electricsheepafrica/africa-cod-ps-cmr

Cameroon - Subnational Population Statistics

Publisher: UNFPA · Source: HDX · License: cc-by · Updated: 2026-01-01

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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 Boundaries layers using the ADM0, and ADM1_PCODE fields.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-01-01. Geographic scope: CMR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	2
Columns	67 (55 numeric, 12 categorical, 0 datetime)
Train split	1 rows
Test split	0 rows
Geographic scope	CMR
Publisher	UNFPA
HDX last updated	2026-01-01

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Variables

Geographic — year (range 2025.0–2025.0), iso3 (CMR).

Identifier / Metadata — adm0_pcode (CM), adm1_pcode (CM005, CM002), adm2_pcode (CM005004, CM002007), esa_source, esa_processed.

Other — adm0_en (Cameroon), adm0_fr (Cameroun (le)), adm1_en (Littoral, Centre), adm1_fr (Littoral, Centre), adm2_fr (Wouri, Mfoundi) and 55 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cod-ps-cmr")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
year	int64	0.0%	2025.0 – 2025.0 (mean 2025.0)
iso3	object	0.0%	CMR
adm0_en	object	0.0%	Cameroon
adm0_fr	object	0.0%	Cameroun (le)
adm0_pcode	object	0.0%	CM
adm1_en	object	0.0%	Littoral, Centre
adm1_fr	object	0.0%	Littoral, Centre
adm1_pcode	object	0.0%	CM005, CM002
adm2_fr	object	0.0%	Wouri, Mfoundi
adm2_pcode	object	0.0%	CM005004, CM002007
metropolis	object	0.0%	Ville de Douala, Ville de Yaoundé
f_tl	int64	0.0%	1879435.0 – 1906354.0 (mean 1892894.5)
m_tl	int64	0.0%	1883496.0 – 1910178.0 (mean 1896837.0)
t_tl	int64	0.0%	3762931.0 – 3816532.0 (mean 3789731.5)
f_00_04	int64	0.0%	191305.0 – 201544.0 (mean 196424.5)
f_05_09	int64	0.0%	181516.0 – 190546.0 (mean 186031.0)
f_10_14	int64	0.0%	189712.0 – 189774.0 (mean 189743.0)
f_15_19	int64	0.0%	176967.0 – 187628.0 (mean 182297.5)
f_20_24	int64	0.0%	161912.0 – 170249.0 (mean 166080.5)
f_25_29	int64	0.0%	140346.0 – 144249.0 (mean 142297.5)
f_30_34	int64	0.0%	133008.0 – 133635.0 (mean 133321.5)
f_35_39	int64	0.0%	146876.0 – 148114.0 (mean 147495.0)
f_40_44	int64	0.0%	151377.0 – 155611.0 (mean 153494.0)
f_45_49	int64	0.0%	120744.0 – 130418.0 (mean 125581.0)
f_50_54	int64	0.0%	82276.0 – 92024.0 (mean 87150.0)
f_55_59	int64	0.0%	59731.0 – 67955.0 (mean 63843.0)
f_60_64	int64	0.0%	43842.0 – 50589.0 (mean 47215.5)
f_65_69	int64	0.0%	32060.0 – 36527.0 (mean 34293.5)
f_70_74	int64	0.0%	18811.0 – 22923.0 (mean 20867.0)
f_75_79	int64	0.0%	8963.0 – 10252.0 (mean 9607.5)
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%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year	2025.0	2025.0	2025.0	2025.0
f_tl	1879435.0	1906354.0	1892894.5	1892894.5
m_tl	1883496.0	1910178.0	1896837.0	1896837.0
t_tl	3762931.0	3816532.0	3789731.5	3789731.5
f_00_04	191305.0	201544.0	196424.5	196424.5
f_05_09	181516.0	190546.0	186031.0	186031.0
f_10_14	189712.0	189774.0	189743.0	189743.0
f_15_19	176967.0	187628.0	182297.5	182297.5
f_20_24	161912.0	170249.0	166080.5	166080.5
f_25_29	140346.0	144249.0	142297.5	142297.5
f_30_34	133008.0	133635.0	133321.5	133321.5
f_35_39	146876.0	148114.0	147495.0	147495.0
f_40_44	151377.0	155611.0	153494.0	153494.0
f_45_49	120744.0	130418.0	125581.0	125581.0
f_50_54	82276.0	92024.0	87150.0	87150.0

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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: adm2_en. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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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 for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cod_ps_cmr,
  title     = {Cameroon - Subnational Population Statistics},
  author    = {UNFPA},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/cod-ps-cmr},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.