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The World Bank World Development Indicators provides 1,600+ time-series indicators covering poverty, health, education, infrastructure, and environment for 217 countries from 1960 onwards. Used in data engineering for global development dashboards, longitudinal analysis pipelines, and economic research systems in Python.
The `wbdata` and `world_bank_data` Python libraries provide full WDI API access. Engineers use `wbdata.get_dataframe(indicators_dict, country=country_codes)` to pull multiple indicators simultaneously into wide-format DataFrames with year rows and country columns.
WDI is the most comprehensive public dataset for building AI development analytics tools. Index all 1,400 indicators for a RAG system that can answer virtually any factual development question with World Bank data. AI forecasting models use WDI historical indicators as features for predicting future development outcomes.
# pip install world_bank_data pandas
import world_bank_data as wb, pandas as pd
# Fetch multiple WDI indicators for all countries
indicators = {
"SP.POP.TOTL": "population",
"NY.GDP.PCAP.CD": "gdp_per_capita",
"SH.DYN.MORT": "child_mortality"
}
df = wb.get_series(list(indicators.keys()), simplify_index=True).unstack(level=0)
df.columns = list(indicators.values())
print(df.dropna().tail(10))Official dataset source
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