Datasets:

electricsheepafrica
/

africa-who-demand-for-family-planning-satisfied-modern-and-traditional

@@ -1,113 +1,139 @@
 ---
 license: cc-by-4.0
-task_categories:
-  - tabular-classification
-  - tabular-regression
 language:
-  - en
-tags:
-  - africa
-  - health
-  - who
-  - gho
-  - "fps"
-pretty_name: "Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%)"
 size_categories:
-  - 1K<n<10K
 ---
-# Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%)
-**Indicator code:** `fps`
-**HuggingFace slug:** `electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional`
-**Source:** [WHO Global Health Observatory](https://www.who.int/data/gho/data/indicators/indicator-details/GHO/fps)
-**License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — WHO Open Data
----
-## Dataset Description
-This dataset contains country-level observations for the WHO GHO indicator **"Demand for family planning satisfied - modern and traditional methods (%)"** (`fps`) across African nations, spanning 1993–2019. It is part of the [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) collection — a unified, ML-ready repository of African data.
-Data is sourced directly from the WHO Global Health Observatory OData API and repackaged as Parquet files with a consistent schema. All values are drawn from `NumericValue` (the float-precision field), not the display string. Confidence interval bounds (`value_low`, `value_high`) are included where available.
----
-## Coverage
-| | |
-|---|---|
-| **Countries** | 40 African nations |
-| **Years** | 1993 – 2019 |
-| **Total rows** | 5,528 |
-| **Region filter** | WHO AFRO (`ParentLocationCode = 'AFR'`) |
-**Countries included:** AGO, BDI, BEN, BFA, CAF, CIV, CMR, COD, COG, COM, DZA, ETH, GAB, GHA, GIN, GMB, GNB, KEN, LBR, LSO … and 20 more
----
-## Sub-dimensions
-- **AGEGROUP**: AGEGROUP_YEARS15-19, AGEGROUP_YEARS20-49
-- **DHSMICSGEOREGION**: DHSMICSGEOREGION_AGODHS201501, DHSMICSGEOREGION_AGODHS201502, DHSMICSGEOREGION_AGODHS201503, DHSMICSGEOREGION_AGODHS201504, DHSMICSGEOREGION_AGODHS201505, DHSMICSGEOREGION_AGODHS201506, DHSMICSGEOREGION_AGODHS201507, DHSMICSGEOREGION_AGODHS201508, DHSMICSGEOREGION_AGODHS201509, DHSMICSGEOREGION_AGODHS201510 …
-- **EDUCATIONLEVEL**: EDUCATIONLEVEL_NHLM, EDUCATIONLEVEL_PRLM, EDUCATIONLEVEL_SHLM
-- **RESIDENCEAREATYPE**: RESIDENCEAREATYPE_RUR, RESIDENCEAREATYPE_URB
-- **WEALTHDECILE**: WEALTHDECILE_WEALTHDECILE01, WEALTHDECILE_WEALTHDECILE02, WEALTHDECILE_WEALTHDECILE03, WEALTHDECILE_WEALTHDECILE04, WEALTHDECILE_WEALTHDECILE05, WEALTHDECILE_WEALTHDECILE06, WEALTHDECILE_WEALTHDECILE07, WEALTHDECILE_WEALTHDECILE08, WEALTHDECILE_WEALTHDECILE09, WEALTHDECILE_WEALTHDECILE10
-- **WEALTHQUINTILE**: WEALTHQUINTILE_WQ1, WEALTHQUINTILE_WQ2, WEALTHQUINTILE_WQ3, WEALTHQUINTILE_WQ4, WEALTHQUINTILE_WQ5
-When an indicator is stratified (e.g., by sex or age group), each unique combination of country × year × dimension produces a separate row. Filter on `dim1` / `dim2` for the stratum you need, or aggregate across strata.
----
-## Schema
-| Column | Type | Description |
-|--------|------|-------------|
-| `indicator_code` | string | GHO indicator code (e.g., `fps`) |
-| `country_iso3` | string | ISO 3166-1 alpha-3 country code |
-| `who_region` | string | WHO region code (always `AFR` here) |
-| `year` | int | Observation year |
-| `value_numeric` | float | Point estimate (primary ML target) |
-| `value_low` | float | Lower confidence bound (if available) |
-| `value_high` | float | Upper confidence bound (if available) |
-| `value_display` | string | Formatted display string, e.g. `"58.3 [57.7–59.0]"` |
-| `dim1_type` | string | Dimension 1 type, e.g. `SEX`, `RESIDENCEAREATYPE` |
-| `dim1` | string | Dimension 1 value, e.g. `SEX_BTSX`, `RURAL` |
-| `dim2_type` | string | Dimension 2 type (if present) |
-| `dim2` | string | Dimension 2 value (if present) |
-| `last_updated` | string | WHO data last-updated timestamp |
----
-## Usage
-```python
-from datasets import load_dataset
-ds = load_dataset("electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional")
-df = ds["train"].to_pandas()
-# Both-sexes, national level only
-national = df[df.get("dim1", "").str.endswith("_BTSX") | df.get("dim1", pd.Series()).isna()]
-# Time series for one country
-kenya = df[df["country_iso3"] == "KEN"].sort_values("year")
-```
----
 ## Citation
 ```bibtex
-@misc{who_gho_fps,
-  title     = {WHO Global Health Observatory: Demand for family planning satisfied - modern and traditional methods (%)},
-  author    = {World Health Organization},
-  year      = {2019},
-  url       = {https://www.who.int/data/gho/data/indicators/indicator-details/GHO/fps},
-  note      = {Repackaged by Electric Sheep Africa}
 }
 ```
 ---
-_Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) from WHO GHO open data. Original data © World Health Organization, licensed CC BY 4.0._

 ---
 license: cc-by-4.0
 language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
+- 1K<n<10K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "who"
+- "gho"
+- "fps"
+pretty_name: "Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) | Africa (World Health Organization)"
 ---
+# Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) | Africa (World Health Organization)
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-21-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
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+Dataset context from the existing Hugging Face card: Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) Indicator code: fps HuggingFace slug: electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional Source: WHO Global Health Observatory License: CC BY 4.0 — WHO Open Data Dataset Description This dataset contains country-level observations for the WHO GHO indicator "Demand for family planning satisfied - modern and traditional methods (%)" (fps)… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional`](https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional) |
+| Sector | health |
+| Topic tags | health, who, gho, fps |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-05-02 13:17:26+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-who-demand-for-family-planning-satisfied-modern-and-traditional")
+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: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** World Health Organization
+- **Publisher/source attribution:** WHO public data
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional](https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional)
+- **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_who_demand_for_family_planning_satisfied_modern_and_traditional_2026,
+  title        = {Africa — WHO GHO: Demand for family planning satisfied - modern and traditional methods (%) | Africa (World Health Organization)},
+  author       = {WHO public data},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-who-demand-for-family-planning-satisfied-modern-and-traditional}}
 }
 ```
+## 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`.