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

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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)

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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.

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	22
Columns	13 (5 numeric, 8 categorical, 0 datetime)
Train split	17 rows
Test split	4 rows
Geographic scope	IRQ
Publisher	Centre for Research on the Epidemiology of Disasters
HDX last updated	2026-05-02

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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).

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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()

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Schema

Column	Type	Null %	Range / Sample Values
year	float64	4.5%	2006.0 – 2026.0 (mean 2017.2381)
country	object	0.0%	Iraq, #country +name
iso	object	0.0%	IRQ, #country +code
disaster_group	object	0.0%	Natural, #cause +group
disaster_subroup	object	0.0%	Hydrological, Meteorological, #cause +subgroup
disaster_type	object	0.0%	Flood, Storm, #cause +type
disaster_subtype	object	0.0%	Flash flood, Flood (General), Riverine flood
total_events	float64	4.5%	1.0 – 2.0 (mean 1.0952)
total_affected	float64	18.2%	600.0 – 7000000.0 (mean 404635.2778)
total_deaths	float64	18.2%	1.0 – 58.0 (mean 10.3889)
cpi	float64	18.2%	64.2648 – 100.0 (mean 79.5583)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-06

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Numeric Summary

Column	Min	Max	Mean	Median
year	2006.0	2026.0	2017.2381	2019.0
total_events	1.0	2.0	1.0952	1.0
total_affected	600.0	7000000.0	404635.2778	6734.5
total_deaths	1.0	58.0	10.3889	6.0
cpi	64.2648	100.0	79.5583	79.0953

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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. 2 column(s) with >80% missing values were removed: total_damage_usd_original, total_damage_usd_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.

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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.

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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)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.