electricsheepafrica/africa-world-bank-external-debt-indicators-for-cabo-verde

Cabo Verde - External Debt

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

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Abstract

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

Debt statistics provide a detailed picture of debt stocks and flows of developing countries. Data presented as part of the Quarterly External Debt Statistics takes a closer look at the external debt of high-income countries and emerging markets to enable a more complete understanding of global financial flows. The Quarterly Public Sector Debt database provides further data on public sector valuation methods, debt instruments, and clearly defined tiers of debt for central, state and local government, as well as extra-budgetary agencies and funds. Data are gathered from national statistical organizations and central banks as well as by various major multilateral institutions and World Bank staff.

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

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

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Dataset Characteristics

Domain	Market and price monitoring
Unit of observation	Country-level aggregates
Rows (total)	2,088
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,670 rows
Test split	417 rows
Geographic scope	CPV
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Cabo Verde), country_iso3 (CPV), year (range 1961.0–2024.0).

Outcome / Measurement — value (range -304537748.8993–2678779840.2111).

Identifier / Metadata — indicator_name (Grants, excluding technical cooperation (BoP, current US$), Net ODA received per capita (current US$), Net official development assistance received (current US$)), indicator_code (BX.GRT.EXTA.CD.WD, DT.ODA.ODAT.PC.ZS, DT.ODA.ODAT.CD), esa_source (HDX), esa_processed (2026-04-16).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-external-debt-indicators-for-cabo-verde")
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
country_name	object	0.0%	Cabo Verde
country_iso3	object	0.0%	CPV
year	int64	0.0%	1961.0 – 2024.0 (mean 2002.2375)
indicator_name	object	0.0%	Grants, excluding technical cooperation (BoP, current US$), Net ODA received per capita (current US$), Net official development assistance received (current US$)
indicator_code	object	0.0%	BX.GRT.EXTA.CD.WD, DT.ODA.ODAT.PC.ZS, DT.ODA.ODAT.CD
value	float64	0.0%	-304537748.8993 – 2678779840.2111 (mean 122831152.3758)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-16

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Numeric Summary

Column	Min	Max	Mean	Median
year	1961.0	2024.0	2002.2375	2003.0
value	-304537748.8993	2678779840.2111	122831152.3758	3142683.85

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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 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.

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Citation

@dataset{hdx_africa_world_bank_external_debt_indicators_for_cabo_verde,
  title     = {Cabo Verde - External Debt},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-external-debt-indicators-for-cabo-verde},
  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.