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The OECD provides macroeconomic, social, and environmental datasets for 38 member countries including GDP, employment, health spending, education attainment, and trade. Used in data engineering for building country comparison dashboards, policy analysis pipelines, and economic indicator tracking systems in Python.
The `pandasdmx` library reads OECD SDMX-JSON APIs, or engineers can use `requests` against the OECD.Stat REST API. The `oecd` Python package simplifies data flow access. Results come as multi-dimensional DataFrames with country, indicator, and time dimensions.
OECD comparative data enables AI systems that benchmark country performance on key policy outcomes. Build RAG systems indexed on OECD Economic Surveys so LLMs can answer 'How does healthcare spending in the US compare to other OECD countries?' with official comparative statistics.
# pip install pandasdmx pandas
import pandasdmx as sdmx, pandas as pd
oecd = sdmx.Request("OECD")
resp = oecd.data("DP_LIVE", key="AUS+CAN+FRA+DEU+GBR+USA.UNEMPLOYMNT.TOT.PC_LF.A",
params={"startTime": "2015"})
df = sdmx.to_pandas(resp).reset_index()
print(df.tail(20))Official dataset source
More datasets used by Python data engineers.
Access 16,000+ development indicators from the World Bank covering GDP, poverty, health, education, infrastructure, and environment for 200+ countries. Used in data engineering for building global development dashboards, time-series analysis pipelines, and cross-country economic comparison systems in Python.
The World Economic Forum publishes datasets and reports covering global competitiveness, gender parity, energy transition, and digital economy indicators across 140+ countries. Used in data engineering for economic research pipelines, policy benchmarking systems, and building global indicator dashboards in Python.
Eurostat, the statistical office of the European Union, offers a comprehensive database of statistical data covering various domains such as economy, population, employment, environment and social issues.