year int64 2.03k 2.03k | iso3 stringclasses 1
value | adm0_en stringclasses 1
value | adm0_fr stringclasses 1
value | adm0_pcode stringclasses 1
value | adm1_en stringclasses 2
values | adm1_fr stringclasses 2
values | adm1_pcode stringclasses 2
values | adm2_fr stringclasses 2
values | adm2_pcode stringclasses 2
values | metropolis stringclasses 2
values | f_tl int64 1.88M 1.91M | m_tl int64 1.88M 1.91M | t_tl int64 3.76M 3.82M | f_00_04 int64 191k 202k | f_05_09 int64 182k 191k | f_10_14 int64 190k 190k | f_15_19 int64 177k 188k | f_20_24 int64 162k 170k | f_25_29 int64 140k 144k | f_30_34 int64 133k 134k | f_35_39 int64 147k 148k | f_40_44 int64 151k 156k | f_45_49 int64 121k 130k | f_50_54 int64 82.3k 92k | f_55_59 int64 59.7k 68k | f_60_64 int64 43.8k 50.6k | f_65_69 int64 32.1k 36.5k | f_70_74 int64 18.8k 22.9k | f_75_79 int64 8.96k 10.3k | f_80plus int64 6.55k 7.75k | m_00_04 int64 193k 199k | m_05_09 int64 183k 189k | m_10_14 int64 189k 192k | m_15_19 int64 177k 190k | m_20_24 int64 167k 172k | m_25_29 int64 140k 144k | m_30_34 int64 129k 129k | m_35_39 int64 137k 138k | m_40_44 int64 141k 145k | m_45_49 int64 124k 124k | m_50_54 int64 92.8k 102k | m_55_59 int64 66.9k 75.4k | m_60_64 int64 49.6k 55.1k | m_65_69 int64 34.5k 39.2k | m_70_74 int64 20.2k 24.7k | m_75_79 int64 8.87k 10.7k | m_80plus int64 4.83k 5.63k | t_00_04 int64 385k 401k | t_05_09 int64 365k 379k | t_10_14 int64 378k 382k | t_15_19 int64 354k 377k | t_20_24 int64 329k 343k | t_25_29 int64 281k 289k | t_30_34 int64 262k 262k | t_35_39 int64 284k 286k | t_40_44 int64 297k 297k | t_45_49 int64 245k 255k | t_50_54 int64 175k 194k | t_55_59 int64 127k 143k | t_60_64 int64 93.5k 106k | t_65_69 int64 66.6k 75.8k | t_70_74 int64 39k 47.6k | t_75_79 int64 17.8k 21k | t_80plus int64 11.4k 13.4k | esa_source stringclasses 1
value | esa_processed stringdate 2026-04-04 00:00:00 2026-04-04 00:00:00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,025 | CMR | Cameroon | Cameroun (le) | CM | Littoral | Littoral | CM005 | Wouri | CM005004 | Ville de Douala | 1,906,354 | 1,910,178 | 3,816,532 | 191,305 | 181,516 | 189,712 | 187,628 | 161,912 | 140,346 | 133,008 | 146,876 | 155,611 | 130,418 | 92,024 | 67,955 | 50,589 | 36,527 | 22,923 | 10,252 | 7,752 | 193,481 | 183,463 | 191,945 | 189,721 | 166,720 | 140,383 | 129,152 | 137,199 | 140,900 | 124,465 | 102,051 | 75,355 | 55,101 | 39,233 | 24,670 | 10,713 | 5,626 | 384,786 | 364,979 | 381,657 | 377,349 | 328,632 | 280,729 | 262,160 | 284,075 | 296,511 | 254,883 | 194,075 | 143,310 | 105,690 | 75,760 | 47,593 | 20,965 | 13,378 | HDX | 2026-04-04 |
2,025 | CMR | Cameroon | Cameroun (le) | CM | Centre | Centre | CM002 | Mfoundi | CM002007 | Ville de Yaoundé | 1,879,435 | 1,883,496 | 3,762,931 | 201,544 | 190,546 | 189,774 | 176,967 | 170,249 | 144,249 | 133,635 | 148,114 | 151,377 | 120,744 | 82,276 | 59,731 | 43,842 | 32,060 | 18,811 | 8,963 | 6,553 | 199,082 | 188,642 | 188,688 | 176,688 | 172,322 | 144,422 | 128,543 | 137,643 | 145,350 | 124,368 | 92,784 | 66,915 | 49,625 | 34,533 | 20,191 | 8,874 | 4,826 | 400,626 | 379,188 | 378,462 | 353,655 | 342,571 | 288,671 | 262,178 | 285,757 | 296,727 | 245,112 | 175,060 | 126,646 | 93,467 | 66,593 | 39,002 | 17,837 | 11,379 | HDX | 2026-04-04 |
Cameroon - Subnational Population Statistics | Africa (original)
Size category: n<1K - Formats: not declared - Sector: demographics_social - Engineered by Electric Sheep Africa
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: Cameroon - Subnational Population Statistics Publisher: UNFPA · Source: HDX · License: cc-by · Updated: 2026-01-01 Abstract Cameroon population statistics disaggregated by administrative levels (0-2), sex and age, projected for the year 2025. The dataset also includes historical data. REFERENCE YEAR 2025 These tables are suitable for database or GIS linkage to the Cameroon - Subnational Administrative Boundaries and Cameroon - Subnational Edge-matched Administrative… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-cmr.
Dataset Profile
| Field | Value |
|---|---|
| Hugging Face repo | electricsheepafrica/africa-cod-ps-cmr |
| Sector | demographics_social |
| Topic tags | humanitarian, hdx, electric-sheep-africa, baseline-population, demographics, gazetteer, gender-and-age-disaggregated-data-gadd, population, cmr |
| Modalities | tabular, text |
| Formats | not declared |
| Size category | n<1K |
| Countries | Cameroon |
| ISO3 coverage | CMR |
| Last modified on HF | 2026-04-04 14:27:02+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
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-cod-ps-cmr")
print(ds)
split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])
Convert To Pandas When Tabular
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, format.
- 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
- Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-cmr
- 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
@misc{electric_sheep_africa_africa_cod_ps_cmr_2026,
title = {Cameroon - Subnational Population Statistics | Africa (original)},
author = {original},
year = {2026},
url = {https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-cmr},
publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-cod-ps-cmr}}
}
License
Released under CC 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.
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