annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- affected-population
- climate-weather
- damage-assessment
- flooding
- hxl
- migration
- population
- mli
pretty_name: 'Mali: Suivi des Inondations'
dataset_info:
splits:
- name: train
num_examples: 16
- name: test
num_examples: 4
Mali: Suivi des Inondations
Publisher: OCHA Mali · Source: HDX · License: cc-by · Updated: 2025-05-05
Abstract
Les données contiennent les impacts causés par les inondations et les fortes pluies au Mali.
Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: MLI.
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) | 21 |
| Columns | 4 (1 numeric, 3 categorical, 0 datetime) |
| Train split | 16 rows |
| Test split | 4 rows |
| Geographic scope | MLI |
| Publisher | OCHA Mali |
| HDX last updated | 2025-05-05 |
Variables
Geographic — admin1_name (#adm1+name, Kayes, Nara).
Demographic — personnes_affectées (range 717.0–84458.0).
Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-18).
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-mali-suivi-des-inondations")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
admin1_name |
object | 0.0% | #adm1+name, Kayes, Nara |
personnes_affectées |
float64 | 4.8% | 717.0 – 84458.0 (mean 18472.1) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-18 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
personnes_affectées |
717.0 | 84458.0 | 18472.1 | 7373.5 |
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. 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 OCHA Mali 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_africa_mali_suivi_des_inondations,
title = {Mali: Suivi des Inondations},
author = {OCHA Mali},
year = {2025},
url = {https://data.humdata.org/dataset/mali-suivi-des-inondations},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.