Datasets:

electricsheepafrica
/

africa-national-top-10-incidences-of-diseases-2009-to-2013

annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: other
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - epidemics-outbreaks
  - governance-and-civil-society
  - health
  - ken
pretty_name: 'National Top 10 Incidences of Diseases: 2009 to 2013'
dataset_info:
  splits:
    - name: train
      num_examples: 8
    - name: test
      num_examples: 2

National Top 10 Incidences of Diseases: 2009 to 2013

Publisher: Kenya National Bureau of Statistics (inactive) · Source: HDX · License: other-pd-nr · Updated: 2025-02-06

Abstract

This dataset shows the National Top 10 Incidences of Diseases in kenya for the period of : 2009 to 2013 as reported by the Kenya National Bureau of statistics

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-02-06. Geographic scope: KEN.

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

Dataset Characteristics


Domain	Public health
Unit of observation	Tabular records
Rows (total)	11
Columns	8 (5 numeric, 3 categorical, 0 datetime)
Train split	8 rows
Test split	2 rows
Geographic scope	KEN
Publisher	Kenya National Bureau of Statistics (inactive)
HDX last updated	2025-02-06

Variables

Identifier / Metadata — disease_name (Accidents (incl. fractures, burns etc), All Other Diseases, Diarrhoea Diseases), esa_source (HDX), esa_processed (2026-04-07).

Other — 2009 (range 387066.0–9833701.0), 2010 (range 419298.0–11371889.0), 2011 (range 374886.0–11150223.0), 2012 (range 357844.0–12215993.0), 2013 (range 349632.0–14823864.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-national-top-10-incidences-of-diseases-2009-to-2013")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`disease_name`	object	0.0%	Accidents (incl. fractures, burns etc), All Other Diseases, Diarrhoea Diseases
`2009`	int64	0.0%	387066.0 – 9833701.0 (mean 2905534.2727)
`2010`	int64	0.0%	419298.0 – 11371889.0 (mean 3478372.4545)
`2011`	int64	0.0%	374886.0 – 11150223.0 (mean 3484770.6364)
`2012`	int64	0.0%	357844.0 – 12215993.0 (mean 3580971.3636)
`2013`	int64	0.0%	349632.0 – 14823864.0 (mean 3966956.0909)
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-07

Numeric Summary

Column	Min	Max	Mean	Median
`2009`	387066.0	9833701.0	2905534.2727	1018151.0
`2010`	419298.0	11371889.0	3478372.4545	1081317.0
`2011`	374886.0	11150223.0	3484770.6364	1100997.0
`2012`	357844.0	12215993.0	3580971.3636	1135046.0
`2013`	349632.0	14823864.0	3966956.0909	1282996.0

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 Kenya National Bureau of Statistics (inactive) 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_national_top_10_incidences_of_diseases_2009_to_2013,
  title     = {National Top 10 Incidences of Diseases: 2009 to 2013},
  author    = {Kenya National Bureau of Statistics (inactive)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/national-top-10-incidences-of-diseases-2009-to-2013},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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