Datasets:

electricsheepafrica
/

africa-population-mauritania

@@ -1,175 +1,144 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: other
-multilinguality:
-- monolingual
-size_categories:
-- n<1K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- affected-population
-- economics
-- fatalities
-- hxl
-- natural-disasters
-- mrt
-pretty_name: "EM-DAT - Country Profiles, Mauritania"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 18
-    - name: test
-      num_examples: 4
 ---
-# EM-DAT - Country Profiles, Mauritania
-**Publisher:** Centre for Research on the Epidemiology of Disasters · **Source:** [HDX](https://data.humdata.org/dataset/emdat-country-profiles-mrt) · **License:** `hdx-other` · **Updated:** 2026-04-24
----
-## Abstract
-# Aggregated figures for natural hazard related events in EM-DAT: *Mauritania*
-Documentation on the Country Profiles [available here](https://doc.emdat.be/docs/data-accessibility/#the-humanitarian-data-exchange-hdx-em-dat-country-profiles)
-How to cite the EM-DAT Project [here](https://doc.emdat.be/docs/introduction/#how-to-cite)
-Main dataset on HDX: [EM-DAT - Country Profiles](https://data.humdata.org/dataset/emdat-country-profiles)
-More on the EM-DAT database :  [website](https://www.emdat.be) / [data portal](https://public.emdat.be)
-Each line corresponds to a given combination of **year, country, disaster subtype** and reports figures for :
-- number of disasters
-- total number of people affected
-- total number of deaths
-- economic losses (original value and adjusted)
----
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-04-24. Geographic scope: **MRT**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Demographics and population |
-| **Unit of observation** | Country-level aggregates |
-| **Rows (total)** | 23 |
-| **Columns** | 13 (5 numeric, 8 categorical, 0 datetime) |
-| **Train split** | 18 rows |
-| **Test split** | 4 rows |
-| **Geographic scope** | MRT |
-| **Publisher** | Centre for Research on the Epidemiology of Disasters |
-| **HDX last updated** | 2026-04-24 |
----
-## Variables
-**Geographic** — `year` (range 2001.0–2023.0), `country` (Mauritania, #country +name), `iso` (MRT, #country +code), `disaster_type` (Flood, Drought, #cause +type), `disaster_subtype` (Riverine flood, Drought, Flood (General)).
-**Outcome / Measurement** — `total_events` (range 1.0–2.0), `total_affected` (range 1619.0–3893774.0), `total_deaths` (range 1.0–25.0).
-**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-29).
-**Other** — `disaster_group` (Natural, #cause +group), `disaster_subroup` (Hydrological, Climatological, #cause +subgroup), `cpi` (range 56.4466–97.135).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-population-mauritania")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `year` | float64 | 4.3% | 2001.0 – 2023.0 (mean 2011.0909) |
-| `country` | object | 0.0% | Mauritania, #country +name |
-| `iso` | object | 0.0% | MRT, #country +code |
-| `disaster_group` | object | 0.0% | Natural, #cause +group |
-| `disaster_subroup` | object | 0.0% | Hydrological, Climatological, #cause +subgroup |
-| `disaster_type` | object | 0.0% | Flood, Drought, #cause +type |
-| `disaster_subtype` | object | 0.0% | Riverine flood, Drought, Flood (General) |
-| `total_events` | float64 | 4.3% | 1.0 – 2.0 (mean 1.0909) |
-| `total_affected` | float64 | 4.3% | 1619.0 – 3893774.0 (mean 402097.3182) |
-| `total_deaths` | float64 | 52.2% | 1.0 – 25.0 (mean 8.9091) |
-| `cpi` | float64 | 4.3% | 56.4466 – 97.135 (mean 71.4836) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-29 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `year` | 2001.0 | 2023.0 | 2011.0909 | 2010.0 |
-| `total_events` | 1.0 | 2.0 | 1.0909 | 1.0 |
-| `total_affected` | 1619.0 | 3893774.0 | 402097.3182 | 24250.0 |
-| `total_deaths` | 1.0 | 25.0 | 8.9091 | 8.0 |
-| `cpi` | 56.4466 | 97.135 | 71.4836 | 69.5133 |
----
-## 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`. 2 column(s) with >80% missing values were removed: `total_damage_usd_original`, `total_damage_usd_adjusted`. 4 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 Centre for Research on the Epidemiology of Disasters 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: `total_deaths`.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/emdat-country-profiles-mrt) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_population_mauritania,
-  title     = {EM-DAT - Country Profiles, Mauritania},
-  author    = {Centre for Research on the Epidemiology of Disasters},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/emdat-country-profiles-mrt},
-  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: other
 language:
 - en
 task_categories:
 - tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "humanitarian-development"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "affected-population"
+- "economics"
+- "fatalities"
+- "hxl"
+- "natural-disasters"
+- "mrt"
+pretty_name: "EM-DAT - Country Profiles, Mauritania | Africa (original)"
 ---
+# EM-DAT - Country Profiles, Mauritania | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** humanitarian_development - *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-humanitarian_development-green)
+![downloads](https://img.shields.io/badge/HF_downloads-9-orange)
+![license](https://img.shields.io/badge/license-other-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: EM-DAT - Country Profiles, Mauritania Publisher: Centre for Research on the Epidemiology of Disasters · Source: HDX · License: hdx-other · Updated: 2026-04-24 Abstract Aggregated figures for natural hazard related events in EM-DAT: Mauritania Documentation on the Country Profiles available here How to cite the EM-DAT Project here Main dataset on HDX: EM-DAT - Country Profiles More on the EM-DAT database : website / data portal Each line corresponds to a given… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-population-mauritania`](https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania) |
+| Sector | humanitarian_development |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, affected-population, economics, fatalities, hxl, natural-disasters, mrt |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Mauritania |
+| ISO3 coverage | `MRT` |
+| Last modified on HF | `2026-04-29 17:55:43+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-population-mauritania")
+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:** Source-specific or other license
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania](https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania)
+- **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_population_mauritania_2026,
+  title        = {EM-DAT - Country Profiles, Mauritania | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-population-mauritania}}
 }
 ```
+## License
+Released under Source-specific or other license.
+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`.