Datasets:

electricsheepafrica
/

africa-cod-ps-nam

@@ -1,212 +1,141 @@
 ---
-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:
-- other
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- baseline-population
-- gazetteer
-- nam
-pretty_name: "Namibia - Subnational Population Statistics"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 0
-    - name: test
-      num_examples: 0
 ---
-# Namibia - Subnational Population Statistics
-**Publisher:** UNFPA · **Source:** [HDX](https://data.humdata.org/dataset/cod-ps-nam) · **License:** `cc-by-igo` · **Updated:** 2025-04-08
----
-## Abstract
-Namibia administrative level 0-2 sex and age disaggregated 2023 population statistic projections
-REFERENCE YEAR:  2023
-The CSV files are suitable for database or GIS linkage to the [Namibia administrative level 0-2 boundaries](https://data.humdata.org/dataset/cod-ab-nam) layers using the ADM0, ADM1, and ADM2_PCODE fields.
-Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-04-08. Geographic scope: **NAM**.
-*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)** | 1 |
-| **Columns** | 59 (55 numeric, 4 categorical, 0 datetime) |
-| **Train split** | 0 rows |
-| **Test split** | 0 rows |
-| **Geographic scope** | NAM |
-| **Publisher** | UNFPA |
-| **HDX last updated** | 2025-04-08 |
----
-## Variables
-**Geographic** — `year` (range 2023.0–2023.0), `iso3` (NAM).
-**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04).
-**Other** — `adm0_en` (Namibia), `f_tl` (range 1414564.0–1414564.0), `m_tl` (range 1362668.0–1362668.0), `t_tl` (range 2777232.0–2777232.0), `f_00_04` (range 161482.0–161482.0) and 50 others.
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-cod-ps-nam")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `year` | int64 | 0.0% | 2023.0 – 2023.0 (mean 2023.0) |
-| `iso3` | object | 0.0% | NAM |
-| `adm0_en` | object | 0.0% | Namibia |
-| `f_tl` | int64 | 0.0% | 1414564.0 – 1414564.0 (mean 1414564.0) |
-| `m_tl` | int64 | 0.0% | 1362668.0 – 1362668.0 (mean 1362668.0) |
-| `t_tl` | int64 | 0.0% | 2777232.0 – 2777232.0 (mean 2777232.0) |
-| `f_00_04` | int64 | 0.0% | 161482.0 – 161482.0 (mean 161482.0) |
-| `f_05_09` | int64 | 0.0% | 158236.0 – 158236.0 (mean 158236.0) |
-| `f_10_14` | int64 | 0.0% | 152756.0 – 152756.0 (mean 152756.0) |
-| `f_15_19` | int64 | 0.0% | 144224.0 – 144224.0 (mean 144224.0) |
-| `f_20_24` | int64 | 0.0% | 133498.0 – 133498.0 (mean 133498.0) |
-| `f_25_29` | int64 | 0.0% | 122089.0 – 122089.0 (mean 122089.0) |
-| `f_30_34` | int64 | 0.0% | 110111.0 – 110111.0 (mean 110111.0) |
-| `f_35_39` | int64 | 0.0% | 92105.0 – 92105.0 (mean 92105.0) |
-| `f_40_44` | int64 | 0.0% | 80564.0 – 80564.0 (mean 80564.0) |
-| `f_45_49` | int64 | 0.0% | 65192.0 – 65192.0 (mean 65192.0) |
-| `f_50_54` | int64 | 0.0% | 53518.0 – 53518.0 (mean 53518.0) |
-| `f_55_59` | int64 | 0.0% | 43633.0 – 43633.0 (mean 43633.0) |
-| `f_60_64` | int64 | 0.0% | 33931.0 – 33931.0 (mean 33931.0) |
-| `f_65_69` | int64 | 0.0% | 23509.0 – 23509.0 (mean 23509.0) |
-| `f_70_74` | int64 | 0.0% | 16524.0 – 16524.0 (mean 16524.0) |
-| `f_75_79` | int64 | 0.0% | 11200.0 – 11200.0 (mean 11200.0) |
-| `f_80plus` | int64 | 0.0% |  |
-| `m_00_04` | int64 | 0.0% |  |
-| `m_05_09` | int64 | 0.0% |  |
-| `m_10_14` | int64 | 0.0% |  |
-| `m_15_19` | int64 | 0.0% |  |
-| `m_20_24` | int64 | 0.0% |  |
-| `m_25_29` | int64 | 0.0% |  |
-| `m_30_34` | int64 | 0.0% |  |
-| `m_35_39` | int64 | 0.0% |  |
-| `m_40_44` | int64 | 0.0% |  |
-| `m_45_49` | int64 | 0.0% |  |
-| `m_50_54` | int64 | 0.0% |  |
-| `m_55_59` | int64 | 0.0% |  |
-| `m_60_64` | int64 | 0.0% |  |
-| `m_65_69` | int64 | 0.0% |  |
-| `m_70_74` | int64 | 0.0% |  |
-| `m_75_79` | int64 | 0.0% |  |
-| `m_80plus` | int64 | 0.0% |  |
-| `t_00_04` | int64 | 0.0% |  |
-| `t_05_09` | int64 | 0.0% |  |
-| `t_10_14` | int64 | 0.0% |  |
-| `t_15_19` | int64 | 0.0% |  |
-| `t_20_24` | int64 | 0.0% |  |
-| `t_25_29` | int64 | 0.0% |  |
-| `t_30_34` | int64 | 0.0% |  |
-| `t_35_39` | int64 | 0.0% |  |
-| `t_40_44` | int64 | 0.0% |  |
-| `t_45_49` | int64 | 0.0% |  |
-| `t_50_54` | int64 | 0.0% |  |
-| `t_55_59` | int64 | 0.0% |  |
-| `t_60_64` | int64 | 0.0% |  |
-| `t_65_69` | int64 | 0.0% |  |
-| `t_70_74` | int64 | 0.0% |  |
-| `t_75_79` | int64 | 0.0% |  |
-| `t_80plus` | int64 | 0.0% |  |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-04 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `year` | 2023.0 | 2023.0 | 2023.0 | 2023.0 |
-| `f_tl` | 1414564.0 | 1414564.0 | 1414564.0 | 1414564.0 |
-| `m_tl` | 1362668.0 | 1362668.0 | 1362668.0 | 1362668.0 |
-| `t_tl` | 2777232.0 | 2777232.0 | 2777232.0 | 2777232.0 |
-| `f_00_04` | 161482.0 | 161482.0 | 161482.0 | 161482.0 |
-| `f_05_09` | 158236.0 | 158236.0 | 158236.0 | 158236.0 |
-| `f_10_14` | 152756.0 | 152756.0 | 152756.0 | 152756.0 |
-| `f_15_19` | 144224.0 | 144224.0 | 144224.0 | 144224.0 |
-| `f_20_24` | 133498.0 | 133498.0 | 133498.0 | 133498.0 |
-| `f_25_29` | 122089.0 | 122089.0 | 122089.0 | 122089.0 |
-| `f_30_34` | 110111.0 | 110111.0 | 110111.0 | 110111.0 |
-| `f_35_39` | 92105.0 | 92105.0 | 92105.0 | 92105.0 |
-| `f_40_44` | 80564.0 | 80564.0 | 80564.0 | 80564.0 |
-| `f_45_49` | 65192.0 | 65192.0 | 65192.0 | 65192.0 |
-| `f_50_54` | 53518.0 | 53518.0 | 53518.0 | 53518.0 |
----
-## 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) with >80% missing values were removed: `adm0_pcode`. 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 UNFPA 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/cod-ps-nam) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_cod_ps_nam,
-  title     = {Namibia - Subnational Population Statistics},
-  author    = {UNFPA},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/cod-ps-nam},
-  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"
+- "humanitarian-development"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "baseline-population"
+- "gazetteer"
+- "nam"
+pretty_name: "Namibia - Subnational Population Statistics | Africa (original)"
 ---
+# Namibia - Subnational Population Statistics | 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-26-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: Namibia - Subnational Population Statistics Publisher: UNFPA · Source: HDX · License: cc-by-igo · Updated: 2025-04-08 Abstract Namibia administrative level 0-2 sex and age disaggregated 2023 population statistic projections REFERENCE YEAR: 2023 The CSV files are suitable for database or GIS linkage to the Namibia administrative level 0-2 boundaries layers using the ADM0, ADM1, and ADM2_PCODE fields. Each row in this dataset represents country-level aggregates. Data was… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-nam.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-cod-ps-nam`](https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-nam) |
+| Sector | humanitarian_development |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, baseline-population, gazetteer, nam |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Namibia |
+| ISO3 coverage | `NAM` |
+| Last modified on HF | `2026-04-04 13:50:46+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-cod-ps-nam")
+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-cod-ps-nam](https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-nam)
+- **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_cod_ps_nam_2026,
+  title        = {Namibia - Subnational Population Statistics | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-nam},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-nam}}
 }
 ```
+## 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`.