electricsheepafrica/africa-climate-change-impact-in-niger

Disaster Loss Data for Niger

Publisher: United Nations Office for Disaster Risk Reduction (UNDRR) · Source: HDX · License: cc-by-igo · Updated: 2023-05-16

---

Abstract

Number of Deaths, Injured, Missing, Houses Destroyed, Houses Damaged, Victims Affected, Relocated, Evacuated, Losses and Damages in crops by climate change event

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the date_ymd column(s). Geographic scope: NER.

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

---

Dataset Characteristics

Domain	Climate and environment
Unit of observation	First-level administrative unit observations
Rows (total)	3,700
Columns	30 (21 numeric, 8 categorical, 1 datetime)
Train split	2,960 rows
Test split	740 rows
Geographic scope	NER
Publisher	United Nations Office for Disaster Risk Reduction (UNDRR)
HDX last updated	2023-05-16

---

Variables

Geographic — code_region (range 1.0–8.0), region (TILLABERI, TAHOUA, ZINDER), location (Tillabéry, Kollo, Filingué), date_ymd, houses_destroyed (range 0.0–3542.0).

Demographic — houses_damaged (range 0.0–218.0), damages_in_crops_ha (range 0.0–370000.0), damages_in_roads_mts (range 0.0–0.0).

Outcome / Measurement — deaths (range 0.0–666.0), affected (range 0.0–445361.0).

Identifier / Metadata — code_department (range 101.0–800.0), code_commune (range 10101.0–80005.0), esa_source (HDX), esa_processed (2026-04-17).

Other — serial (range 1.0–3700.0), event (EPIDEMIC, FLOOD, EPIZOOTY), department (TILLABERI, MADAROUNFA, KOLLO), commune (TILLABERI, MIRRIAH, DIFFA), description_of_cause (Rougeole, suite à une importante quantité de pluie tombée, manque de pluie) and 11 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-climate-change-impact-in-niger")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
serial	int64	0.0%	1.0 – 3700.0 (mean 1848.7641)
event	object	0.0%	EPIDEMIC, FLOOD, EPIZOOTY
code_region	int64	0.0%	1.0 – 8.0 (mean 4.6811)
region	object	0.0%	TILLABERI, TAHOUA, ZINDER
code_department	float64	2.9%	101.0 – 800.0 (mean 474.3403)
department	object	2.9%	TILLABERI, MADAROUNFA, KOLLO
code_commune	float64	8.4%	10101.0 – 80005.0 (mean 47901.1209)
commune	object	8.4%	TILLABERI, MIRRIAH, DIFFA
location	object	37.9%	Tillabéry, Kollo, Filingué
date_ymd	datetime64[ns]	0.5%
description_of_cause	object	52.9%	Rougeole, suite à une importante quantité de pluie tombée, manque de pluie
datacards	int64	0.0%	1.0 – 1.0 (mean 1.0)
deaths	float64	0.1%	0.0 – 666.0 (mean 2.8755)
injured	int64	0.0%	0.0 – 139.0 (mean 0.1505)
missing	int64	0.0%	0.0 – 0.0 (mean 0.0)
houses_destroyed	int64	0.0%	0.0 – 3542.0 (mean 19.4422)
houses_damaged	int64	0.0%	0.0 – 218.0 (mean 0.4389)
victims	int64	0.0%	0.0 – 567954.0 (mean 4134.3024)
affected	int64	0.0%	0.0 – 445361.0 (mean 373.7105)
relocated	int64	0.0%	0.0 – 13146.0 (mean 16.1114)
evacuated	int64	0.0%	0.0 – 0.0 (mean 0.0)
losses_usd	int64	0.0%	0.0 – 59000.0 (mean 21.4865)
losses_local	int64	0.0%	0.0 – 1794000000.0 (mean 623402.1108)
education_centers	int64	0.0%	0.0 – 5.0 (mean 0.0054)
hospitals	int64	0.0%	0.0 – 1.0 (mean 0.0003)
damages_in_crops_ha	float64	0.0%	0.0 – 370000.0 (mean 711.8333)
damages_in_roads_mts	int64	0.0%	0.0 – 0.0 (mean 0.0)
lost_cattle	int64	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

---

Numeric Summary

Column	Min	Max	Mean	Median
serial	1.0	3700.0	1848.7641	1849.5
code_region	1.0	8.0	4.6811	5.0
code_department	101.0	800.0	474.3403	505.0
code_commune	10101.0	80005.0	47901.1209	50605.0
datacards	1.0	1.0	1.0	1.0
deaths	0.0	666.0	2.8755	0.0
injured	0.0	139.0	0.1505	0.0
missing	0.0	0.0	0.0	0.0
houses_destroyed	0.0	3542.0	19.4422	0.0
houses_damaged	0.0	218.0	0.4389	0.0
victims	0.0	567954.0	4134.3024	0.0
affected	0.0	445361.0	373.7105	0.0
relocated	0.0	13146.0	16.1114	0.0
evacuated	0.0	0.0	0.0	0.0
losses_usd	0.0	59000.0	21.4865	0.0

---

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) with >80% missing values were removed: cause, source, magnitude, glidenumber, other_sectors. 1 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 United Nations Office for Disaster Risk Reduction (UNDRR) 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: location, description_of_cause.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

---

Citation

@dataset{hdx_africa_climate_change_impact_in_niger,
  title     = {Disaster Loss Data for Niger},
  author    = {United Nations Office for Disaster Risk Reduction (UNDRR)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/climate-change-impact-in-niger},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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