electricsheepafrica/africa-world-bank-climate-change-indicators-for-sao-tome-and-principe

Sao Tome and Principe - Climate Change

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.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

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

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Country-level aggregates
Rows (total)	1,272
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	1,017 rows
Test split	254 rows
Geographic scope	STP
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Sao Tome and Principe), country_iso3 (STP), year (range 1960.0–2024.0).

Outcome / Measurement — value (range -3.7366–360000.0).

Identifier / Metadata — indicator_name (Urban population (% of total population), Urban population, Population, total), indicator_code (SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL), esa_source (HDX), esa_processed (2026-04-13).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-climate-change-indicators-for-sao-tome-and-principe")
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%	Sao Tome and Principe
country_iso3	object	0.0%	STP
year	int64	0.0%	1960.0 – 2024.0 (mean 1999.272)
indicator_name	object	0.0%	Urban population (% of total population), Urban population, Population, total
indicator_code	object	0.0%	SP.URB.TOTL.IN.ZS, SP.URB.TOTL, SP.POP.TOTL
value	float64	0.0%	-3.7366 – 360000.0 (mean 12473.574)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-13

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2024.0	1999.272	2003.0
value	-3.7366	360000.0	12473.574	38.15

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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_climate_change_indicators_for_sao_tome_and_principe,
  title     = {Sao Tome and Principe - Climate Change},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-climate-change-indicators-for-sao-tome-and-principe},
  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.