electricsheepafrica/africa-eswatini-languages

Eswatini: Languages

Publisher: CLEAR Global (previously Translators without Borders) · Source: HDX · License: cc-by-sa · Updated: 2026-04-06

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Abstract

Data on languages spoken in Eswatini, showing the main language spoken in the household by proportion of the population. Data is drawn from AfroBarometer. For more resources on the languages of Eswatini and language use in humanitarian contexts please visit: https://clearglobal.org/language-maps-and-data/

Each row in this dataset represents time-series observations. Temporal coverage is indicated by the datetime_published, date_creation column(s). Geographic scope: SWZ.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Time-series observations
Rows (total)	3
Columns	16 (4 numeric, 10 categorical, 2 datetime)
Train split	2 rows
Test split	0 rows
Geographic scope	SWZ
Publisher	CLEAR Global (previously Translators without Borders)
HDX last updated	2026-04-06

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Variables

Geographic — location_code (SWZ), location_name (Eswatini), location_level (range 0.0–0.0), reliability_score (range 0.6298–0.6298), representivity_rating (moderate).

Temporal — datetime_published, date_creation.

Demographic — language_code (swat1243, stan1293, zulu1248), language_name (Swati, English, Zulu), language_rank (range 1.0–3.0).

Outcome / Measurement — proportion_value (range 0.0026–0.9295).

Identifier / Metadata — dataset_name (Eswatini Round 9 data (2022)), source (AfroBarometer), esa_source (HDX), esa_processed (2026-04-06).

Other — url (https://www.afrobarometer.org/wp-content/uploads/2024/02/ESW_R9.data_.final_.wtd_release.30May23.sav).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eswatini-languages")
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
location_code	object	0.0%	SWZ
location_name	object	0.0%	Eswatini
location_level	int64	0.0%	0.0 – 0.0 (mean 0.0)
language_code	object	0.0%	swat1243, stan1293, zulu1248
language_name	object	0.0%	Swati, English, Zulu
language_rank	int64	0.0%	1.0 – 3.0 (mean 2.0)
proportion_value	float64	0.0%	0.0026 – 0.9295 (mean 0.3333)
reliability_score	float64	0.0%	0.6298 – 0.6298 (mean 0.6298)
dataset_name	object	0.0%	Eswatini Round 9 data (2022)
url	object	0.0%	https://www.afrobarometer.org/wp-content/uploads/2024/02/ESW_R9.data_.final_.wtd_release.30May23.sav
source	object	0.0%	AfroBarometer
datetime_published	datetime64[ns]	0.0%
date_creation	datetime64[ns]	0.0%
representivity_rating	object	0.0%	moderate
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-06

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Numeric Summary

Column	Min	Max	Mean	Median
location_level	0.0	0.0	0.0	0.0
language_rank	1.0	3.0	2.0	2.0
proportion_value	0.0026	0.9295	0.3333	0.0679
reliability_score	0.6298	0.6298	0.6298	0.6298

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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. 2 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 CLEAR Global (previously Translators without Borders) 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_eswatini_languages,
  title     = {Eswatini: Languages},
  author    = {CLEAR Global (previously Translators without Borders)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/eswatini-languages},
  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.