Africa · Geodata & Infrastructure
Collection
Geospatial and infrastructure data for Africa. HOT OpenStreetMap, COD admin boundaries, airports, and road networks. • 121 items • Updated
adm0_en string | adm0_pcode string | f_tl int64 | m_tl int64 | t_tl int64 | t_00_04 int64 | t_05_09 int64 | t_10_14 int64 | t_15_19 int64 | t_20_24 int64 | t_25_29 int64 | t_30_34 int64 | t_35_39 int64 | t_40_44 int64 | t_45_49 int64 | t_50_54 int64 | t_55_59 int64 | t_60_64 int64 | t_65_69 int64 | t_70_74 int64 | t_75_79 int64 | t_80plus int64 | esa_source string | esa_processed string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sierra Leone | SL | 3,893,510 | 4,015,077 | 7,907,338 | 1,046,571 | 1,236,489 | 945,186 | 974,071 | 738,369 | 677,122 | 483,634 | 469,383 | 333,835 | 270,529 | 208,515 | 123,323 | 125,583 | 82,206 | 73,039 | 44,302 | 75,152 | HDX | 2026-04-04 |
Sierra Leone - Subnational Population Statistics
Publisher: OCHA West and Central Africa (ROWCA) · Source: HDX · License: cc-by-igo · Updated: 2025-07-22
Abstract
Sierra Leone administrative levels 0 (country), 1 (province), 2 (district), and 3 (chiefdom) sex-disaggregated population statistics
REFERENCE YEAR: 2020 projections
Endorsed by the Information Management Working Group, March 2018.
These CSV tables are suitable for database or GIS linkage to the https://data.humdata.org/dataset/cod-ab-sle administrative level 0-3 shapefiles and geodatabase features.
Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-07-22. Geographic scope: SLE.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Demographics and population |
| Unit of observation | Tabular records |
| Rows (total) | 1 |
| Columns | 24 (20 numeric, 4 categorical, 0 datetime) |
| Train split | 0 rows |
| Test split | 0 rows |
| Geographic scope | SLE |
| Publisher | OCHA West and Central Africa (ROWCA) |
| HDX last updated | 2025-07-22 |
Variables
Identifier / Metadata — adm0_pcode (SL), esa_source (HDX), esa_processed (2026-04-04).
Other — adm0_en (Sierra Leone), f_tl (range 3893510.0–3893510.0), m_tl (range 4015077.0–4015077.0), t_tl (range 7907338.0–7907338.0), t_00_04 (range 1046571.0–1046571.0) and 16 others.
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-cod-ps-sle")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
adm0_en |
object | 0.0% | Sierra Leone |
adm0_pcode |
object | 0.0% | SL |
f_tl |
int64 | 0.0% | 3893510.0 – 3893510.0 (mean 3893510.0) |
m_tl |
int64 | 0.0% | 4015077.0 – 4015077.0 (mean 4015077.0) |
t_tl |
int64 | 0.0% | 7907338.0 – 7907338.0 (mean 7907338.0) |
t_00_04 |
int64 | 0.0% | 1046571.0 – 1046571.0 (mean 1046571.0) |
t_05_09 |
int64 | 0.0% | 1236489.0 – 1236489.0 (mean 1236489.0) |
t_10_14 |
int64 | 0.0% | 945186.0 – 945186.0 (mean 945186.0) |
t_15_19 |
int64 | 0.0% | 974071.0 – 974071.0 (mean 974071.0) |
t_20_24 |
int64 | 0.0% | 738369.0 – 738369.0 (mean 738369.0) |
t_25_29 |
int64 | 0.0% | 677122.0 – 677122.0 (mean 677122.0) |
t_30_34 |
int64 | 0.0% | 483634.0 – 483634.0 (mean 483634.0) |
t_35_39 |
int64 | 0.0% | 469383.0 – 469383.0 (mean 469383.0) |
t_40_44 |
int64 | 0.0% | 333835.0 – 333835.0 (mean 333835.0) |
t_45_49 |
int64 | 0.0% | 270529.0 – 270529.0 (mean 270529.0) |
t_50_54 |
int64 | 0.0% | 208515.0 – 208515.0 (mean 208515.0) |
t_55_59 |
int64 | 0.0% | 123323.0 – 123323.0 (mean 123323.0) |
t_60_64 |
int64 | 0.0% | 125583.0 – 125583.0 (mean 125583.0) |
t_65_69 |
int64 | 0.0% | 82206.0 – 82206.0 (mean 82206.0) |
t_70_74 |
int64 | 0.0% | 73039.0 – 73039.0 (mean 73039.0) |
t_75_79 |
int64 | 0.0% | 44302.0 – 44302.0 (mean 44302.0) |
t_80plus |
int64 | 0.0% | 75152.0 – 75152.0 (mean 75152.0) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-04 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
f_tl |
3893510.0 | 3893510.0 | 3893510.0 | 3893510.0 |
m_tl |
4015077.0 | 4015077.0 | 4015077.0 | 4015077.0 |
t_tl |
7907338.0 | 7907338.0 | 7907338.0 | 7907338.0 |
t_00_04 |
1046571.0 | 1046571.0 | 1046571.0 | 1046571.0 |
t_05_09 |
1236489.0 | 1236489.0 | 1236489.0 | 1236489.0 |
t_10_14 |
945186.0 | 945186.0 | 945186.0 | 945186.0 |
t_15_19 |
974071.0 | 974071.0 | 974071.0 | 974071.0 |
t_20_24 |
738369.0 | 738369.0 | 738369.0 | 738369.0 |
t_25_29 |
677122.0 | 677122.0 | 677122.0 | 677122.0 |
t_30_34 |
483634.0 | 483634.0 | 483634.0 | 483634.0 |
t_35_39 |
469383.0 | 469383.0 | 469383.0 | 469383.0 |
t_40_44 |
333835.0 | 333835.0 | 333835.0 | 333835.0 |
t_45_49 |
270529.0 | 270529.0 | 270529.0 | 270529.0 |
t_50_54 |
208515.0 | 208515.0 | 208515.0 | 208515.0 |
t_55_59 |
123323.0 | 123323.0 | 123323.0 | 123323.0 |
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. 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 OCHA West and Central Africa (ROWCA) 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.
Citation
@dataset{hdx_africa_cod_ps_sle,
title = {Sierra Leone - Subnational Population Statistics},
author = {OCHA West and Central Africa (ROWCA)},
year = {2025},
url = {https://data.humdata.org/dataset/cod-ps-sle},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.
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