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sum indicator problem solve

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Debby
2026-04-07 20:04:52 +07:00
parent 00cdf961a9
commit e00e9c569d
1 changed files with 321 additions and 144 deletions
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scripts/bigquery_aggraget_fact_selected_layer.py
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@@ -247,91 +247,140 @@ def _format_yoy(yoy: float, unit: str, lower_better: bool) -> tuple:
def _build_narrative(row: pd.Series) -> str: def _build_narrative(row: pd.Series) -> str:
""" """
Bangun 1 paragraf narasi ASEAN-level untuk satu baris (year x indicator_id). Bangun 1 paragraf narasi per indikator, merangkum seluruh negara ASEAN
dan seluruh tahun yang tersedia.
""" """
year = int(row["year"])
ind_name = str(row["indicator_name"]).strip() ind_name = str(row["indicator_name"]).strip()
unit = str(row["unit"]).strip() if row["unit"] else "" unit = str(row["unit"]).strip() if row["unit"] else ""
direction = str(row["direction"]).strip() direction = str(row["direction"]).strip()
pillar = str(row["pillar_name"]).strip() pillar = str(row["pillar_name"]).strip()
framework = str(row["framework"]).strip() framework = str(row["framework"]).strip()
avg_val = row["avg_value"] year_min = int(row["year_min"])
year_max = int(row["year_max"])
n_years = int(row["n_years"])
n_countries = int(row["n_countries"])
avg_score = row["avg_norm_score_1_100"] avg_score = row["avg_norm_score_1_100"]
performance = row["performance"] performance = str(row["performance"]) if not pd.isna(row["performance"]) else None
yoy = row["yoy_avg_value"] first_val = row["asean_avg_first"]
n_countries = int(row["n_countries"]) if not pd.isna(row["n_countries"]) else 0 last_val = row["asean_avg_last"]
cum_change = row["cumulative_change"]
best_country = str(row["best_country"]).strip()
worst_country = str(row["worst_country"]).strip()
yoy_improved = int(row["yoy_improved_count"]) if not pd.isna(row["yoy_improved_count"]) else 0
yoy_total = int(row["yoy_total_transitions"]) if not pd.isna(row["yoy_total_transitions"]) else 0
best_year = int(row["best_year"]) if not pd.isna(row["best_year"]) else None
worst_year = int(row["worst_year"]) if not pd.isna(row["worst_year"]) else None
lower_better = _is_lower_better(direction) lower_better = _is_lower_better(direction)
direction_label = ( direction_label = (
"lower values indicate better outcomes" if lower_better "lower values indicate better outcomes"
else "higher values indicate better outcomes" if lower_better else "higher values indicate better outcomes"
) )
# --- Bagian 1: Nilai rata-rata ASEAN --- year_span = (
val_str = _format_value(avg_val, unit) f"from {year_min} to {year_max}"
sentence1 = ( if year_min != year_max else f"in {year_min}"
f"In {year}, the ASEAN regional average for {ind_name} stood at {val_str}"
) )
if n_countries > 0: n_years_label = f"{n_years} year{'s' if n_years > 1 else ''} of data"
sentence1 += (
f", based on data from {n_countries} "
f"ASEAN member state{'s' if n_countries > 1 else ''}"
)
sentence1 += "."
# --- Bagian 2: Score dan performance --- # --- Bagian 1: Pembuka ---
s1 = (
f"The {ind_name} under the {framework} framework ({pillar} pillar) "
f"was monitored {year_span} across {n_countries} ASEAN member state{'s' if n_countries > 1 else ''}, "
f"covering {n_years_label}."
)
# --- Bagian 2: Arah interpretasi ---
s2 = f"Since {direction_label} for this indicator, performance is evaluated accordingly."
# --- Bagian 3: Trend kumulatif (first -> last) ---
if not pd.isna(first_val) and not pd.isna(last_val) and not pd.isna(cum_change):
first_str = _format_value(first_val, unit)
last_str = _format_value(last_val, unit)
chg_str = _format_value(abs(cum_change), unit)
if lower_better:
is_improving = cum_change < 0
else:
is_improving = cum_change > 0
moved = "declined" if cum_change < 0 else "increased"
trend_lbl = "an overall improving trend" if is_improving else "an overall deteriorating trend"
s3 = (
f"The ASEAN regional average {moved} from {first_str} in {year_min} "
f"to {last_str} in {year_max}, a cumulative shift of {chg_str}, "
f"indicating {trend_lbl} over the observed period."
)
else:
s3 = "Insufficient data is available to assess the cumulative trend across the period."
# --- Bagian 4: Score dan performance ---
if not pd.isna(avg_score): if not pd.isna(avg_score):
score_str = f"{avg_score:.1f} out of 100" score_str = f"{avg_score:.1f} out of 100"
if performance == "Good": if performance == "Good":
perf_phrase = ( s4 = (
f"The region achieved a normalized score of {score_str}, " f"The average normalized score across all years and countries stood at {score_str}, "
f"classified as Good performance meeting the 60-point threshold " f"placing the region in the Good performance category and consistently above the 60-point threshold."
f"under the {framework} framework ({pillar} pillar)."
) )
elif performance == "Bad": elif performance == "Bad":
perf_phrase = ( s4 = (
f"The region recorded a normalized score of {score_str}, " f"The average normalized score across all years and countries stood at {score_str}, "
f"classified as Bad performance falling below the 60-point threshold " f"placing the region in the Bad performance category and below the 60-point threshold."
f"under the {framework} framework ({pillar} pillar)."
) )
else: else:
perf_phrase = ( s4 = f"The average normalized score across all years and countries stood at {score_str}."
f"The region recorded a normalized score of {score_str} "
f"under the {framework} framework ({pillar} pillar)."
)
else: else:
perf_phrase = ( s4 = "The normalized score could not be assessed due to insufficient data."
f"Performance could not be assessed due to insufficient data "
f"under the {framework} framework ({pillar} pillar)."
)
# --- Bagian 3 & 4: Arah + YoY --- # --- Bagian 5: Best dan worst country ---
direction_phrase = f"Since {direction_label} for this indicator" if best_country and worst_country and best_country != worst_country:
s5 = (
if not pd.isna(yoy) and yoy != 0: f"Among member states, {best_country} recorded the strongest performance throughout the period, "
direction_word, change_desc, is_positive = _format_yoy(yoy, unit, lower_better) f"while {worst_country} showed the weakest performance."
if is_positive:
trend_word = "a positive trend"
tone = "reflecting improvements in regional food security performance"
else:
trend_word = "a deteriorating trend"
tone = "signaling the need for greater regional attention and policy response"
yoy_phrase = (
f"{direction_phrase}, the regional average {direction_word} {change_desc} "
f"compared to {year - 1}, reflecting {trend_word} — {tone}."
) )
elif pd.isna(yoy): elif best_country:
yoy_phrase = ( s5 = f"{best_country} recorded the strongest overall performance among ASEAN member states."
f"No prior year data is available for comparison, " else:
f"as this is the earliest recorded year for this indicator in the dataset." s5 = ""
# --- Bagian 6: Best dan worst year ---
if best_year and worst_year and best_year != worst_year:
s6 = (
f"The best regional score was recorded in {best_year}, "
f"while {worst_year} represented the weakest year across the region."
)
elif best_year:
s6 = f"The strongest regional performance was recorded in {best_year}."
else:
s6 = ""
# --- Bagian 7: YoY consistency ---
if yoy_total > 0:
yoy_worsened = yoy_total - yoy_improved
if yoy_improved > yoy_worsened:
consistency = "predominantly positive"
elif yoy_improved < yoy_worsened:
consistency = "predominantly negative"
else:
consistency = "mixed"
s7 = (
f"Year-on-year, the region showed improvement in {yoy_improved} out of {yoy_total} "
f"transitions, reflecting a {consistency} trajectory over the period."
) )
else: else:
yoy_phrase = ( s7 = ""
f"{direction_phrase}, the regional average remained stable "
f"compared to {year - 1}, with no measurable change year-on-year."
)
return f"{sentence1} {perf_phrase} {yoy_phrase}" # --- Gabungkan semua bagian ---
parts = [s1, s2, s3, s4]
if s5:
parts.append(s5)
if s6:
parts.append(s6)
if s7:
parts.append(s7)
return " ".join(parts)
# ============================================================================= # =============================================================================
@@ -947,111 +996,233 @@ class IndicatorNormAggregator:
Pipeline agg_narrative_indicator yang dijalankan otomatis Pipeline agg_narrative_indicator yang dijalankan otomatis
setelah agg_indicator_norm selesai. Memakai df_final yang sudah ada setelah agg_indicator_norm selesai. Memakai df_final yang sudah ada
di memori, tanpa re-load dari BigQuery. di memori, tanpa re-load dari BigQuery.
Granularity: per indicator_id (1 baris = 1 indikator,
merangkum SELURUH negara ASEAN + SELURUH tahun yang tersedia).
Total rows = jumlah indikator unik.
""" """
self.logger.info("\n" + "=" * 80) self.logger.info("\n" + "=" * 80)
self.logger.info("STEP 12-16: agg_narrative_indicator") self.logger.info("STEP 12-16: agg_narrative_indicator")
self.logger.info(" Level : ASEAN (year x indicator_id)") self.logger.info(" Level : per indicator_id (all ASEAN countries x all years)")
self.logger.info("=" * 80) self.logger.info("=" * 80)
# -- STEP 12: Agregasi ke level ASEAN -- df = df_final.copy()
self.logger.info("\n--- STEP 12: AGGREGATE TO ASEAN LEVEL ---")
# -- STEP 12: Agregasi per indicator_id (all countries, all years) --
self.logger.info("\n--- STEP 12: AGGREGATE PER INDICATOR (all countries x all years) ---")
dim_cols = ["indicator_name", "unit", "direction", "pillar_name", "framework"] dim_cols = ["indicator_name", "unit", "direction", "pillar_name", "framework"]
agg_dict = {col: "first" for col in dim_cols}
agg_dict["value"] = "mean"
agg_dict["norm_score_1_100"] = "mean"
agg_dict["country_id"] = "count"
df_agg = ( # Agregat dasar
df_final.groupby(["year", "indicator_id"]) base = (
.agg(agg_dict) df.groupby("indicator_id")
.agg(
indicator_name = ("indicator_name", "first"),
unit = ("unit", "first"),
direction = ("direction", "first"),
pillar_name = ("pillar_name", "first"),
framework = ("framework", "first"),
year_min = ("year", "min"),
year_max = ("year", "max"),
n_years = ("year", "nunique"),
n_countries = ("country_id", "nunique"),
avg_norm_score_1_100 = ("norm_score_1_100", "mean"),
)
.reset_index() .reset_index()
.rename(columns={ )
"value" : "avg_value",
"norm_score_1_100": "avg_norm_score_1_100", # ASEAN avg value per tahun -> ambil first year & last year value
"country_id" : "n_countries", asean_yr = (
df.groupby(["indicator_id", "year"])["value"]
.mean()
.reset_index()
.rename(columns={"value": "avg_val_yr"})
)
def _first_last(grp):
grp = grp.sort_values("year")
return pd.Series({
"asean_avg_first": grp["avg_val_yr"].iloc[0],
"asean_avg_last" : grp["avg_val_yr"].iloc[-1],
}) })
first_last = (
asean_yr.groupby("indicator_id")
.apply(_first_last)
.reset_index()
) )
self.logger.info(f" Rows : {len(df_agg):,}") base = base.merge(first_last, on="indicator_id", how="left")
self.logger.info(f" Inds : {df_agg['indicator_id'].nunique()}") base["cumulative_change"] = base["asean_avg_last"] - base["asean_avg_first"]
self.logger.info(
f" Years : {int(df_agg['year'].min())} - {int(df_agg['year'].max())}" # Best & worst country (berdasarkan avg norm_score_1_100 per negara per indikator)
country_score = (
df.groupby(["indicator_id", "country_name"])["norm_score_1_100"]
.mean()
.reset_index()
) )
# -- STEP 13: YoY avg_value per indikator -- def _best_worst_country(grp):
self.logger.info("\n--- STEP 13: COMPUTE YoY avg_value ---") grp = grp.sort_values("norm_score_1_100")
parts = [] return pd.Series({
for ind_id, grp in df_agg.groupby("indicator_id"): "worst_country": grp["country_name"].iloc[0],
grp = grp.sort_values("year").copy() "best_country" : grp["country_name"].iloc[-1],
grp["prev_avg_value"] = grp["avg_value"].shift(1) })
grp["yoy_avg_value"] = np.where(
grp["avg_value"].notna() & grp["prev_avg_value"].notna(),
grp["avg_value"] - grp["prev_avg_value"],
np.nan,
)
grp = grp.drop(columns=["prev_avg_value"])
parts.append(grp)
df_agg = pd.concat(parts, ignore_index=True)
self.logger.info(f" yoy_avg_value nulls: {df_agg['yoy_avg_value'].isna().sum():,}")
# -- STEP 14: Assign performance -- bw_country = (
self.logger.info("\n--- STEP 14: ASSIGN PERFORMANCE ---") country_score.groupby("indicator_id")
df_agg["performance"] = pd.NA .apply(_best_worst_country)
has_score = df_agg["avg_norm_score_1_100"].notna() .reset_index()
df_agg.loc[has_score & (df_agg["avg_norm_score_1_100"] >= _PERFORMANCE_THRESHOLD), "performance"] = "Good" )
df_agg.loc[has_score & (df_agg["avg_norm_score_1_100"] < _PERFORMANCE_THRESHOLD), "performance"] = "Bad" base = base.merge(bw_country, on="indicator_id", how="left")
n_good = (df_agg["performance"] == "Good").sum()
n_bad = (df_agg["performance"] == "Bad").sum() # Best & worst year (berdasarkan avg norm_score_1_100 ASEAN per tahun)
yr_score = (
df.groupby(["indicator_id", "year"])["norm_score_1_100"]
.mean()
.reset_index()
)
def _best_worst_year(grp):
grp = grp.sort_values("norm_score_1_100")
return pd.Series({
"worst_year": int(grp["year"].iloc[0]),
"best_year" : int(grp["year"].iloc[-1]),
})
bw_year = (
yr_score.groupby("indicator_id")
.apply(_best_worst_year)
.reset_index()
)
base = base.merge(bw_year, on="indicator_id", how="left")
# YoY consistency: hitung berapa transisi membaik vs memburuk
# (berdasarkan avg ASEAN value per tahun, direction-aware)
def _yoy_consistency(grp):
ind_id = grp["indicator_id"].iloc[0]
direction = base.loc[base["indicator_id"] == ind_id, "direction"].iloc[0]
lb = _is_lower_better(direction)
grp = grp.sort_values("year")
diffs = grp["avg_val_yr"].diff().dropna()
if lb:
improved = (diffs < 0).sum()
else:
improved = (diffs > 0).sum()
return pd.Series({
"yoy_improved_count" : int(improved),
"yoy_total_transitions": int(len(diffs)),
})
yoy_cons = (
asean_yr.groupby("indicator_id")
.apply(_yoy_consistency)
.reset_index()
)
base = base.merge(yoy_cons, on="indicator_id", how="left")
self.logger.info(f" Rows (= n_indicators): {len(base):,}")
self.logger.info(f" Years span : {int(base['year_min'].min())} - {int(base['year_max'].max())}")
# -- STEP 13: Assign performance berdasarkan avg_norm_score_1_100 --
self.logger.info("\n--- STEP 13: ASSIGN PERFORMANCE ---")
base["performance"] = pd.NA
has_score = base["avg_norm_score_1_100"].notna()
base.loc[has_score & (base["avg_norm_score_1_100"] >= _PERFORMANCE_THRESHOLD), "performance"] = "Good"
base.loc[has_score & (base["avg_norm_score_1_100"] < _PERFORMANCE_THRESHOLD), "performance"] = "Bad"
n_good = (base["performance"] == "Good").sum()
n_bad = (base["performance"] == "Bad").sum()
self.logger.info(f" Good: {n_good:,} | Bad: {n_bad:,}") self.logger.info(f" Good: {n_good:,} | Bad: {n_bad:,}")
# -- STEP 15: Build narrative -- # -- STEP 14: Build narrative --
self.logger.info("\n--- STEP 15: BUILD NARRATIVE ---") self.logger.info("\n--- STEP 14: BUILD NARRATIVE ---")
df_agg["narrative"] = df_agg.apply(_build_narrative, axis=1) base["narrative"] = base.apply(_build_narrative, axis=1)
self.logger.info(f" Narratives generated: {len(df_agg):,}") self.logger.info(f" Narratives generated: {len(base):,}")
self.logger.info("\n Sample (first 2):") self.logger.info("\n Sample (first 2):")
for _, row in df_agg.head(2).iterrows(): for _, row in base.head(2).iterrows():
self.logger.info( self.logger.info(
f"\n [{int(row['year'])}] {row['indicator_name'][:50]}" f"\n [{row['indicator_name'][:55]}]"
f"\n -> {row['narrative'][:250]}..." f"\n -> {row['narrative'][:300]}..."
) )
# -- STEP 16: Save -- # -- STEP 15: Save --
self.logger.info("\n--- STEP 16: SAVE -> [Gold] agg_narrative_indicator ---") self.logger.info("\n--- STEP 15: SAVE -> [Gold] agg_narrative_indicator ---")
out = df_agg[[ out = base[[
"year", "indicator_id", "indicator_name", "unit", "direction", "indicator_id",
"pillar_name", "framework", "indicator_name",
"avg_value", "avg_norm_score_1_100", "performance", "unit",
"yoy_avg_value", "n_countries", "narrative", "direction",
"pillar_name",
"framework",
"year_min",
"year_max",
"n_years",
"n_countries",
"asean_avg_first",
"asean_avg_last",
"cumulative_change",
"avg_norm_score_1_100",
"performance",
"best_country",
"worst_country",
"best_year",
"worst_year",
"yoy_improved_count",
"yoy_total_transitions",
"narrative",
]].copy() ]].copy()
out = out.sort_values(["year", "pillar_name", "indicator_name"]).reset_index(drop=True) out = out.sort_values(["pillar_name", "indicator_name"]).reset_index(drop=True)
out["year"] = out["year"].astype(int)
out["indicator_id"] = out["indicator_id"].astype(int) out["indicator_id"] = out["indicator_id"].astype(int)
out["indicator_name"] = out["indicator_name"].astype(str) out["indicator_name"] = out["indicator_name"].astype(str)
out["unit"] = out["unit"].fillna("").astype(str) out["unit"] = out["unit"].fillna("").astype(str)
out["direction"] = out["direction"].astype(str) out["direction"] = out["direction"].astype(str)
out["pillar_name"] = out["pillar_name"].astype(str) out["pillar_name"] = out["pillar_name"].astype(str)
out["framework"] = out["framework"].astype(str) out["framework"] = out["framework"].astype(str)
out["avg_value"] = out["avg_value"].astype(float) out["year_min"] = out["year_min"].astype(int)
out["year_max"] = out["year_max"].astype(int)
out["n_years"] = out["n_years"].astype(int)
out["n_countries"] = out["n_countries"].astype(int)
out["asean_avg_first"] = out["asean_avg_first"].astype(float)
out["asean_avg_last"] = out["asean_avg_last"].astype(float)
out["cumulative_change"] = out["cumulative_change"].astype(float)
out["avg_norm_score_1_100"] = out["avg_norm_score_1_100"].astype(float) out["avg_norm_score_1_100"] = out["avg_norm_score_1_100"].astype(float)
out["performance"] = out["performance"].astype(str).replace("nan", pd.NA).astype("string") out["performance"] = out["performance"].astype(str).replace("nan", pd.NA).astype("string")
out["yoy_avg_value"] = pd.to_numeric(out["yoy_avg_value"], errors="coerce").astype(float) out["best_country"] = out["best_country"].astype(str)
out["n_countries"] = out["n_countries"].astype(int) out["worst_country"] = out["worst_country"].astype(str)
out["best_year"] = pd.to_numeric(out["best_year"], errors="coerce").astype("Int64")
out["worst_year"] = pd.to_numeric(out["worst_year"], errors="coerce").astype("Int64")
out["yoy_improved_count"] = pd.to_numeric(out["yoy_improved_count"], errors="coerce").astype("Int64")
out["yoy_total_transitions"]= pd.to_numeric(out["yoy_total_transitions"], errors="coerce").astype("Int64")
out["narrative"] = out["narrative"].astype(str) out["narrative"] = out["narrative"].astype(str)
self.logger.info(f" Columns : {list(out.columns)}")
self.logger.info(f" Total rows : {len(out):,}")
self.logger.info(f" Frameworks : {dict(out['framework'].value_counts())}")
self.logger.info(f" Performance: {dict(out['performance'].value_counts())}")
schema = [ schema = [
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("unit", "STRING", mode="NULLABLE"), bigquery.SchemaField("unit", "STRING", mode="NULLABLE"),
bigquery.SchemaField("direction", "STRING", mode="REQUIRED"), bigquery.SchemaField("direction", "STRING", mode="REQUIRED"),
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"), bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
bigquery.SchemaField("framework", "STRING", mode="REQUIRED"), bigquery.SchemaField("framework", "STRING", mode="REQUIRED"),
bigquery.SchemaField("avg_value", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("year_min", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("year_max", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("n_years", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("n_countries", "INTEGER", mode="REQUIRED"),
bigquery.SchemaField("asean_avg_first", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("asean_avg_last", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("cumulative_change", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("avg_norm_score_1_100", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("avg_norm_score_1_100", "FLOAT", mode="NULLABLE"),
bigquery.SchemaField("performance", "STRING", mode="NULLABLE"), bigquery.SchemaField("performance", "STRING", mode="NULLABLE"),
bigquery.SchemaField("yoy_avg_value", "FLOAT", mode="NULLABLE"), bigquery.SchemaField("best_country", "STRING", mode="NULLABLE"),
bigquery.SchemaField("n_countries", "INTEGER", mode="REQUIRED"), bigquery.SchemaField("worst_country", "STRING", mode="NULLABLE"),
bigquery.SchemaField("best_year", "INTEGER", mode="NULLABLE"),
bigquery.SchemaField("worst_year", "INTEGER", mode="NULLABLE"),
bigquery.SchemaField("yoy_improved_count", "INTEGER", mode="NULLABLE"),
bigquery.SchemaField("yoy_total_transitions", "INTEGER", mode="NULLABLE"),
bigquery.SchemaField("narrative", "STRING", mode="NULLABLE"), bigquery.SchemaField("narrative", "STRING", mode="NULLABLE"),
] ]
@@ -1076,17 +1247,23 @@ class IndicatorNormAggregator:
"completeness_pct" : 100.0, "completeness_pct" : 100.0,
"config_snapshot" : json.dumps({ "config_snapshot" : json.dumps({
"source_table" : "agg_indicator_norm (in-memory df_final)", "source_table" : "agg_indicator_norm (in-memory df_final)",
"granularity" : "year x indicator_id (ASEAN level)", "granularity" : "indicator_id (all ASEAN countries x all years)",
"aggregation" : "mean across ASEAN countries", "aggregation" : "mean across all countries and years per indicator",
"performance_threshold": _PERFORMANCE_THRESHOLD, "performance_threshold": _PERFORMANCE_THRESHOLD,
"yoy_column" : "yoy_avg_value", "narrative_components" : [
"layer" : "gold", "year span", "n_countries", "direction interpretation",
"cumulative trend (first to last year)",
"avg_norm_score + performance",
"best/worst country", "best/worst year",
"YoY consistency count",
],
"layer": "gold",
}), }),
"validation_metrics" : json.dumps({ "validation_metrics" : json.dumps({
"total_rows" : rows_loaded, "total_rows" : rows_loaded,
"n_indicators": int(out["indicator_id"].nunique()), "n_indicators": int(out["indicator_id"].nunique()),
"year_min" : int(out["year"].min()), "year_min" : int(out["year_min"].min()),
"year_max" : int(out["year"].max()), "year_max" : int(out["year_max"].max()),
}), }),
} }
save_etl_metadata(self.client, metadata) save_etl_metadata(self.client, metadata)