electricsheepasia/asia-world-bank-health-indicators-for-iran-islamic-rep

Iran, Islamic Rep. - Health

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-04-28

Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-28. Geographic scope: IRN.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — country_name (Iran, Islamic Rep.), country_iso3 (IRN), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -1696091.0–91567738.0).

Identifier / Metadata — indicator_name (Net migration, Survival to age 65, female (% of cohort), Population ages 00-04, female (% of female population)), indicator_code (SM.POP.NETM, SP.DYN.TO65.FE.ZS, SP.POP.0004.FE.5Y), esa_source (HDX), esa_processed (2026-05-04).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-world-bank-health-indicators-for-iran-islamic-rep")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

• —Data originates from World Bank Group and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_world_bank_health_indicators_for_iran_islamic_rep,
  title     = {Iran, Islamic Rep. - Health},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-health-indicators-for-iran-islamic-rep},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.