electricsheepasia/asia-humanitarian-needs-who-historical-data-for-philippines

Philippines - Historical Health Indicators

Publisher: World Health Organization · Source: HDX · License: hdx-other · Updated: 2025-02-07

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Abstract

This dataset contains historical data from WHO's data portal.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: PHL.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	16,814
Columns	18 (6 numeric, 12 categorical, 0 datetime)
Train split	13,451 rows
Test split	3,362 rows
Geographic scope	PHL
Publisher	World Health Organization
HDX last updated	2025-02-07

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Variables

Geographic — gho_display (Mean BMI (kg/m²) (crude estimate), Composite coverage index (%), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)), year_display (range 1961.0–2025.0), startyear (range 1961.0–2025.0), endyear (range 1961.0–2025.0), region_code (WPR, #region+code) and 4 others.

Outcome / Measurement — value (No data, No, Yes).

Identifier / Metadata — gho_code (NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19), dimension_code (SEX_FMLE, SEX_MLE, SEX_BTSX), dimension_name (Female, Male, Both sexes), esa_source, esa_processed.

Other — numeric (range 0.0–93260800.0), low (range 0.0–771359.0), high (range 0.0–1237902.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-humanitarian-needs-who-historical-data-for-philippines")
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
gho_code	object	0.0%	NCD_BMI_MEANC, SA_0000001400_ARCHIVED, CHILDMORT10TO19
gho_display	object	0.0%	Mean BMI (kg/m²) (crude estimate), Composite coverage index (%), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)
year_display	float64	0.0%	1961.0 – 2025.0 (mean 2006.2184)
startyear	float64	0.0%	1961.0 – 2025.0 (mean 2006.2087)
endyear	float64	0.0%	1961.0 – 2025.0 (mean 2006.2184)
region_code	object	0.0%	WPR, #region+code
region_display	object	0.0%	Western Pacific, #region+name
country_code	object	0.0%	PHL, #country+code
country_display	object	0.0%	Philippines, #country+name
dimension_type	object	14.1%	DHSMICSGEOREGION, SEX, WEALTHDECILE
dimension_code	object	14.1%	SEX_FMLE, SEX_MLE, SEX_BTSX
dimension_name	object	15.6%	Female, Male, Both sexes
numeric	float64	25.4%	0.0 – 93260800.0 (mean 12834.8381)
value	object	0.8%	No data, No, Yes
low	float64	40.0%	0.0 – 771359.0 (mean 1780.6124)
high	float64	40.0%	0.0 – 1237902.0 (mean 2701.8601)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
year_display	1961.0	2025.0	2006.2184	2008.0
startyear	1961.0	2025.0	2006.2087	2008.0
endyear	1961.0	2025.0	2006.2184	2008.0
numeric	0.0	93260800.0	12834.8381	45.7
low	0.0	771359.0	1780.6124	39.2527
high	0.0	1237902.0	2701.8601	54.2431

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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. 1 column(s) with >80% missing values were removed: gho_url. 36 exact duplicate rows were removed. 6 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 World Health Organization and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: numeric, low, high.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_humanitarian_needs_who_historical_data_for_philippines,
  title     = {Philippines - Historical Health Indicators},
  author    = {World Health Organization},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/who-historical-data-for-phl},
  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.