electricsheepafrica/africa-cod-ps-cpv

Cabo Verde - Subnational Population Statistics

Publisher: UNFPA · Source: HDX · License: cc-by-igo · Updated: 2025-05-05

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Abstract

Cabo Verde administrative level 0-1 and island sex and age disaggregated 2021 projected population statistics

UPDATED 24 January 2023 to correctly assign ADM1 feature "São Salvador do Mundo" [CV19] to island "Santiago" [CV6].

REFERENCE YEAR: 2022

Gazetteer available to administrative level 4

The administrative level 0, 1, and island tables are suitable for database or GIS linkage to the Cabo Verde - Administrative Boundaries administrative level 0, 1, and 2 shapefiles using the ADM0, ADM1, and ISL_PCODE items.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-05-05. Geographic scope: CPV.

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)	1
Columns	69 (64 numeric, 5 categorical, 0 datetime)
Train split	0 rows
Test split	0 rows
Geographic scope	CPV
Publisher	UNFPA
HDX last updated	2025-05-05

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Variables

Geographic — year (range 2022.0–2022.0), iso3 (CPV).

Identifier / Metadata — adm0_pcode (CV), esa_source (HDX), esa_processed (2026-04-04).

Other — adm0_pt (Cabo Verde), f_tl (range 280362.0–280362.0), m_tl (range 289150.0–289150.0), t_tl (range 569523.0–569523.0), f_00_04 (range 25262.0–25262.0) and 59 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cod-ps-cpv")
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%	2022.0 – 2022.0 (mean 2022.0)
iso3	object	0.0%	CPV
adm0_pt	object	0.0%	Cabo Verde
adm0_pcode	object	0.0%	CV
f_tl	int64	0.0%	280362.0 – 280362.0 (mean 280362.0)
m_tl	int64	0.0%	289150.0 – 289150.0 (mean 289150.0)
t_tl	int64	0.0%	569523.0 – 569523.0 (mean 569523.0)
f_00_04	int64	0.0%	25262.0 – 25262.0 (mean 25262.0)
f_05_09	int64	0.0%	25359.0 – 25359.0 (mean 25359.0)
f_10_14	int64	0.0%	24532.0 – 24532.0 (mean 24532.0)
f_15_19	int64	0.0%	23498.0 – 23498.0 (mean 23498.0)
f_20_24	int64	0.0%	21749.0 – 21749.0 (mean 21749.0)
f_25_29	int64	0.0%	24670.0 – 24670.0 (mean 24670.0)
f_30_34	int64	0.0%	25924.0 – 25924.0 (mean 25924.0)
f_35_39	int64	0.0%	22422.0 – 22422.0 (mean 22422.0)
f_40_44	int64	0.0%	17165.0 – 17165.0 (mean 17165.0)
f_45_49	int64	0.0%	13738.0 – 13738.0 (mean 13738.0)
f_50_54	int64	0.0%	12504.0 – 12504.0 (mean 12504.0)
f_55_59	int64	0.0%	12390.0 – 12390.0 (mean 12390.0)
f_60_64	int64	0.0%	9907.0 – 9907.0 (mean 9907.0)
f_65_69	int64	0.0%	7236.0 – 7236.0 (mean 7236.0)
f_70_74	int64	0.0%	3912.0 – 3912.0 (mean 3912.0)
f_75_79	int64	0.0%	2797.0 – 2797.0 (mean 2797.0)
f_80_84	int64	0.0%
f_85_89	int64	0.0%
f_90_94	int64	0.0%
f_95plus	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_80_84	int64	0.0%
m_85_89	int64	0.0%
m_90_94	int64	0.0%
m_95plus	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_80_84	int64	0.0%
t_85_89	int64	0.0%
t_90_94	int64	0.0%
t_95plus	int64	0.0%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-04

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Numeric Summary

Column	Min	Max	Mean	Median
year	2022.0	2022.0	2022.0	2022.0
f_tl	280362.0	280362.0	280362.0	280362.0
m_tl	289150.0	289150.0	289150.0	289150.0
t_tl	569523.0	569523.0	569523.0	569523.0
f_00_04	25262.0	25262.0	25262.0	25262.0
f_05_09	25359.0	25359.0	25359.0	25359.0
f_10_14	24532.0	24532.0	24532.0	24532.0
f_15_19	23498.0	23498.0	23498.0	23498.0
f_20_24	21749.0	21749.0	21749.0	21749.0
f_25_29	24670.0	24670.0	24670.0	24670.0
f_30_34	25924.0	25924.0	25924.0	25924.0
f_35_39	22422.0	22422.0	22422.0	22422.0
f_40_44	17165.0	17165.0	17165.0	17165.0
f_45_49	13738.0	13738.0	13738.0	13738.0
f_50_54	12504.0	12504.0	12504.0	12504.0

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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. 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_cpv,
  title     = {Cabo Verde - Subnational Population Statistics},
  author    = {UNFPA},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cod-ps-cpv},
  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.