electricsheepafrica/africa-cod-ps-nam

Namibia - Subnational Population Statistics

Publisher: UNFPA · Source: HDX · License: cc-by-igo · Updated: 2025-04-08

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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 last updated on HDX on 2025-04-08. Geographic scope: NAM.

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	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

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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.

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Quick Start

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()

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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

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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

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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: 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.

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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_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)}
}

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