electricsheepasia/asia-afghanistan-casualties

Afghanistan - Casualties

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-04-25

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Abstract

Total number of civilian casualties documented in each of Afghanistan’s 34 provinces, the top three causes of civilian casualties in each province, and the percentage increase or decrease compared to 2017. For more, refer to the report from UNAMA

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-04-25. Geographic scope: AFG.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	35
Columns	10 (4 numeric, 6 categorical, 0 datetime)
Train split	28 rows
Test split	7 rows
Geographic scope	AFG
Publisher	OCHA Afghanistan
HDX last updated	2025-04-25

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Variables

Geographic — province (#adm1+name, Kabul, Nangarhar).

Outcome / Measurement — total_civilian_casualties (range 0.0–1866.0), deaths (range 0.0–681.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-05-04).

Other — leading_tactic_or_cause (Ground Engagements, IEDs (non-suicide), Suicide/Complex Attacks), second_highest_tactic (IEDs (non-suicide), Aerial attacks, Ground Engagements), third_highest_tactic (Targeted Killings, IEDs (non-suicide), Aerial attacks), injuries (range 0.0–1270.0), compared_to_2017 (range -0.7–1.7).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-afghanistan-casualties")
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
province	object	0.0%	#adm1+name, Kabul, Nangarhar
leading_tactic_or_cause	object	2.9%	Ground Engagements, IEDs (non-suicide), Suicide/Complex Attacks
second_highest_tactic	object	5.7%	IEDs (non-suicide), Aerial attacks, Ground Engagements
third_highest_tactic	object	5.7%	Targeted Killings, IEDs (non-suicide), Aerial attacks
total_civilian_casualties	float64	2.9%	0.0 – 1866.0 (mean 323.3529)
deaths	float64	2.9%	0.0 – 681.0 (mean 111.8824)
injuries	float64	2.9%	0.0 – 1270.0 (mean 211.4412)
compared_to_2017	float64	2.9%	-0.7 – 1.7 (mean 0.1515)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-04

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Numeric Summary

Column	Min	Max	Mean	Median
total_civilian_casualties	0.0	1866.0	323.3529	179.0
deaths	0.0	681.0	111.8824	67.5
injuries	0.0	1270.0	211.4412	124.5
compared_to_2017	-0.7	1.7	0.1515	-0.03

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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. 3 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 OCHA Afghanistan 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_afghanistan_casualties,
  title     = {Afghanistan - Casualties},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-casualties},
  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.