ledger: GDP (nominal, current US$) top-20 countries 2023 from World Bank API
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#!/usr/bin/env python3
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"""Extract GDP (nominal, current US$) for the top 20 countries, 2023.
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Source: World Bank API, indicator NY.GDP.MKTP.CD (GDP, current US$), country:all, date=2023.
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Filter: keep only records whose country.id is a real ISO 3166-1 alpha-3 code
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(iso3_codes.txt from the Wikipedia ISO3 list), dropping World Bank aggregates.
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Artifacts (written alongside this script):
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- gdp_nominal_usd_2023.csv (rank, country, iso3, gdp_nominal_usd_billions)
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- gdp_nominal_usd_2023.md (markdown table of the same top-20)
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"""
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import csv
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import json
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import os
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HERE = os.path.dirname(os.path.abspath(__file__))
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ISO3_PATH = os.path.join(HERE, "iso3_codes.txt")
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JSON_PATH = os.path.join(HERE, "gdp_nominal_usd_2023.json")
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CSV_PATH = os.path.join(HERE, "gdp_nominal_usd_2023.csv")
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MD_PATH = os.path.join(HERE, "gdp_nominal_usd_2023.md")
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TOP_N = 20
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with open(ISO3_PATH, "r", encoding="utf-8") as f:
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iso3_codes = set(line.strip() for line in f if line.strip())
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with open(JSON_PATH, "r", encoding="utf-8") as f:
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payload = json.load(f)
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records = payload[1] # payload[0] is the metadata envelope
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rows = []
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for r in records:
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country = r.get("country", {})
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value = r.get("value")
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code = r.get("countryiso3code") # ISO 3166-1 alpha-3; aggregates use non-ISO codes
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if value is None or code not in iso3_codes:
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continue
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rows.append(
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{
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"country": country.get("value", ""),
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"iso3": code,
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"gdp_raw_usd": float(value),
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}
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)
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rows.sort(key=lambda x: -x["gdp_raw_usd"])
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top = rows[:TOP_N]
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with open(CSV_PATH, "w", newline="", encoding="utf-8") as f:
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writer = csv.writer(f)
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writer.writerow(["rank", "country", "iso3", "gdp_nominal_usd_billions"])
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for i, r in enumerate(top, 1):
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writer.writerow([i, r["country"], r["iso3"], round(r["gdp_raw_usd"] / 1e9, 3)])
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lines = [
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"# Top 20 countries by GDP (nominal, current US$) — 2023",
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"",
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"Source: World Bank API (`NY.GDP.MKTP.CD`, 2023). Only ISO 3166-1 alpha-3 countries "
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"are counted (World Bank aggregates excluded).",
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"",
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"| Rank | Country | ISO3 | GDP nominal (USD billions) |",
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"|---:|---|:--:|---:|",
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]
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for i, r in enumerate(top, 1):
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lines.append(f"| {i} | {r['country']} | {r['iso3']} | {r['gdp_raw_usd'] / 1e9:.3f} |")
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with open(MD_PATH, "w", encoding="utf-8") as f:
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f.write("\n".join(lines) + "\n")
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print(f"PARSED rows={len(rows)} kept_countries={len(top)}")
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for r in top:
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print(f" {r['country']} ({r['iso3']}) {r['gdp_raw_usd'] / 1e9:.3f}B")
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@@ -0,0 +1,82 @@
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#!/usr/bin/env python3
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"""Real cross-check: re-fetches the World Bank GDP nominal-2023 API and compares the
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committed gdp_nominal_usd_2023.csv (next to this script) against a fresh computation.
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Exit code: 0 = artifact matches the live source of truth; 1 = mismatch.
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"""
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import csv
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import json
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import os
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import sys
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import urllib.request
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HERE = os.path.dirname(os.path.abspath(__file__))
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CSV_PATH = os.path.join(HERE, "gdp_nominal_usd_2023.csv")
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ISO3_PATH = os.path.join(HERE, "iso3_codes.txt")
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API_URL = ("https://api.worldbank.org/v2/country/all/indicator/NY.GDP.MKTP.CD"
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"?format=json&date=2023:2023&per_page=1000")
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TOLERANCE_USD = 1_000_000 # 1e6 USD == 0.001B (artifact rounds to 3 decimals)
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def fetch_json(url):
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req = urllib.request.Request(url, headers={"User-Agent": "ledger/extract/1.0"})
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with urllib.request.urlopen(req, timeout=40) as r:
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return json.loads(r.read().decode("utf-8"))
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def main():
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with open(ISO3_PATH, encoding="utf-8") as f:
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iso3 = {ln.strip() for ln in f if ln.strip()}
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payload = fetch_json(API_URL)
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records = payload[1] if isinstance(payload, list) and payload else []
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api = []
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for rec in records:
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iso = rec.get("countryiso3code")
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val = rec.get("value")
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if iso in iso3 and val is not None:
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api.append((rec["country"]["value"], iso, float(val)))
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api.sort(key=lambda x: -x[2])
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top20 = api[:20]
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with open(CSV_PATH, encoding="utf-8") as f:
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rows = [r for r in csv.reader(f) if r]
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if not rows:
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print("FAIL: artifact CSV is empty")
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sys.exit(1)
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data = rows[1:]
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if len(data) != 20:
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print(f"FAIL: artifact has {len(data)} data rows, expected 20")
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sys.exit(1)
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ok = True
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for i, (cname, ciso, cval) in enumerate(top20):
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row = data[i]
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try:
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artifact_usd = float(row[3]) * 1e9
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except (ValueError, IndexError):
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artifact_usd = None
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good = (row[1] == cname) and (row[2] == ciso) and (artifact_usd is not None
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and abs(artifact_usd - cval) < TOLERANCE_USD)
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if not good:
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ok = False
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print(f"MISMATCH rank {i+1}: source={cname} {ciso} {cval:.3f}B; "
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f"artifact={row}")
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values = [float(r[3]) for r in data]
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if values != sorted(values, reverse=True):
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ok = False
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print("FAIL: artifact rows are not in descending GDP order")
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if ok:
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print(f"CHECK OK: {len(top20)} countries match the live World Bank API "
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f"(US={top20[0][1]}, China={top20[1][1]}).")
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sys.exit(0)
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print("CHECK FAIL")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,21 @@
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rank,country,iso3,gdp_nominal_usd_billions
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1,United States,USA,27811.517
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2,China,CHN,18270.357
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3,Germany,DEU,4562.208
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4,Japan,JPN,4384.854
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5,India,IND,3500.906
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6,United Kingdom,GBR,3420.797
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7,France,FRA,3056.251
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8,Italy,ITA,2316.882
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9,Canada,CAN,2196.594
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10,Brazil,BRA,2191.132
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11,Russian Federation,RUS,2046.285
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12,"Korea, Rep.",KOR,1844.801
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13,Mexico,MEX,1794.41
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14,Australia,AUS,1734.451
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15,Spain,ESP,1619.482
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16,Indonesia,IDN,1371.167
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17,Saudi Arabia,SAU,1218.585
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18,Turkiye,TUR,1141.243
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19,Netherlands,NLD,1135.476
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20,Switzerland,CHE,928.435
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@@ -0,0 +1 @@
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[{"page":1,"pages":1,"per_page":1000,"total":266,"sourceid":"2","lastupdated":"2026-10-08"},[{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"ZH","value":"Africa Eastern and Southern"},"countryiso3code":"AFE","date":"2023","value":1179121535535.41,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"ZI","value":"Africa Western and Central"},"countryiso3code":"AFW","date":"2023","value":938480674635.085,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"1A","value":"Arab World"},"countryiso3code":"ARB","date":"2023","value":3617895070373.04,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"S3","value":"Caribbean small states"},"countryiso3code":"CSS","date":"2023","value":79594075306.6477,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"B8","value":"Central Europe and the Baltics"},"countryiso3code":"CEB","date":"2023","value":2273564082260.43,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"V2","value":"Early-demographic dividend"},"countryiso3code":"EAR","date":"2023","value":14791485754640,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"Z4","value":"East Asia & Pacific"},"countryiso3code":"EAS","date":"2023","value":31597454903562.8,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"4E","value":"East Asia & Pacific (excluding high income)"},"countryiso3code":"EAP","date":"2023","value":21645526054900.4,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"T4","value":"East Asia & Pacific (IDA & IBRD countries)"},"countryiso3code":"TEA","date":"2023","value":21618798469116.4,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XC","value":"Euro area"},"countryiso3code":"EMU","date":"2023","value":15957338405375.8,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"Z7","value":"Europe & Central Asia"},"countryiso3code":"ECS","date":"2023","value":27758730809930.4,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"7E","value":"Europe & Central Asia (excluding high income)"},"countryiso3code":"ECA","date":"2023","value":2141252006536.72,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"T7","value":"Europe & Central Asia (IDA & IBRD countries)"},"countryiso3code":"TEC","date":"2023","value":5535571828711.02,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"EU","value":"European Union"},"countryiso3code":"EUU","date":"2023","value":18659279022583.3,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"F2","value":"Fragility, Conflict, and Violence (FCV) situations"},"countryiso3code":"FCV","date":"2023","value":2043142894382.41,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XE","value":"Heavily indebted poor countries (HIPC)"},"countryiso3code":"HPC","date":"2023","value":1060851525652.8,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XD","value":"High income"},"countryiso3code":"","date":"2023","value":69198099195981.1,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XF","value":"IBRD only"},"countryiso3code":"IBD","date":"2023","value":39083500948084.9,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"ZT","value":"IDA & IBRD total"},"countryiso3code":"IBT","date":"2023","value":42011330091982.8,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XH","value":"IDA blend"},"countryiso3code":"IDB","date":"2023","value":1277421993702.09,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XI","value":"IDA only"},"countryiso3code":"IDX","date":"2023","value":1650407150195.89,"unit":"","obs_status":"","decimal":0},{"indicator":{"id":"NY.GDP.MKTP.CD","value":"GDP (current US$)"},"country":{"id":"XG","value":"IDA total"},"countryiso3code":"IDA","date":"2023","value":2927829143897.98,"unit":"","obs_status":Line truncated
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@@ -0,0 +1,26 @@
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# Top 20 countries by GDP (nominal, current US$) — 2023
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Source: World Bank API (`NY.GDP.MKTP.CD`, 2023). Only ISO 3166-1 alpha-3 countries are counted (World Bank aggregates excluded).
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| Rank | Country | ISO3 | GDP nominal (USD billions) |
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|---:|---|:--:|---:|
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| 1 | United States | USA | 27811.517 |
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| 2 | China | CHN | 18270.357 |
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| 3 | Germany | DEU | 4562.208 |
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| 4 | Japan | JPN | 4384.854 |
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| 5 | India | IND | 3500.906 |
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| 6 | United Kingdom | GBR | 3420.797 |
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| 7 | France | FRA | 3056.251 |
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| 8 | Italy | ITA | 2316.882 |
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| 9 | Canada | CAN | 2196.594 |
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| 10 | Brazil | BRA | 2191.132 |
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| 11 | Russian Federation | RUS | 2046.285 |
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| 12 | Korea, Rep. | KOR | 1844.801 |
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| 13 | Mexico | MEX | 1794.410 |
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| 14 | Australia | AUS | 1734.451 |
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| 15 | Spain | ESP | 1619.482 |
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| 16 | Indonesia | IDN | 1371.167 |
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| 17 | Saudi Arabia | SAU | 1218.585 |
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| 18 | Turkiye | TUR | 1141.243 |
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| 19 | Netherlands | NLD | 1135.476 |
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| 20 | Switzerland | CHE | 928.435 |
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@@ -0,0 +1,261 @@
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ABW
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AFG
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AGO
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AIA
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ALA
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ALB
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AND
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ARE
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ARG
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ARM
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ASM
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ATA
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ATF
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ATG
|
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AUS
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AUT
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AZE
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BDI
|
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BEL
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BEN
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BES
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BFA
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BGD
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BGR
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BHR
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BHS
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BIH
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BLM
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BLR
|
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BLZ
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BMU
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BOL
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BRA
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BRB
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BRN
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BTN
|
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BVT
|
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BWA
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CAF
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CAN
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CCK
|
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CHE
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CHL
|
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CHN
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||||
CIV
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||||
CMR
|
||||
COD
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||||
COG
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||||
COK
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||||
COL
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||||
COM
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||||
CPV
|
||||
CRI
|
||||
CUB
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||||
CUW
|
||||
CXR
|
||||
CYM
|
||||
CYP
|
||||
CZE
|
||||
DEU
|
||||
DJI
|
||||
DMA
|
||||
DNK
|
||||
DOM
|
||||
DZA
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||||
ECU
|
||||
EGY
|
||||
ERI
|
||||
ESH
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||||
ESP
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||||
EST
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||||
ETH
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||||
FIN
|
||||
FJI
|
||||
FLK
|
||||
FRA
|
||||
FRO
|
||||
FSM
|
||||
GAB
|
||||
GBR
|
||||
GEO
|
||||
GGY
|
||||
GHA
|
||||
GIB
|
||||
GIN
|
||||
GLP
|
||||
GMB
|
||||
GNB
|
||||
GNQ
|
||||
GRC
|
||||
GRD
|
||||
GRL
|
||||
GTM
|
||||
GUF
|
||||
GUM
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||||
GUY
|
||||
HKG
|
||||
HMD
|
||||
HND
|
||||
HRV
|
||||
HTI
|
||||
HUN
|
||||
IDN
|
||||
IMN
|
||||
IND
|
||||
IOT
|
||||
IRL
|
||||
IRN
|
||||
IRQ
|
||||
ISL
|
||||
ISR
|
||||
ITA
|
||||
JAM
|
||||
JEY
|
||||
JOR
|
||||
JPN
|
||||
KAZ
|
||||
KEN
|
||||
KGZ
|
||||
KHM
|
||||
KIR
|
||||
KNA
|
||||
KOR
|
||||
KWT
|
||||
LAO
|
||||
LBN
|
||||
LBR
|
||||
LBY
|
||||
LCA
|
||||
LIE
|
||||
LKA
|
||||
LSO
|
||||
LTU
|
||||
LUX
|
||||
LVA
|
||||
MAC
|
||||
MAF
|
||||
MAR
|
||||
MCO
|
||||
MDA
|
||||
MDG
|
||||
MDV
|
||||
MEX
|
||||
MHL
|
||||
MKD
|
||||
MLI
|
||||
MLT
|
||||
MMR
|
||||
MNE
|
||||
MNG
|
||||
MNP
|
||||
MOZ
|
||||
MRT
|
||||
MSR
|
||||
MTQ
|
||||
MUS
|
||||
MWI
|
||||
MYS
|
||||
MYT
|
||||
NAM
|
||||
NCL
|
||||
NER
|
||||
NFK
|
||||
NGA
|
||||
NIC
|
||||
NIU
|
||||
NLD
|
||||
NOR
|
||||
NPL
|
||||
NRU
|
||||
NZL
|
||||
OMN
|
||||
PAK
|
||||
PAN
|
||||
PCN
|
||||
PER
|
||||
PHL
|
||||
PLW
|
||||
PNG
|
||||
POL
|
||||
PRI
|
||||
PRK
|
||||
PRT
|
||||
PRY
|
||||
PSE
|
||||
PYF
|
||||
QAT
|
||||
REU
|
||||
ROU
|
||||
RUS
|
||||
RWA
|
||||
SAU
|
||||
SDN
|
||||
SEN
|
||||
SGP
|
||||
SGS
|
||||
SHN
|
||||
SJM
|
||||
SLB
|
||||
SLE
|
||||
SLV
|
||||
SMR
|
||||
SOM
|
||||
SPM
|
||||
SRB
|
||||
SSD
|
||||
STP
|
||||
SUR
|
||||
SVK
|
||||
SVN
|
||||
SWE
|
||||
SWZ
|
||||
SXM
|
||||
SYC
|
||||
SYR
|
||||
TCA
|
||||
TCD
|
||||
TGO
|
||||
THA
|
||||
TJK
|
||||
TKL
|
||||
TKM
|
||||
TLS
|
||||
TON
|
||||
TTO
|
||||
TUN
|
||||
TUR
|
||||
TUV
|
||||
TWN
|
||||
TZA
|
||||
UGA
|
||||
UKR
|
||||
UMI
|
||||
URY
|
||||
USA
|
||||
UZB
|
||||
VAT
|
||||
VCT
|
||||
VEN
|
||||
VGB
|
||||
VIR
|
||||
VNM
|
||||
VUT
|
||||
WLF
|
||||
WSM
|
||||
XXB
|
||||
XXE
|
||||
XXG
|
||||
XXI
|
||||
XXL
|
||||
XXM
|
||||
XXN
|
||||
XXP
|
||||
XXR
|
||||
XXS
|
||||
XXW
|
||||
XXY
|
||||
YEM
|
||||
ZAF
|
||||
ZMB
|
||||
ZWE
|
||||
Reference in new issue
Block a user