electricsheepafrica/africa-population-senegal

EM-DAT - Country Profiles, Senegal

Publisher: Centre for Research on the Epidemiology of Disasters · Source: HDX · License: hdx-other · Updated: 2026-04-24

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Abstract

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

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 2026-04-24. Geographic scope: SEN.

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	SEN
Publisher	Centre for Research on the Epidemiology of Disasters
HDX last updated	2026-04-24

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Variables

Geographic — year (range 2002.0–2024.0), country (Senegal, #country +name), iso (SEN, #country +code), disaster_type (Flood, Drought, #cause +type), disaster_subtype (Riverine flood, Drought, Flood (General)).

Outcome / Measurement — total_events (range 1.0–2.0), total_affected (range 1000.0–850000.0), total_deaths (range 1.0–28.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-29).

Other — disaster_group (Natural, #cause +group), disaster_subroup (Hydrological, Climatological, #cause +subgroup), cpi (range 57.3418–100.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-population-senegal")
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%	2002.0 – 2024.0 (mean 2011.1429)
country	object	0.0%	Senegal, #country +name
iso	object	0.0%	SEN, #country +code
disaster_group	object	0.0%	Natural, #cause +group
disaster_subroup	object	0.0%	Hydrological, Climatological, #cause +subgroup
disaster_type	object	0.0%	Flood, Drought, #cause +type
disaster_subtype	object	0.0%	Riverine flood, Drought, Flood (General)
total_events	float64	4.5%	1.0 – 2.0 (mean 1.0476)
total_affected	float64	9.1%	1000.0 – 850000.0 (mean 153231.85)
total_deaths	float64	45.5%	1.0 – 28.0 (mean 8.6667)
cpi	float64	4.5%	57.3418 – 100.0 (mean 71.85)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-29

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Numeric Summary

Column	Min	Max	Mean	Median
year	2002.0	2024.0	2011.1429	2011.0
total_events	1.0	2.0	1.0476	1.0
total_affected	1000.0	850000.0	153231.85	52800.0
total_deaths	1.0	28.0	8.6667	7.0
cpi	57.3418	100.0	71.85	71.7077

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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.
• The following columns have >20% missing values and should be treated with caution in modelling: total_deaths.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_population_senegal,
  title     = {EM-DAT - Country Profiles, Senegal},
  author    = {Centre for Research on the Epidemiology of Disasters},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/emdat-country-profiles-sen},
  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.