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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 `wbdata` and `world_bank_data` Python libraries provide clean access to the World Bank API. Engineers use `wbdata.get_dataframe()` to pull indicator time-series for multiple countries into pandas DataFrames with date-indexed columns.
World Bank data builds authoritative AI knowledge bases for global development topics. RAG pipelines index country indicator time-series so LLMs can answer 'Which countries reduced extreme poverty the most in the last decade?' with verified World Bank statistics rather than hallucinated figures.
# pip install wbdata pandas
import wbdata, pandas as pd
from datetime import datetime
indicators = {"NY.GDP.MKTP.CD": "GDP", "SP.POP.TOTL": "Population"}
df = wbdata.get_dataframe(indicators, country=["US", "CN", "DE"],
date=(datetime(2010, 1, 1), datetime(2023, 1, 1)))
print(df.head(10))Official dataset source
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