electricsheepafrica/africa-world-bank-public-sector-indicators-for-central-african-republic

Central African Republic - Public Sector

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

---

Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: CAF.

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

---

Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	2,048
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,638 rows
Test split	409 rows
Geographic scope	CAF
Publisher	World Bank Group
HDX last updated	2026-03-27

---

Variables

Geographic — country_name (Central African Republic), country_iso3 (CAF), year (range 1961.0–2024.0).

Outcome / Measurement — value (range -88741648960.0–187134432970.651).

Identifier / Metadata — indicator_name (Armed forces personnel, total, Armed forces personnel (% of total labor force), Military expenditure (current USD)), indicator_code (MS.MIL.TOTL.P1, MS.MIL.TOTL.TF.ZS, MS.MIL.XPND.CD), esa_source (HDX), esa_processed (2026-04-16).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-public-sector-indicators-for-central-african-republic")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Central African Republic
country_iso3	object	0.0%	CAF
year	int64	0.0%	1961.0 – 2024.0 (mean 2011.9648)
indicator_name	object	0.0%	Armed forces personnel, total, Armed forces personnel (% of total labor force), Military expenditure (current USD)
indicator_code	object	0.0%	MS.MIL.TOTL.P1, MS.MIL.TOTL.TF.ZS, MS.MIL.XPND.CD
value	float64	0.0%	-88741648960.0 – 187134432970.651 (mean 4231414681.9686)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-16

---

Numeric Summary

Column	Min	Max	Mean	Median
year	1961.0	2024.0	2011.9648	2013.0
value	-88741648960.0	187134432970.651	4231414681.9686	3.8835

---

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 World Bank Group 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_world_bank_public_sector_indicators_for_central_african_republic,
  title     = {Central African Republic - Public Sector},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-public-sector-indicators-for-central-african-republic},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

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