_operation,
chg_diff_operation,
chg_diff_period,
rebase_start,
rebase_end,
rebase_base,
decompose_method,
decompose_component,
order,
query_data,
query_metadata,
):
if not order:
return query_data, query_metadata
if not query_data:
return {}, {}
if (
("resample" in order and (not resample_freq or not resample_operation))
or ("rolling" in order and (not rolling_periods or not rolling_operation))
or ("chg-diff" in order and (not chg_diff_operation or not chg_diff_period))
or ("rebase" in order and (not rebase_start or not rebase_base))
or (
"decompose" in order
and (not decompose_method or not decompose_component)
)
):
raise PreventUpdate
data = pd.DataFrame.from_records(query_data, coerce_float=True, index="index")
data.index = pd.to_datetime(data.index)
metadata = pd.DataFrame.from_records(query_metadata)
data.columns = pd.MultiIndex.from_frame(metadata)
p = Pipeline(location=db.engine, download=False)
transformations = {
"usd": lambda x: convert_usd(x, pipeline=p, errors="ignore"),
"real": lambda x: convert_real(
x, start_date=real_start, end_date=real_end, pipeline=p, errors="ignore"
),
"gdp": lambda x: convert_gdp(x, pipeline=p, errors="ignore"),
"resample": lambda x: resample(
x, rule=resample_freq, operation=resample_operation
),
"rolling": lambda x: rolling(
x, window=rolling_periods, operation=rolling_operation
),
"chg-diff": lambda x: chg_diff(
x, operation=chg_diff_operation, period=chg_diff_period
),
"rebase": lambda x: rebase(
x, start_date=rebase_start, end_date=rebase_end, base=rebase_base
),
"decompose": lambda x: decompose(
x,
component=decompose_component,
method=decompose_method,
force_x13=True,
errors="ignore",
),
}
transformed_data = data.copy()
for t in order:
transformed_data = transformations[t](transformed_data)
transformed_metadata = transformed_data.columns.to_frame()
transformed_data.columns = transformed_data.columns.get_level_values(0)
transformed_data.reset_index(inplace=True)
return transformed_data.to_dict("records"), transformed_metadata.to_dict(
"records"
)
{
"base": "balance_cg-bps",
"transformations": [lambda x: transform.convert_gdp(x, pipeline=p)],
},
"Public sector - Uruguay - Fiscal balance: public enterprises (% GDP) - Monthly":
{
"base": "balance_pe",
"transformations": [lambda x: transform.convert_gdp(x, pipeline=p)],
},
"Public sector - Uruguay - Tax revenue by source (real, YoY % chg) - Monthly":
{
"base":
"tax_revenue",
"transformations": [
lambda x: transform.convert_real(x, pipeline=p),
lambda x: transform.chg_diff(x, period="inter"),
],
},
"Public sector - Uruguay - General government debt: by contractual term, residual, currency and residence (% GDP) - Quarterly":
{
"base": "public_debt_gps",
"transformations": [lambda x: transform.convert_gdp(x, pipeline=p)],
},
"Public sector - Uruguay - Non-monetary government debt: by contractual term, residual, currency and residence (% GDP) - Quarterly":
{
"base": "public_debt_nfps",
"transformations": [lambda x: transform.convert_gdp(x, pipeline=p)],
},
"Public sector - Uruguay - Central bank debt: by contractual term, residual, currency and residence (% GDP) - Quarterly":
{
"base": "public_debt_cb",