electricsheepasia/asia-afghanistan-casualties

Afghanistan - Casualties

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

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](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

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).

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()

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 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.

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.

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)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.