README.md

---
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](https://data.humdata.org/dataset/mali-suivi-des-inondations) · **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](https://huggingface.co/electricsheepafrica).*

---

## 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

```python
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](https://data.humdata.org/dataset/mali-suivi-des-inondations) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*