electricsheepasia/asia-population-emdat-country-profiles-iraq

EM-DAT - Country Profiles, Iraq

Publisher: Centre for Research on the Epidemiology of Disasters · Source: HDX · License: hdx-other · Updated: 2026-05-02

Abstract

Aggregated figures for natural hazard related events in EM-DAT: Iraq

Documentation on the Country Profiles available here

How to cite the EM-DAT Project here

Main dataset on HDX: EM-DAT - Country Profiles

More on the EM-DAT database : website / data portal

Each line corresponds to a given combination of year, country, disaster subtype and reports figures for :

• —number of disasters
• —total number of people affected
• —total number of deaths
• —economic losses (original value and adjusted)

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-05-02. Geographic scope: IRQ.

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

Dataset Characteristics

Variables

Geographic — year (range 2006.0–2026.0), country (Iraq, #country +name), iso (IRQ, #country +code), disaster_type (Flood, Storm, #cause +type), disaster_subtype (Flash flood, Flood (General), Riverine flood).

Outcome / Measurement — total_events (range 1.0–2.0), total_affected (range 600.0–7000000.0), total_deaths (range 1.0–58.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-06).

Other — disaster_group (Natural, #cause +group), disaster_subroup (Hydrological, Meteorological, #cause +subgroup), cpi (range 64.2648–100.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-population-emdat-country-profiles-iraq")
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 snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 2 column(s) with >80% missing values were removed: `totaldamageusdoriginal, totaldamageusd_adjusted`. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Centre for Research on the Epidemiology of Disasters 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_population_emdat_country_profiles_iraq,
  title     = {EM-DAT - Country Profiles, Iraq},
  author    = {Centre for Research on the Epidemiology of Disasters},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/emdat-country-profiles-irq},
  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.