electricsheepafrica/africa-world-bank-combined-indicators-for-south-sudan

South Sudan - Economic, Social, Environmental, Health, Education, Development and Energy

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 covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Climate Change, External Debt, Trade.

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

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Country-level aggregates
Rows (total)	18,339
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	14,671 rows
Test split	3,667 rows
Geographic scope	SSD
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (South Sudan), country_iso3 (SSD), year (range 1960.0–2025.0).

Outcome / Measurement — value (range -1078293666537.33–2504703858099.18).

Identifier / Metadata — indicator_name (Population in the largest city (% of urban population), Population in largest city, Net migration), indicator_code (EN.URB.LCTY.UR.ZS, EN.URB.LCTY, SM.POP.NETM), esa_source (HDX), esa_processed (2026-04-10).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-combined-indicators-for-south-sudan")
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%	South Sudan
country_iso3	object	0.0%	SSD
year	int64	0.0%	1960.0 – 2025.0 (mean 2004.0339)
indicator_name	object	0.0%	Population in the largest city (% of urban population), Population in largest city, Net migration
indicator_code	object	0.0%	EN.URB.LCTY.UR.ZS, EN.URB.LCTY, SM.POP.NETM
value	float64	0.0%	-1078293666537.33 – 2504703858099.18 (mean 634628754.2693)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-10

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	2004.0339	2010.0
value	-1078293666537.33	2504703858099.18	634628754.2693	34.3598

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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. 6,406 exact duplicate rows were removed. 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_combined_indicators_for_south_sudan,
  title     = {South Sudan - Economic, Social, Environmental, Health, Education, Development and Energy},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-combined-indicators-for-south-sudan},
  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.