Datasets:

electricsheepafrica
/

africa-mali-suivi-des-inondations

@@ -1,145 +1,147 @@
 ---
-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.*

 ---
+license: cc-by-4.0
 language:
 - en
 task_categories:
 - tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "climate-environment"
+- "parquet"
+- "text"
+- "humanitarian"
+- "hdx"
+- "affected-population"
+- "climate-weather"
+- "damage-assessment"
+- "flooding"
+- "hxl"
+- "migration"
+- "population"
+- "mli"
+- "climate"
+- "weather"
+pretty_name: "Mali: Suivi des Inondations | Africa (original)"
 ---
+# Mali: Suivi des Inondations | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** climate_environment - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-n%3C1K-blue)
+![sector](https://img.shields.io/badge/sector-climate_environment-green)
+![downloads](https://img.shields.io/badge/HF_downloads-18-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+Dataset context from the existing Hugging Face card: 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… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-mali-suivi-des-inondations`](https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations) |
+| Sector | climate_environment |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, affected-population, climate-weather, damage-assessment, flooding, hxl, migration, population, mli |
+| Modalities | `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Mali |
+| ISO3 coverage | `MLI` |
+| Last modified on HF | `2026-04-20 08:29:14+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
 ```python
 from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-mali-suivi-des-inondations")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations](https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
 ## Citation
 ```bibtex
+@misc{electric_sheep_africa_africa_mali_suivi_des_inondations_2026,
+  title        = {Mali: Suivi des Inondations | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mali-suivi-des-inondations}}
 }
 ```
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 ---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.