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

EM-DAT - Country Profiles, Bangladesh

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: Bangladesh

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: BGD.

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)	107
Columns	15 (7 numeric, 8 categorical, 0 datetime)
Train split	85 rows
Test split	21 rows
Geographic scope	BGD
Publisher	Centre for Research on the Epidemiology of Disasters
HDX last updated	2026-05-02

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Variables

Geographic — year (range 2000.0–2025.0), country (Bangladesh, #country +name), iso (BGD, #country +code), disaster_type (Storm, Flood, Extreme temperature), disaster_subtype (Tropical cyclone, Riverine flood, Flood (General)).

Demographic — total_damage_usd_original (range 4000000.0–2300000000.0), total_damage_usd_adjusted (range 5293948.0–3653660868.0).

Outcome / Measurement — total_events (range 1.0–5.0), total_affected (range 50.0–36600000.0), total_deaths (range 1.0–4275.0).

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

Other — disaster_group (Natural, #cause +group), disaster_subroup (Meteorological, Hydrological, Geophysical), cpi (range 54.8952–100.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-population-emdat-country-profiles-bangladesh")
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	0.9%	2000.0 – 2025.0 (mean 2010.8868)
country	object	0.0%	Bangladesh, #country +name
iso	object	0.0%	BGD, #country +code
disaster_group	object	0.0%	Natural, #cause +group
disaster_subroup	object	0.0%	Meteorological, Hydrological, Geophysical
disaster_type	object	0.0%	Storm, Flood, Extreme temperature
disaster_subtype	object	0.0%	Tropical cyclone, Riverine flood, Flood (General)
total_events	float64	0.9%	1.0 – 5.0 (mean 1.3774)
total_affected	float64	12.1%	50.0 – 36600000.0 (mean 1930943.5745)
total_deaths	float64	9.3%	1.0 – 4275.0 (mean 120.7629)
total_damage_usd_original	float64	77.6%	4000000.0 – 2300000000.0 (mean 443774375.0)
total_damage_usd_adjusted	float64	78.5%	5293948.0 – 3653660868.0 (mean 657977267.1304)
cpi	float64	3.7%	54.8952 – 100.0 (mean 70.7785)
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	2000.0	2025.0	2010.8868	2010.0
total_events	1.0	5.0	1.3774	1.0
total_affected	50.0	36600000.0	1930943.5745	83106.0
total_deaths	1.0	4275.0	120.7629	31.0
total_damage_usd_original	4000000.0	2300000000.0	443774375.0	174800000.0
total_damage_usd_adjusted	5293948.0	3653660868.0	657977267.1304	212009108.0
cpi	54.8952	100.0	70.7785	69.5133

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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. 5 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.
• The following columns have >20% missing values and should be treated with caution in modelling: total_damage_usd_original, total_damage_usd_adjusted.
• 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_bangladesh,
  title     = {EM-DAT - Country Profiles, Bangladesh},
  author    = {Centre for Research on the Epidemiology of Disasters},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/emdat-country-profiles-bgd},
  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.